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15 November 2024

Illuminating Malaria: Spectroscopy’s Vital Role in Diagnosis and Research

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1
School of Chemistry, Monash University, Wellington Rd., Clayton, VIC 3800, Australia
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IIT-B Monash Research Academy, Indian Institute of Technology Bombay, Mumbai 400076, MH, India
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School of Mechanical & Aerospace Engineering, Monash University, Wellington Rd., Clayton, VIC 3800, Australia
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International Centre for Genetic Engineering and Biotechnology, AREA Science Park Padriciano 99, 34149 Trieste, Italy
Spectrosc. J.2024, 2(4), 216-263;https://doi.org/10.3390/spectroscj2040015
This article belongs to the Special Issue Vibrational Spectroscopy and Biospectroscopy: Commemorative Issue Saluting the Pioneering Contributions of Prof. Henry Mantsch
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Abstract

Spectroscopic techniques have emerged as crucial tools in the field of malaria research, offering immense potential for improved diagnosis and enhanced understanding of the disease. This review article pays tribute to the pioneering contributions of Professor Henry Mantsch in the realm of clinical biospectroscopy, by comprehensively exploring the diverse applications of spectroscopic methods in malaria research. From the identification of reliable biomarkers to the development of innovative diagnostic approaches, spectroscopic techniques spanning the ultraviolet to far-infrared regions have played a pivotal role in advancing our knowledge of malaria. This review will highlight the multifaceted ways in which spectroscopy has contributed to the field, with a particular emphasis on its impact on diagnostic advancements and drug research. By leveraging the minimally invasive and highly accurate nature of spectroscopic techniques, researchers have made significant strides in improving the detection and monitoring of malaria parasites. These advancements hold the promise of enhancing patient outcomes and aiding in the global efforts towards the eradication of this devastating disease.

1. Introduction

Malaria remains a formidable global health challenge, with an estimated 249 million cases in 2022, a 55% increase from pre-COVID-19 levels [1]. To effectively combat this disease, there is a critical need for affordable and highly sensitive diagnostic tests that can identify asymptomatic carriers [2]. Nucleic acid amplification tests, such as Polymerase Chain Reaction (PCR) assays, are highly sensitive methods for detecting low-density malaria infections. However, their use is limited to well-equipped laboratory settings due to their complexity [2].
In response to the need for more accessible diagnostic tools, ultrasensitive Rapid Diagnostic Tests (uRDTs) have been developed. These tests, which detect proteins like Histidine-Rich Protein 2 (HRP2), have shown promise in laboratory conditions, following the same principles as conventional Rapid Diagnostic Tests (cRDTs) [3]. However, the sensitivity and specificity of these uRDTs are still lower compared to the gold standard PCR assays. In a recent meta-analysis, the Alere™ Ultra-sensitive Malaria Ag P. falciparum RDT had a sensitivity of 72.1% for symptomatic patients, higher than the 67.4% sensitivity reported for cRDTs in the same field conditions [3]. Yet, these values remain well below the 95% sensitivity achieved by PCR assays [3].
To strengthen the fight against malaria, continued research and development are critical. Improving the performance of malaria diagnostic tests is essential, bringing them closer to the accuracy of laboratory-based molecular tests while maintaining the affordability and portability that are crucial for widespread deployment, especially in resource-limited settings. In this context, spectroscopic approaches offer a viable alternative as point-of-care tests in remote villages and resource-poor clinical settings. These spectroscopic techniques can also play a role in developing new drugs and understanding the mechanisms of drug interaction, along with monitoring the therapeutic effects of drugs by quantifying parasitemia—a capability currently lacking in Rapid Diagnostic Tests (RDTs). By enhancing the accuracy, affordability, and accessibility of malaria diagnostics, we can bolster the tools available to combat this devastating disease, particularly in the most vulnerable communities.
The use of spectroscopic techniques for disease diagnosis was eloquently summarized by Professor Henry Mantsch, who stated that “changes in tissue biochemistry must precede any morphological or symptomatic manifestations, thus allowing spectroscopic diagnosis at an earlier stage of the disease” [4]. This principle has been instrumental in the fight against malaria, where spectroscopic methods have provided a minimally invasive and accurate approach to detecting and monitoring the disease. These diagnostic tools span a range of spectroscopic modalities, including Fourier transform infrared (FTIR), near-infrared (NIR), Raman, surface enhanced Raman scattering (SERS) tip enhanced Raman scattering (TERS), atomic force microscopy–infrared (AFM-IR), ultraviolet/visible (UV/Vis) and photoacoustic spectroscopy.
Infrared spectroscopy has shown great promise in malaria diagnosis, as it can detect specific biomarkers associated with Plasmodium-infected red blood cells. These biomarkers include lipids, proteins, and hemozoin, a by-product of the malaria parasite’s hemoglobin digestion. Raman spectroscopic approaches have relied on detecting hemozoin but other markers including proteins and lipids can also be detected using Raman spectroscopy. Near-infrared spectroscopy, on the other hand, leverages the unique optical properties of hemozoin and lipids to differentiate between infected and uninfected red blood cells using the overtone and combination bands of these biomarkers. UV/Visible spectroscopy has been employed to detect changes in the optical absorption spectra of blood samples, which can be correlated with the presence and density of malaria parasites.
The advantages of spectroscopic approaches for malaria diagnosis are numerous. These techniques are minimally invasive, requiring only a small blood sample, and can provide rapid, objective results without the need for skilled personnel. Moreover, they have the potential to detect asymptomatic infections, which are crucial for interrupting disease transmission. Importantly, spectroscopic methods can also quantify parasitemia levels, enabling healthcare providers to monitor the efficacy of antimalarial drug treatments. The field of spectroscopy-based malaria diagnostics has seen remarkable advancements with the development of miniaturized chip-based spectrometers [5,6]. These compact devices integrate multiple spectroscopic functions onto a single electronic chip, enabling greater affordability and portability compared to traditional bulky spectrometers. The integration of microelectromechanical systems (MEMS) and complementary metal–oxide–semiconductor (CMOS) technology has been pivotal in realizing this compact and versatile design, allowing for the seamless incorporation of spectroscopic capabilities into smaller, more accessible platforms. This technological progress has significantly expanded the applications of clinical spectroscopy, empowering healthcare professionals and researchers with powerful analytical tools that can be readily deployed in various clinical settings.
While spectroscopic techniques have shown promising results in laboratory settings, their translation to the clinic and point-of-care settings is not without limitations. Factors such as sample preparation, environmental interference, and variations in individual physiology can affect the accuracy and reliability of these methods. Addressing these challenges through continued research and development is essential for a broader adoption of spectroscopic approaches in the field. This review will provide an overview of the application of optical spectroscopy techniques in the diagnosis and research of malaria. The review will highlight the key spectroscopic biomarkers that have been identified for the detection of malaria infection before delineating the diverse range of spectroscopic methods that have been utilized in malaria research, including FTIR, Raman, SERS, NIR, AFM/IR, photothermal imaging, TERS photoacoustic and UV/Vis spectroscopy. For each technique, the review will discuss how spectroscopic signatures have contributed to advancing our understanding of malaria, from disease diagnosis to investigating drug interactions and mechanisms of action. Additionally, the review will address the barriers that have hindered the translation of these spectroscopic approaches from the research lab to clinical practice. Finally, the review will speculate on future applications and potential of optical spectroscopy in advancing malaria research and improving disease management. Overall, this review aims to provide a comprehensive survey of the role optical spectroscopy has played, and continues to play, in the fight against the global health challenge of malaria.

2. Life Cycle of the Parasite

The life cycle of the malaria parasite involves both a sexual and an asexual phase (Figure 1). Most current diagnostic techniques have focused on detecting markers of the erythrocytic (red blood cell) stage of the parasite’s life cycle in peripheral blood. However, the future may see the exploration of alternative, non-invasive sample types such as saliva, breath, urine, and stool as potential targets for malaria diagnosis. These non-blood-based approaches could detect markers of infection without the need to identify circulating parasites in the peripheral blood.
Figure 1. Asexual and sexual phases of the malaria parasite in RBC. After sporozoites enter the bloodstream, they travel to the liver, where they invade hepatocytes and develop into schizonts, each containing thousands of merozoites. These merozoites are then released and invade erythrocytes, initiating the intraerythrocytic asexual phase. During this phase, the parasites grow and divide within the food vacuole, progressing through three distinct morphological stages: ring, trophozoite, and schizont. When schizonts rupture, they release merozoites, continuing the erythrocytic cycle. Some merozoites, instead of replicating, differentiate into male and female gametocytes capable of transmission to mosquitoes. The digestion of hemoglobin by the parasite leads to the accumulation of Hz. In the circulation, only ring-stage parasites and late-stage gametocytes are observed. Reproduced with permission from the Royal Society of Chemistry [7].

3. Spectral Biomarkers for Malaria

The malaria parasite, Plasmodium, produces distinct biochemical signatures during its complex life cycle within human hosts that serve as valuable diagnostic and therapeutic targets. Among these biomarkers, hemozoin, a crystalline byproduct of hemoglobin digestion, has emerged as a unique indicator of infection due to its specific magnetic and spectroscopic properties. The parasite also exhibits characteristic changes in lipid metabolism, producing unique lipid profiles that differ significantly from those of healthy cells. Additionally, specific nucleic acid sequences and protein expressions unique to Plasmodium provide molecular fingerprints of infection, while also offering insights into drug resistance and disease progression. Understanding these biomarkers not only advances our knowledge of parasite biology but also enables the development of more sensitive and specific diagnostic tools.

3.1. Hemozoin

3.1.1. Discovery of Hemozoin and Malaria Infection

Hemozoin, also known as malaria pigment, is a dark brown/black molecule that results from the catabolization of hemoglobin by the malaria parasite. For a detailed review of the molecular analysis of hemozoin, the reader is referred to a recent in-depth review by Rathi et al. [8] that comprehensively examines the structure, function, and biosynthesis of this important malaria pigment. Johann Friedrich Meckel, a German pathologist, made early observations of the malaria pigment in 1847. He noticed this dark pigment in the organs of individuals who had died of pernicious fever, often found in the spleen, liver, brain, or kidneys on autopsy. Meckel associated this pigment accumulation with the presence of malaria in the blood [9]. In 1871, Meckel’s observations were later confirmed by two scientists—Rudolf Virchow in Germany and Maxime Cornu in France. However, their findings were not widely recognized at the time as they were unpublished [9]. In 1879, Philipp Friedrich Hermann Klencke, a German scientist, was also recognized for his early observations of the malaria parasite [10]. However, due to the lack of publication and differences in his drawings compared to later photomicrographs, his contributions were not widely acknowledged [9]. The ground-breaking discovery of the malaria parasite is credited to Alphonse Lavéran in 1880 [11]. Lavéran’s understanding of the significance of his discovery and its potential impact on the treatment and transmission of malaria earned him the Nobel Prize in Physiology or Medicine in 1907. While there was some debate over the priority of the discovery, Lavéran’s work is considered the most significant in understanding and identifying the malaria parasite [9].

3.1.2. Organisms Producing Hemozoin

Human malaria parasites, including P. falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi, all produce hemozoin during their life cycles. Hemozoin formation has also been documented in the New World monkey malaria parasite P. brasilianum, the rodent malaria parasite P. yoelii, and the avian malaria parasite P. gallinaceum [12]. Beyond the Plasmodium genus, hemozoin has been identified in another protozoan parasite that infects birds, Hemoproteus columbae. Interestingly, the unrelated human parasitic worms Schistosoma mansoni [13] and Echinostoma trivolvis also dispose of heme through hemozoin production, though E. trivolvis only forms the pigment when residing in its intermediate snail host. Even insects, such as kissing bugs of the genus Rhodnius, excrete excess heme as hemozoin in their feces [12]. So, while hemozoin is closely associated with and vital to the lifecycle of the malaria parasite, trace amounts may occasionally be detected in a few other severe infectious or hematological conditions, though it remains a uniquely defining feature of Plasmodium infections.

3.1.3. Hemozoin Location in Humans

For P. falciparum infection, infected red blood cells (RBCs) containing trophozoites and schizonts are often absent from the peripheral circulation, especially at low levels of infection [14]. However, at high levels of infection, trophozoites have been reported in peripheral blood [15]. The low number of schizonts and trophozoites in peripheral blood is due to a process called sequestration, where the infected RBCs adhere to the endothelium of blood vessels, particularly in the venules [14]. The infected RBCs develop electron-dense structures on their membrane, known as knobs, which facilitate their attachment to the venular endothelium. By sequestering in the vasculature, the mature parasites (trophozoites and schizonts) can evade destruction in the spleen. Importantly, the sequestered parasites can continue to release new merozoites, which can then invade uninfected RBCs, thereby perpetuating the asexual cycle of the parasite [14]. This sequestration of P. falciparum-infected RBCs in the vasculature presents a potential opportunity for non-invasive diagnostic approaches. The stationary, sequestered cells containing hemozoin-rich trophozoites and schizonts could serve as targets for spectroscopic detection, such as NIR or Raman spectroscopy. Late-stage rings and mature stage IV–V gametocytes, which are found in peripheral blood, do contain hemozoin [16]. The female gametocyte is characterized by a centralized accumulation of the hemozoin pigment. The hemozoin granules are condensed and localized within the center of the female gametocyte. In contrast, the hemozoin pigment in the male gametocyte is dispersed throughout the infected red blood cell rather than being concentrated in the center [16]. Hemozoin is also found in leukocytes, including neutrophils [15,17] and monocytes [17]. While schizonts and trophozoites are not common in peripheral blood, other cell types including late-stage rings, gametocytes, and leukocytes still make hemozoin an attractive marker for spectroscopy-based diagnosis.

