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Reversal Nanoimprinted 3D Plasmonic Sensor Around Microposts for Cell and DNA Detection
Chemiresistive Gas Sensors for the Detection of Listeria monocytogenes Metabolite: Recent Progress and Challenges
A Ready-to-Use Recombinant Yeast Two-Hybrid Assay for Thyroxine Detection
Smart Wearable EEG Devices: A Review of Lightweight, Multi-Sensor Systems for Sleep and Everyday Neurophysiology
Wearable Wireless EMG Sensors for Monitoring Post-Error Neuromuscular Responses During a Sport-Specific Inhibitory Control Task

Journal Description

Biosensors

Biosensors is an international, peer-reviewed, open access journal on the technology and science of biosensors, published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Embase, CAPlus / SciFinder, Inspec, and other databases.
Journal Rank: JCR - Q1 (Instruments and Instrumentation) / CiteScore - Q1 (Instrumentation)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.3 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2026).
Recognition of Reviewers: Reviewers whose reports are timely and of high quality receive an APC discount voucher for a future publication in an MDPI journal. Become a reviewer.
Journal Cluster of Analysis and Sensing Technologies: Analytica, Biosensors, Chemosensors, Purification, Separations and Spectroscopy Journal.

Impact Factor: 6.2 (2025); 5-Year Impact Factor: 6.2 (2025)

Imprint Information Journal Flyer Open Access ISSN: 2079-6374

Latest Articles

23 pages, 5763 KB

Open AccessArticle

A Microfluidic Gradient Platform for High-Throughput Evaluation of Blue-Light-Induced Oxidative Stress and Antioxidant Protection in Retinal Pigment Epithelial Cells

by Hon-Man-Herman Tam, Sheng-Yen Wang, Yung-Shin Sun and Kai-Yin Lo

Biosensors 2026, 16(9), 516; https://doi.org/10.3390/bios16090516 (registering DOI) - 12 Sep 2026

The retinal pigment epithelium (RPE) is a monolayer of cells located between retinal photoreceptors and the choroid, playing a critical role in maintaining visual function by protecting the retina and supporting photoreceptor metabolism. Damage to RPE cells can lead to visual disorders, including [...] Read more.

The retinal pigment epithelium (RPE) is a monolayer of cells located between retinal photoreceptors and the choroid, playing a critical role in maintaining visual function by protecting the retina and supporting photoreceptor metabolism. Damage to RPE cells can lead to visual disorders, including macular degeneration. Chronic exposure to high-energy blue light has been shown to elevate intracellular reactive oxygen species (ROS) in RPE cells, causing oxidative stress and cellular damage. In this study, a microfluidic platform incorporating a gradient-generating structure was developed to establish controllable and stable gradients of blue light intensity and chemical concentrations. This platform was used to investigate the effects of varying blue light intensities and antioxidant concentrations on oxidative stress in human RPE cells ARPE-19. Cells cultured within the microfluidic channels were exposed to different blue light intensities in combination with chemical treatments. Results demonstrated that ROS production increased with higher blue light intensity, whereas higher antioxidant concentrations effectively reduced ROS accumulation, supporting the ability of these antioxidants to attenuate blue-light-induced intracellular oxidative stress. The present microfluidic device enables simultaneous evaluation of multiple conditions within a single experiment, reducing reagent consumption and enhancing experimental efficiency. This in vitro microfluidic platform integrates chemical and light gradients to assess retinal oxidative damage and antioxidant effects, offering significant potential for ophthalmic drug screening and investigations of retinal protective mechanisms. Full article

(This article belongs to the Special Issue Microfluidics in Biomedicine: Current Advances and Future Directions)

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37 pages, 18816 KB

Open AccessReview

Research Progress of Terahertz Technology in Microbiology

by Ding Cao, Ruibing Dong, Guangyou Fang and Xuequan Chen

Biosensors 2026, 16(9), 515; https://doi.org/10.3390/bios16090515 - 11 Sep 2026

Microorganisms are ubiquitous in nature, and microbial activities are closely intertwined with the entire life cycle system and human life. Developing novel technologies for the detection, characterization and manipulation of microorganisms promotes their applications in clinical, environmental and industrial areas. Over the last [...] Read more.

Microorganisms are ubiquitous in nature, and microbial activities are closely intertwined with the entire life cycle system and human life. Developing novel technologies for the detection, characterization and manipulation of microorganisms promotes their applications in clinical, environmental and industrial areas. Over the last two decades, terahertz (THz) technology has emerged as a new optical tool for microbiology. The great potential originates from the unique advantages of THz waves including the high sensitivity to water and inter-/intra-molecular motions, the non-invasive and label-free detecting scheme, and their low photon energy. THz waves have been utilized as a stimulus to alter microbial functions or as a sensing approach for quantitative measurement and qualitative differentiation. This review specifically focuses on recent research progress of THz technology applied in the field of microbiology, including two major parts of THz biological effects and the microbial detection applications. At the end of this paper, we summarize the research progress and discuss the challenges currently faced by THz technology in microbiology, along with potential solutions. We also provide a perspective on future development directions. This review aims to build a bridge between THz photonics and microbiology, promoting both fundamental research and application development in this interdisciplinary field. Full article

(This article belongs to the Special Issue Terahertz Biophotonics: Advancing Biosensing Technologies)

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34 pages, 12394 KB

Open AccessReview

Artificial Intelligence for Alzheimer’s Disease Diagnosis: From Traditional Machine Learning to Large Language Models

by Xiayao Guo, Yanqi Sun, Yang Chen, Hongde Liu, Xiaohui Liu and Xuemei Wang

Biosensors 2026, 16(9), 514; https://doi.org/10.3390/bios16090514 - 11 Sep 2026

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and a leading cause of dementia worldwide, characterized by progressive cognitive decline, memory impairment, and functional deterioration. With the rapid growth of the aging population, AD has become a major global health challenge, imposing [...] Read more.

