Search Results (347)

Exosomes are nanoscale extracellular vesicles (EVs) that carry biomolecular signatures reflective of their parent cells, making them powerful tools for non-invasive diagnostics and therapeutic monitoring. Despite their potential, clinical application is hindered by challenges such as low abundance, heterogeneity, and the complexity of biological samples. To address these limitations, plasmonic biosensing technologies—particularly propagating surface plasmon resonance (PSPR), localized surface plasmon resonance (LSPR), and surface-enhanced Raman scattering (SERS)—have been developed to enable label-free, highly sensitive, and multiplexed detection at the single-vesicle level. This review outlines recent advancements in nanoplasmonic platforms for exosome detection and profiling, emphasizing innovations in nanostructure engineering, microfluidic integration, and signal enhancement. Representative applications in oncology, neurology, and immunology are discussed, along with the increasingly critical role of artificial intelligence (AI) in spectral interpretation and diagnostic classification. Key technical and translational challenges—such as assay standardization, substrate reproducibility, and clinical validation—are also addressed. Overall, this review highlights the synergy between exosome biology and plasmonic nanotechnology, offering a path toward real-time, precision diagnostics via sub-femtomolar detection of exosomal miRNAs through next-generation biosensing strategies. Full article

We present a miniaturized, inexpensive, and user-friendly microfluidic platform to support biological applications. The system integrates a mini-incubator providing controlled environmental conditions and housing a microfluidic device for long-term cell culture experiments. The incubator is designed to be compatible with standard inverted optical microscopes and Raman spectrometers, allowing for the non-invasive imaging and spectroscopic analysis of cell cultures in vitro. The microfluidic device, which reproduces a dynamic environment, was optimized to sustain a passive, gravity-driven flow of medium, eliminating the need for an external pumping system and reducing mechanical stress on the cells. The platform was tested using Raman analysis and adherent tumoral cells to assess proliferation prior and subsequent to hydrogen peroxide treatment for oxidative stress induction. The results demonstrated a successful adhesion of cells onto the substrate and their proliferation. Furthermore, the platform is suitable for carrying out optical monitoring of cultures and Raman analysis. In fact, it was possible to discriminate spectra deriving from control and hydrogen peroxide-treated cells in terms of DNA backbone and cellular membrane modification effects provoked by reactive oxygen species (ROS) activity. The 800–1100 cm⁻¹ band highlights the destructive effects of ROS on the DNA backbone’s structure, as its rupture modifies its vibration; moreover, unpaired nucleotides are increased in treated sample, as shown in the 1154–1185 cm⁻¹ band. Protein synthesis deterioration, led by DNA structure damage, is highlighted in the 1257–1341 cm⁻¹, 1440–1450 cm⁻¹, and 1640–1670 cm⁻¹ bands. Furthermore, membrane damage is emphasized in changes in the 1270, 1301, and 1738 cm⁻¹ frequencies, as phospholipid synthesis is accelerated in an attempt to compensate for the membrane damage brought about by the ROS attack. This study highlights the potential use of this platform as an alternative to conventional culturing and analysis procedures, considering that cell culturing, optical imaging, and Raman spectroscopy can be performed simultaneously on living cells with minimal cellular stress and without the need for labeling or fixation. Full article

In clinical applications of surface-enhanced Raman spectroscopy (SERS) immunosensors, accurately determining analyte biomarker concentrations is essential. This study presents a non-invasive approach for quantifying various breast cancer biomarkers—including human epidermal growth factor receptor II (HER-II) (2+, 3+ (I), 3+ (II), 3+ (III), and positive IV) and CA 15-3—using a directional, plasmonically active, label-free SERS sensor. Each stage of sensor functionalization, conjugation, and biomarker interaction was verified by UV–Vis spectroscopy. Atomic force microscopy (AFM) characterized the morphology of gold nanourchin (GNU)-immobilized printed circuit board (PCB) substrates. An enhancement factor of ≈ 0.5 × 10⁵ was achieved using Rhodamine 6G as the probe molecule. Calibration curves were initially established using standard HER-II solutions at concentrations ranging from 1 to 100 ng/mL and CA 15-3 at concentrations from 10 to 100 U/mL. The SERS signal intensities in the 620–720 nm region were plotted against concentration, yielding linear sensitivity with R² values of 0.942 and 0.800 for HER-II and CA15-3, respectively. The same procedure was applied to breast cancer serum (BCS) samples, allowing unknown biomarker concentrations to be determined based on the corresponding calibration curves. SERS data were processed using the filtfilt filter from scipy.signal for smoothing and then baseline-corrected with the Improved Asymmetric Least Squares (IASLS) algorithm from the pybaselines.Whittaker library. Principal Component Analysis (PCA) effectively distinguished the sample groups and revealed spectral differences before and after biomarker interactions. Key Raman peaks were attributed to functional groups including N–H (primary and secondary amines), C–H antisymmetric stretching, C–N (amines), C=O antisymmetric stretching, NH₃⁺ (amines), carbohydrates, glycine, alanine, amides III, C=N stretches, and NH₂ in primary amides. Full article

