Biosensors

17 pages, 4367 KB

Open AccessArticle

Intelligent Distributed Optical Fiber Pressure Sensing for Dental Bite-Force Analysis

by Zhanerke Katrenova, Dauren Kussaiyn, Shakhrizat Alisherov, Wilfried Blanc, Alexandr Dostavalov, Amin Zollanvari and Carlo Molardi

Biosensors 2026, 16(9), 528; https://doi.org/10.3390/bios16090528 - 21 Sep 2026

Viewed by 332

Abstract

Measuring bite force is essential for assessing the masticatory system and diagnosing oral disease. Existing measurement devices have low spatial resolution and susceptibility to electromagnetic interference. This paper presents a machine learning (ML)-assisted distributed fiber optic sensing system based on Scattering Level Multiplexing [...] Read more.

Measuring bite force is essential for assessing the masticatory system and diagnosing oral disease. Existing measurement devices have low spatial resolution and susceptibility to electromagnetic interference. This paper presents a machine learning (ML)-assisted distributed fiber optic sensing system based on Scattering Level Multiplexing (SLMux) for high-resolution bite force analysis. Enhanced backscattered data were acquired through optical backscattered reflectometry from 88 sensing points along the dental arch. Measured data were reconstructed into a two-dimensional map of bite force and analyzed through an ML pipeline. Sector classification across 4 regions and weight prediction were processed by an end-to-end fine-tuned ResNet-18 Convolutional Neural Network (CNN) and classical ML approaches. ResNet-18 is compared with Logistic Regression, Support Vector Machine (SVM), Random Forest, XGBoost (Extreme Gradient Boosting), Extra Trees, and k-Nearest Neighbors (kNN) trained on handcrafted features. On sector classification, Logistic Regression achieved the best performance (98.71% accuracy). On the weight prediction task, formulated as a 12-class problem, the end-to-end ResNet-18 CNN substantially outperformed all classical models, reaching 51.28% accuracy and a mean absolute error of 78 g, versus 124 g for the best classical model. A regression-based ResNet-18 variant was also trained on the wavelength-shift and weight data, resulting in a mean absolute error of 62.6 g. The results indicate that integrating ML with distributed fiber-optic sensing has the potential to enhance dental diagnostics and treatment planning. Full article

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

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22 pages, 16074 KB

Open AccessArticle

Electric-Field-Assisted Enrichment and Electrochemical Detection of Gram-Positive Bacteria Using an FEM-Guided Interdigitated Electrode Biosensor

by Zeeshan, Naeem Iqbal and Jaeyoung Choi

Biosensors 2026, 16(9), 527; https://doi.org/10.3390/bios16090527 - 21 Sep 2026

Viewed by 268

Abstract

Dielectrophoretic (DEP) enrichment is a powerful strategy to enhance bacterial capture efficiency and accelerate the response of electrochemical biosensors by actively concentrating target bioparticles at the sensing interface. In this study, a combined numerical–experimental framework is developed to rationally design a DEP-assisted electrochemical [...] Read more.

Dielectrophoretic (DEP) enrichment is a powerful strategy to enhance bacterial capture efficiency and accelerate the response of electrochemical biosensors by actively concentrating target bioparticles at the sensing interface. In this study, a combined numerical–experimental framework is developed to rationally design a DEP-assisted electrochemical biosensor with improved bacterial capture and detection performance. A finite-element modeling (FEM) approach was used to model the coupled electric field, dielectrophoretic force, and particle-transport phenomena, providing a quantitative basis for comparing bacterial trapping efficiency across different interdigitated electrode geometries. The modeling reveals that a wave-shaped interdigitated electrode (W_IDE) generates extended high-field regions and an enlarged effective capture area, resulting in an improved bacterial capture efficiency of 19% compared to 12% for the conventional rectangular interdigitated electrodes (R_IDE) under positive DEP (pDEP) conditions. Based on these insights, the W_IDE was fabricated on a printed circuit board (PCB) substrate, modified with platinum-black (Pt-black) to increase electroactive surface area, and interfaced with a custom-built 16-channel portable potentiostat unit, enabling sequential impedance measurements. The developed biosensor was applied to pDEP-assisted impedance detection of Staphylococcus aureus and Micrococcus luteus using vancomycin as the capture probe. The pDEP-assisted operation enabled rapid and highly sensitive detection down to 10 CFU/mL within 30 min with a wide linear range (10–10⁵ CFU/mL) in 0.1× PBS, outperforming passive detection (10²–10⁵ CFU/mL) for both Staphylococcus aureus and Micrococcus luteus. In skim milk, however, the linear detection ranges shifted to 10²–10⁵ CFU/mL with pDEP-assisted detection and 10³–10⁵ CFU/mL under passive detection conditions. Overall, this work highlights the significance of combining FEM-optimized electrodes with DEP-driven enrichment to achieve improved bacterial capture, sensitivity, and robustness in electrochemical biosensors. Full article