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and a leading cause of dementia worldwide, characterized by progressive cognitive decline, memory impairment, and functional deterioration. With the rapid growth of the aging population, AD has become a major global health challenge, imposing substantial burdens on patients, families, and healthcare systems. Despite extensive research, early and accurate diagnosis of AD remains challenging due to disease heterogeneity, overlapping clinical manifestations, and the lack of easily accessible, highly sensitive, and specific diagnostic markers. Recent advances in biomedical technologies, including neuroimaging, multi-omics profiling, electronic health records, and digital health tools, have generated large-scale and heterogeneous datasets, providing new opportunities for improving AD diagnosis. However, extracting clinically meaningful information from these complex data sources remains difficult using conventional statistical approaches. Artificial intelligence (AI) has progressively transformed AD diagnosis by evolving from traditional machine learning (ML) approaches based on handcrafted feature engineering to deep learning (DL) models capable of automated representation learning and multimodal information integration. More recently, large language models (LLMs) have further expanded the scope of AI-driven AD diagnosis by enabling contextual understanding of unstructured clinical information, knowledge-guided reasoning, and integration of multimodal biomedical evidence. This transition reflects a shift from feature-based prediction toward more flexible and intelligent diagnostic frameworks. This review synthesizes recent advances in AI-based AD diagnosis, tracing the evolution from traditional ML to DL and LLMs. Particular emphasis is placed on the emerging role of LLMs in extracting disease-related information from speech and clinical narratives, integrating heterogeneous biomedical data sources, and enabling multimodal frameworks for AD assessment. Full article

(This article belongs to the Special Issue The Smart Biosensors Era: AI in Cancer Detection and Imaging)

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19 pages, 11764 KB

Open AccessArticle

Wearable-Derived Evening Motion Entropy and Morning Light Exposure Are Associated with Depressive Symptoms in University Students

by Guanxiang Ding, Qizhi Zhao, Linxin Zou, Yuezhou Zhang, Xianghong Zhao, Hao Li and Zhengxiang Yu

Biosensors 2026, 16(9), 513; https://doi.org/10.3390/bios16090513 - 11 Sep 2026

Depressive symptoms are prevalent in university students, yet scalable screening is constrained by self-report and intermittent assessment. Wearable monitoring of motion and light offers an objective alternative, though their combined screening characterization is limited. Undergraduate volunteers wore a self-designed wristband for seven days, [...] Read more.

Depressive symptoms are prevalent in university students, yet scalable screening is constrained by self-report and intermittent assessment. Wearable monitoring of motion and light offers an objective alternative, though their combined screening characterization is limited. Undergraduate volunteers wore a self-designed wristband for seven days, recording triaxial acceleration and ambient light at 1 min resolution. Depressive symptoms were assessed via nine-item Patient Health Questionnaire (PHQ-9) and grouped as Dep (≥5) or HC (<5). Motion and light rhythm features were extracted from 24 h profiles and predefined windows; logistic regression (LR) and support vector machine (SVM) compared motion-only, light-only, and joint sets, interpreted with Shapley additive explanations (SHAP). After preprocessing, 152 participants provided valid activity data (HC = 132, Dep = 20) and 218 valid light data (HC = 186, Dep = 32). The Dep group showed delayed activity timing, lower daily light exposure, and greater evening motion irregularity, with differences most prominent during 18:00–22:00 for motion entropy and variance. The joint motion–light SVM performed best (accuracy 85.2%, recall 60.0%, F1 Score 46.2%, Area Under Curve 0.892). SHAP highlighted evening motion entropy and variance, morning light exposure, and rhythm-related wavelet features. These findings indicate that wearable motion and light rhythms are associated with depressive symptoms; evening motion entropy and morning light exposure may aid campus risk screening as interpretable markers, although the cross-sectional design and PHQ-9-based grouping preclude diagnostic or causal conclusions. Full article

(This article belongs to the Special Issue Advances in Flexible and Wearable Biosensors)

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22 pages, 3633 KB

Open AccessArticle

Combining Asymmetric PCR with PAM-Independent Cas12a Analysis of Single-Stranded Amplicons for On-Site Nucleic Acid Testing

by Konstantin G. Ptitsyn, Olga S. Timoshenko, Svetlana A. Khmeleva, Leonid K. Kurbatov, Adelina A. Stepanova, Evgenia M. Nikiforova, Elena V. Suprun, Sergey P. Radko and Andrey V. Lisitsa

Biosensors 2026, 16(9), 512; https://doi.org/10.3390/bios16090512 - 11 Sep 2026

A biosensing system relying on asymmetric PCR (aPCR) and PAM-independent recognition of single-stranded DNA amplicons by a Cas12a/gRNA complex is adapted for on-site nucleic acid testing (NAT). aPCR was performed on a low-cost, portable, smartphone-controlled thermocycler operating in endpoint mode. Subsequently, a selective [...] Read more.