3.1.4. Crystal Structure

Hemozoin is a crystalline pigment that has a well-characterized molecular and crystal structure. At the molecular level, hemozoin is a dimer of heme (ferriprotoporphyrin IX) molecules, where each heme molecule consists of a central ferric (Fe3+) iron atom coordinated to a porphyrin ring and various side chains. Synchrotron X-ray fluorescence powder diffraction data indicated that the heme molecules are linked together through a reciprocal coordination bond between the central iron atom of one heme and a carboxylate group of the propionate side chain of the adjacent heme [18]. The Fe–O bond distance converged to a value of 1.886(2) Å, which was found to be consistent with other high-spin ferric porphyrins [18]. These hemozoin dimers then crystallize into a unique monoclinic crystal structure, with the heme dimers arranged in stacks and held together through hydrogen bonding and π–π stacking interactions between the porphyrin rings, as well as Van der Waals interactions between the alkyl side chains (Figure 2). The resulting hemozoin crystals typically range in size from 0.2 to 1.0 μm in length and exhibit a characteristic needle-like or rod-shaped morphology with a hexagonal cross-section. This unique crystal structure allows the malaria parasite to sequester the potentially toxic heme molecules, preventing them from causing oxidative damage to the parasite’s cellular components, and the presence of hemozoin crystals in the blood of infected individuals serves as a diagnostic marker for malaria.
Figure 2. Hematin and β-hematin structure. (A) Schematic representation of hematin, the monomeric precursor of β-hematin. (B) Structure and packing arrangement of β-hematin (synthetic malaria pigment) viewed along the c-axis. Some (h,k,l) planes are indicated. Reprinted with permission from the American Chemical Society [19].

3.1.5. Raman Spectroscopy of Hemozoin

Raman spectroscopy is a powerful tool for the analysis of hemozoin, as it can provide detailed information about the molecular structure and vibrational modes of this important biomolecule. When analyzing hemozoin using Raman spectroscopy, several distinct and characteristic bands are observed in the spectrum. Excitation of hemozoin with a laser wavelength in the Soret band region (e.g., 406 nm) results in a significant enhancement of the Raman signal, a phenomenon known as type-A resonance Raman scattering (or Frank–Condon scattering), due to the π→π* electronic transitions of the porphyrin [20], which is in resonance with the excitation wavelength, allowing for the detection of very low concentrations of hemozoin and making it a highly sensitive technique for the identification and quantification of this biomolecule. The Raman bands observed in this region are primarily associated with the porphyrin ring vibrations, such as the intense ν4 mode at around 1375 cm−1, which corresponds to the C-N stretching of the porphyrin macrocycle. Excitation of hemozoin in the near-infrared region, such as with a 785 or 830 nm laser, also results in enhanced Raman scattering, attributed to the presence of the central iron atom in the heme group, which can undergo charge transfer transitions with the porphyrin macrocycle in this wavelength range. Dramatic enhancement of certain Raman modes when irradiating β-hematin and hemin (the precursor to hemozoin) with 780 nm and 830 nm laser excitation wavelengths is observed (Figure 3). Specifically, the A1g modes at 1570, 1371, 795, 677, and 3 cm−1, the ring breathing modes in the 850–650 cm−1 range, and the out-of-plane modes including iron ligand modes in the 400–200 cm−1 range were significantly enhanced. This enhancement was more pronounced in beta-hematin compared to hemin. The absorbance spectra recorded during the transformation of hemin to beta-hematin showed a red shift of the Soret and Q (0–1) bands, which has been interpreted as resulting from excitonic coupling due to porphyrin aggregation. Additionally, a small broad electronic transition observed at 867 nm was assigned to a z-polarized charge transfer transition dxz → eg(π*).
Figure 3. Raman excitation wavelength measurements recorded of β-hematin. The asterisks (*) highlight the bands enhanced relative to the other excitation wavelengths at 830 nm. Reproduced with permission from the American Chemical Society [20].
The extraordinary band enhancement observed when exciting beta-hematin with near-infrared wavelengths, compared to hemin, can be explained by the theory of aggregate-enhanced Raman scattering. This occurs due to intermolecular excitonic interactions between the porphyrinic units, leading to a superposition of electronic transitions that result in enhanced Raman scattering. Hemozoin pigment, like other haem pigments, produces intense overtone tones when excited with green laser light [21]. Enhancement with the 514.5 and 532 nm excitation laser lines, which are in close proximity to the vibronic Qv band of the visible spectrum of hemoporphyrins, enables the C–Term enhancement mechanism to dominate, which occurs between forbidden electronic transitions which are prohibited at the equilibrium geometry of the molecule [21]. The enhanced Raman scattering observed in both the Soret band and near-infrared regions is a characteristic feature of hemozoin and allows for its sensitive and selective detection in complex biological samples, such as those obtained from malaria-infected individuals, making Raman spectroscopy a valuable technique for the diagnosis and study of malaria.

3.1.6. FTIR Spectroscopy of Hemozoin

The FTIR spectra of β-hematin and hemozoin are shown in Figure 4. Slater et al. [22,23] were the first to report the infrared (IR) spectrum of hemozoin and β-hematin. They identified two distinct absorption bands in the hemozoin spectrum at 1664 cm−1 and 1211 cm−1, which were absent in the spectra of free heme (hematin) and heme complexes (hemin). By comparing the hemozoin spectrum to other iron-carboxylate-containing compounds, Slater et al. [23] proposed that the heme units in hemozoin are coordinated in a unidentate fashion, where one of the carboxylate C-O bonds exhibits double-bond character, resulting in a C=O stretching vibration between 1700 and 1600 cm−1. The strong absorption at 1211 cm−1 was also attributed to the C-O stretching of an axial carboxylate ligand, based on studies of metalloporphyrin compounds with O-methyl groups, which showed similar C-O stretching bands in the 1270–1080 cm−1 region [23]. These distinct spectroscopic signatures of hemozoin, compared to the heme precursors, provided a valuable IR-based marker that could potentially be exploited for the development of diagnostic tools for malaria infection. The unique IR absorption features highlighted the structural differences between the crystalline hemozoin and the soluble heme species, which have important implications for understanding the biochemistry of malaria parasites.
Figure 4. (A) FTIR spectrum of β-hematin. (B) FTIR spectrum of hemozoin extracted from malaria trophozoites. Reproduced with permission from the American Chemical Society [20].

3.1.7. UV/Visible Spectroscopy of Hemozoin

Hemin exhibits a distinct Soret band (or B band) that is resolved into two bands at 363 nm (S′) and 385 nm (S), along with Q bands at 495 nm, 521 nm, 550 nm (very weak), and 612 nm. In contrast, the Soret band of β-hematin collapses into a single broad band centered at 389 nm, along with a Q band at 649 nm, when observed at low pH. This distinction helps to discriminate β-hematin from hematin and hemin. Other bands observed in the absorbance spectrum of β-hematin include bands at 513 nm and 550 nm [20]. The absorbance spectrum of β-hematin at low pH exhibits characteristics consistent with those reported by Bohle et al. [24] Their study, using a potassium bromide pellet technique, revealed distinct spectral bands at 406 nm, 510 nm, 538 nm, and 644 nm [24]. These findings align closely with our observed spectrum, providing corroborative evidence for the spectral properties of β-hematin under acidic conditions [20]. They also reported a band at 580 nm that is not observed in the solution spectra reported here [24]. The 649 nm band is characteristic of hemin aggregates, as determined by micro-spectrophotometry [25] and photoacoustic spectroscopy [26]. This band sequentially shifts (640 nm, 645 nm, 649 nm) as the pH decreases. Figure 5 shows representative spectra of the conversion of hemin to β-hematin. The decrease in pH causes broadening and reduced intensity of the Soret band in β-hematin, attributed to aggregation and precipitation. An apparent red shift occurs from 363 nm to 389 nm, though accurate maxima determination is difficult due to the broad nature of the band at low pH. Red shifts in both Soret and Q-bands indicate excitonic coupling from porphyrin aggregation. Interestingly, a small, broad band centered around 867 nm is observed in both hemin and β-hematin [20]. This band slightly red-shifts as the reaction progresses and appears less intense in β-hematin compared to hemin, likely due to β-hematin precipitation. This previously unreported band is tentatively assigned to a charge transfer (CT) transition, specifically band I (dxz → eg(π*)) [20]. These spectral changes provide insight into the formation and characteristics of β-hematin, offering valuable information about the molecular processes involved in its synthesis and structure.
Figure 5. Absorbance spectra recorded during the acidification of hemin to form β-hematin. Reproduced with permission from the American Chemical Society [20].

3.1.8. NIR Spectroscopy of Hemozoin

The near-infrared (NIR) spectra of the hemozoin standard purchased from Invivogen and hemozoin extracted from infected red blood cells, along with synthetic β-hematin, are presented in Figure 6a–c. The spectra do not show much consistency, indicating a lot of impurities. Notably, the spectrum of hemozoin isolated from infected red blood cells (Figure 6b) exhibits slight differences in the 1960 nm–2500 nm region compared to the hemozoin purchased from Invivogen. Specifically, four intense bands are observed at 1975 nm, 2055 nm, 2133 nm, and 2228 nm in the extracted hemozoin, whereas the synthetic β-hematin (Figure 6c) shows a single intense peak at 2216 nm [5]. These additional bands in the extracted hemozoin sample are tentatively assigned to ν(CH3/CH2) vibrations, possibly originating from residual hemoglobin left behind during the isolation process. The experimental NIR wavelength values are based on the second derivative spectra, providing enhanced spectral resolution and allowing for the identification of these subtle differences between the natural and synthetic forms of the pigment possibly resulting from impurities [5].
Figure 6. Representative second derivative spectra, (a) β-hematin (green), (b) dry hemozoin isolated from infected red blood cells (red), (c) dry crystalline hemozoin purchased from Invivogen (blue). Reproduced with permission from the American Chemical Society [5].

3.2. Lipids

The role of lipids in the formation of hemozoin, a by-product of the malaria parasite’s digestion of host hemoglobin, has been the subject of investigation. Bendrat et al. [27] suggested that this process is mediated by lipids, as they observed that an acetonitrile extract of P. falciparum promoted the formation of beta-hematin, a synthetic analog of hemozoin. Further evidence supporting the involvement of lipids in hemozoin formation comes from the dramatic increase in lipid content within P. falciparum-infected erythrocytes [28,29,30,31] and observations of hemozoin localization in close proximity to neutral lipid bodies (NLBs) [30,32]. Specifically, the parasites synthesize and package triacylglycerol (TAG) and diacylglycerol (DAG) into NLBs in a stage-specific manner within the digestive vacuole (DV) [30,33,34]. These lipids are barely detectable in uninfected erythrocytes [28,35]. However, the precise function of NLBs during parasite growth remains unclear. It has been hypothesized that NLBs may serve as a depot of lipid intermediates generated during the digestion of phospholipids [33] or, alternatively, as a source of lipids that can be mobilized to supply the growing parasite with fatty acids and acylglycerols for membrane generation [34]. Importantly, the specific composition of the P. falciparum NLB lipid blend, identified by mass spectrometry, has been shown to be sufficient for mediating hemozoin formation [32]. This lipid blend consists of a 4:2:1:1:1 ratio of the monoglycerides monostearoylglycerol (MSG) and monopalmitoylglycerol (MPG), and the diglycerides 1,3-dioleoylglycerol (DOG), 1,3-dipalmitoylglycerol (DPG), and 1,3-dilinoleoylglycerol (DLG) [32]. Research has revealed some fascinating insights into the formation of β-hematin in the presence of lipids. Egan et al. [36] discovered that β-hematin can rapidly form under conditions that closely mimic the physiological environment, particularly in the presence of interfaces between octanol/water, pentanol/water, and lipid/water. Molecular dynamics simulations have provided further elucidation of this process. These simulations have shown that a precursor to the hemozoin dimer can spontaneously form in the absence of the competing hydrogen bonds that are typically present in water. This suggests that beta-hematin likely self-assembles near a lipid/water interface within the living organism (in vivo), as confirmed by Raman spectroscopy [36].
The ability of β-hematin to form so readily under these realistic conditions is a significant finding. It sheds light on the mechanisms underlying the production of this important biological substance and highlights the importance of examining these processes in the context of relevant interfaces and environmental factors. Further research in this direction could yield valuable insights into the physiological relevance and potential applications of β-hematin.
FTIR synchrotron spectra recorded of single cells from the different phases of the erythrocytic life cycle show the continuous increase in lipid components as the cells progress from being uninfected to the ring, schizont, and trophozoite stages [31]. Figure 7 shows synchrotron FTIR spectra of single uninfected red blood cells compared with ring, trophozoite, and schizont stages cells. The ester carbonyl band from triglyceride fatty acids at 1742 cm−1 increases through the different stages and is barely discernible in uninfected cells. Bands at 2922 cm−1 and 2852 cm−1, assigned to the νasym(CH2 acyl chain lipids) and νsym(CH2 acyl chain lipids), respectively, increase as the parasite matures from its early ring stage to the trophozoite and finally to the schizont stage [15]. The principal component analysis (PCA) enabled discrimination between uninfected, ring, trophozoite and schizont stages on a PC1 versus PC2 Scores plot [31]. Raman spectroscopy was employed to differentiate between P. falciparum and P. vivax on a PCA 2D scores plot. The PCA performed in the CH stretching region 3100–2800 cm−1 showed a clear clustering of the two species, indicating that the lipidomic component can be used to distinguish species of sp. using Raman spectroscopy. This finding is crucial for informing appropriate drug treatment strategies against different species [37].
Figure 7. FTIR averaged normalized spectra of the C-H stretching region and fingerprint region from the Australian Synchrotron of RBCs (control) and the three stages of the parasitic life cycle (ring, trophozoite, and schizont) within a fixed RBC. Standard deviation spectra are shown below each spectrum for both spectral regions. Reproduced with permission from the American Chemical Society [31].

