Search Results (290)

Point-of-care testing (POCT) offers a transformative approach to diagnostics by enabling rapid and accurate results at or near the site of patient care. This is especially valuable in critical care, emergency settings, and resource-limited areas. However, one major limitation of POCT remains its analytical sensitivity, particularly in detecting low concentrations of analytes. To address this, various innovations are being explored, including advanced sensors, signal amplification, and sensitive labels. Among these, bioluminescent proteins have gained attention for their high sensitivity, fast readout, minimal background interference, and simplified instrumentation. Bioluminescence—light emission from biochemical reactions—presents an ideal platform for enhancing POCT sensitivity. In parallel, metal–organic frameworks (MOFs), especially structures like ZIF-8, are emerging as valuable materials in biosensing. Their high porosity, tunable surface properties, and ability to host biomolecules make them excellent candidates for improving analyte capture and signal transduction. When integrated with bioluminescent systems, MOFs can stabilize proteins, concentrate targets, and enhance overall assay performance. This review highlights the role of bioluminescent proteins in medical diagnostics and their application in POCT platforms. We also discuss the potential synergy between MOFs and bioluminescence to overcome current sensitivity limitations. Finally, we examine existing challenges and strategies to optimize these technologies for robust, field-deployable diagnostic tools. By leveraging both the natural sensitivity of bioluminescence and the structural advantages of MOFs, next-generation POCT systems can achieve superior performance, driving forward diagnostic accessibility and patient care outcomes. 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

The utilization of insects as a source of essential nutrients holds considerable promise, with the potential to serve as both feed and food. Consequently, there is a necessity to develop control systems, as the undeclared addition of insects to food products and/or non-compliance with labelling regulations may pose health risks and result in financial losses for consumers. This review describes methods for identifying and detecting insect species by targeting biomolecules such as DNA, proteins, saccharides, and metabolites, with a particular focus on DNA-based approaches. This review provides a detailed overview of the application of polymerase chain reaction (PCR) and DNA sequencing methods that are suitable for the analysis of edible and forage insects. The main focus is on identifying species that are approved for use as novel foods or insect feeds within the European Union (e.g., house cricket (Acheta domesticus), common mealworm (Tenebrio molitor), migratory locust (Locusta migratoria), lesser mealworm (Alphitobius diaperinus), black soldier fly (Hermetia illucens), banded cricket (Gryllodes sigillatus), field cricket (Gryllus assimilis), silkworm (Bombyx mori)). However, insect species of global relevance are also discussed. The suitability of DNA analysis methods for accurate species identification, detection of (un)labeled contaminants, and monitoring of genetic diversity has been demonstrated. Full article

SPR biosensors operate on the principle of evanescent wave propagation at metal–dielectric interfaces in total internal reflection conditions, with consequent photonic energy attenuation. This plasmonic excitation occurs in specific conditions of incident light wavelength, angle, and the dielectric refractive index. This principle has been the basis for SPR-based biosensor setups wherein mass/concentration-induced changes in the refractive indices of dielectric media reflect as plasmonic resonance condition changes quantitatively reported as arbitrary response units. SPR biosensors operating on this conceptual framework have been designed to study biomolecular interactions with real-time readout and in label-free setups, providing key kinetic characterization that has been valuable in various applications. SPR biosensors often feature antibodies as target affinity probes. Notably, the operational challenges encountered with antibodies have led to the development of aptamers—oligonucleotide biomolecules rationally designed to adopt tertiary structures, enabling high affinity and specific binding to a wide range of targets. Aptamers have been extensively adopted in SPR biosensor setups with promising clinical and industrial prospects. In this paper, we explore the growing literature on SPR setups featuring aptamers, specifically providing expert commentary on the current state and future implications of these SPR aptasensors for drug discovery as well as disease diagnosis and monitoring. Full article

The creation of surface relief gratings using hydrogels for label-free biomolecule detection represents a significant advance in the development of versatile, cutting-edge biosensors. Central to this innovation is the formulation of materials with enhanced mechanical properties, especially for applications in soft, wearable technologies. In this work, we have developed novel biofunctional hydrogels that incorporate slide-ring supramolecular structures into their network, enabling the production of surface relief gratings with superior mechanical characteristics for biomolecule detection without the need for labels. These hydrogels, functionalized with bovine serum albumin and goat anti-rabbit antibodies, demonstrated excellent selectivity and sensitivity toward anti-bovine serum albumin and rabbit IgGs in blood serum, evaluated using a label-free format. Remarkably, the new materials matched the analytical performance of conventional hydrogels based on static networks while offering dramatically improved toughness and elasticity, with a compressive modulus comparable to human skin. This demonstrates the potential of slide-ring hydrogels for fabricating robust, label-free biosensing platforms. Furthermore, the flexibility of this system allows for the incorporation of various recognition elements tailored to specific applications. Full article