A biosensing system relying on asymmetric PCR (aPCR) and PAM-independent recognition of single-stranded DNA amplicons by a Cas12a/gRNA complex is adapted for on-site nucleic acid testing (NAT). aPCR was performed on a low-cost, portable, smartphone-controlled thermocycler operating in endpoint mode. Subsequently, a selective visual readout of the aPCR outcome was achieved using a Cas12a-based assay. A mechanism to prevent false positives was incorporated into the aPCR/Cas12a biosensing system by introducing uracil into the amplicons produced during amplification. Pectobacterium species—harmful bacterial plant pathogens—and genus-specific primers Y1 and Y2 against Pectobacterium spp. were utilized as a convenient model. The absence of a PAM requirement allowed for fine-tuning the selectivity of the Cas12a assay against a strain of a particular Pectobacterium species, P. polaris, by properly positioning the gRNA spacer on the ssDNA. P. polaris was detected down to 10 copies of the bacterial genome per reaction, with an overall testing time of about 2 h and either instrumental or visual readouts. Similar selectivity and sensitivity toward a P. polaris strain were observed with the aPCR/Cas12a biosensing system for potato samples artificially contaminated with Pectobacterium species. The overall findings indicate aPCR/Cas12a-based biosensing as a technique suitable for on-site NAT. Full article

(This article belongs to the Section Environmental, Agricultural, and Food Biosensors)

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87 pages, 4941 KB

Open AccessReview

Surface-Enhanced Raman Spectroscopy in Breast Cancer Detection: A Bibliometric Review and Landscape of Global Trends

by Alitzel B. García-Hernández, Gethzemani M. Estrada-Villegas, Ana L. Gómez-Gómez, Ma. de la Paz Salgado-Cruz and Dana M. Cortez Landa

Biosensors 2026, 16(9), 511; https://doi.org/10.3390/bios16090511 - 10 Sep 2026

Surface-Enhanced Raman Spectroscopy (SERS) has emerged as a powerful analytical platform for breast cancer (BC) detection, offering ultrasensitive, multiplexed, and label-free molecular recognition. However, despite the rapid expansion of the field, no prior study has combined quantitative bibliometric mapping with a cluster-validated technical [...] Read more.

Surface-Enhanced Raman Spectroscopy (SERS) has emerged as a powerful analytical platform for breast cancer (BC) detection, offering ultrasensitive, multiplexed, and label-free molecular recognition. However, despite the rapid expansion of the field, no prior study has combined quantitative bibliometric mapping with a cluster-validated technical and translational synthesis, limiting a comprehensive understanding of the field’s structure, evolution and clinical projection. In this review, a PRISMA-guided bibliometric analysis was conducted; 199 articles on SERS-based BC detection (2016–2025) were retrieved from SCOPUS, Web of Science and Google Scholar, mapping publication trends, keyword co-occurrence networks (VOSviewer), and Multiple Correspondence Analysis (MCA) with hierarchical clustering on principal components. The results reveal sustained growth in scientific output, led by Asia, North America, and Europe. The 20 most-cited articles (271 citations maximum) showed a shift from substrate optimization toward AI-assisted liquid biopsy platforms. MCA identified five clusters, corroborated by the co-occurrence network: (1) nanostructured platforms for diagnosis; (2) biofunctionalization strategies; (3) liquid biopsy approaches targeting exosomes, circulating tumor cells, and alternative biofluids; (4) diagnostic interpretation based on chemometrics, machine learning (ML) and artificial intelligence (AI); and (5) translational achievements in preclinical and clinical studies. Each cluster was anchored by a technical sub-analysis of its landmark studies, an integration largely absent from prior SERS reviews. This framework clarifies the field’s trajectory and positions SERS as a key technology for non-invasive, personalized diagnostics in precision oncology. Full article

(This article belongs to the Special Issue SERS-Based Diagnostic Systems: Innovation and Precision Biomedical Applications)

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27 pages, 1344 KB

Open AccessArticle

Retention-Based, Dissociation-Sensitive Pre-Screening of Aptamers Using a Centrifugal Microspin Filter

by Cheeyoon Ahn, Hwayeon Jeong, Kyungjin Jeon and Cheulhee Jung

Biosensors 2026, 16(9), 510; https://doi.org/10.3390/bios16090510 (registering DOI) - 10 Sep 2026

Efficient evaluation of aptamer candidates after systematic evolution of ligands by exponential enrichment (SELEX) remains limited by the time and cost required for direct kinetic characterization. We developed a centrifugal microspin filtration (MSF)-based MSF–k_off assay for retention-based, dissociation-sensitive pre-screening of aptamer [...] Read more.

Efficient evaluation of aptamer candidates after systematic evolution of ligands by exponential enrichment (SELEX) remains limited by the time and cost required for direct kinetic characterization. We developed a centrifugal microspin filtration (MSF)-based MSF–k_off assay for retention-based, dissociation-sensitive pre-screening of aptamer candidates. Repeated washing preferentially removes rapidly dissociating aptamers from target-immobilized magnetic beads, and the retained aptamers are quantified by quantitative polymerase chain reaction and expressed as Wash 0-normalized retention (W0-NRT). In a thrombin aptamer panel, W0-NRT showed a strong inverse relationship with SPR-derived k_off. A preliminary analysis of streptavidin aptamers likewise showed a W0-NRT-based ranking consistent with the literature-reported k_off values, although the two target systems exhibited distinct empirical relationships. In a wash-based thrombin sandwich assay, two affinity-matched sensor aptamers differing 1.76-fold in k_off were ranked in the same order by post-wash retention as by W0-NRT. The agreement was in rank order rather than in the magnitude of retention. The parallel microspin filter format was also estimated to offer advantages in analysis time and assay-related cost. The MSF–k_off assay therefore provides a simple post-SELEX approach for prioritizing candidates with higher retention under dissociation-sensitive washing before detailed kinetic characterization. Full article

(This article belongs to the Special Issue Aptamer-Based Biosensing: Innovations in Molecular Recognition, Signal Transduction, and Applications)

14 pages, 21375 KB

Open AccessArticle

A Rapid and Sensitive Loop-Mediated Isothermal Amplification Assay for the Detection of Mulberry Mosaic Dwarf-Associated Virus

by Shaoshuang Sun, Xueping Zhou and Xiuling Yang

Biosensors 2026, 16(9), 509; https://doi.org/10.3390/bios16090509 - 10 Sep 2026

Mulberry mosaic dwarf-associated virus (MMDaV), a member of the genus Mulcrilevirus in the family Geminiviridae, poses a serious threat to mulberry cultivation and growth in China. Early and accurate diagnosis is a prerequisite for the effective prevention and control of MMDaV-related disease. [...] Read more.