3.3. Nucleic Acids

The Plasmodium genome is a circular, AT-rich DNA molecule of approximately 23 megabases, containing around 5400 genes [38]. Compared to the genomes of free-living eukaryotic microorganisms, the genome of the intracellular malaria parasite Plasmodium encodes a smaller number of enzymes and transporters. However, a substantial proportion of the Plasmodium genome is dedicated to genes involved in immune evasion mechanisms and host–parasite interactions [38]. During the blood stage of the Plasmodium life cycle, the parasite’s DNA content or the number of daughter cells can increase dramatically within a single round of replication. On average, studies have documented a 20-fold to 30-fold rise in DNA content or daughter cell number over the course of one proliferative cycle [39,40]. This large amount of Plasmodium DNA inside the host red blood cell can be detected and used for diagnosis of malaria infection. In addition to the DNA within infected red blood cells, Plasmodium parasites also release DNA fragments into the host’s bloodstream. These cell-free, circulating Plasmodium DNA molecules can be detected in the modiumma or serum of infected individuals [40]. The presence and quantification of circulating malaria DNA in plasma has emerged as a valuable biomarker for the diagnosis, monitoring, and management of malaria [41]. Circulating Plasmodium DNA can be detected even in low-density infections or in cases where the parasites are sequestered in the deep vasculature, making it a more sensitive method compared to traditional microscopy. Analysis of circulating malaria DNA has also provided insights into parasite dynamics, drug resistance, and the genetic diversity of sp. during infection.
In terms of RNA, malaria parasites exhibit some unique genomic features when compared to other eukaryotic organisms. Most notably, the Plasmodium genome lacks the long, tandemly repeated arrays of ribosomal RNA (rRNA) genes that are characteristic of many other eukaryotes [38]. Instead, Plasmodium parasites contain multiple single 18S-5.8S-28S rRNA units distributed across different chromosomes [38]. Furthermore, the expression of these rRNA units is tightly regulated throughout the various stages of the Plasmodium life cycle [38]. This results in the selective expression of different sets of rRNAs at different points during the parasite’s development. This developmental regulation of rRNA gene expression contrasts with the more uniform rRNA profiles seen in many other eukaryotic organisms [38].
Vibrational spectroscopic techniques, such as Fourier transform infrared (FTIR) and Raman spectroscopy, have faced challenges in the direct detection of DNA within malaria-infected cells. In an early FTIR spectroscopy study investigating single infected rings, trophozoites, and schizonts, no clear DNA phosphodiester bands were identified at the expected wavenumber values of ~1240 cm−1 and ~1080 cm−1, corresponding to the asymmetric and symmetric PO2− vibrations, respectively [31]. Similarly, Raman spectroscopy studies did not detect the characteristic Raman bands at 813 cm−1 or 840 cm−1 that would be anticipated for RNA or DNA [42]. The apparent lack of detectable DNA/RNA signals in these vibrational spectra suggests that the parasite DNA may be present at levels below the sensitivity threshold of these techniques. Another potential explanation for this observation lies in the conformational dependence of the DNA molar extinction coefficient. When DNA is in the dried, A-DNA conformation, the molar extinction coefficient is significantly lower compared to the hydrated, B-DNA state. This is likely due to the more ordered arrangement of the phosphodiester groups in B-DNA, in contrast to the less organized structure of A-DNA. Specifically, in the case of FTIR spectra of DNA, the symmetric PO2− vibration is approximately 3 times less absorbing in the A-DNA form compared to the B-DNA form, and the asymmetric PO2− band is shifted from ~1240 cm−1 in the A-form to ~1220 cm−1 in the B-form [43]. These conformational-dependent changes in the DNA FTIR spectrum may explain the apparent lack of detectable DNA signals in FTIR analyses of dried, malaria-infected red blood cells, as the less IR-active A-DNA conformation predominates under those conditions.

3.4. Proteins

The Plasmodium genome exhibits a high degree of uniqueness, with almost two-thirds of its proteins appearing to be unique to this organism. This proportion is much higher than what is typically observed in other eukaryotes [44]. This finding may be a reflection of the greater evolutionary distance between Plasmodium and the other eukaryotic organisms that have been sequenced to date [44]. Additionally, the (A+T) richness of the Plasmodium genome may have contributed to the reduction in sequence similarity, further exacerbating the observed uniqueness [44]. Another 257 proteins, constituting approximately 5% of the total, showed significant similarity to hypothetical proteins found in other organisms [44].
The increase in parasitic load leads to elevated plasma levels of molecules such as C-reactive protein (CRP), lipopolysaccharide binding protein (LBP), and various cytokines, including tumor necrosis factor (TNF), interleukin-10 (IL-10), and interferon-gamma, are typically seen during this phase, which is common for other types of infection [45]. The most specific protein for malaria infection, particularly for Plasmodium falciparum, is Plasmodium falciparum Histidine-Rich Protein 2 (PfHRP2), which is produced by the parasite and released into the bloodstream during infection and is widely used in rapid diagnostic tests. However, gene deletions can allow certain parasites to remain undetected [46,47,48,49,50,51]. Another parasite-specific protein, not mentioned in the previous list, is Plasmodium falciparum Lactate Dehydrogenase (PfLDH), with different species of Plasmodium producing slightly different forms of LDH, making it useful for species-specific diagnosis [52,53,54]. Plasmodium Aldolase, a parasite-specific enzyme found in all human malaria species, is less commonly used as a biomarker than PfHRP2 or PfLDH but can be useful for pan-malarial detection [54]. It’s important to note that while these proteins are specific to malaria, their detection doesn’t always indicate an active infection; for example, PfHRP2 can persist in the bloodstream for weeks after successful treatment, potentially leading to false-positive results.

3.4.1. FTIR Spectroscopy of Proteins

The FTIR spectra of uninfected red blood cells are dominated by the alpha-helical protein hemoglobin. Red blood cells contain a remarkably high concentration of hemoglobin, comprising approximately 95% of the total cytosolic proteins within these cells [55]. This hemoglobin is present at a concentration of 5 millimolar (mM) inside the red blood cell [55]. The percentage of hemoglobin in different stages of malaria parasites (rings, trophozoites, and schizonts) varies as the parasite develops within the red blood cell. In the early ring stage, the parasite has consumed relatively little hemoglobin, with approximately 0–20% of the host cell hemoglobin digested. The trophozoite stage is the most active feeding stage, during which about 60–80% of the host cell hemoglobin is typically digested [55]. By the late schizont stage, most of the hemoglobin has been consumed, with approximately 80–100% of the host cell hemoglobin digested. These percentages are approximate and can vary depending on the specific sp. and individual parasites.
FTIR spectra of both infected and uninfected red blood cells at all stages are dominated by strong amide bands appearing at 1650 cm−1 and 1544 cm−1 assigned to the amide I mode (ν(C=O) + ν(C-N) + δ(NH2)) and amide II mode (ν(C-N) + δ(NH2) + ν(C-C) + ν(C=O)), respectively, and a weaker band at ~1300 cm−1 assigned to the amide III mode ((ν(C-N) + δ(CH2) + δ(NH2) +δ(C-C-N) + δ(C-O)). Other bands, including the band at 1450 cm−1 and ~3300 cm−1, are assigned to the carboxylate group of amino acid side chains and the amide A mode (N-H stretching) from the peptide functional group. The amide I mode is very sensitive to protein conformational change, and the profile can be used to predict the relative contribution of alpha-helical, β-pleated sheet, random coil, and other protein conformational motifs [56]. The amide I and II band profile has been shown to change radically between the different stages of the malaria parasite, especially after performing a second derivative [31]. However, in our field trial using ATR-FTIR technology, the amide I and II region was found not to be particularly useful in diagnosing infected from uninfected patients, and instead, the region between 3000–2700 cm−1 and 1200–900 cm−1 proved to be better in terms of sensitivity and specificity for diagnosing patients with malaria infection [57].

3.4.2. Raman Spectroscopy of Proteins

Raman spectroscopy provides valuable information about protein structure and composition through characteristic vibrational bands. In addition to the amide modes mentioned above in the context of FTIR, Raman also shows bands from aromatic amino acid side chains which result in distinct bands from phenylalanine, which shows a sharp peak near 1000 cm−1, tyrosine exhibits a doublet at approximately 830 and 850 cm−1, and tryptophan displays bands at about 760 and 1340 cm−1. The S-S stretching vibration of disulfide bonds appears around 500–550 cm−1. C-H stretching vibrations from aliphatic amino acids are observed in the 2800–3000 cm−1 region. The band near 1450 cm−1 is attributed to CH2 and CH3 deformations. Additionally, the region between 500 and 800 cm−1 contains various skeletal vibrations that can provide information about protein conformation. These Raman bands collectively offer insights into protein secondary structure, side chain environments, and overall conformation, making Raman spectroscopy a powerful tool for protein analysis.

3.4.3. UV/Visible Spectroscopy of Proteins

The UV/Visible spectrum of proteins is characterized by several key absorption bands that provide valuable information about their structure and composition. The primary absorption band in the UV region, known as the peptide bond absorption, occurs around 190–230 nm and is due to the n→π* and π→π* transitions of the peptide bond. This band is present in all proteins and is sensitive to secondary structure. The far-UV region (180–240 nm) is particularly useful for analyzing protein secondary structure, with α-helices showing a characteristic double minimum at 208 and 222 nm, and β-sheets displaying a single minimum near 215 nm. In the near-UV region (250–300 nm), absorption is primarily due to aromatic amino acid side chains: phenylalanine absorbs weakly near 257 nm, tyrosine shows a peak around 274 nm, and tryptophan exhibits the strongest absorption with a maximum near 280 nm. The exact positions and intensities of these aromatic peaks can provide information about the local environment and tertiary structure of the protein. Disulfide bonds (cystine) contribute a weak absorption band around 260 nm. Some proteins containing metal ions or other chromophores may show additional absorption bands in the visible region, such as the heme group in hemoglobin and myoglobin, which gives rise to the Soret band around 400 nm and Q-bands between 500 and 600 nm. Flavoproteins typically show absorption bands in the 350–500 nm range. The overall shape and intensity of the UV/Visible spectrum can be used to estimate protein concentration and purity and to monitor conformational changes. It is worth noting that the exact positions and intensities of these bands can vary depending on the specific protein, its environment, and any modifications or ligands present.

3.4.4. Near Infrared Spectroscopy of Proteins

NIR spectroscopy has been widely used to investigate protein structures, folding patterns in polypeptides, and amino acid composition [58]. The primary functional groups for near-infrared (NIR) spectroscopic analysis in protein studies are the amides and C-H modes. NIR absorption bands related to proteins, particularly amides, are thoroughly detailed in the review by Salzer [59]. The 1500–1530 nm region includes the NH stretching first overtone, while the 2050–2060 nm region pertains to the NH-stretching combination bands. Additionally, absorption bands within the 2148 to 2200 nm wavelength range are valuable for constructing calibration and prediction models in protein research [59,60].
Around the early 2000s, more compact, rapid, and user-friendly spectrometers equipped with state-of-the-art software began to emerge. Miniaturization of NIR devices has significantly reduced the high capital costs associated with traditional large NIR instruments. Over the past decade, there have been substantial advancements in instrument miniaturization, including those designed for use in non-traditional environments [61,62]. Miniaturized NIR instruments are now being utilized by the military for security surveillance, farmers for rapid analysis of agricultural produce and pest control, and pharmacies for drug screening. Additionally, NIR handheld instruments have shown strong potential for disease diagnosis, though this area has not yet been fully explored [63].

5. Clinical Field Trials of ATR-FTIR Spectroscopy

Henry Mantsch was vociferous in promoting biospectroscopy into medicine, with the development of a widely implemented point-of-care (PoC) spectroscopic disease diagnostic being a major aim. Malaria diagnosis by IR spectroscopy shows great promise in this respect. Laboratory experiments with mid-IR spectroscopy using spiked red blood cells [7,91] had demonstrated potential for detection of parasitemia at levels somewhere between the limits of light microscopy and PCR detection, as well as being sensitive to different stages in the malaria parasite life cycle [31]. Indeed, the need for a cheap, rapid, and accurate spectroscopic test with the potential to detect parasitemia at levels necessary to detect asymptomatic carriers in mass screening efforts in low-income settings has made IR spectroscopy-based testing very attractive. This has led to date to two pilot field trials designed to test this notion, published concurrently in the Malaria Journal [57,92].
Both trials were similar, employing portable FTIR ATR spectrometers to measure extracted red blood cell (RBC) fractions isolated from clinical samples obtained using venipuncture. The studies differed in terms of sampling strategy. The Heraud et al. [57] study obtained samples at regional hospitals in Northeast Thailand from patients presenting with symptoms consistent with malaria infection. Whereas, in Mwanga et al. [92], samples were obtained in a cross-sectional malaria survey in Tanzania, with subjects included regardless of the perceived state of their health. The patient sample size was similar in both studies, with Mwanga et al. [92] considering 296 and Heraud et al. [57] 318 patients. Neither of the studies was point-of-care, with both measuring stored samples. In the Heraud et al. [57] study, methanol-fixed RBCs were stored and measured subsequently by placing an aliquot of the fixed cell slurry on the ATR crystal that was then dried by the air stream and measured. Mwanga et al. [92] placed a drop of the RBC extract on Whatman filter paper with the dried sample spot pressed down onto the ATR crystal with the instrument anvil.
Analysis was similar in overall approach for both studies, with the use of PCR as the gold standard. However, the details differed. Heraud et al. [57] employed quality testing of spectra using water vapor and signal-to-noise ratio as testing criteria as well as examining the similarity of individual spectra to the model as a basis for accepting or rejecting spectra, whereas Mwanga et al. [92] appeared to not quality test spectra at all. Heraud et al. [57] tested the effects of spectral pre-processing and spectral regions on classification model performance, emphasizing the need for the incorporation of pre-processing into spectroscopy-based malaria diagnostic, conceiving a system where quality testing, pre-processing, and classification would utilize “cloud-based” algorithms and demonstrated the ability of their measurement system to receive and send data from a remote computer. By contrast, Mwanga et al. [92] used an arbitrarily chosen pre-processing scheme.
Classification modeling approaches were similar in both studies, with data split 70:30 and 80:20 into calibration and validation sets for Heraud et al. [57] and Mwanga et al. [92], respectively. Prospective validation of calibration models was not completed in either study. Heraud et al. [57] compared support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) classification modeling, finding SVM performed slightly better with 92% sensitivity (3 false negatives out of 39 true positives) and 97% specificity (2 false positives out of 57 true negatives) and an area under the receiver operation curve (AUROC) of 0.98. Whereas Mwanga et al. [92] compared k-nearest neighbors (KNN), logistic regression (LR), (SVM), naïve Bayes (NB), XGBoost (XGB), random forest (RF), and multilayer perceptron (MLP), finding LR performed slightly better than the other approaches with a sensitivity of 92% (2 false negatives out of set of 28 true positives) and specificity of 93% (2 false positives out of a set of 24 true negatives). Mwanga et al. [92] did not state AUROC performance. One reason for the much smaller validation set in the Mwanga et al. [92] study was that it only included patients with monospecific infection (P. falciparum). When mixed infections (P. falciparum plus P. ovale) were included, the sensitivity dropped to 82% and specificity to 91%. Unlike the study of Mwanga et al. [92], Heraud et al. [57] employed positive malaria samples infected by both P. falciparum, P. vivax, and mixed infections (P. falciparum, n = 58; P. vivax, n = 77; P. falciparum/P. vivax, n = 16) in both calibration and validation sets. The comparison of classification performance between the two studies is summarized in Table 4.
Table 4. Comparison of the classification performance of spectroscopy-based models in the studies of Heraud et al. [57] and Mwanga et al. [92].
The advantage of PLS-DA modeling, as described by Heraud et al. [57], was the ability to examine regression loadings explicitly and, hence, determine the spectral regions most heavily weighted for classifying malaria positive and negative spectra. This is shown in Figure 21.
Figure 21. A partial least squares discriminant analysis (PLS-DA) prediction plot showing the classification of either malaria positive (<0.5) or negative (>0.5); spectra color-coded malaria positive (red) or negative (green) by PCR. (B) Same as in (A), except support vector machine (SVM) learning is used for the classification. (C) Receiver operating characteristic (ROC) curves showing the diagnostic of the PLS-DA and SVM classification. (D) ROC curve for data where samples were assigned positive- and negative, based on PCR versus randomized models. (E) Average spectra over the three spectral ranges used for PLS-DA classification. Superimposed is a color code showing the regression loadings for malaria positive (“warm colors”) or negative (“cool colors”) classification for each absorbance value. This figure is reproduced from an open-access article published by Biomedical Central (BMC), a part of Springer Nature [57].
Analysis of the partial least squares discriminant analysis (PLS-DA) loadings reveals a striking pattern: the most significant positive weightings for malaria-infected samples are predominantly associated with lipid-related spectral bands. Specifically, the CH3/CH2 bending modes (~1370 cm−1 and 1450 cm−1) and CH2 stretching modes (~2850 cm−1 and 2920 cm−1) show particularly strong correlations with malarial infection (Figure 21). This pronounced lipid signature in the spectral data suggests a potential biochemical marker for malaria infection, likely reflecting the parasite’s impact on host cell lipid metabolism or its own lipid-rich structures. This is consistent with the increase in lipid absorbance observed in malaria-spiked RBC samples in all life stages compared with controls [31]. By contrast, the spectral regions most heavily weighted for malaria positive and negative samples with the Logistic Regression modeling employed by Mwanga et al. [92] are harder to determine with any precision with only single wavenumber values influencing model prediction provided. Contrary to what one would presume to be logical, and remaining unexplained, wavenumbers 1729 cm−1 and 1730 cm−1, presumably relating to lipid ester carbonyl absorbance, were those most strongly determining the classification of non-infected control samples in the LR modeling.
Little information could be gained about the differences between infected and non-infected samples from the average spectra presented by Mwanga et al. [92], which were dominated by intense bands from cellulose. The spectral contamination from the cellulose background was intense across the entire fingerprint region and potentially could obscure bands related to malaria parasitemia. It is likely to be difficult to fully account for the spectral contamination by spectral pre-processing, a less than perfect situation in terms of modeling accuracy, limiting the usefulness of this sampling approach, compared to the Heraud et al. [57] study where fixed aliquots of RBCs were measured directly. The very small sample volume probed by ATR FTIR spectroscopy may also be a factor limiting detection sensitivity in both these studies. Other techniques that, by their nature, measure larger sample volumes, such as IR transflection [161] and NIR spectroscopy [5], would be expected to have lower detection limits by probing larger cell volumes, thus proving superior to ATR methods for the development of a reliable malaria field diagnostic. These alternative approaches also have the potential to use smaller handheld devices better suited to point-of-care measurements in the field than the still rather cumbersome ATR spectrometers used in these studies. The ease of use of these handheld devices for use by minimally trained users would certainly be enhanced by employing remote “Cloud-computing” to handle spectral quality control, pre-processing, modeling, and return of the diagnostic result to the user as advocated by Heraud et al. [57].