Cancer diagnosis requires alternative techniques that allow for early, non-invasive, or minimally invasive identification. Traditional methods, like tissue biopsies, are highly invasive and can be traumatic for patients. Liquid biopsy, a less invasive option, detects cancer biomarkers in body fluids such as blood and urine. However, early-stage cancer often presents low biomarker levels, making sensitivity a challenge for integrating liquid biopsy into early diagnosis. Recent studies revealed that extracellular vesicles (EVs) secreted by cells are apt markers for liquid biopsy. Detecting extracellular vesicles (EVs) for liquid biopsy faces challenges like low sensitivity, EV subtype heterogeneity, and difficulty isolating pure populations. Label-free methods, such as plasmonic biosensors and Raman spectroscopy, offer potential solutions by enabling direct analysis without markers, improving accuracy, and reducing complexity. This review paper discusses current challenges in EV-based liquid biopsy for cancer diagnosis and prognosis. It addresses the effective use of microfluidics and nano-plasmonic approaches to address these challenges. Enhancing label-free EV detection in liquid biopsy could revolutionize early cancer diagnosis by offering non-invasive, cost-effective, and rapid testing. This could improve patient outcomes through personalized treatment and ease the burden on healthcare systems. Full article

Sub-wavelength metallic nanostructures allow the squeezing of light within nanoscale regions, called plasmonic hotspots. Squeezed near-field light has been demonstrated to detect, modulate, and generate light in more effective ways. The enhanced electric field in the plasmonic hotspots are also utilized for identifying molecular fingerprints in a more sensitive manner, i.e., surface-enhanced Raman spectroscopy (SERS). SERS is a versatile tool used to characterize chemicals and biomolecules with the advantages of label-free detection, specificity, and high sensitivity compared to fluorescence and colorimetric sensing methods. With its practical and diverse applications such as biomedical sensing, the evaluation of SERS on diverse nano-structure platforms and materials is highly in demand. Nanogap structures are promising SERS platforms which can be fabricated over a large area with uniform nanoscale gap size. Here, we demonstrate the fabrication of large-area metal–insulator–metal nanogap structures with different metals (i.e., Au and Ag) and analyze material dependence on SERS. While both nanometer-sized gap structures exhibit a large enhancement factor for Raman spectroscopy, Ag-based structures exhibit 58- and 15-times-larger enhancement factors for bottom and top plasmonic hotspots, respectively. The enhanced detection on a silver nanogap platform is attributed to enhanced electric field in the gap, as confirmed by simulation. Our findings provide not only a way to better understand SERS in different metallic nano platforms but also insights for designing highly sensitive nanoscale chemical and biomedical sensors. Full article