Mulberry mosaic dwarf-associated virus (MMDaV), a member of the genus Mulcrilevirus in the family Geminiviridae, poses a serious threat to mulberry cultivation and growth in China. Early and accurate diagnosis is a prerequisite for the effective prevention and control of MMDaV-related disease. In this study, a loop-mediated isothermal amplification (LAMP)-based detection method was established for the specific identification of MMDaV. Three pairs of specific primers were designed targeting the nucleotide sequences of the MMDaV V2 gene. The optimized LAMP reaction was performed at a constant temperature of 65 °C for 60 min, and the established method exhibited high specificity with no cross-reactivity observed against other tested geminiviruses. The LAMP method achieved a detection limit of 200 pg of target MMDaV DNA, which was tenfold more sensitive than conventional PCR. Furthermore, the amplification results could be directly visualized via distinct color change. Collectively, the developed LAMP method enables efficient, sensitive, and specific isothermal detection of MMDaV and holds great promise for rapid diagnosis of mulberry viral disease. Full article

(This article belongs to the Section Environmental, Agricultural, and Food Biosensors)

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16 pages, 4587 KB

Open AccessArticle

Hierarchical Au–Pt Nanostructured Film-Enabled Electrochemical Sensor for Highly-Sensitive Determination of Salvianolic Acid B

by Yujiao Hou, Fang Lin, Xiang Gao, Jianhua Yang, Longfei Sun and Weijun Kong

Biosensors 2026, 16(9), 508; https://doi.org/10.3390/bios16090508 - 10 Sep 2026

Salvianolic acid B is an important bioactive biomarker in traditional Chinese medicines (TCMs). Current methods for its quantitation are time- and cost-consuming; a rapid and reliable method is urgently needed. In this work, we have developed a simple and sensitive electrochemical (EC) sensor [...] Read more.

Salvianolic acid B is an important bioactive biomarker in traditional Chinese medicines (TCMs). Current methods for its quantitation are time- and cost-consuming; a rapid and reliable method is urgently needed. In this work, we have developed a simple and sensitive electrochemical (EC) sensor on screen-printed carbon electrode (SPCE) that was modified with three-dimensional hierarchical Au–Pt nanostructured film. This highly conductive structure facilitates direct electron transfer when the sample solution containing salvianolic acid B is dropped, thereby amplifying its generated current response. Under optimized conditions, the fabricated label-free EC sensor exhibited a wide linear range (4–500 μg/mL) with excellent conductivity and a detection limit as low as 3.76 μg/mL for salvianolic acid B, as well as high reproducibility and outstanding stability. Furthermore, the practical applicability was validated by satisfactory spiked recovery rates (97.5–101.4%) in complex Salvia miltiorrhiza matrices. These findings indicated that the Au–Pt nanostructure-modified SPCE could achieve reliable and stable quantitation of salvianolic acid B. The developed EC sensor provided an economical, efficient, user-friendly, and reliable on-site strategy for accurate determination of salvianolic acid B and other components in more TCMs. Full article

(This article belongs to the Special Issue Functional Nanomaterials for Advanced Biosensing: From Molecular Design to Real-World Applications)

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20 pages, 2525 KB

Open AccessReview

Artificial Pancreas and Closed-Loop Insulin Delivery: From Early Concepts to AI-Driven Diabetes Automation

by Reem Emad Al-Dhaleai, Mustafa Tariq Khan, Zaid Chilmeran, Abdulrahman Husain AlSadeq and Alexandra E. Butler

Biosensors 2026, 16(9), 507; https://doi.org/10.3390/bios16090507 - 10 Sep 2026

Closed-loop insulin delivery, or the artificial pancreas, has evolved from an ambitious engineering concept into one of the most consequential advances in diabetes technology. By integrating continuous glucose monitoring, insulin pumps, and control algorithms into a single feedback system, these platforms aim to [...] Read more.

Closed-loop insulin delivery, or the artificial pancreas, has evolved from an ambitious engineering concept into one of the most consequential advances in diabetes technology. By integrating continuous glucose monitoring, insulin pumps, and control algorithms into a single feedback system, these platforms aim to shift diabetes care from repeated manual correction toward more anticipatory and adaptive glucose regulation. The field has progressed from early proof-of-concept systems to contemporary hybrid closed-loop platforms, reflecting a deeper shift in diabetes management itself: from treating glucose excursions after they occur to trying to blunt them in real time. Its clinical relevance is greatest in type 1 diabetes, where the burden of self-management is high and the consequences of glycemic instability are immediate. This review introduces the major technologies and evidence shaping the field, with emphasis on the control strategies that drive system behavior, and asks which currently available closed-loop systems offer the best balance of glycemic benefit, usability, and translational readiness for routine diabetes care. Across randomized trials and real-world studies, automated insulin delivery has consistently improved time in range, reduced hypoglycemia, and enhanced patient experience, particularly in pediatric populations. At the same time, important limitations remain: sensor lag, the physiologic constraints of subcutaneous insulin, device complexity, cost, and unequal access limit full autonomy and widespread adoption. Looking ahead, the next phase of progress will likely depend on more adaptive artificial intelligence, improved meal detection, multimodal wearable data, and multi-hormone systems that move the field closer to truly physiologic glucose control. Full article