6. Pathway and Obstacles to Translation

In addition to improvements in measurement technologies, further scope for improvement of the diagnostic concept may lie in reducing the blood sample volume for the measurement. The two field studies relied on venepuncture for sampling however, only a small quantity of the blood sample was used for spectroscopy. Development of less invasive methods is required, especially to compete with existing technology, such as rapid antigen testing used as a comparison diagnostic in both studies. Given the very small measurement volumes that are probed by spectroscopic methods, it should be possible to engineer a more refined sample processing technology that could work with finger-prick volumes of blood and provide a direct presentation of the processed sample to the measurement device. This development is essential for making the diagnostic more rapid, easier to use, and improving measurement consistency. A potentially ground-breaking approach in malaria diagnostics would be the in vivo detection of parasitemia using Near-Infrared (NIR) spectroscopy, where NIR light is transmitted directly through the patient’s skin. While this non-invasive method is theoretically feasible, it currently faces significant practical challenges. The primary hurdle is the high detection limit, which surpasses the sensitivity required for accurate diagnosis of low-level parasitemia. This limitation stems from factors such as signal attenuation by skin and other tissues, the complex optical properties of blood, and the relatively low concentration of parasites in peripheral circulation during the early stages of infection. Despite these current constraints, ongoing advancements in NIR technology, signal processing algorithms, and our understanding of malaria pathophysiology may eventually overcome these barriers, making this non-invasive approach a reality in future malaria diagnostics. How many patient spectra are needed to develop classification models powerful enough to accurately diagnose malaria prospectively?
Heraud et al. [57] attempted to estimate empirically the size of the calibration set necessary to achieve optimal modelling accuracy by using field data from their trial with classification performance being monitored by cross validation using successive PLS-DA models with increasing number of samples in the calibration data sets (up to n = 200; using 20 replicates in each case). Classification error was observed to decrease exponentially with sample size (n), with extrapolation of the trend line predicting very low error rate at n = 500. A “cloud” based diagnostic would enhance the opportunity to improve classification power in real time by enabling the updating of models on a continuous basis as new spectra were acquired.
Apart from having adequate sample size in the calibration set, the need to control for environmental factors resulting in measurement variability is paramount for developing a reliable spectroscopy-based diagnostics. Unlike genetic testing such as PCR, it can be argued that phenotypic testing such as spectroscopy-based diagnostics is more vulnerable to perturbation by uncontrolled environmental factors. For example, the study by Martin et al. [90] demonstrated that the use of different anticoagulant tubes could affect the detection and quantification of malaria parasitemia in human red blood cells by ATR-FTIR spectroscopy. This underscores the need to develop standardized methods and protocols that attempt to minimize and control for as many of these environmental factors as possible. An advantage of a spectroscopy-based diagnostic compared to PCR testing, for example, is that other important diagnostic information apart from the presence of parasitemia might be gleaned from the single sample spectrum using multiple classification models applied in parallel. Examples are the determination of blood chemistry such as glucose, urea and hemoglobin levels [91] or diagnosis of other key indicators such as glucose-6 phosphate dehydrogenase deficiency [162], in addition to the malaria diagnosis, making the test more valuable to the practitioner in deciding the best course of treatment.

7. Application of Spectroscopy to Monitor Drug Interactions

Vibrational spectroscopy including techniques like FTIR and Raman spectroscopy, offers a powerful tool for monitoring drug interactions in malaria-infected red blood cells. These non-destructive methods allow for real-time analysis of biochemical changes at the molecular level, enabling researchers to observe how antimalarial drugs affect the parasite’s metabolism and the host cell environment. This approach provides insights into the mode of action of drugs, parasite resistance mechanisms, and the overall biochemical impact of the treatment. Furthermore, combining these spectroscopic techniques with advanced chemometric modelling enhances the ability to discriminate between healthy and infected cells, facilitating high-throughput drug screening and personalized therapeutic strategies against malaria.
Many antimalarial drugs exhibit strong Raman scattering, making this technique highly suitable for (i) conducting structural studies both in the presence and absence of metabolites, and (ii) detecting and quantifying these drugs under various conditions [163]. Many studies have investigated the significance of complex formation between antimalarial drugs such as chloroquine and ferriprotoporphyrin IX Fe(III)PPIX in solution [164]. For instance, Frosch et al. [165] reported the presence of a non-covalent interaction in the electronic ground state of the drug–target complex using polarization-resolved resonance Raman spectroscopy. They suggested that the non-covalent interaction of chloroquine with hematin induces a change in the excited-state geometry along specific ground-state normal coordinates. In another study by the same team, the density functional theory (DFT) calculations were used to perform the mode assignment, which indicated that the protonation states of CQ significantly affect its molecular geometry, vibrational modes, and molecular orbitals. These alterations are crucial for its π–π interactions with hemozoin [166]. Furthermore, the team has performed a number of investigations to elucidate the structure of antimalarial drugs such as halafantine [167], the antiplasmodial naphthylisoquinoline alkaloid dioncophylline A [168,169], quinine in cinchona bark [166], and mefloquine [170] using Raman spectroscopy and DFT calculations.
Webster et al. [31] utilized resonance Raman spectroscopy to monitor the effects of chloroquine (CQ) treatment on cultures of falciparum trophozoites. This vibrational spectroscopic study is the first of its kind to investigate the effect of drug treatment on single P. falciparum-infected red blood cells. PCA reveals that the intensity of the A1g modes; 1570, 1376, 796, 678 cm−1 and the B1g modes: 1552, 751 cm−1 is reduced in CQ treated cells compared to the untreated controls. The exact mechanism by which CQ exerts its antimalarial effects remains not fully elucidated. Theories have suggested that the CQ binds to ferriprotoporphyrin IX (FPIX) to form the FPIX–CQ complex and can lead to parasite cell autodigestion. Kozicki et al. [171] investigated and compared the CQ-treated and untreated cultured falciparum-infected human red blood cells (iRBCs) using attenuated total reflection (ATR-FTIR) and Raman spectroscopy. The intensities of bands correspond to biochemical moieties such as proteins, lipids, nucleic acids and carbohydrates were changed in response to CQ treatment. The ATR-FTIR analysis reveals an increase in the CH stretching bands within the 3100–2800 cm−1 range in CQ-treated iRBCs, indicating a higher concentration of saturated lipids. The PCA showed a characteristic of ferric heme band at 1379 cm−1 due to high oxygenated hemoglobin concentration in the CQ-treated iRBCs. Recently, Wolf et al. [172] manifested the significance of quinoline chloroquine–hematin interaction in solution using an advanced, highly parallelized Raman difference spectroscopy setup. A shift of (−1.12 ± 0.05) cm−1 was observed in the core-size marker band ν(CαCm)asym peak position of the 1:1 chloroquine-hematin mixture compared to pure hematin. Additionally, the oxidation-state marker band ν(pyrrole half-ring)sym showed a shift of (+0.93 ± 0.13) cm−1. This is consistent with the results from DFT calculations. The study has provided significant insights to the antimalarial action of CQ.
These findings demonstrate the capability of vibrational spectroscopy to capture molecular-level changes induced by drug binding and metabolism. However, a multimodal spectroscopic approach, combining both Raman and FTIR techniques, would provide more comprehensive insights by offering complementary information on the molecular structure, biochemical environment, and interaction dynamics. Furthermore, integrating computational chemistry tools such as DFT and molecular dynamics simulations can enhance the interpretation of spectral data, helping to elucidate the underlying mechanisms of drug action and resistance at the atomic level. This holistic approach holds promise for optimizing antimalarial drug design and improving our understanding of their interactions with both the parasite and the host cell.

8. Conclusions

The application of spectroscopic techniques in malaria research represents a paradigm shift in our approach to disease diagnosis, management, and drug development. As we stand at the crossroads of technological innovation and global health challenges, these advanced spectroscopic methods offer a glimpse into a future where rapid, accurate, and minimally invasive diagnostics could revolutionize malaria control efforts. Moreover, spectroscopy plays a crucial role in the development and evaluation of new antimalarial drugs. Providing detailed molecular insights into drug–parasite interactions enables researchers to design more effective compounds and optimize their efficacy against resistant strains.
The work pioneered by Professor Henry Mantsch and furthered by numerous researchers has laid a foundation for translating laboratory successes into real-world applications. Spectroscopic techniques, such as Raman and infrared spectroscopy, offer powerful tools for assessing the effectiveness of antimalarials. These methods can monitor drug uptake, metabolism, and distribution within infected cells, providing valuable information on drug action mechanisms and potential resistance development.
However, the journey from bench to bedside is fraught with challenges. The variability in field conditions, the need for standardization, and the complexities of biological systems all pose significant hurdles. Yet, these very challenges present opportunities for interdisciplinary collaboration and innovation. The integration of spectroscopic techniques with emerging technologies such as artificial intelligence and cloud computing could potentially overcome current limitations and usher in a new era of personalized malaria management and tailored drug therapies.
As we look to the future, we must ask ourselves: How can we harness the full potential of spectroscopic techniques to not only diagnose but also predict and prevent malaria outbreaks? How can we leverage these technologies to accelerate the discovery and development of novel antimalarial compounds? What role will these technologies play in the broader context of global health equity and disease eradication efforts? The answers to these questions may well determine the course of malaria control and treatment in the coming decades.
Ultimately, the true measure of success for these spectroscopic approaches will not be in their technical sophistication, but in their ability to make a tangible difference in the lives of those most affected by malaria. This includes not only improving diagnostics but also facilitating the development of more effective and accessible antimalarial drugs. As we continue to push the boundaries of what is possible with spectroscopy, we must remain focused on the end goal: a world free from the burden of malaria. The path forward requires not only scientific ingenuity but also a commitment to translating these promising technologies into accessible, affordable, and effective tools for communities around the globe, encompassing both diagnostic capabilities and enhanced therapeutic interventions.