Recently, sensing for biomolecules has become increasingly popular in the fields of environmental monitoring, personal health, and food safety. Plasmonic biosensors have been a powerful tool due to their high sensitivity and label-free operation. However, when it comes to molecules with different kinds and concentrations, detection technology and data processing remain a challenging task. In this study, we investigate the qualitative and quantitative detection of two kinds of biomolecules in the mid-infrared region simultaneously by the utilization of a plasmonic sensor. The strong coupling between each plasmonic resonance and the corresponding molecular vibration is found to significantly enhance the absorption signal of molecules, and the obtained Rabi splitting is not only a proof of molecular existence but also an indicator of molecular concentration. However, the amount of the molecular solution with a background refractive index in turn affects the plasmonic resonance position. In more general situations, it is not easy to achieve the match between plasmonic resonance and molecular resonance, and thus the quantitative detection by the Rabi splitting depth is not always feasible. Hence, we propose a machine learning algorithm called principal component analysis (PCA), providing a versatile approach for analyzing the proportion of each molecule in the mixture. Our work opens up new routes in noninvasive optical sensing and the integration of AI-driven data analysis further strengthens its potential for real-world applications. Full article

Polysaccharides are important biomolecules, which play a key role in biological, medical, and industrial processes due to their diverse structures and important functions. This paper looks into the significant progress made in the structural and functional analysis of polysaccharides by nuclear magnetic resonance (NMR) spectroscopy. NMR spectroscopy, including solution-state and solid-state technology, has revolutionized this field through detailed molecular insights into the structure, conformation, and dynamics of polysaccharides at the molecular level. There have been some important historical breakthroughs in 1D and 2D NMR, which have led to modern methods like multidimensional NMR and nuclear dynamic polarization (DNP). These modern methods offer a high level of resolution and sensitivity and have made it easier to come up with innovative applications. The applications range from the structural elucidation of microbial and plant structures of polysaccharides to improving food texture, developing therapies, and creating sustainable materials. Despite challenges such as signal overlap and limited sensitivity, emerging solutions are making significant progress. Computational modeling, isotope labeling, and integrated methods that combine complementary technologies are driving the boundaries of polysaccharide research. This review demonstrates the transformative role of NMR in revealing the complexity of polysaccharides and its potential to promote future discoveries and new ideas in the dynamic field. Full article

In recent years, field-effect transistors (FETs) based on graphene have attracted significant interest due to their unique electrical properties and their potential for biosensing and molecular detection applications. This study uses FETs with a nanocrystalline graphite (NCG) channel to detect DNA nucleobases. The exceptional electronic properties of NCG, and its high surface area, enable strong π–π stacking interactions with DNA nucleobases, promoting efficient adsorption and stabilization of the biomolecules. The direct attachment of nucleobases to the NCG channel leads to substantial changes in the device’s electrical characteristics, which can be measured in real time to assess DNA binding and sequence recognition. This method enables highly sensitive, label-free DNA detection, opening up new possibilities for rapid genetic analysis and diagnostics. Understanding the interactions between DNA nucleobases and graphene-based materials is crucial for advancing genetic research and biotechnology, paving the way for more accurate and efficient diagnostic tools. Full article

Biomolecule detection is pivotal in disease diagnosis. In this study, we present a novel aptamer–antibody sandwich module integrated with an imaging weak measurement system to enhance the sensitivity and specificity of biomolecule detection. The feasibility of this approach is demonstrated using CA125. CA125 is a glycoprotein tumor marker widely used for ovarian cancer diagnosis and monitoring, with its level changes closely associated with disease progression. Given its clinical significance, developing highly sensitive and specific CA125 detection methods is crucial for precision medicine. The dual-recognition mechanism combines the high affinity of aptamers and the specificity of antibodies, significantly improving detection performance while utilizing antibodies for signal amplification. In the presence of CA125, the anti-CA125 aptamer immobilized on the chip surface captures the target, which is then specifically bound by the CA125 antibody, forming the aptamer–CA125–antibody complex. This interaction induces a change in the refractive index of the chip surface, which is detected by the imaging weak measurement system and ultimately manifested as a variation in light intensity in the resulting images. The method achieves the highly sensitive detection of CA125 in the 0.01 mU/mL range to 100 U/mL, with preliminary results showing a detection resolution of 3.98 μU/mL and high specificity against non-target proteins. Additionally, detecting CA125 in serum samples further validates the feasibility of the method’s applicability in complex biological matrices. The proposed method offers significant advantages, including high sensitivity, high specificity, label-free, multiplexed detection, low cost, and real-time detection, making it a promising platform for bio-molecule detection with a wide range of applications. Full article