(This article belongs to the Special Issue Recent Advances in Glucose Biosensors—2nd Edition)

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26 pages, 5991 KB

Open AccessArticle

Development and Preliminary Evaluation of RR-TB GoldDx and Hr-TB GoldDx: Rapid Visual RPA-Gold Nanoparticle Assays for Detecting Rifampicin- and Isoniazid-Resistance-Associated Mutations in Mycobacterium tuberculosis

by Usanee Wattananandkul, Sukanya Saikaew, Sirikwan Sangboonruang, Rodjana Pongsararuk, Prapaporn Srilohasin, Bordin Butr-Indr, Sorasak Intorasoot, Chayada Sitthidet Tharinjaroen, Natthawat Semakul, Surachet Arunothong, Angkana Chaiprasert and Khajornsak Tragoolpua

Biosensors 2026, 16(9), 506; https://doi.org/10.3390/bios16090506 - 10 Sep 2026

Drug-resistant tuberculosis remains a major challenge to tuberculosis control, particularly in settings with limited access to conventional molecular testing. This study developed and preliminarily evaluated RR-TB GoldDx and Hr-TB GoldDx, rapid visual assays for detecting rifampicin- and isoniazid-resistance-associated mutations in Mycobacterium tuberculosis. [...] Read more.

Drug-resistant tuberculosis remains a major challenge to tuberculosis control, particularly in settings with limited access to conventional molecular testing. This study developed and preliminarily evaluated RR-TB GoldDx and Hr-TB GoldDx, rapid visual assays for detecting rifampicin- and isoniazid-resistance-associated mutations in Mycobacterium tuberculosis. The assays combine recombinase polymerase amplification with unmodified gold nanoparticles (AuNPs) for amplification confirmation and thiol-probe-modified AuNPs for detecting resistance-associated mutations at rpoB codons 516, 526, and 531, katG codon 315, and position −15 of the fabG1–inhA promoter. Evaluation using DNA from 50 M. tuberculosis isolates yielded positive internal-control results for all samples. RR-TB GoldDx achieved accuracies of 84% and 86% against phenotypic drug-susceptibility testing and DNA sequencing, respectively, with Cohen’s kappa values of 0.68 and 0.72. Hr-TB GoldDx achieved 80% accuracy and a kappa value of 0.60 against phenotypic testing, while the inhA promoter and katG assays achieved sequencing-based accuracies of 84% and 86%, respectively. Results were available within 1 h at an estimated reagent cost of THB 500–550 (approximately USD 15–16) per test without conventional thermocycling, real-time fluorescence detection, or gel electrophoresis. These instrument-minimal assays show potential for rapid drug-resistance screening but require validation in larger, genetically diverse cohorts and direct respiratory specimens. Full article

(This article belongs to the Special Issue Point-of-Care Testing: Advances and Perspectives)

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22 pages, 6814 KB

Open AccessArticle

Continuous Measurement of Spatially Resolved Red Blood Cell Aggregation Using Multiple Side-Branch Channels

by Minjae Kim and Yang Jun Kang

Biosensors 2026, 16(9), 505; https://doi.org/10.3390/bios16090505 - 8 Sep 2026

Red blood cell (RBC) aggregation is an important hemorheological property that influences blood viscosity, microcirculation, and stored-blood quality. However, conventional measurements commonly require repeated flow cessation or flow-rate modulation, limiting continuous monitoring and providing little information on spatial heterogeneity. This study presents a [...] Read more.

Red blood cell (RBC) aggregation is an important hemorheological property that influences blood viscosity, microcirculation, and stored-blood quality. However, conventional measurements commonly require repeated flow cessation or flow-rate modulation, limiting continuous monitoring and providing little information on spatial heterogeneity. This study presents a microfluidic platform for the spatiotemporal mapping of RBC aggregation during continuous blood flow. Multiple high-resistance side chambers connected to a main channel create low-shear-rate regions for aggregation while maintaining high shear in the main channel for RBC disaggregation. Flow rates and shear rates are evaluated using a hydraulic circuit model, numerical simulation, and micro-PIV measurements. An aggregation index (AI) map is introduced to quantify spatial and temporal changes in the side chambers. AI remains high and stable at flow rates of 0.5~1 mL/h. RBC aggregation increases significantly at concentrations of 15 mg/mL or with more dextran solution (C_dex). At the selected flow rate of 1 mL/h and C_dex = 40 mg/mL, the 95% confidence interval of AI is 0.477~0.600 for the proposed method, compared with 0.364~0.460 for the previous method. The proposed AI value is approximately 30% higher than that obtained using the previous method. Moreover, AI decreases progressively during four weeks of RBC storage. These findings demonstrate continuous and multiple-location detection of RBC aggregation and support the use of this platform for assessing hemorheological alterations and storage-induced RBC deterioration. Full article

(This article belongs to the Special Issue Design and Application of Microfluidic Biosensors in Biomedicine—2nd Edition)

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21 pages, 2828 KB

Open AccessArticle

Entropy-Based Analysis of Olfactory EEG as a Candidate Biomarker for Early Mild Cognitive Impairment Detection: A Proof-of-Concept Study

by Sabatina Criscuolo, Andrea De Maria, Annarita Tedesco, Pasquale Arpaia and Egidio De Benedetto

Biosensors 2026, 16(9), 504; https://doi.org/10.3390/bios16090504 - 8 Sep 2026

A decline in olfactory ability represents one of the earliest signs of Alzheimer’s disease (AD) and can be valuable information for early diagnosis at the stage of mild cognitive impairment (MCI). Nevertheless, the underlying neurophysiological mechanisms of olfactory impairment have not been systematically [...] Read more.