This study investigates a novel approach to moderate-temperature carbon capture by examining the enhanced performance of thermally pre-treated dolomite. To obtain mixed oxides, dolomite samples were prepared via calcination in a quartz cylindrical furnace under an ambient atmosphere at 900 °C, and subsequently thermally pre-treated under an inert (argon) stream at 650 °C. Characterization of the as-prepared samples involved morphological, structural, textural, and optical features examined through XRD, BET, SEM-EDS, FT-IR, and RAMAN, XPS, and UV-vis spectroscopy, whereas TGA and subsequent multicyclic tests were used to study the CO₂ sorption. The dolomite sample calcined at 900 °C for 60 min, and after being activated under an inert atmosphere (argon), labeled PCD60Act, exhibited the highest CO₂ uptake of 0.477 g_CO2/g_sorbent; after 15 sorption–regeneration cycles, it still retained a CO₂ uptake of 0.38 g_CO2/g_sorbent at 650 °C, and it was also successfully regenerated at this moderate temperature, demonstrating 84% capture capacity retention. These remarkable results are explained by the crystalline defects generated during the thermal pre-treatments of the dolomite. This research offers valuable perspectives on the viability of employing thermally pre-treated dolomite as an inexpensive, thermally stable, and moderate-temperature regenerable CaO-based sorbent for applications in CO₂ removal in the context of integrated carbon capture and conversion (ICCC) for the production of high-purity hydrogen. Full article

This investigation presents an overview of various optical biosensors utilized for the detection of cancer cells. It covers a comprehensive range of technologies, including surface plasmon resonance $\left(SPR\right)$ sensors, which exploit changes in refractive index $\left(RI\right)$ at the sensor surface to detect biomolecular interactions. Localized surface plasmon resonance $\left(LSPR\right)$ sensors offer high sensitivity and versatility in detecting cancer biomarkers. Colorimetric sensors, based on color changes induced via specific biochemical reactions, provide a cost-effective and simple approach to cancer detection. Sensors based on fluorescence work using the light emitted from fluorescent molecules detect cancer-specific targets with specificity and high sensitivity. Photonics and waveguide sensors utilize optical waveguides to detect changes in light propagation, offering real-time and label-free detection of cancer biomarkers. Raman spectroscopy-based sensors utilize surface-enhanced Raman scattering $\left(SERS\right)$to provide molecular fingerprint information for cancer diagnosis. Lastly, fiber optic sensors offer flexibility and miniaturization, making them suitable for in vivo and point-of-care applications in cancer detection. This study provides insights into the principles, applications, and advancements of these optical biosensors in cancer diagnostics, highlighting their potential in improving early detection and patient outcomes. Full article

Raman microspectroscopy is a powerful, label-free technique for the biochemical characterization of cells, but its complex spectral data require advanced computational methods for meaningful interpretation. Clustering analysis is widely used in spectroscopic imaging to extract meaningful biochemical information. Traditional methods, such as K-means clustering with Euclidean distance, often struggle to capture subtle spectral variations, leading to suboptimal segmentation. Alternative distance metrics, including cosine and Mahalanobis distances, have been explored to enhance cluster separability, yet challenges remain in distinguishing chemically relevant features while minimizing redundancy and noise. In this study, we introduce an asymmetric metric distance matrix with a tunable eccentricity parameter to improve clustering performance in Raman hyperspectral imaging. Our results demonstrate that suitable eccentricity values enhance the identification of subcellular structures while requiring fewer clusters than Euclidean-based approaches. Compared to polar metrics, the proposed asymmetric metric achieves better stability and reduced noise, leading to more accurate segmentation. Future research could explore its application in other clustering techniques and machine learning frameworks, as well as its application in broader spectral imaging techniques where the distance metric plays a fundamental role. Full article

Three-dimensional (3D) cellular models, such as spheroids, serve as pivotal systems for understanding complex biological phenomena in histology, oncology, and tissue engineering. In response to the growing need for advanced imaging capabilities, we present a novel multi-modal Raman light sheet microscope designed to capture elastic (Rayleigh) and inelastic (Raman) scattering, along with fluorescence signals, in a single platform. By leveraging a shorter excitation wavelength (532 nm) to boost Raman scattering efficiency and incorporating robust fluorescence suppression, the system achieves label-free, high-resolution tomographic imaging without the drawbacks commonly associated with near-infrared modalities. An accompanying Deep Image Prior (DIP) seamlessly integrates with the microscope to provide unsupervised denoising and resolution enhancement, preserving critical molecular details and minimizing extraneous artifacts. Altogether, this synergy of optical and computational strategies underscores the potential for in-depth, 3D imaging of biomolecular and structural features in complex specimens and sets the stage for future advancements in biomedical research, diagnostics, and therapeutics. Full article