(This article belongs to the Special Issue Electrochemical (Bio-)Sensors in Biological Applications—3rd Edition)

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23 pages, 5480 KB

Open AccessArticle

New Concept of Intraoperative Fluorescence Diagnostics for Photodynamic Dosimetry of Tumors Using Chlorin-Based Photosensitizers

by Kanamat Efendiev, Arkadii Moskalev, Polina Alekseeva, Alexander Borodkin, Alexey Skobeltsin, Vladimir Makarov, Nina Kalyagina, Kirill Linkov, Aida Gilyadova, Olga Shpileva, Dinara Ilyasova, Tatiana Pisareva, Polina Kozlova, Aidai Rakhmanova, Artem Shiryaev and Victor Loschenov

Biosensors 2026, 16(9), 526; https://doi.org/10.3390/bios16090526 - 20 Sep 2026

Viewed by 406

Abstract

Photodynamic therapy (PDT) with chlorin-based photosensitizers requires real-time monitoring of photosensitizer distribution and photobleaching to guide treatment, yet existing endoscopic fluorescence systems rely on short-wavelength (375–450 nm) excitation that is spectrally incompatible with 660 nm chlorin e6 (Ce6)-PDT, forcing interruption of therapy for [...] Read more.

Photodynamic therapy (PDT) with chlorin-based photosensitizers requires real-time monitoring of photosensitizer distribution and photobleaching to guide treatment, yet existing endoscopic fluorescence systems rely on short-wavelength (375–450 nm) excitation that is spectrally incompatible with 660 nm chlorin e6 (Ce6)-PDT, forcing interruption of therapy for diagnostic imaging. We developed VENERA-red-660, a dual-channel endoscopic system using a single 660 nm laser for simultaneous white-light and Ce6 fluorescence imaging (>670 nm detection) without interrupting irradiation. System sensitivity was validated on optical phantoms (Ce6 0.1–5.0 mg/L), and phototheranostics was performed in patients with basal cell carcinoma (n = 5), vulvar intraepithelial neoplasia grade III (n = 3), and peritoneal carcinomatosis (n = 10) using intravenous chlorin-based photosensitizer. Phantom testing showed a linear fluorescence response (R² = 0.998) down to the lowest tested concentration of 0.1 mg/L. Pre-PDT fluorescence contrast indices confirmed photosensitizer selectivity in all three tumor types, decreasing by 57%, 66%, and 60% after treatment, consistent with photobleaching; continuous intra-PDT monitoring confirmed progressive photobleaching kinetics, and confocal microscopy verified cellular-level Ce6 selectivity. Increased backscattered laser signal after PDT was consistent with treatment-induced changes in tissue optical properties. These pilot findings demonstrate that single-source phototheranostic monitoring is feasible across external and laparoscopic access routes, supporting further evaluation of VENERA-red-660 as a tool for intraoperative PDT dosimetry. Full article

(This article belongs to the Special Issue Optical Biosensing for Diagnostics)

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53 pages, 1455 KB

Open AccessReview

The Evolution of Next-Generation Glucose Monitoring Systems: Developments from Conventional Diagnostics to Wearable and Continuous Sensing Platforms—A Narrative Review

by Sumedha Nitin Prabhu

Biosensors 2026, 16(9), 525; https://doi.org/10.3390/bios16090525 - 20 Sep 2026

Viewed by 502

Abstract

For the diagnosis of diabetes, glycemic control, treatment modification, and the avoidance of both acute and long-term consequences, accurate glucose monitoring is crucial. This study examines the development of glucose-monitoring systems, from traditional diagnostic tests and finger-prick electrochemical glucometers to wearable, continuous, minimally [...] Read more.