A decline in olfactory ability represents one of the earliest signs of Alzheimer’s disease (AD) and can be valuable information for early diagnosis at the stage of mild cognitive impairment (MCI). Nevertheless, the underlying neurophysiological mechanisms of olfactory impairment have not been systematically studied and, so far, have not been applied to make objective diagnoses using electroencephalography (EEG). To fill this gap, this proof-of-concept study investigates the possibility of using olfactory-evoked EEG complexity to discriminate between healthy subjects (HSs) and MCI patients. To this purpose, a publicly available olfactory oddball EEG-recording dataset was considered. First, a strategy for cleaning the EEG signals was implemented and applied, including exclusion of participants, channels, and epochs affected by substantial artifacts and noise. Then, a dedicated preprocessing pipeline was implemented: in particular, the cleaned signals were partitioned into three temporal intervals according to the stimulus onsets (i.e., pre-stimulus, early post-stimulus, and late post-stimulus periods). For each window of interest, a novel metric—namely, the Multivariate Multiscale Multi-Frequency Entropy (M3FrEn)—was computed across 10 temporal scales. The obtained results showed significant main effects of group and stimulus, as well as a significant group-by-stimulus interaction across all scales, as assessed by linear mixed-effects models. Post hoc analysis revealed a significantly reduced stimulus-related entropy modulation in MCI subjects compared to healthy controls across several early post-stimulus scales, with the strongest effect at scale 6 (adjusted

p = 0.0189

,

H e d g e s ’ g = - 2.88

). An exploratory, fully nested subject-level classification analysis, in which feature selection was performed independently within each cross-validation fold, achieved an accuracy of approximately 92% with all classifiers, consistently relying on the early post-stimulus feature. These preliminary findings suggest that M3FrEn captures olfactory-related EEG alterations in MCI, providing proof-of-concept evidence for its potential as a candidate biomarker. Full article

(This article belongs to the Special Issue AI-Based Biosensors and Biomedical Imaging)

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25 pages, 7849 KB

Open AccessArticle

Rapid Bacterial Detection on Surfaces by Field-Deployable Respirometric Sensor Sachets

by Valeria Ferraro, Loris Pinto, Liang Li, Federico Baruzzi, Dmitri B. Papkovsky and Elisa Santovito

Biosensors 2026, 16(9), 503; https://doi.org/10.3390/bios16090503 - 8 Sep 2026

Monitoring bacterial contamination on surfaces is critical for hygiene and safety assurance in food, healthcare, and pharmaceutical settings, although routine methods still remain slow and laboratory dependent. Here, we report a portable respirometric platform based on sealed sensor sachets incorporating optical oxygen sensors [...] Read more.

Monitoring bacterial contamination on surfaces is critical for hygiene and safety assurance in food, healthcare, and pharmaceutical settings, although routine methods still remain slow and laboratory dependent. Here, we report a portable respirometric platform based on sealed sensor sachets incorporating optical oxygen sensors to rapidly detect and quantify total aerobic viable counts (TVC) from swabbed surfaces. Following standardized surface swabbing, samples were incubated in the sachets and oxygen depletion kinetics were recorded with a handheld reader and microbial activity was inferred from oxygen consumption. Reference quantification was obtained by serial dilution and aerobic plate counting, enabling direct benchmarking of the respirometric readout against an industry-accepted culture method such as ISO 4833:2013. The platform demonstrated strong agreement with plate counts (R² > 0.94), achieving a median detection limit of 2.69 log₁₀ CFU/cm² and a dynamic range of 0–6 log₁₀ CFU/cm² for surface-associated microbial loads. The sensor system was also applied in a semi-industrial setting in a meat processing plant. Across replicate measurements, the assay provided consistent kinetic signatures and quantitative outputs, suitable for rapid and practical decision-making. Compared with traditional culture-based approaches (requiring up to 72 h), the respirometric sachets delivered actionable results within 10 h using a portable, low-infrastructure workflow, supporting rapid on-site hygiene verification and sanitation control. Full article

(This article belongs to the Special Issue Advanced Biosensors for Food Safety)

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26 pages, 17499 KB

Open AccessArticle

Detection of Polysaccharide Markers of Fungal Infections by Surface-Enhanced Raman Scattering and Machine Learning Methods

by Julia Yu. Zvyagina, Robert R. Safiullin, Andrey S. Naboko, Victor I. Polozov, Irina A. Boginskaya, Marina V. Sedova, Vadim B. Krylov, Dmitry V. Yashunsky, Dmitry A. Argunov, Nikolay E. Nifantiev, Ilya A. Ryzhikov, Alexander M. Merzlikin and Andrey N. Lagarkov

Biosensors 2026, 16(9), 502; https://doi.org/10.3390/bios16090502 - 8 Sep 2026

In this study, we used the SERS method for the first time to measure the spectra of four polysaccharide markers of fungal infections: linear β-(1→3)- and β-(1→6)-linked D-glucans, branched mannan of Candida albicans and galactomannan of Aspergillus fumigatus. Aqueous solutions of the [...] Read more.