Author Contributions

Conceptualization: B.R.W.; Resources: B.R.W.; writing—original: B.R.W. Abstract, 1. Introduction, 3. Spectral biomarkers, 8. Conclusions B.R.W.; 3.4.4 Near Infrared Spectroscopy of Proteins, 4.6 Near Infrared Spectroscopy, 4.7. UV/Visible Spectroscopy, 4.8. Multimodal Spectroscopy J.A.A.; 2. Life cycle of the parasite, 7. Application of Spectroscopy to Monitor Drug Inter-actions T.C.C.V.; 4.1. Raman Spectroscopy, 4.1.1. Resonance Raman Spectroscopy, 4.1.2. Raman Acoustic Levitation Spectroscopy (RALS), 4.1.3. Raman Spectroscopy Coupled to Quantitative Phase Microscopy (QPM), 4.1.4. Raman Analysis of Malaria Analytes in Serum, Plasma, and Blood Samples, 4.1.5. Raman Analysis of Malaria Parasites in Tissues A.D.; 4.13. Other Non-Invasive Approaches K.D.; 4.2. Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy N.M.; 4.5. Optical Photothermal Infrared (O-PTIR) Imaging C.G.; 4.3. Focal Plane Array Fourier Transform Infrared (FPA-FTIR) Imaging Spectroscopy V.S.; 4.1.6. Surface Enhanced Raman Spectroscopy (SERS), 4.1.7. Magnetic Field-Assisted SERS S.J.; 4.11. Atomic Force Microscopy–Infrared (AFM-IR) Spectroscopy, 4.12. Tip-Enhanced Raman Spectroscopy (TERS) M.G.; 4.4. Synchrotron FTIR Spectroscopy, 4.9. Photoacoustic Spectroscopy, 4.10. Photoacoustic Imaging (PAI) D.B.; 5. Clinical Field Trials of ATR-FTIR Spectroscopy, 6. Pathway and Obstacles to Translation P.H. All authors proof read the manuscript. Supervision: B.R.W.; Project administration: B.R.W.; Funding acquisition: B.R.W. and D.E.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