(1→3)-α-d-Glucan is an important component of the cell wall of most fungi. The polymer has many applications, including as a therapeutic agent in the prevention or treatment of various diseases, as well as a heavy metal sorbent and a component of new materials used in the plastics industry. The presence of (1→3)-α-d-glucan (water-insoluble, alkali-soluble polysaccharide) in the cell wall of Pleurotus djamor (pink oyster mushroom) was confirmed using specific fluorophore-labeled antibodies. Therefore, the water-insoluble fraction (WI-ASF) of P. djamor B123 fruiting bodies was isolated by alkaline extraction and used for further analyses. The structural features of the WI-ASF were determined by composition analysis, linkage analysis, Fourier transform infrared and Raman spectroscopy, ¹H and ¹³C nuclear magnetic resonance spectroscopy, scanning electron microscopy, as well as viscosity, specific rotation, and gel permeation chromatography. These studies revealed the presence of glucose units linked by α-glycosidic bonds and scanty amounts of mannose and xylose. Furthermore, methylation analysis of WI-ASF demonstrated that the (1→3)-linked glucopyranose (Glcp) is the primary moiety (86.4%) of the polymer, while the 3,4- and 3,6-substituted hexoses are the branching residues of the glucan. The results of chemical and spectroscopic investigations indicated that the analyzed WI-ASF is a (1→3)-linked α-d-glucan type with a molecular weight of 552 kDa. Full article

Due to rising concerns regarding the adulteration and mislabeling of honey, new directives at the European level encourage researchers to develop reliable honey authentication models based on rapid and cost-effective analytical techniques, such as vibrational spectroscopies. The present study discusses the identification of the main vibrational bands of the FT-Raman and ATR-IR spectra of the most consumed honey varieties in Transylvania: acacia, honeydew, and rapeseed, exposing the ways the spectral fingerprint differs based on the honey’s varietal-dependent composition. Additionally, a pilot study on honey authentication describes a new methodology of processing the combined vibrational data with the most efficient machine learning algorithms. By employing the proposed methodology, the developed model was capable of distinguishing honey produced in a narrow geographical region (Transylvania) with an accuracy of 85.2% and 93.8% on training and testing datasets when the Trilayered Neural Network algorithm was applied to the combined IR and Raman data. Moreover, acacia honey was differentiated against fifteen other sources with a 87% accuracy on training and testing datasets. The proposed methodology proved efficiency and can be further employed for label control and food safety enhancement. Full article

In this work, we present a low-cost, label-free cellulose-based paper SERS (Surface-Enhanced Raman Scattering) substrate for the sensitive detection of thiol compounds. Uniform silver nanoparticles (AgNPs) were synthesized on cellulose filter paper via in situ reduction of a silver precursor under UVC irradiation, achieving a high SERS enhancement factor of 8.5 × 10⁶. The Ag-cellulose substrate demonstrated reliable detection of benzenethiol, capturing its characteristic SERS signals with remarkable sensitivity. Quantitative analysis was enabled by adjusting exposure times for accurate calibration. Furthermore, Principal Component Analysis (PCA) was successfully employed to distinguish mixed samples of benzenethiol, hexanethiol, and propanethiol, showcasing the substrate’s capability in separating complex mixtures. This cellulose-based AgNP platform offers a sustainable, cost-effective solution for rapid chemical detection, with significant potential for real-world applications such as environmental monitoring and food safety. Full article

Raman spectroscopy enables fast, label-free, qualitative, and quantitative observation of the physical and chemical properties of various substances. Here, we present a 785 nm custom-built Raman spectroscopy instrument designed for sensing applications in the 400–1700 cm⁻¹ spectral range. We demonstrate the performance of the instrument by fingerprinting 14 pesticide reference samples with over twenty technical repeats per sample. We present molecular Raman fingerprints of the pesticides comprehensively and distinguish similarities and differences among them using multivariate analysis and machine learning techniques. The same pesticides were additionally investigated using a commercial 532 nm Raman instrument to see the potential variations in peak shifts and intensities. We developed a unique Raman fingerprint library for 14 reference pesticides, which is comprehensively documented in this study for the first time. The comparison shows the importance of selecting an appropriate excitation wavelength based on the target analyte. While 532 nm may be advantageous for certain compounds due to resonance enhancement, 785 nm is generally more effective for reducing fluorescence and achieving clearer Raman spectra. By employing machine learning techniques like the Random Forest Classifier, the study automates the classification of 14 different pesticides, streamlining data interpretation for non-experts. Applying such combined techniques to a wider range of agricultural chemicals, clinical biomarkers, or pollutants could provide an impetus to develop monitoring technologies in food safety, diagnostics, and cross-industry quality control applications. Full article