For the diagnosis of diabetes, glycemic control, treatment modification, and the avoidance of both acute and long-term consequences, accurate glucose monitoring is crucial. This study examines the development of glucose-monitoring systems, from traditional diagnostic tests and finger-prick electrochemical glucometers to wearable, continuous, minimally invasive, and non-invasive sensing platforms. Major invasive technologies, including enzymatic and non-enzymatic electrochemical sensors, microdialysis systems, hexokinase-based photometric assays, and field-effect transistor biosensors, are critically evaluated. The ability of minimally invasive methods to access interstitial fluid with less pain is discussed, including fluorescence probes, surface-enhanced Raman spectroscopy, and microneedle arrays. Emerging non-invasive approaches are also evaluated. These include reverse iontophoresis, optical coherence tomography, infrared and photoacoustic spectroscopy, microwave resonators, smart contact lenses, and AI-assisted wearable systems. Finally, the review identifies major barriers related to sensitivity, selectivity, calibration, biofouling, biocompatibility, physiological variability, and clinical translation. It also outlines opportunities for intelligent, painless, and patient-centered glucose monitoring. Full article

(This article belongs to the Special Issue Nano-Biosensors and Their Applications for In Vivo/Vitro Diagnosis—3rd Edition)

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26 pages, 3003 KB

Open AccessArticle

Tumor-Aware Unsupervised Digital HER2 Immunohistochemistry via Dual Contrastive Learning

by Dong-Bum Kim and Jong-Ha Lee

Biosensors 2026, 16(9), 524; https://doi.org/10.3390/bios16090524 - 19 Sep 2026

Viewed by 376

Abstract

Breast cancer is the most common cancer and the second leading cause of cancer-related death among women worldwide. For accurate diagnosis, pathologists use immunohistochemical (IHC) staining for biomarkers such as human epidermal growth factor receptor 2 (HER2) as an auxiliary test, in addition [...] Read more.

Breast cancer is the most common cancer and the second leading cause of cancer-related death among women worldwide. For accurate diagnosis, pathologists use immunohistochemical (IHC) staining for biomarkers such as human epidermal growth factor receptor 2 (HER2) as an auxiliary test, in addition to hematoxylin and eosin (H&E) staining for evaluating tissue morphology. Traditional IHC staining is limited by high cost, time, and labor, with a shortage of pathologists to meet demand. In this work, we propose a digital HER2 IHC staining algorithm based on tumor masks. The proposed model builds on the Dual Contrastive Learning Generative Adversarial Network (DCLGAN), adding tumor mask loss, structural similarity index measure (SSIM) loss, and a modified identity loss that differentiates between active and inactive regions. Performance was quantitatively assessed by comparing HER2 scores using the Fréchet Inception Distance (FID) and a grade classification model. Compared to DCLGAN, the proposed model achieved a 27.2% decrease in FID and a 2.57% increase in soft accuracy. Visual evaluation further showed that it prevents yellow discoloration and suppresses nonspecific region staining seen in unsupervised learning. These results indicate that effective learning is achievable even with limited and unaligned data, thereby accelerating digital pathology workflows. Full article

(This article belongs to the Section Biosensors and Healthcare)

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33 pages, 9524 KB

Open AccessReview

Preserving Sample, Interface, and Signal Fidelity in Wearable Sweat Electrolyte Monitoring During Exercise

by Qi Qi, Jinjie Duan, Yuning Lei, Shuang Zhou, Qian Liu, Qiong Wang, Nan Gao, Wenjuan Sun, Zhiming Zhan and Yuxiang Wu

Biosensors 2026, 16(9), 523; https://doi.org/10.3390/bios16090523 - 19 Sep 2026

Viewed by 333

Abstract

Sweat electrolytes, including Na⁺, K⁺, and Cl⁻, provide dynamic information on exercise-induced fluid and electrolyte regulation. Wearable ion-selective electrode (ISE) platforms now enable continuous, in situ monitoring of these ions, but performance in standard solutions does not [...] Read more.

Sweat electrolytes, including Na⁺, K⁺, and Cl⁻, provide dynamic information on exercise-induced fluid and electrolyte regulation. Wearable ion-selective electrode (ISE) platforms now enable continuous, in situ monitoring of these ions, but performance in standard solutions does not guarantee reliable on-body data. During exercise, measurement distortion can arise before sweat contacts the electrode, at the electrochemical interface, and during signal acquisition and interpretation. This review synthesizes these coupled distortion pathways through a three-layer fidelity framework. Sample fidelity describes whether collected sweat preserves its temporal sequence, concentration state, and spatial origin during collection and microfluidic transport. Interface fidelity concerns stable ion recognition, ion-to-electron transduction, and reference-potential output in complex sweat matrices under deformation. Signal fidelity covers potential readout, transmission, calibration, temperature compensation, drift correction, and concentration estimation. By organizing recent work around these layers, we identify how sampling lag, residual-sweat mixing, evaporation, fouling, component leaching, interfacial drift, reference instability, and algorithmic overprocessing can propagate into biased electrolyte profiles. We argue that wearable sweat electrolyte monitoring should move from isolated electrode optimization toward end-to-end fidelity preservation and validation under realistic exercise conditions. This framework provides a practical basis for sensor design, performance reporting, and translation to sports, occupational safety, and digital health applications. Full article