D.E.B. thanks the European Union’s Horizon Europe Marie Sklodowska-Curie grant (Grant agreement No. 101106307).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Venkatesan, P. The 2023 WHO World malaria report. Lancet Microbe 2024, 5, e214. [Google Scholar] [CrossRef] [PubMed]
  2. Abebaw, A.; Aschale, Y.; Kebede, T.; Hailu, A. The prevalence of symptomatic and asymptomatic malaria and its associated factors in Debre Elias district communities, Northwest Ethiopia. Malar. J. 2022, 21, 167. [Google Scholar] [CrossRef] [PubMed]
  3. Danwang, C.; Kirakoya-Samadoulougou, F.; Samadoulougou, S. Assessing field performance of ultrasensitive rapid diagnostic tests for malaria: A systematic review and meta-analysis. Malar. J. 2021, 20, 245. [Google Scholar] [CrossRef] [PubMed]
  4. Mantsch, H.; Jackson, M. Molecular spectroscopy in biodiagnostics (from Hippocrates to Herschel and beyond). J. Mol. Struct. 1995, 347, 187–206. [Google Scholar] [CrossRef]
  5. Adegoke, J.A.; Kochan, K.; Heraud, P.; Wood, B.R. A Near-Infrared A near-infrared “matchbox size” spectrometer to detect and quantify malaria parasitemia. Anal. Chem. 2021, 93, 5451–5458. [Google Scholar] [CrossRef]
  6. Adegoke, J.A.; Raper, H.; Gassner, C.; Heraud, P.; Wood, B.R. Visible microspectrophotometry coupled with machine learning to discriminate the erythrocytic life cycle stages of P. falciparum malaria parasites in functional single cells. Analyst 2022, 147, 2662–2670. [Google Scholar] [CrossRef]
  7. Khoshmanesh, A.; Dixon, M.W.; Kenny, S.; Tilley, L.; McNaughton, D.; Wood, B.R. Detection and quantification of early-stage malaria parasites in laboratory infected erythrocytes by attenuated total reflectance infrared spectroscopy and multivariate analysis. Anal. Chem. 2014, 86, 4379–4386. [Google Scholar] [CrossRef]
  8. Rathi, A.; Chowdhry, Z.; Patel, A.; Zuo, S.; Veettil, T.C.P.; Adegoke, J.A.; Heidari, H.; Wood, B.R.; Bhallamudi, V.P.; Peng, W.K. Hemozoin in malaria eradication—From material science, technology to field test. NPG Asia Mater. 2023, 15, 70. [Google Scholar] [CrossRef]
  9. Drouin, E.; Hautecoeur, P.; Markus, M. Who was the first to visualize the malaria parasite? Parasites Vectors 2024, 17, 184. [Google Scholar] [CrossRef]
  10. Klencke, P.F.H. Neue Physiologische Abhandlungen auf Selbständige Beobachtungen Gegründet für Aerzte und Naturforscher; LH Bösenberg: Leipzig, Germany, 1843; Volume VIII. [Google Scholar]
  11. Lavéran, A. Note sur un nouveau parasite trouvé dans le sang de plusieurs malades atteints de fièvre palustre. Bull. Acad. Méd. 1880, 1880, 1235–1236. [Google Scholar]
  12. Egan, T.J. Hemozoin Biophysics and Drug Target. In Encylopedia Malar; Hommel, M., Kremsner, P., Eds.; Springer: New York, NY, USA, 2013; pp. 1–11. [Google Scholar] [CrossRef]
  13. Xiao, S.H.; Sun, J. Schistosoma hemozoin and its possible roles. Int. J. Parasitol. 2017, 47, 171–183. [Google Scholar] [CrossRef] [PubMed]
  14. Schmidt, J.A.; Udeinya, I.J.; Leech, J.H.; Hay, R.J.; Aikawa, M.; Barnwell, J.; Green, I.; Miller, L.H. Plasmodium falciparum malaria. An amelanotic melanoma cell line bears receptors for the knob ligand on infected erythrocytes. J. Clin. Investig. 1982, 70, 379–386. [Google Scholar] [CrossRef]
  15. Qadir, H.; Baig, M.M.; Ojla, M.D.; Aisha, M.; Adil, A. Malarial Pigment Hemozoin in Neutrophils: A Manifestation of Severe Malaria. Turk. J. Haematol. 2023, 40, 137–138. [Google Scholar] [CrossRef]
  16. Dixon, M.W.; Thompson, J.; Gardiner, D.L.; Trenholme, K.R. Sex in Plasmodium: A sign of commitment. Trends Parasitol. 2008, 24, 168–175. [Google Scholar] [CrossRef]
  17. Birhanu, M.; Asres, Y.; Adissu, W.; Yemane, T.; Zemene, E.; Gedefaw, L. Hematological Parameters and Hemozoin-Containing Leukocytes and Their Association with Disease Severity among Malaria Infected Children: A Cross-Sectional Study at Pawe General Hospital, Northwest Ethiopia. Interdiscip. Perspect. Infect. Dis. 2017, 2017, 8965729. [Google Scholar] [CrossRef] [PubMed]
  18. Pagola, S.; Stephens, P.W.; Bohle, D.S.; Kosar, A.D.; Madsen, S.K. The structure of malaria pigment β-haematin. Nature 2000, 404, 307–310. [Google Scholar] [CrossRef] [PubMed]
  19. Solomonov, I.; Osipova, M.; Feldman, Y.; Baehtz, C.; Kjaer, K.; Robinson, I.K.; Webster, G.T.; McNaughton, D.; Wood, B.R.; Weissbuch, I.; et al. Crystal nucleation, growth, and morphology of the synthetic malaria pigment β-hematin and the effect thereon by quinoline additives: The malaria pigment as a target of various antimalarial drugs. J. Am. Chem. Soc. 2007, 129, 2615–2627. [Google Scholar] [CrossRef]
  20. Wood, B.R.; Langford, S.J.; Cooke, B.M.; Lim, J.; Glenister, F.K.; Duriska, M.; Unthank, J.K.; McNaughton, D. Resonance Raman spectroscopy reveals new insight into the electronic structure of β-hematin and malaria pigment. J. Am. Chem. Soc. 2004, 126, 9233–9239. [Google Scholar] [CrossRef]
  21. Marzec, K.M.; Perez-Guaita, D.; de Veij, M.; McNaughton, D.; Baranska, M.; Dixon, M.W.A.; Tilley, L.; Wood, B.R. Red Blood Cells Polarize Green Laser Light Revealing Hemoglobin’s Enhanced Non-Fundamental Raman Modes. ChemPhysChem 2014, 15, 3963–3968. [Google Scholar] [CrossRef]
  22. Slater, A.F. Malaria pigment. Exp. Parasitol. 1992, 74, 362–365. [Google Scholar] [CrossRef]
  23. Slater, A.F.; Swiggard, W.J.; Orton, B.R.; Flitter, W.D.; Goldberg, D.E.; Cerami, A.; Henderson, G.B. An iron-carboxylate bond links the heme units of malaria pigment. Proc. Natl. Acad. Sci. USA 1991, 88, 325–329. [Google Scholar] [CrossRef] [PubMed]
  24. Bohle, D.S.; Conklin, B.J.; Cox, D.; Madsen, S.K.; Paulson, S.; Stephens, P.W.; Yee, G.T. Structural and Spectroscopic Studies of beta-Hematin (the Heme Coordination Polymer in Malaria Pigment). In Inorganic and Organometallic Polymers II: Advanced Materials and Intermediates; Wisian-Neilson, P., Allcock, H.R., Wynne, K.J., Eds.; ACS Symposium Series; American Chemical Society: Washington, DC, USA, 1994; Volume 572, pp. 497–515. [Google Scholar]
  25. Morselt, A.F.; Glastra, A.; James, J. Microspectrophotometric analysis of malarial pigment. Exp. Parasitol. 1973, 33, 17–22. [Google Scholar] [CrossRef] [PubMed]
  26. Lukianova-Hleb, E.Y.; Campbell, K.M.; Constantinou, P.E.; Braam, J.; Olson, J.S.; Ware, R.E.; Sullivan, D.J., Jr.; Lapotko, D.O. Hemozoin-generated vapor nanobubbles for transdermal reagent- and needle-free detection of malaria. Proc. Natl. Acad. Sci. USA 2014, 111, 900–905. [Google Scholar] [CrossRef] [PubMed]
  27. Bendrat, K.; Berger, B.J.; Cerami, A. Haem polymerization in malaria. Nature 1995, 378, 138–139. [Google Scholar] [CrossRef] [PubMed]
  28. Ancelin, M.L.; Vial, H.J. Saturable and non-saturable components of choline transport in Plasmodium-infected mammalian erythrocytes: Possible role of experimental conditions. Biochem. J. 1992, 283 Pt 2, 619–621. [Google Scholar] [CrossRef][Green Version]
  29. Holz, G.G., Jr. Lipids and the malarial parasite. Bull. World Health Organ. 1977, 55, 237–248. [Google Scholar]
  30. Vielemeyer, O.; McIntosh, M.T.; Joiner, K.A.; Coppens, I. Neutral lipid synthesis and storage in the intraerythrocytic stages of Plasmodium falciparum. Mol. Biochem. Parasitol. 2004, 135, 197–209. [Google Scholar] [CrossRef]
  31. Webster, G.T.; de Villiers, K.A.; Egan, T.J.; Deed, S.; Tilley, L.; Tobin, M.J.; Bambery, K.R.; McNaughton, D.; Wood, B.R. Discriminating the intraerythrocytic lifecycle stages of the malaria parasite using synchrotron FT-IR microspectroscopy and an artificial neural network. Anal. Chem. 2009, 81, 2516–2524. [Google Scholar] [CrossRef]
  32. Pisciotta, J.M.; Coppens, I.; Tripathi, A.K.; Scholl, P.F.; Shuman, J.; Bajad, S.; Shulaev, V.; Sullivan, D.J., Jr. The role of neutral lipid nanospheres in Plasmodium falciparum haem crystallization. Biochem. J. 2007, 402, 197–204. [Google Scholar] [CrossRef]
  33. Palacpac, N.M.; Hiramine, Y.; Seto, S.; Hiramatsu, R.; Horii, T.; Mitamura, T. Evidence that Plasmodium falciparum diacylglycerol acyltransferase is essential for intraerythrocytic proliferation. Biochem. Biophys. Res. Commun. 2004, 321, 1062–1068. [Google Scholar] [CrossRef]
  34. Jackson, K.E.; Klonis, N.; Ferguson, D.J.; Adisa, A.; Dogovski, C.; Tilley, L. Food vacuole-associated lipid bodies and heterogeneous lipid environments in the malaria parasite, Plasmodium falciparum. Mol. Microbiol. 2004, 54, 109–122. [Google Scholar] [CrossRef] [PubMed]
  35. Nawabi, P.; Lykidis, A.; Ji, D.; Haldar, K. Neutral-lipid analysis reveals elevation of acylglycerols and lack of cholesterol esters in Plasmodium falciparum-infected erythrocytes. Eukaryot. Cell 2003, 2, 1128–1131. [Google Scholar] [CrossRef]
  36. Egan, T.J.; Chen, J.Y.; de Villiers, K.A.; Mabotha, T.E.; Naidoo, K.J.; Ncokazi, K.K.; Langford, S.J.; McNaughton, D.; Pandiancherri, S.; Wood, B.R. Haemozoin (β-haematin) biomineralization occurs by self-assembly near the lipid/water interface. FEBS Lett. 2006, 580, 5105–5110. [Google Scholar] [CrossRef]
  37. Birczyńska-Zych, M.; Czepiel, J.; Łabanowska, M.; Kraińska, M.; Biesiada, G.; Moskal, P.; Kozicki, M.; Garlicki, A.; Wesełucha-Birczyńska, A. Could Raman spectroscopy distinguish between P. falciparum and P. vivax Infection? Clin. Spectrosc. 2021, 3, 100015. [Google Scholar] [CrossRef]
  38. Gardner, M.J.; Hall, N.; Fung, E.; White, O.; Berriman, M.; Hyman, R.W.; Carlton, J.M.; Pain, A.; Nelson, K.E.; Bowman, S.; et al. Genome sequence of the human malaria parasite Plasmodium falciparum. Nature 2002, 419, 498–511. [Google Scholar] [CrossRef] [PubMed]
  39. Ganter, M.; Goldberg, J.M.; Dvorin, J.D.; Paulo, J.A.; King, J.G.; Tripathi, A.K.; Paul, A.S.; Yang, J.; Coppens, I.; Jiang, R.H.; et al. Plasmodium falciparum CRK4 directs continuous rounds of DNA replication during schizogony. Nat. Microbiol. 2017, 2, 17017. [Google Scholar] [CrossRef]
  40. Machado, M.; Steinke, S.; Ganter, M. Plasmodium Reproduction, Cell Size, and Transcription: How to Cope with Increasing DNA Content? Front. Cell. Infect. Microbiol. 2021, 11, 660679. [Google Scholar] [CrossRef]
  41. Sambe, B.S.; Diagne, A.; Diatta, H.A.M.; Gaba, F.M.; Sarr, I.; Diatta, A.S.; Diaw, S.O.M.; Sané, R.; Diouf, B.; Vigan-Womas, I.; et al. Molecular detection and quantification of Plasmodium vivax DNA in blood pellet and plasma samples from patients in Senegal. Front. Parasitol. 2023, 2, 1149738. [Google Scholar] [CrossRef]
  42. Hobro, A.J.; Konishi, A.; Coban, C.; Smith, N.I. Raman spectroscopic analysis of malaria disease progression via blood and plasma samples. Analyst 2013, 138, 3927–3933. [Google Scholar] [CrossRef]
  43. Wood, B.R. The importance of hydration and DNA conformation in interpreting infrared spectra of cells and tissues. Chem. Soc. Rev. 2016, 45, 1980–1998. [Google Scholar] [CrossRef]
  44. Reuterswärd, P.; Bergström, S.; Orikiiriza, J.; Lindquist, E.; Bergström, S.; Andersson Svahn, H.; Ayoglu, B.; Uhlén, M.; Wahlgren, M.; Normark, J.; et al. Levels of human proteins in plasma associated with acute paediatric malaria. Malar. J. 2018, 17, 426. [Google Scholar] [CrossRef] [PubMed]
  45. Ikegbunam, M.; Maurer, M.; Abone, H.; Ezeagwuna, D.; Sandri, T.L.; Esimone, C.; Ojurongbe, O.; Woldearegai, T.G.; Kreidenweiss, A.; Held, J.; et al. Evaluating Malaria Rapid Diagnostic Tests and Microscopy for Detecting Plasmodium Infection and Status of Plasmodium falciparum Histidine-Rich Protein 2/3 Gene Deletions in Southeastern Nigeria. Am. J. Trop. Med. Hyg. 2024, 110, 902–909. [Google Scholar] [CrossRef] [PubMed]
  46. Kojom Foko, L.P.; Eboumbou Moukoko, C.E.; Jakhan, J.; Narang, G.; Hawadak, J.; Kouemo Motse, F.D.; Pande, V.; Singh, V. Deletions of Histidine-Rich Protein 2/3 Genes in Natural Plasmodium falciparum Populations from Cameroon and India: Role of Asymptomatic and Submicroscopic Infections. Am. J. Trop. Med. Hyg. 2024, 110, 1100–1109. [Google Scholar] [CrossRef] [PubMed]
  47. Oyegoke, O.O.; Akoniyon, O.P.; Maharaj, L.; Adewumi, T.S.; Malgwi, S.A.; Aderoju, S.A.; Fatoba, A.J.; Adeleke, M.A.; Maharaj, R.; Okpeku, M. Molecular detection of sub-microscopic infections and Plasmodium falciparum histidine-rich protein-2 and 3 gene deletions in pre-elimination settings of South Africa. Sci. Rep. 2024, 14, 16024. [Google Scholar] [CrossRef]
  48. Ranjan, P.; Ghoshal, U.; Prakash, S.; Pandey, A.; Shukla, R. Genetic variability of histidine-rich protein 2 repeat sequences: Misleading factor in true determination of Plasmodium falciparum in different population. Indian J. Med. Microbiol. 2024, 49, 100616. [Google Scholar] [CrossRef] [PubMed]
  49. Schreidah, C.; Giesbrecht, D.; Gashema, P.; Young, N.W.; Munyaneza, T.; Muvunyi, C.M.; Thwai, K.; Mazarati, J.B.; Bailey, J.A.; Juliano, J.J.; et al. Expansion of artemisinin partial resistance mutations and lack of histidine rich protein-2 and -3 deletions in Plasmodium falciparum infections from Rukara, Rwanda. Malar. J. 2024, 23, 150. [Google Scholar] [CrossRef]
  50. Xu, S.; Tang, J. Biological threats to global malaria elimination II Deletion in the malaria rapid diagnostic test target Plasmodium falciparum histidine-rich protein 2/3 genes. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2024, 36, 239–242. [Google Scholar] [CrossRef]
  51. Bachman, C.M.; Cate, D.M.; Grant, B.; Burkot, S.; Mulondo, J.; Hsieh, H.V.; Chamai, M.; Odongo, B.; Olwoch, P.; Nalubega, M.; et al. A Novel Malaria Lateral Flow Assay for Detecting Plasmodium falciparum Lactate Dehydrogenase in Busia, Uganda. Am. J. Trop. Med. Hyg. 2022, 106, 850–852. [Google Scholar] [CrossRef]
  52. Feleke, S.M.; Gidey, B.; Mohammed, H.; Nega, D.; Dillu, D.; Haile, M.; Solomon, H.; Parr, J.B.; Tollera, G.; Tasew, G.; et al. Field performance of Plasmodium falciparum lactate dehydrogenase rapid diagnostic tests during a large histidine-rich protein 2 deletion survey in Ethiopia. Malar. J. 2022, 21, 236. [Google Scholar] [CrossRef]
  53. Tan, A.F.; Sakam, S.S.B.; Rajahram, G.S.; William, T.; Abd Rachman Isnadi, M.F.; Daim, S.; Barber, B.E.; Kho, S.; Sutherland, C.J.; Anstey, N.M.; et al. Diagnostic accuracy and limit of detection of ten malaria parasite lactate dehydrogenase-based rapid tests for Plasmodium knowlesi and P. falciparum. Front. Cell. Infect. Microbiol. 2022, 12, 1023219. [Google Scholar] [CrossRef]
  54. Saxena, N.; Pandey, V.C.; Dutta, G.P.; Ghatak, S. Levels of FDP-aldolase enzyme activity in Plasmodium knowlesi parasitized monkey erythrocytes. Indian J. Exp. Biol. 1986, 24, 502–504. [Google Scholar] [PubMed]