Recent years have seen a swift rise in the use of α-emitting radionuclides such as ²²⁵Ac and ²²³Ra as various radiopharmaceuticals to treat (micro)metastasized tumors. They have shown remarkable effectiveness in clinical practice owing to the highly cytotoxic α-particles that are emitted, which have a very short range in tissue, causing mainly double-stranded DNA breaks. However, it is essential that both chelation and targeting strategies are optimized for their successful translation to clinical application, as α-emitting radionuclides have distinctly different features compared to β⁻-emitters, including their much larger atomic radius. Furthermore, upon α-decay, any daughter nuclide irrevocably breaks free from the targeting molecule, known as the recoil effect, dictating the need for faster targeting to prevent healthy tissue toxicity. In this review we provide a brief overview of the current status of targeted α-therapy and highlight innovations in α-emitter-based chelator design, focusing on the role of click chemistry to allow for fast complexation to biomolecules at mild labeling conditions. Finally, an outlook is provided on different targeting strategies and the role that pre-targeting can play in targeted alpha therapy. Full article

Artificial intelligence (AI)-assisted prediction of adverse drug reactions (ADRs) has significant potential for improving drug safety and reducing financial costs. Early studies often relied on limited dimensions such as the molecular structure of drugs or interactions with biomolecules. In contrast, integrating these characteristics provides valuable insights into ADR predictions from multiple perspectives, enhancing the comprehensiveness and accuracy of the prediction models. In addition, previous studies have focused on whether a specific adverse drug reaction occurs with a particular drug, ignoring the fact that multiple adverse drug reactions may occur concurrently with a single drug. To address these, we developed a predictor that identifies ADRs early in drug discovery, using a deep learning model designed to fuse multiple drug characteristics. Our approach employed four modules to extract one- and two-dimensional sequence structure information of drug molecules, drug–protein interaction data, and drug similarity. A fusion model integrated these characteristics to predict the precise probability of ADRs. The receiver operating characteristic–area under curve (ROC-AUC), area under precision–recall curve (AUPR), and F1 scores on the benchmark dataset are 0.7002, 0.6619, and 0.6330, respectively. The AUPR is significantly improved compared to the conventional multi-label classifier (from 64.02% to 66.19%). In addition, we compared the results with the state-of-the-art methods on LIU’s dataset and the AUPR increased from 34.65% to 68.82%, which shows that our model outperforms them in terms of accuracy and robustness. Ablation experiments further validated the effectiveness of the individual modules. This model accurately predicted the probability of various ADR classes by integrating comprehensive information, thereby offering significant value in enhancing monitoring measures for new drug development and clinical use. Full article

Personalized monitoring of disease biomarkers is of great interest in women’s health. However, existing approaches typically involve invasive inspection or bulky equipment, making them challenging to implement at home. Hence, we present a general strategy for label-free and specific detection of disease biomarkers in physiological media using an aptamer-based biosensor. The biosensor is a graphene field-effect transistor that involves immobilizing the aptamer and a biomolecule-permeable polyethylene glycol (PEG) layer on the graphene surface. The aptamer is capable of specifically binding with the target biomarker, thus inducing a change in the sensing responses. The PEG layer can effectively reduce the nonspecific adsorption of nontarget molecules in the solution, and increase the effective Debye screening length in the region directly adjacent to the graphene. In this work, studies of a biosensor with modification of the aptamer and PEG show that cervical carcinoma biomarkers such as tumor necrosis factor-α and interleukin 6 can be sensitively and specifically detected in undiluted physiological media, with detection limits as low as 0.13 pM for TNF-a and 0.20 pM for IL-6. This work presents a significant method for the general application of the biosensor for disease diagnosis in women’s health. Full article