(This article belongs to the Special Issue Advances in Flexible Bioelectronics and Intelligent Biosensing Systems—2nd Edition)

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15 pages, 5296 KB

Open AccessArticle

A Portable Personal Glucometer-Based Immunoassay Platform for Horseradish Peroxidase-Related Biomarker Detection

by Jia-Yuan He, Ming-Shan Shuai, Hao Zhang and Dan-Ni Yang

Biosensors 2026, 16(9), 522; https://doi.org/10.3390/bios16090522 - 19 Sep 2026

Viewed by 294

Abstract

Given the prevalent use of horseradish peroxidase (HRP) as an enzyme label in immunoassays, the practical importance of point-of-care testing for the portable identification of HRP-related biomarkers is evident. Accordingly, this study devised a portable immunoassay platform using a personal glucometer (PGM) to [...] Read more.

Given the prevalent use of horseradish peroxidase (HRP) as an enzyme label in immunoassays, the practical importance of point-of-care testing for the portable identification of HRP-related biomarkers is evident. Accordingly, this study devised a portable immunoassay platform using a personal glucometer (PGM) to achieve rapid detection of HRP-related biomarkers. 3,3′,5,5′-tetramethylbenzidine (TMB) and dopamine (DA) were used as signal-regulating substrates, and the assay principle was based on the HRP-mediated redox reaction. In the absence of HRP, TMB and DA directly reacted with [Fe(CN)₆]³⁻ on commercial glucose test strips, producing a strong PGM response. When HRP and H₂O₂ were present, HRP facilitated the oxidation of TMB to generate oxidized TMB, which further reacted with DA to produce oxidized products with weak reactivity toward [Fe(CN)₆]³⁻, resulting in a decreased PGM signal. Conducting the quantitative assay involved measuring the HRP-dependent change in the PGM readout. Unlike many PGM immunoassays that rely on additional glucose-producing enzyme labels or amplified nanomaterial tags, the proposed method directly converts the signal of the conventional HRP label into a PGM-readable response. The sensing platform demonstrated a good linear response to HRP concentrations ranging from 156 to 625 ng/mL, with a low detection limit of 26 ng/mL. Interference experiments indicated good tolerance to the tested nonspecific proteins. The preliminary applicability of the platform was further evaluated by detecting cardiac troponin I (cTnI) and heparin-binding protein (HBP) in spiked serum samples, with recoveries ranging from 93.0% to 117.1% for cTnI and from 88.3% to 112.5% for HBP. In summary, the proposed platform provides a portable digital readout for HRP-linked immunoassays and reduces the dependence on dedicated readout instruments. Full article

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

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25 pages, 628 KB

Open AccessSystematic Review

Wearable Sweat Biosensors for Inflammatory Biomarkers (CRP, IL-6 and TNF-α): A Systematic Review of Analytical and Clinical Evidence

by Raghad S. Alsharidah, Razique Anwer, Maysoon Anwar Abdelhamid, Eman Amer Alanazi, Farah Mfwadh Alanazi, Haneen Fahaid Alanazi, Jumanah Naif Alotaibi, Lama Abdulaziz Alshathri, Renad Ali Alshehri, Rania Alsuhibani and Raghad H. Majrashi

Biosensors 2026, 16(9), 521; https://doi.org/10.3390/bios16090521 - 18 Sep 2026

Viewed by 466

Abstract

Background: Wearable sweat biosensors have emerged as a promising non-invasive approach for monitoring inflammatory biomarkers, offering an alternative to repeated blood sampling. This systematic review evaluated the current evidence on the detection of C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-α [...] Read more.