  55. Francis, S.E.; Sullivan, D.J., Jr.; Goldberg, D.E. Hemoglobin metabolism in the malaria parasite Plasmodium falciparum. Annu. Rev. Microbiol. 1997, 51, 97–123. [Google Scholar] [CrossRef]
  56. Wang, Y.; Boysen, R.I.; Wood, B.R.; Kansiz, M.; McNaughton, D.; Hearn, M.T. Determination of the secondary structure of proteins in different environments by FTIR-ATR spectroscopy and PLS regression. Biopolymers 2008, 89, 895–905. [Google Scholar] [CrossRef]
  57. Heraud, P.; Chatchawal, P.; Wongwattanakul, M.; Tippayawat, P.; Doerig, C.; Jearanaikoon, P.; Perez-Guaita, D.; Wood, B.R. Infrared spectroscopy coupled to cloud-based data management as a tool to diagnose malaria: A pilot study in a malaria-endemic country. Malar. J. 2019, 18, 348. [Google Scholar] [CrossRef]
  58. Elliott, A.; Ambrose, E.J. Evidence of chain folding in polypeptides and proteins. Discuss. Faraday Soc. 1950, 9, 246–251. [Google Scholar] [CrossRef]
  59. Salzer, R. Practical Guide to Interpretive Near-Infrared Spectroscopy. By Jerry Workman, Jr. and Lois Weyer. Angew. Chem. Int. Ed. 2008, 47, 4628–4629. [Google Scholar] [CrossRef]
  60. Miyazawa, M.; Sonoyama, M. Second Derivative near Infrared Studies on the Structural Characterisation of Proteins. J. Near Infrared Spectrosc. 1998, 6, A253–A257. [Google Scholar] [CrossRef]
  61. Beć, K.B.; Huck, C.W. Breakthrough Potential in Near-Infrared Spectroscopy: Spectra Simulation. A Review of Recent Developments. Front. Chem. 2019, 7, 48. [Google Scholar] [CrossRef]
  62. Beć, K.B.; Grabska, J.; Huck, C.W. Principles and Applications of Miniaturized Near-Infrared (NIR) Spectrometers. Chem. Eur. J. 2021, 27, 1514–1532. [Google Scholar] [CrossRef]
  63. Antila, J.; Tuohiniemi, M.; Rissanen, A.; Kantojärvi, U.; Lahti, M.; Viherkanto, K.; Kaarre, M.; Malinen, J. MEMS- and MOEMS-Based Near-Infrared Spectrometers. In Encyclopedia of Analytical Chemistry; Wiley: Hoboken, NJ, USA, 2014; pp. 1–36. [Google Scholar]
  64. Wood, B.R.; McNaughton, D. Resonance Raman spectroscopy in malaria research. Expert Rev. Proteom. 2006, 3, 525–544. [Google Scholar] [CrossRef]
  65. Luthra, A.; Denisov, I.G.; Sligar, S.G. Spectroscopic features of cytochrome P450 reaction intermediates. Arch. Biochem. Biophys. 2011, 507, 26–35. [Google Scholar] [CrossRef] [PubMed]
  66. Ong, C.W.; Shen, Z.X.; Ang, K.K.H.; Kara, U.A.K.; Tang, S.H. Resonance Raman Microspectroscopy of Normal Erythrocytes and Plasmodium berghei-Infected Erythrocytes. Appl. Spectrosc. 1999, 53, 1097–1101. [Google Scholar] [CrossRef]
  67. Wood, B.R.; Langford, S.J.; Cooke, B.M.; Glenister, F.K.; Lim, J.; McNaughton, D. Raman imaging of hemozoin within the food vacuole of Plasmodium falciparum trophozoites. FEBS Lett. 2003, 554, 247–252. [Google Scholar] [CrossRef]
  68. Wood, B.R.; Hermelink, A.; Lasch, P.; Bambery, K.R.; Webster, G.T.; Khiavi, M.A.; Cooke, B.M.; Deed, S.; Naumann, D.; McNaughton, D. Resonance Raman microscopy in combination with partial dark-field microscopy lights up a new path in malaria diagnostics. Analyst 2009, 134, 1119–1125. [Google Scholar] [CrossRef] [PubMed]
  69. Puskar, L.; Tuckermann, R.; Frosch, T.; Popp, J.; Ly, V.; McNaughton, D.; Wood, B.R. Raman acoustic levitation spectroscopy of red blood cells and Plasmodium falciparum trophozoites. Lab Chip 2007, 7, 1125–1131. [Google Scholar] [CrossRef]
  70. Kang, J.W.; Lue, N.; Kong, C.-R.; Barman, I.; Dingari, N.C.; Goldfless, S.J.; Niles, J.C.; Dasari, R.R.; Feld, M.S.; Choi, W.; et al. Combined confocal Raman and quantitative phase microscopy system for biomedical diagnosis. Biomed. Opt. Express 2011, 2, 2484–2492. [Google Scholar] [CrossRef]
  71. Brückner, M.; Becker, K.; Popp, J.; Frosch, T. Fiber array based hyperspectral Raman imaging for chemical selective analysis of malaria-infected red blood cells. Anal. Chim. Acta 2015, 894, 76–84. [Google Scholar] [CrossRef]
  72. Kozicki, M.; Czepiel, J.; Biesiada, G.; Nowak, P.; Garlicki, A.; Wesełucha-Birczyńska, A. The ring-stage of Plasmodium falciparum observed in RBCs of hospitalized malaria patients. Analyst 2015, 140, 8007–8016. [Google Scholar] [CrossRef]
  73. Patel, S.K.; Rajora, N.; Kumar, S.; Sahu, A.; Kochar, S.K.; Krishna, C.M.; Srivastava, S. Rapid Discrimination of Malaria- and Dengue-Infected Patients Sera Using Raman Spectroscopy. Anal. Chem. 2019, 91, 7054–7062. [Google Scholar] [CrossRef]
  74. Hackett, M.J.; Aitken, J.B.; El-Assaad, F.; McQuillan, J.A.; Carter, E.A.; Ball, H.J.; Tobin, M.J.; Paterson, D.; De Jonge, M.D.; Siegele, R.; et al. Neuroscience: Mechanisms of murine cerebral malaria: Multimodal imaging of altered cerebral metabolism and protein oxidation at hemorrhage sites. Sci. Adv. 2015, 1, e1500911. [Google Scholar] [CrossRef]
  75. Frame, L.; Brewer, J.; Lee, R.; Faulds, K.; Graham, D. Development of a label-free Raman imaging technique for differentiation of malaria parasite infected from non-infected tissue. Analyst 2017, 143, 157–163. [Google Scholar] [CrossRef] [PubMed]
  76. Hobro, A.J.; Pavillon, N.; Fujita, K.; Ozkan, M.; Coban, C.; Smith, N.I. Label-free Raman imaging of the macrophage response to the malaria pigment hemozoin. Analyst 2015, 140, 2350–2359. [Google Scholar] [CrossRef]
  77. Gardiner, D.J. Introduction to Raman Scattering. In Practical Raman Spectroscopy; Gardiner, D.J., Graves, P.R., Eds.; Springer: Berlin/Heidelberg, Germany, 1989; pp. 1–12. [Google Scholar]
  78. Bell, S.E.J.; Charron, G.; Cortés, E.; Kneipp, J.; de la Chapelle, M.L.; Langer, J.; Procházka, M.; Tran, V.; Schlücker, S. Towards Reliable and Quantitative Surface-Enhanced Raman Scattering (SERS): From Key Parameters to Good Analytical Practice. Angew. Chem. Int. Ed. 2020, 59, 5454–5462. [Google Scholar] [CrossRef]
  79. Chen, F.; Flaherty, B.R.; Cohen, C.E.; Peterson, D.S.; Zhao, Y. Direct detection of malaria infected red blood cells by surface enhanced Raman spectroscopy. Nanomedicine 2016, 12, 1445–1451. [Google Scholar] [CrossRef]
  80. Chen, K.; Perlaki, C.; Xiong, A.; Preiser, P.; Liu, Q. Review of Surface Enhanced Raman Spectroscopy for Malaria Diagnosis and a New Approach for the Detection of Single Parasites in the Ring Stage. IEEE J. Sel. Top. Quantum Electron. 2016, 22, 179–187. [Google Scholar] [CrossRef]
  81. Garrett, N.L.; Sekine, R.; Dixon, M.W.; Tilley, L.; Bambery, K.R.; Wood, B.R. Bio-sensing with butterfly wings: Naturally occurring nano-structures for SERS-based malaria parasite detection. Phys. Chem. Chem. Phys. 2015, 17, 21164–21168. [Google Scholar] [CrossRef] [PubMed]
  82. Radziuk, D.; Moehwald, H. Highly effective hot spots for SERS signatures of live fibroblasts. Nanoscale 2014, 6, 6115–6126. [Google Scholar] [CrossRef] [PubMed]
  83. Heidelberger, M.; Prout, C.; Hindle, J.A.; Rose, A.S. Studies in Human Malaria1: III. An Attempt at Vaccination of Paretics Against Blood-Borne Infection with Pl. Vivax. J. Immunol. 1946, 53, 109–112. [Google Scholar] [CrossRef]
  84. Liu, Q.; Yuen, C.; Chen, K.; Ju, J.; Xiong, A.; Preiser, P. Surface Enhanced Raman Spectroscopy for Malaria Diagnosis and Intradermal Measurements; SPIE: Bellingham, WA, USA, 2018; Volume 10509. [Google Scholar]
  85. Chen, K.; Yuen, C.; Aniweh, Y.; Preiser, P.; Liu, Q. Towards ultrasensitive malaria diagnosis using surface enhanced Raman spectroscopy. Sci. Rep. 2016, 6, 20177. [Google Scholar] [CrossRef]
  86. Wang, W.; Dong, R.-l.; Gu, D.; He, J.-a.; Yi, P.; Kong, S.-K.; Ho, H.-P.; Loo, J.; Wang, W.; Wang, Q. Antibody-free rapid diagnosis of malaria in whole blood with surface-enhanced Raman Spectroscopy using Nanostructured Gold Substrate. Adv. Med. Sci. 2020, 65, 86–92. [Google Scholar] [CrossRef]
  87. Yuen, C.; Gao, X.; Yong, J.J.M.; Prakash, P.; Shobana, C.R.; Kaushalya, P.A.T.; Luo, Y.; Bai, Y.; Yang, C.; Preiser, P.R.; et al. Towards malaria field diagnosis based on surface-enhanced Raman scattering with on-chip sample preparation and near-analyte nanoparticle synthesis. Sens. Actuators B Chem. 2021, 343, 130162. [Google Scholar] [CrossRef]
  88. Yuen, C.; Liu, Q. Optimization of Fe3O4@Ag nanoshells in magnetic field-enriched surface-enhanced resonance Raman scattering for malaria diagnosis. Analyst 2013, 138, 6494–6500. [Google Scholar] [CrossRef]
  89. Oliveira, M.J.; Caetano, S.; Dalot, A.; Sabino, F.; Calmeiro, T.R.; Fortunato, E.; Martins, R.; Pereira, E.; Prudêncio, M.; Byrne, H.J.; et al. A simple polystyrene microfluidic device for sensitive and accurate SERS-based detection of infection by malaria parasites. Analyst 2023, 148, 4053–4063. [Google Scholar] [CrossRef] [PubMed]
  90. Martin, M.; Perez-Guaita, D.; Andrew, D.W.; Richards, J.S.; Wood, B.R.; Heraud, P. The effect of common anticoagulants in detection and quantification of malaria parasitemia in human red blood cells by ATR-FTIR spectroscopy. Analyst 2017, 142, 1192–1199. [Google Scholar] [CrossRef] [PubMed]
  91. Roy, S.; Perez-Guaita, D.; Andrew, D.W.; Richards, J.S.; McNaughton, D.; Heraud, P.; Wood, B.R. Simultaneous ATR-FTIR Based Determination of Malaria Parasitemia, Glucose and Urea in Whole Blood Dried onto a Glass Slide. Anal. Chem. 2017, 89, 5238–5245. [Google Scholar] [CrossRef] [PubMed]
  92. Mwanga, E.P.; Minja, E.G.; Mrimi, E.; Jimenez, M.G.; Swai, J.K.; Abbasi, S.; Ngowo, H.S.; Siria, D.J.; Mapua, S.; Stica, C.; et al. Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis. Malar. J. 2019, 18, 341. [Google Scholar] [CrossRef]
  93. Perez-Guaita, D.; Perez-Guaita, D.; Andrew, D.; Heraud, P.; Beeson, J.; Anderson, D.; Richards, J.; Wood, B.R. High resolution FTIR imaging provides automated discrimination and detection of single malaria parasite infected erythrocytes on glass. Faraday Discuss. 2016, 187, 341–352. [Google Scholar] [CrossRef]
  94. Perez-Guaita, D.; Kochan, K.; Martin, M.; Andrew, D.W.; Heraud, P.; Richards, J.S.; Wood, B.R. Multimodal vibrational imaging of cells. Vib. Spectrosc. 2017, 91, 46–58. [Google Scholar] [CrossRef]
  95. Banas, A.M.; Banas, K.; Chu, T.T.T.; Naidu, R.; Hutchinson, P.E.; Agrawal, R.; Lo, M.K.F.; Kansiz, M.; Roy, A.; Chandramohanadas, R.; et al. Comparing infrared spectroscopic methods for the characterization of Plasmodium falciparum-infected human erythrocytes. Commun. Chem. 2021, 4, 129. [Google Scholar] [CrossRef]
  96. Idro, R.; Jenkins, N.E.; Newton, C.R.J.C. Pathogenesis, clinical features, and neurological outcome of cerebral malaria. Lancet Neurol. 2005, 4, 827–840. [Google Scholar] [CrossRef]
  97. Dvorin, J.D. Getting Your Head around Cerebral Malaria. Cell Host Microbe 2017, 22, 586–588. [Google Scholar] [CrossRef] [PubMed]
  98. Hackett, M.J.; Lee, J.; El-Assaad, F.; McQuillan, J.A.; Carter, E.A.; Grau, G.E.; Hunt, N.H.; Lay, P.A. FTIR Imaging of Brain Tissue Reveals Crystalline Creatine Deposits Are an Ex Vivo Marker of Localized Ischemia during Murine Cerebral Malaria: General Implications for Disease Neurochemistry. ACS Chem. Neurosci. 2012, 3, 1017–1024. [Google Scholar] [CrossRef] [PubMed]
  99. Wood, B.R.; Bambery, K.R.; Dixon, M.W.A.; Tilley, L.; Nasse, M.J.; Mattson, E.; Hirschmugl, C.J. Diagnosing malaria infected cells at the single cell level using focal plane array Fourier transform infrared imaging spectroscopy. Anal. Chem. 2014, 139, 4769–4774. [Google Scholar] [CrossRef] [PubMed]
  100. Phal, Y.; Pfister, L.; Carney, P.S.; Bhargava, R. Resolution Limit in Infrared Chemical Imaging. J. Phys. Chem. C 2022, 126, 9777–9783. [Google Scholar] [CrossRef]
  101. Lasch, P.; Naumann, D. Spatial resolution in infrared microspectroscopic imaging of tissues. Biochim. Biophys. Acta Biomembr. 2006, 1758, 814–829. [Google Scholar] [CrossRef]
  102. Edgar, R.C.S.; Counihan, N.A.; McGowan, S.; de Koning-Ward, T.F. Methods Used to Investigate the Plasmodium falciparum Digestive Vacuole. Front. Cell. Infect. Microbiol. 2021, 11, 829823. [Google Scholar] [CrossRef]
  103. Shibeshi, M.A.; Kifle, Z.D.; Atnafie, S.A. Antimalarial Drug Resistance and Novel Targets for Antimalarial Drug Discovery. Infect. Drug Resist. 2020, 13, 4047–4060. [Google Scholar] [CrossRef]
  104. Matz, J.M. Plasmodium’s bottomless pit: Properties and functions of the malaria parasite’s digestive vacuole. Trends Parasitol. 2022, 38, 525–543. [Google Scholar] [CrossRef]
  105. Noland, G.S.; Briones, N.; Sullivan, D.J. The shape and size of hemozoin crystals distinguishes diverse Plasmodium species. Mol. Biochem. Parasitol. 2003, 130, 91–99. [Google Scholar] [CrossRef]
  106. Bannister, L.H.; Hopkins, J.M.; Margos, G.; Dluzewski, A.R.; Mitchell, G.H. Three-Dimensional Ultrastructure of the Ring Stage of Plasmodium falciparum: Evidence for Export Pathways. Microsc. Microanal. 2004, 10, 551–562. [Google Scholar] [CrossRef]
  107. Kansiz, M.; Prater, C.; Dillon, E.; Lo, M.; Anderson, J.; Marcott, C.; Demissie, A.; Chen, Y.; Kunkel, G. Optical Photothermal Infrared Microspectroscopy with Simultaneous Raman—A New Non-Contact Failure Analysis Technique for Identification of <10 μm Organic Contamination in the Hard Drive and other Electronics Industries. Microsc. Today 2020, 28, 26–36. [Google Scholar] [CrossRef]
  108. Reffner, J. Advances in infrared microspectroscopy and mapping molecular chemical composition at submicrometer spatial resolution. Spectroscopy 2018, 33, 12–17. [Google Scholar]
  109. Mclean, A.; Veettil, T.C.P.; Giergiel, M.; Wood, B.R. Evolution of Vibrational Biospectroscopy: Multimodal techniques and Miniaturisation supported by Machine Learning. Vib. Spectrosc. 2024, 133, 103708. [Google Scholar] [CrossRef]
  110. Manley, M. Near-infrared spectroscopy and hyperspectral imaging: Non-destructive analysis of biological materials. Chem. Soc. Rev. 2014, 43, 8200–8214. [Google Scholar] [CrossRef]
  111. Blanco, M.; Villarroya, I. NIR spectroscopy: A rapid-response analytical tool. TrAC Trends Anal. Chem. 2002, 21, 240–250. [Google Scholar] [CrossRef]
  112. Reich, G. Near-infrared spectroscopy and imaging: Basic principles and pharmaceutical applications. Adv. Drug Deliv. Rev. 2005, 57, 1109–1143. [Google Scholar] [CrossRef] [PubMed]
  113. Schwanninger, M.; Rodrigues, J.C.; Fackler, K. A Review of Band Assignments in near Infrared Spectra of Wood and Wood Components. J. Near Infrared Spectrosc. 2011, 19, 287–308. [Google Scholar] [CrossRef]