This review into recent advancements in silicon-based technology, with a particular emphasis on the biomedical applications of silicon sensors. Owing to their diminutive size, high sensitivity, and intrinsic compatibility with electronic systems, silicon-based sensors have found widespread utilization across healthcare, industrial, and environmental monitoring domains. In the realm of biomedical sensing, silicon has demonstrated significant potential to enhance human health outcomes while simultaneously driving progress in microfabrication techniques for multifunctional device development. The review systematically examines the versatile roles of silicon in the fabrication of electrodes, sensing channels, and substrates. Silicon electrodes are widely used in electrochemical biosensors for glucose monitoring and neural activity recording, while sensing channels in field-effect transistor biosensors enable the detection of cancer biomarkers and small molecules. Porous silicon substrates are applied in optical biosensors for label-free protein and pathogen detection. Key challenges in this field, including the interaction of silicon with biomolecules, the economic barriers to miniaturization, and issues related to signal stability, are critically analyzed. Proposed strategies to address these challenges and improve sensor functionality and reliability are also discussed. Furthermore, the article explores emerging developments in silicon-based biosensors, particularly their integration into wearable technologies. The pivotal role of artificial intelligence (AI) in enhancing the performance, functionality, and real-time capabilities of these sensors is also highlighted. This review provides a comprehensive overview of the current state, challenges, and future directions in the field of silicon-based biomedical sensing technologies. Full article

Peptides are an important class of biomolecules that perform many physiological functions and which occupy a significant and increasing share of the pharmaceutical market. Methods to determine the solid-state structures of peptides in different environments are important to help understand their biological functions and to aid the development of drug formulations. Here, a new magic-angle spinning (MAS) solid-state nuclear magnetic resonance (SSNMR) approach is described for the structural analysis of uniformly ¹³C-labeled solid peptides. Double-quantum (DQ) coherence between selective pairs of ¹³C nuclei in peptide backbone and side-chain CH₃ groups is excited to provide restraints on (i) ¹³C–¹³C internuclear distances and (ii) the relative orientations of C–H bonds. DQ coherence is selected by adjusting the MAS frequency to the difference in the resonance frequencies of selected nuclear pairs (the rotational resonance condition), which reintroduces the dipolar coupling between the nuclei. Interatomic distances are then measured using a constant time SSNMR experiment to eliminate uncertainties arising from relaxation effects. Further, the relative orientations of C–H bond vectors are determined using a DQ heteronuclear local field SSNMR experiment, employing ¹³C–¹H coupling amplification to increase sensitivity. These methods are applied to determine the molecular conformation of a uniformly ¹³C-labeled peptide, N-formyl-l-methionyl-l-leucyl-l-phenylalanine (fMLF). From just six distance and six angular restraints, two possible molecular conformations are determined, one of which is in excellent agreement with the crystal structure of a closely related peptide. The method is envisaged to a useful addition to the SSNMR repertoire for the solid-state structure determination of peptides in a variety of forms, including amyloid fibrils and pharmaceutical formulations. Full article

Surface acoustic wave (SAW) sensor technology is a promising approach to diagnosing cancer through the detection of cancer biomarkers due to its high sensitivity, potential label-free operation, and fast response times, and, fundamentally, because it is a non-invasive technique in comparison with the current traditional diagnostic techniques for cancer. This review focuses on this application, and for this purpose, the recent literature on cancer biomarkers detected by this advanced technology has been compiled, including that on volatile organic compounds (VOCs) from exhaled breath and larger biomolecules such as proteins, DNA, and microRNAs in body fluids, which demonstrates its great versatility. The conventional techniques for cancer biomarker detection in biofluids, such as ELISA, PCR, SPR, and UV absorbance, exhibit limitations including high costs, slow response times, a reduced sensitivity, the need for specialized instrumentation, and the requirement for highly trained personnel. Different SAW sensor configurations are discussed with attention paid to their specific properties, wave propagation modes, and suitability for different environments. Detailed studies are reviewed, highlighting biomarkers for lung, colorectal, prostate, breast, and ovarian cancer diagnostics, as well as the detection of circulating tumor cells and cancerous cell growth. This review identifies current challenges, including optimizing sensitivity, addressing environmental interferences, and the need for clinical validation. Finally, future research directions are proposed, emphasizing the use of VOC biomarkers and the integration of SAW technology into hybrid systems and microfluidic platforms to enable the creation of scalable, non-invasive diagnostic tools for the detection of cancer in early stages, and, in this way, to minimize the morbidity and mortality associated with this disease. Full article