In this study, we used the SERS method for the first time to measure the spectra of four polysaccharide markers of fungal infections: linear β-(1→3)- and β-(1→6)-linked D-glucans, branched mannan of Candida albicans and galactomannan of Aspergillus fumigatus. Aqueous solutions of the polysaccharides were studied in concentrations from 10 pg/mL to 100 μg/mL. The spectra were analyzed using machine learning methods: principal component analysis for data visualization and partial least squares with a ridge regularizer, which were used to construct metrics reflecting the accuracy of substance recognition relative to each other. The spectral changes with varying analyte concentration were observed and stable calibration has been achieved. Subsequent measurements of fungal polysaccharides in the presence of a physiological concentration of human serum albumin (45 mg/mL), used to model blood serum, enabled accurate analyte detection in a clinically relevant concentration range of 10 pg/mL to 100 ng/mL. In this case, the calibration dependence was calculated using the partial least squares method with the L1-regularizer. Blind testing was evaluated using a train-derived applicability-domain criterion based on the disagreement between the model prediction and an independent concentration estimate. Full article

(This article belongs to the Special Issue Optical Biosensors for Healthcare: An Artificial Intelligence Approach)

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15 pages, 2715 KB

Open AccessArticle

Explainable AI-Assisted Label-Free Raman Biosensing Reveals Therapy-Associated Spectral Signatures in Melanoma Tumors

by Muhammad Nouman Khan, Qingsong Zhou, Jiaqing Guo, Asif Khalid and Rui Hu

Biosensors 2026, 16(9), 501; https://doi.org/10.3390/bios16090501 - 8 Sep 2026

Sensitive detection of treatment-associated Raman spectral alterations in tumor tissues remains challenging, particularly when such changes are not readily apparent from conventional morphological evaluation. Here, we developed a label-free Raman biosensing strategy combined with explainable machine learning to characterise treatment-associated spectral signatures in [...] Read more.

Sensitive detection of treatment-associated Raman spectral alterations in tumor tissues remains challenging, particularly when such changes are not readily apparent from conventional morphological evaluation. Here, we developed a label-free Raman biosensing strategy combined with explainable machine learning to characterise treatment-associated spectral signatures in melanoma tumours. A B16-F10 melanoma-bearing mouse model was used to compare untreated and PBS-treated controls with cohorts receiving immune checkpoint blockade, anti-angiogenic intervention, or combination therapy. Raman spectra were acquired from multiple spatial regions of melanoma tissues and analyzed using nonlinear dimensionality reduction, supervised classification, and SHAP-based feature interpretation. Although cohort-averaged spectra showed substantial overlap, multivariate analysis revealed treatment-dependent spectral organization, with the combination-treatment cohort showing the most compact and distinguishable spectral profile. Supervised models, including convolutional neural networks, support vector machines, and k-nearest neighbors, further supported the reproducibility of treatment-associated Raman signatures when evaluated using mouse-level validation strategies. SHAP analysis identified discriminative Raman features mainly located within lipid, phospholipid, ester, protein, and collagen-associated vibrational domains, suggesting potential contributions from metabolic- and extracellular-matrix-related biochemical components to treatment-associated spectral discrimination. These findings indicate that Raman spectroscopy integrated with explainable machine learning provides a sensitive, label-free method for distinguishing treatment-associated spectral differences among melanoma tissues. The proposed approach may serve as a complementary spectroscopic tool alongside conventional histological and molecular analyses for investigating treatment-associated tissue-state alterations. Full article

(This article belongs to the Section Optical and Photonic Biosensors)

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15 pages, 5812 KB

Open AccessArticle

Microfluidic Light-Scattering Imaging Coupled with Deep Learning for Label-Free Single-Cell Classification of Lymphoma Cells

by Linyan Xie, Mengfei Wang, Xijia Luo, Shuoxian Xia, Qiongqiong Ren and Xuezhi Zhou

Biosensors 2026, 16(9), 500; https://doi.org/10.3390/bios16090500 - 7 Sep 2026

Accurate classification of lymphoma cell subtypes is essential for disease diagnosis and therapeutic decision-making, yet conventional approaches often rely on fluorescence labeling, labor-intensive sample preparation, and specialized instrumentation, limiting their applicability for rapid, label-free single-cell analysis. Here, we present an AI-assisted microfluidic light-scattering [...] Read more.

Accurate classification of lymphoma cell subtypes is essential for disease diagnosis and therapeutic decision-making, yet conventional approaches often rely on fluorescence labeling, labor-intensive sample preparation, and specialized instrumentation, limiting their applicability for rapid, label-free single-cell analysis. Here, we present an AI-assisted microfluidic light-scattering imaging platform for label-free classification of lymphoma cells. The platform integrates hydrodynamic focusing within a microfluidic chip, continuous acquisition of two-dimensional (2D) light-scattering patterns, automated image preprocessing, and transfer learning based on a pretrained ResNet50 network for intelligent optical feature extraction and classification. Human B lymphoma (Daudi) and T lymphoblastic lymphoma (SUP-T1) cells were used to evaluate the proposed framework. The optical imaging system was first validated using standard microspheres, demonstrating reliable acquisition of light-scattering patterns under continuous-flow conditions. A dataset comprising 800 single-cell scattering patterns was subsequently established and evaluated using stratified five-fold cross-validation. The proposed framework achieved an average classification accuracy of 94.75% with an average area under the receiver operating characteristic (ROC) curve of 0.986. By integrating microfluidic optical biosensing with deep learning, this work enables automated interpretation of intrinsic optical scattering signatures and provides a promising AI-enabled strategy for rapid, label-free lymphoma screening and intelligent healthcare applications. Full article