  114. Huck, C.W.; Ozaki, Y.; Huck-Pezzei, V.A. Critical Review Upon the Role and Potential of Fluorescence and Near-Infrared Imaging and Absorption Spectroscopy in Cancer Related Cells, Serum, Saliva, Urine and Tissue Analysis. Curr. Med. Chem. 2016, 23, 3052–3077. [Google Scholar] [CrossRef]
  115. Pellicer, A.; del Carmen Bravo, M. Near-infrared spectroscopy: A methodology-focused review. Semin. Fetal Neonatal Med. 2011, 16, 42–49. [Google Scholar] [CrossRef]
  116. Pasquini, C. Near infrared spectroscopy: A mature analytical technique with new perspectives—A review. Anal. Chim. Acta 2018, 1026, 8–36. [Google Scholar] [CrossRef]
  117. Ozaki, Y. Near-infrared spectroscopy—Its versatility in analytical chemistry. Anal. Sci. 2012, 28, 545–563. [Google Scholar] [CrossRef] [PubMed]
  118. Beć, K.B.; Grabska, J.; Huck, C.W. Chapter 5—Miniaturized near-infrared spectroscopy in current analytical chemistry: From natural products to forensics. In Molecular and Laser Spectroscopy; Gupta, V.P., Ed.; Elsevier: Amsterdam, The Netherlands, 2022; pp. 141–188. [Google Scholar]
  119. Beć, K.B.; Grabska, J.; Huck, C.W. The essential role of omni-capable research laboratories in advancing analytical spectroscopy. NIR News 2019, 30, 30–34. [Google Scholar] [CrossRef]
  120. Sakudo, A. Near-infrared spectroscopy for medical applications: Current status and future perspectives. Clin. Chim. Acta 2016, 455, 181–188. [Google Scholar] [CrossRef] [PubMed]
  121. Domján, G.; Kaffka, K.J.; Jákó, J.M.; Vályi-Nagy, I.T. Rapid Analysis of Whole Blood and Blood Serum Using near Infrared Spectroscopy. J. Near Infrared Spectrosc. 1994, 2, 67–78. [Google Scholar] [CrossRef]
  122. Lukianova-Hleb, E.Y.; Lapotko, D.O. Malaria theranostics using hemozoin-generated vapor nanobubbles. Theranostics 2014, 4, 761–769. [Google Scholar] [CrossRef][Green Version]
  123. Maia, M.F.; Kapulu, M.; Muthui, M.; Wagah, M.G.; Ferguson, H.M.; Dowell, F.E.; Baldini, F.; Ranford-Cartwright, L. Detection of Plasmodium falciparum infected Anopheles gambiae using near-infrared spectroscopy. Malar. J. 2019, 18, 85. [Google Scholar] [CrossRef]
  124. Da, D.F.; McCabe, R.; Some, B.M.; Esperanca, P.M.; Sala, K.A.; Blight, J.; Blagborough, A.M.; Dowell, F.; Yerbanga, S.R.; Lefevre, T.; et al. Detection of Plasmodium falciparum in laboratory-reared and naturally infected wild mosquitoes using near-infrared spectroscopy. Sci. Rep. 2021, 11, 10289. [Google Scholar] [CrossRef]
  125. Ong, O.T.W.; Kho, E.A.; Esperança, P.M.; Freebairn, C.; Dowell, F.E.; Devine, G.J.; Churcher, T.S. Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions. Parasites Vectors 2020, 13, 160. [Google Scholar] [CrossRef]
  126. Fernandes, J.N.; Dos Santos, L.M.B.; Chouin-Carneiro, T.; Pavan, M.G.; Garcia, G.A.; David, M.R.; Beier, J.C.; Dowell, F.E.; Maciel-de-Freitas, R.; Sikulu-Lord, M.T. Rapid, noninvasive detection of Zika virus in Aedes aegypti mosquitoes by near-infrared spectroscopy. Sci. Adv. 2018, 4, eaat0496. [Google Scholar] [CrossRef]
  127. Garcia, G.A.; Lord, A.R.; Santos, L.M.B.; Kariyawasam, T.N.; David, M.R.; Couto-Lima, D.; Tátila-Ferreira, A.; Pavan, M.G.; Sikulu-Lord, M.T.; Maciel-de-Freitas, R. Rapid and Non-Invasive Detection of Aedes aegypti Co-Infected with Zika and Dengue Viruses Using Near Infrared Spectroscopy. Viruses 2022, 15, 11. [Google Scholar] [CrossRef]
  128. Some, B.M.; Da, D.F.; McCabe, R.; Djegbe, N.D.C.; Pare, L.I.G.; Werme, K.; Mouline, K.; Lefevre, T.; Ouedraogo, A.G.; Churcher, T.S.; et al. Adapting field-mosquito collection techniques in a perspective of near-infrared spectroscopy implementation. Parasites Vectors 2022, 15, 338. [Google Scholar] [CrossRef] [PubMed]
  129. Lambert, B.; Sikulu-Lord, M.T.; Mayagaya, V.S.; Devine, G.; Dowell, F.; Churcher, T.S. Monitoring the Age of Mosquito Populations Using Near-Infrared Spectroscopy. Sci. Rep. 2018, 8, 5274. [Google Scholar] [CrossRef] [PubMed]
  130. Santos, M.C.D.; Viana, J.L.S.; Monteiro, J.D.; Freire, R.C.M.; Freitas, D.L.D.; Câmara, I.M.; da Silva, G.J.S.; Gama, R.A.; Araújo, J.M.G.; Lima, K.M.G. Infrared spectroscopy (NIRS and ATR-FTIR) together with multivariate classification for non-destructive differentiation between female mosquitoes of Aedes aegypti recently infected with dengue vs. uninfected females. Acta Trop. 2022, 235, 106633. [Google Scholar] [CrossRef] [PubMed]
  131. Sikulu-Lord, M.T.; Edstein, M.D.; Goh, B.; Lord, A.R.; Travis, J.A.; Dowell, F.E.; Birrell, G.W.; Chavchich, M. Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. PLoS ONE 2024, 19, e0289232. [Google Scholar] [CrossRef]
  132. Adegoke, J.A.; De Paoli, A.; Afara, I.O.; Kochan, K.; Creek, D.J.; Heraud, P.; Wood, B.R. Ultraviolet/Visible and Near-Infrared Dual Spectroscopic Method for Detection and Quantification of Low-Level Malaria Parasitemia in Whole Blood. Anal. Chem. 2021, 93, 13302–13310. [Google Scholar] [CrossRef]
  133. Briehl, R.W.; Hobbs, J.F. Ultraviolet Difference Spectra in Human Hemoglobin: I. Difference Spectra in Hemoglobin A and Their Relation to the Function of Hemoglobin. J. Biol. Chem. 1970, 245, 544–554. [Google Scholar] [CrossRef] [PubMed]
  134. Briehl, R.W.; Ranney, H.M. Ultraviolet Difference Spectra in Human Hemoglobin: II. Difference Spectra in Isolated Subunits of Hemoglobin. J. Biol. Chem. 1970, 245, 555–558. [Google Scholar] [CrossRef]
  135. Serebrennikova, Y.M.; Patel, J.; Garcia-Rubio, L.H. Interpretation of the ultraviolet-visible spectra of malaria parasite Plasmodium falciparum. Appl. Opt. 2010, 49, 180–188. [Google Scholar] [CrossRef]
  136. Frosch, T.; Koncarevic, S.; Becker, K.; Popp, J. Morphology-sensitive Raman modes of the malaria pigment hemozoin. Analyst 2009, 134, 1126–1132. [Google Scholar] [CrossRef]
  137. Queral-Beltran, A.; Marín-García, M.; Lacorte, S.; Tauler, R. Multivariate curve resolution of incomplete and partly trilinear multiblock datasets. Chemom. Intell. Lab. Syst. 2024, 247, 105081. [Google Scholar] [CrossRef]
  138. Scepanovic, O.R.; Volynskaya, Z.; Kong, C.R.; Galindo, L.H.; Dasari, R.R.; Feld, M.S. A multimodal spectroscopy system for real-time disease diagnosis. Rev. Sci. Instrum. 2009, 80, 043103. [Google Scholar] [CrossRef] [PubMed]
  139. Szymoński, K.; Chmura, Ł.; Lipiec, E.; Adamek, D. Vibrational spectroscopy—Are we close to finding a solution for early pancreatic cancer diagnosis? World J. Gastroenterol. 2023, 29, 96–109. [Google Scholar] [CrossRef] [PubMed]
  140. Yao, J.; Wang, L.V. Sensitivity of photoacoustic microscopy. Photoacoustics 2014, 2, 87–101. [Google Scholar] [CrossRef]
  141. Memeu, D.M.; Sallorey, A.M.; Maina, C.; Kinyua, D.M. Review of Photoacoustic Malaria Diagnostic Techniques. Open J. Clin. Diagn. 2021, 11, 59–75. [Google Scholar] [CrossRef]
  142. Samson, E.B.; Goldschmidt, B.S.; Whiteside, P.J.; Sudduth, A.S.; Custer, J.R.; Beerntsen, B.; Viator, J.A. Photoacoustic spectroscopy of β-hematin. J. Opt. 2012, 14, 065302. [Google Scholar] [CrossRef] [PubMed]
  143. Menyaev, Y.A.; Carey, K.A.; Nedosekin, D.A.; Sarimollaoglu, M.; Galanzha, E.I.; Stumhofer, J.S.; Zharov, V.P. Preclinical photoacoustic models: Application for ultrasensitive single cell malaria diagnosis in large vein and artery. Biomed. Opt. Express 2016, 7, 3643–3658. [Google Scholar] [CrossRef]
  144. Cai, C.; Carey, K.A.; Nedosekin, D.A.; Menyaev, Y.A.; Sarimollaoglu, M.; Galanzha, E.I.; Stumhofer, J.S.; Zharov, V.P. In vivo photoacoustic flow cytometry for early malaria diagnosis. Cytometry 2016, 89, 531–542. [Google Scholar] [CrossRef]
  145. Lukianova-Hleb, E.; Bezek, S.; Szigeti, R.; Khodarev, A.; Kelley, T.; Hurrell, A.; Berba, M.; Kumar, N.; D’Alessandro, U.; Lapotko, D. Transdermal Diagnosis of Malaria Using Vapor Nanobubbles. Emerg. Infect. Dis. 2015, 21, 1122–1127. [Google Scholar] [CrossRef]
  146. Dean-Ben, X.L.; Gottschalk, S.; Mc Larney, B.; Shoham, S.; Razansky, D. Advanced optoacoustic methods for multiscale imaging of in vivo dynamics. Chem. Soc. Rev. 2017, 46, 2158–2198. [Google Scholar] [CrossRef]
  147. Veverka, M.; Menozzi, L.; Yao, J. The sound of blood: Photoacoustic imaging in blood analysis. Med. Nov. Technol. Devices 2023, 18, 100219. [Google Scholar] [CrossRef]
  148. Hu, S.; Wang, L.V. Optical-Resolution Photoacoustic Microscopy: Auscultation of Biological Systems at the Cellular Level. Biophys. J. 2013, 105, 841–847. [Google Scholar] [CrossRef] [PubMed]
  149. Park, S.; Lee, C.; Kim, J.; Kim, C. Acoustic resolution photoacoustic microscopy. Biomed. Eng. Lett. 2014, 4, 213–222. [Google Scholar] [CrossRef]
  150. Zhou, Y.; Yao, J.; Wang, L.V. Tutorial on photoacoustic tomography. J. Biomed. Opt. 2016, 21, 061007. [Google Scholar] [CrossRef] [PubMed]
  151. Periyasamy, V.; Das, N.; Sharma, A.; Pramanik, M. 1064 nm acoustic resolution photoacoustic microscopy. J. Biophotonics 2019, 12, e201800357. [Google Scholar] [CrossRef]
  152. Tian, C.; Zhang, C.; Zhang, H.; Xie, D.; Jin, Y. Spatial resolution in photoacoustic computed tomography. Rep. Prog. Phys. 2021, 84, 036701. [Google Scholar] [CrossRef]
  153. Moothanchery, M.; Bi, R.; Kim, J.Y.; Jeon, S.; Kim, C.; Olivo, M. Optical resolution photoacoustic microscopy based on multimode fibers. Biomed. Opt. Express 2018, 9, 1190. [Google Scholar] [CrossRef]
  154. Liu, W.; Yao, J. Photoacoustic microscopy: Principles and biomedical applications. Biomed. Eng. Lett. 2018, 8, 203–213. [Google Scholar] [CrossRef]
  155. Pleitez, M.A.; Khan, A.A.; Soldà, A.; Chmyrov, A.; Reber, J.; Gasparin, F.; Seeger, M.R.; Schätz, B.; Herzig, S.; Scheideler, M.; et al. Label-free metabolic imaging by mid-infrared optoacoustic microscopy in living cells. Nat. Biotechnol. 2020, 38, 293–296. [Google Scholar] [CrossRef]
  156. Perez-Guaita, D.; Kochan, K.; Batty, M.; Doerig, C.; Garcia-Bustos, J.; Espinoza, S.; McNaughton, D.; Heraud, P.; Wood, B.R. Multispectral Atomic Force Microscopy-Infrared Nano-Imaging of Malaria Infected Red Blood Cells. Anal. Chem. 2018, 90, 3140–3148. [Google Scholar] [CrossRef]
  157. Wood, B.R.; Bailo, E.; Khiavi, M.A.; Tilley, L.; Deed, S.; Deckert-Gaudig, T.; McNaughton, D.; Deckert, V. Tip-Enhanced Raman Scattering (TERS) from Hemozoin Crystals within a Sectioned Erythrocyte. Nano Lett. 2011, 11, 1868–1873. [Google Scholar] [CrossRef]
  158. Berna, A.Z.; Wang, X.R.; Bollinger, L.B.; Banda, J.; Mawindo, P.; Evanoff, T.; Culbertson, D.L.; Seydel, K.; John, A.R.O. Breath Biomarkers of Pediatric Malaria: Reproducibility and Response to Antimalarial Therapy. J. Infect. Dis. 2024, 230, 1013–1022. [Google Scholar] [CrossRef] [PubMed]
  159. Schaber, C.L.; Katta, N.; Bollinger, L.B.; Mwale, M.; Mlotha-Mitole, R.; Trehan, I.; Raman, B.; John, A.R.O. Breathprinting Reveals Malaria-Associated Biomarkers and Mosquito Attractants. J. Infect. Dis. 2018, 217, 1553–1560. [Google Scholar] [CrossRef] [PubMed]
  160. Chai, H.C.; Chua, K.H. Urine and Saliva: Relevant Specimens for Malaria Diagnosis? Diagnostics 2022, 12, 2989. [Google Scholar] [CrossRef]
  161. Wood, B.R.; Kochan, K.; Bedolla, D.E.; Salazar-Quiroz, N.; Grimley, S.L.; Perez-Guaita, D.; Baker, M.J.; Vongsvivut, J.; Tobin, M.J.; Bambery, K.R.; et al. Infrared Based Saliva Screening Test for COVID-19. Angew. Chem. Int. Ed. 2021, 60, 17102–17107. [Google Scholar] [CrossRef] [PubMed]
  162. Martin, M.; Wongwattanakul, M.; Khemtonglang, N.; Kiatchoosakun, P.; Heraud, P.; Jearanaikoon, P.; Wood, B.R. Identification of Glucose-6 Phosphate Dehydrogenase Deficient Patients Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Using Partial Least Squares Discriminant Analysis in Aqueous Blood Samples. Appl. Spectrosc. 2023, 77, 513–520. [Google Scholar] [CrossRef]
  163. Perez-Guaita, D.; Marzec, K.M.; Hudson, A.; Evans, C.; Chernenko, T.; Matthäus, C.; Miljkovic, M.; Diem, M.; Heraud, P.; Richards, J.S.; et al. Parasites under the Spotlight: Applications of Vibrational Spectroscopy to Malaria Research. Chem. Rev. 2018, 118, 5330–5358. [Google Scholar] [CrossRef]
  164. Egan, T.J. Interactions of quinoline antimalarials with hematin in solution. J. Inorg. Biochem. 2006, 100, 916–926. [Google Scholar] [CrossRef]
  165. Frosch, T.; Küstner, B.; Schlücker, S.; Szeghalmi, A.; Schmitt, M.; Kiefer, W.; Popp, J. In vitro polarization-resolved resonance Raman studies of the interaction of hematin with the antimalarial drug chloroquine. J. Raman Spectrosc. 2004, 35, 819–821. [Google Scholar] [CrossRef]
  166. Frosch, T.; Schmitt, M.; Bringmann, G.; Kiefer, W.; Popp, J. Structural Analysis of the Anti-Malaria Active Agent Chloroquine under Physiological Conditions. J. Phys. Chem. B 2007, 111, 1815–1822. [Google Scholar] [CrossRef]
  167. Frosch, T.; Popp, J. Structural analysis of the antimalarial drug halofantrine by means of Raman spectroscopy and density functional theory calculations. J. Biomed. Opt. 2010, 15, 041516. [Google Scholar] [CrossRef]
  168. Frosch, T.; Schmitt, M.; Schenzel, K.; Faber, J.H.; Bringmann, G.; Kiefer, W.; Popp, J. In vivo localization and identification of the antiplasmodial alkaloid dioncophylline A in the tropical liana Triphyophyllum peltatum by a combination of fluorescence, near infrared Fourier transform Raman microscopy, and density functional theory calculations. Biopolymers 2006, 82, 295–300. [Google Scholar] [CrossRef] [PubMed]
  169. Frosch, T.; Schmitt, M.; Noll, T.; Bringmann, G.; Schenzel, K.; Popp, J. Ultrasensitive in situ Tracing of the Alkaloid Dioncophylline A in the Tropical Liana Triphyophyllum peltatum by Applying Deep-UV Resonance Raman Microscopy. Anal. Chem. 2007, 79, 986–993. [Google Scholar] [CrossRef] [PubMed]
  170. Frosch, T.; Schmitt, M.; Popp, J. Raman spectroscopic investigation of the antimalarial agent mefloquine. Anal. Bioanal. Chem. 2007, 387, 1749–1757. [Google Scholar] [CrossRef] [PubMed]
  171. Kozicki, M.; Creek, D.J.; Sexton, A.; Morahan, B.J.; Wesełucha-Birczyńska, A.; Wood, B.R. An attenuated total reflection (ATR) and Raman spectroscopic investigation into the effects of chloroquine on Plasmodium falciparum-infected red blood cells. Analyst 2015, 140, 2236–2246. [Google Scholar] [CrossRef]
  172. Wolf, S.; Domes, R.; Domes, C.; Frosch, T. Spectrally Resolved and Highly Parallelized Raman Difference Spectroscopy for the Analysis of Drug–Target Interactions between the Antimalarial Drug Chloroquine and Hematin. Anal. Chem. 2024, 96, 3345–3353. [Google Scholar] [CrossRef]
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