Background: Wearable sweat biosensors have emerged as a promising non-invasive approach for monitoring inflammatory biomarkers, offering an alternative to repeated blood sampling. This systematic review evaluated the current evidence on the detection of C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) in sweat for infection and inflammatory disease monitoring. Methods: Following PRISMA 2020 guidelines (PROSPERO: CRD420261371221), PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar were searched through April 2026. Human studies measuring CRP, IL-6, or TNF-α in sweat using wearable or biosensor-based platforms were included. Risk of bias and certainty of evidence were assessed using a modified Newcastle–Ottawa Scale/JBI checklist and GRADE. Results: Thirteen studies (2020–2025, 5–80 participants) were included. CRP showed the most consistent sweat-serum agreement (r = 0.844) and good diagnostic performance in one inflammatory bowel disease cohort (AUC = 0.845). TNF-α demonstrated the highest diagnostic accuracy in a single longitudinal study (AUC = 0.962), whereas IL-6 showed moderate sweat-serum correlations (R² = 0.60–0.72) but limited diagnostic discrimination. IL-6 and TNF-α were more consistently detected in passively collected eccrine sweat, although IL-6 was also successfully measured following pilocarpine iontophoresis in one study. Most studies had moderate methodological quality. Conclusions: Sweat-based measurement of CRP, IL-6, and TNF-α is analytically feasible for non-invasive inflammatory biomarker monitoring. However, current evidence remains limited by small, heterogeneous studies, underscoring the need for standardized protocols and larger prospective clinical validation. Full article

(This article belongs to the Section Wearable Biosensors)

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13 pages, 2224 KB

Open AccessArticle

Modeling and Sensitivity Analysis of GaN HEMT Biosensor

by Ashkhen Yesayan and Jean-Michel Sallese

Biosensors 2026, 16(9), 520; https://doi.org/10.3390/bios16090520 - 17 Sep 2026

Viewed by 320

Abstract

Gallium nitride (GaN) high-electron-mobility transistor (HEMT) biosensors have recently emerged as promising platforms for highly sensitive and label-free detection of biomolecules. Their exceptional electrical properties, including high carrier mobility, together with the wide bandgap and strong chemical bonds of GaN materials, provide excellent [...] Read more.

Gallium nitride (GaN) high-electron-mobility transistor (HEMT) biosensors have recently emerged as promising platforms for highly sensitive and label-free detection of biomolecules. Their exceptional electrical properties, including high carrier mobility, together with the wide bandgap and strong chemical bonds of GaN materials, provide excellent stability under high temperatures, ionizing radiation, and chemically harsh environments. Despite significant experimental progress, comprehensive analytical models capable of linking biomolecular recognition events to the electrical response of GaN biosensors remain limited. This work presents a physics-based, design-oriented analytical modeling framework for AlGaN/GaN HEMT biosensors. The model incorporates biomolecular binding kinetics, the dielectric properties of the hybrid system, and electrostatic coupling to the transistor channel conductivity. Numerical simulations are performed using COMSOL Multiphysics to validate the analytical model. The developed framework provides physical insight into the mechanisms governing biosensor operation and offers practical guidelines for the optimization and comparative assessment of HEMT/MIS-HEMT biosensor architectures. Full article

(This article belongs to the Section Biosensor and Bioelectronic Devices)

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19 pages, 3295 KB

Open AccessArticle

Design and Evaluation of Multi-Electrode Configurations for Transcutaneous Frontal Nerve Activation

by Enver Salkim

Biosensors 2026, 16(9), 519; https://doi.org/10.3390/bios16090519 - 17 Sep 2026

Viewed by 266

Abstract

Conventional migraine treatments often come with undesirable side effects. One alternative approach uses a wearable neuromodulation device that stimulates nerves in the forehead. However, clinical studies have reported modest effectiveness, and some users experience discomfort or pain. These issues may be associated with [...] Read more.

Conventional migraine treatments often come with undesirable side effects. One alternative approach uses a wearable neuromodulation device that stimulates nerves in the forehead. However, clinical studies have reported modest effectiveness, and some users experience discomfort or pain. These issues may be associated with electrode design, which can require higher stimulation currents to achieve activation. This study aims to evaluate the stimulation currents required by the migraine neuromodulator device and to explore whether modifying electrode arrangement can lower these current levels, which may potentially improve patient comfort and treatment compliance. To accomplish this, a hybrid computational framework combining a 3D volume conductor model with biophysical nerve dynamics was deployed. To capture population diversity, forty distinct nerve models were generated based on statistical variations in trajectory paths and evaluated across five electrode array configurations. The findings show that the proposed electrode array (EA) and neuroanatomical variability significantly influence stimulation thresholds. Simulations of the Cefaly electrode produced current thresholds ranging from 9 mA to 30 mA across the models. When the proposed EA was applied by aligning with nerve pathways, the required currents dropped substantially, ranging from 0.5 mA to 9 mA. Additionally, this design reduced calculated peak spatial current density across superficial tissue layers—a factor associated with cutaneous discomfort—which may support improved tolerance and long-term treatment adherence in clinical practice. Full article