(This article belongs to the Special Issue Optical Biosensors for Healthcare: An Artificial Intelligence Approach)

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41 pages, 16238 KB

Open AccessReview

Rapid Diagnostics for Distinguishing Bacterial and Viral Infections: A Review of Technologies, Clinical Utility, and Stewardship Implications

by Mohammad Javanmard, Rohan Vellanki, Ali Fardoost and Mehdi Javanmard

Biosensors 2026, 16(9), 499; https://doi.org/10.3390/bios16090499 - 6 Sep 2026

Antimicrobial resistance (AMR) is a growing global health threat driven in part by inappropriate and unnecessary antibiotic use resulting from diagnostic uncertainty at the point of care. In outpatient and acute-care settings, clinicians are often unable to rapidly distinguish between viral and bacterial [...] Read more.

Antimicrobial resistance (AMR) is a growing global health threat driven in part by inappropriate and unnecessary antibiotic use resulting from diagnostic uncertainty at the point of care. In outpatient and acute-care settings, clinicians are often unable to rapidly distinguish between viral and bacterial infections, leading to empiric antibiotic prescribing that contributes to the emergence and spread of resistant pathogens. This review examines current and emerging rapid diagnostic technologies for differentiating bacterial and viral infections, including molecular assays, rapid antigen tests, biomarker-based diagnostics, host-response platforms, hematologic methods, and artificial intelligence-based decision-support systems. These technologies are evaluated based on diagnostic accuracy, turnaround time, cost, accessibility, and clinical actionability within real-world healthcare settings. Although several emerging and point-of-care (POC) technologies can provide results within approximately 6–15 min, their ability to consistently align with the timing, workflow, and clinical decision-making requirements of frontline outpatient and emergency-care settings remains variable and incompletely established. Future progress in antimicrobial stewardship will depend on developing rapid, clinically actionable diagnostic systems that integrate seamlessly into patient care and reduce unnecessary antibiotic use. Full article

(This article belongs to the Special Issue Interdisciplinary Advances: Lab-on-a-Chip Biosensors Shaping Precision Diagnosis)

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29 pages, 4327 KB

Open AccessReview

Optical and Electrochemical Biosensors Using Electrochemically Etched Porous Silicon

by Teodora Despotovski Kiš, Marko Radović, Brankica Kartalović and Nikola Knežević

Biosensors 2026, 16(9), 498; https://doi.org/10.3390/bios16090498 - 6 Sep 2026

Versatile nanostructured materials based on electrochemically etched porous silicon (pSi) are being developed, which have tuneable pore morphology and unique optical and electrochemical properties that enable their effective biosensing applications. It has been shown that fabrication parameters critically influence pore formation and sensor [...] Read more.

Versatile nanostructured materials based on electrochemically etched porous silicon (pSi) are being developed, which have tuneable pore morphology and unique optical and electrochemical properties that enable their effective biosensing applications. It has been shown that fabrication parameters critically influence pore formation and sensor performance, yet challenges remain in reproducible synthesis, structural stability and device integration. Here we review the electrochemical etching synthesis of pSi and recent advances in pSi-based optical and electrochemical biosensors for detecting bacteria, biomolecules, and viruses. We highlight strategies such as surface functionalisation, incorporation of nanomaterials, and integration with microfluidic and lab-on-a-chip technologies that enhance sensitivity and response times by addressing mass transfer limitations. These developments highlight pSi’s potential as a low-cost, adaptable biosensing material with applications in clinical diagnostics and environmental monitoring, while mapping future directions to overcome current fabrication and stability challenges. Full article

(This article belongs to the Special Issue Development and Application of Functional Nanomaterial-Based Biosensors)

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27 pages, 2570 KB

Open AccessReview

Photoacoustic Imaging for Liver Disease: The Systems and the Molecules

by Bowen Jiang, Zhixian Lin and Xiaoquan Yang

Biosensors 2026, 16(9), 497; https://doi.org/10.3390/bios16090497 - 5 Sep 2026

Liver disease represents a significant global health burden, and its effective management relies on early, accurate diagnosis. Established assessments for liver diseases, ranging from invasive biopsies to conventional noninvasive imaging (e.g., ultrasound, MRI, and CT), are often limited by inadequate specificity, potential safety [...] Read more.

Liver disease represents a significant global health burden, and its effective management relies on early, accurate diagnosis. Established assessments for liver diseases, ranging from invasive biopsies to conventional noninvasive imaging (e.g., ultrasound, MRI, and CT), are often limited by inadequate specificity, potential safety risks, or unsuitability for dynamic tracking. Photoacoustic imaging (PAI), a hybrid modality that combines optical absorption contrast with deep ultrasonic detection, offers an attractive solution for noninvasive, high-sensitivity, and high-specificity imaging in deep organs such as the liver. This review summarizes recent advances in photoacoustic imaging for liver pathophysiology, beginning with the evolution of imaging systems and extending to the diverse molecules employed for preclinical studies and early clinical trials. Specifically, novel reconstruction algorithms improved the spatial resolution and acquisition speed by up to threefold, Monte Carlo-based fluence compensation increased the deep-tissue signal-to-background ratio by approximately 50%, and a 7-azaindole-modified probe exhibited one-magnitude-higher superoxide-triggered activation than conventional hemicyanine dyes. Furthermore, we discuss key challenges and future perspectives, highlighting the translational potential of PAI as an emerging liver imaging modality. Full article

(This article belongs to the Section Optical and Photonic Biosensors)

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