We have fabricated a new highly reproducible, stable, and sensitive cellulose paper-based Surfaced-enhanced Raman scattering (SERS) sensor substrate for non-enzymatic label-free glucose detection. To enhance reproducibility, stability, and sensitivity, the cellulose paper (CP) substrate has been modified with a naturally derived biocompatible polymer, chitosan (CS), followed by depositing enormous amount of plasmonic silver nanoparticles (AgNPs) on CP/CS and finally forming a self-assembling monolayer of 4-mercaptophenyl boronic acid (MPBA) on CP/CS/AgNPs (CP/CS/AgNPs/MPBA). The SERS sensor substrate is characterized by scanning electron microscopy (SEM), energy dispersive X-ray (EDX), Fourier transform infrared (FT-IR), and X-ray diffraction (XRD) spectroscopy techniques. The glucose sensing is achieved by monitoring the SERS intensity of C-S and B-O stretching vibrations at 1072 cm⁻¹ in MPBA, which is gradually increased with increasing concentration of glucose due to the increasing orientation change of MPBA on AgNPs. The results show that the proposed glucose paper-based SERS sensor exhibits a high analytical enhancement factor (AEF) (3.4 × 10⁷), enhanced reproducibility (<7%), improved stability (>5 weeks), excellent selectivity towards other metabolic compounds, and high sensitivity with a limit of detection (LOD) of 0.74 mM and a linear dynamic range between 1.0 and 7.0 mM. The practical application of this SERS sensor is examined in real spiked and non-spiked human blood serum samples for the detection of glucose, and satisfactory recovery results have been obtained, demonstrating the potentiality of the present paper-based SERS sensor for non-enzymatic label-free glucose detection in real biological samples. Full article

Background: The development of nanostructured materials for cancer therapy has garnered significant interest due to their unique physicochemical properties, including enhanced surface area and tunable electronic structures, which can facilitate targeted drug delivery and oxidative stress modulation. This study investigates the anticancer potential of monoclinic silver dimolybdate nanorods (m-Ag₂Mo₂O₇) against aggressive breast (MDA-MB-231) and prostate (PC-3) cancer cells and explores their in vivo pharmacokinetic behavior. Methods: m-Ag₂Mo₂O₇ nanorods were synthesized via a hydrothermal method and characterized using XRD, SEM, Raman, and FTIR spectroscopy. In vitro cytotoxicity was evaluated using MTT assays on MDA-MB-231 and PC-3 cell lines across concentrations ranging from 1.56 to 100 µg/mL. In vivo biodistribution and radiopharmacokinetics were assessed using technetium-99m-labeled nanorods in male Swiss rats, with gamma counting employed for tissue uptake analysis and pharmacokinetic parameter determination. Results: m-Ag₂Mo₂O₇ nanorods exhibited a modest cytotoxic effect on MDA-MB-231 cells, with 50 µg/mL reducing cell viability by 23.5% (p < 0.05), while no significant cytotoxicity was observed in PC-3 cells. In vivo studies revealed predominant accumulation in the stomach, liver, spleen, and bladder, indicating reticuloendothelial system uptake and renal clearance. Pharmacokinetic analysis showed a rapid systemic clearance (half-life ~6.76 h) and a low volume of distribution (0.0786 L), suggesting primary retention in circulation with minimal off-target diffusion. Conclusions: While m-Ag₂Mo₂O₇ nanorods display limited standalone cytotoxicity, their ability to induce oxidative stress and favorable pharmacokinetic profile support their potential as adjuvant agents in cancer therapy, particularly for chemoresistant breast cancers. Further studies are warranted to elucidate their molecular mechanisms, optimize combinatorial treatment strategies, and assess long-term safety in preclinical models. Full article