(This article belongs to the Special Issue Wearable and Flexible Biosensors for Brain–Computer Interface Applications)

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30 pages, 1572 KB

Open AccessArticle

PathoSafe: Sub-1 KB Out-of-Distribution Rejection and Cluster-Aware Certification for Point-of-Care Pathogen Biosensing

by Gaoming He, Mengdi Hou, Jianbo Huang, Longquan Chen, Qihuang Gao and Bitie Lan

Biosensors 2026, 16(9), 518; https://doi.org/10.3390/bios16090518 - 17 Sep 2026

Viewed by 378

Abstract

A point-of-care pathogen-microscopy readout must signal when a sample falls outside what its detector was trained on. PathoSafe is an 816-byte int8-projection low-rank Mahalanobis out-of-distribution (OOD) head reading a frozen detector’s penultimate feature. It reaches a cross-site area under the receiver operating characteristic [...] Read more.

A point-of-care pathogen-microscopy readout must signal when a sample falls outside what its detector was trained on. PathoSafe is an 816-byte int8-projection low-rank Mahalanobis out-of-distribution (OOD) head reading a frozen detector’s penultimate feature. It reaches a cross-site area under the receiver operating characteristic curve (AUROC) of 0.980 and multi-source AUROC of 0.964 (parasitic-egg-dominated), within 0.01 of a 16,896-byte full-precision dense baseline, and adds a measured 30.76 µs and 816 bytes of read-only memory (ROM) constants on an STM32H743 development board, so abstention adds only a small measured cost on-chip. The advantage is the trunk feature rather than the integer form. Certification is the harder problem and carries our central result: slide clustering silently breaks the standard independent-sample certificate, since distribution-free risk control assumes exchangeable samples that patch-clustered medical data do not supply. On a degraded-input risk certificate, the naive cell-level version holds on only 20% of leakage-free re-splits, whereas the correct slide-level one is valid at 0.266 with its width set by the slide count rather than by any bound we evaluate. The deployed threshold inherits this less severely. A Hoeffding–Bentkus budget gives an illustrative count of 38 independent slides under the stated assumptions (35 when five seeds are used), which is about

1.3 \times

what this benchmark provides. We release that leakage-free slide-disjoint benchmark: an in-distribution malaria task with cross-site (BBBC041) and multi-source (SIPaKMeD, parasitic egg, white blood cells) regimes. Full article

(This article belongs to the Special Issue Recent Advances in Artificial Intelligence (AI)-Enabled Biosensing Technologies)

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25 pages, 16653 KB

Open AccessReview

Applications of Acoustic Waves in Micro-Droplet Technologies

by Jianing Liu, Lijun Chen, Zheng Peng, Zachary B. Cluff, Pam Van Ry, Joseph Rich and Ruoyu Zhong

Biosensors 2026, 16(9), 517; https://doi.org/10.3390/bios16090517 - 13 Sep 2026

Viewed by 638

Abstract

Acoustofluidics has emerged as a powerful technique for the precise generation, sorting, manipulation, and stimulation of cell-laden droplets within emerging droplet microfluidic studies. Droplet acoustofluidics leverages various mechanisms, including acoustic radiation forces and acoustic streaming, to actively manipulate droplets non-invasively. The remarkable speed, [...] Read more.

Acoustofluidics has emerged as a powerful technique for the precise generation, sorting, manipulation, and stimulation of cell-laden droplets within emerging droplet microfluidic studies. Droplet acoustofluidics leverages various mechanisms, including acoustic radiation forces and acoustic streaming, to actively manipulate droplets non-invasively. The remarkable speed, precision, and biocompatibility of acoustic waves make it an ideal tool for droplet-based single-cell-level biomedical research. This review paper provides a comprehensive overview of recent advancements in droplet acoustofluidics technologies, highlighting their mechanisms, advantages, applications, and challenges. We have compiled the fundamental principles of acoustic waves and summarized major applications, including single-cell droplet generation, sorting, and active in-droplet manipulation. Despite the advances that current droplet acoustofluidics have achieved, we point out the challenges and give out our thoughts on future improvement directions. These insights will have important implications for advancing interdisciplinary research in cell mechanobiology, drug screening, cell sorting, and mechanophenotyping. Full article

(This article belongs to the Special Issue Biosensors in Biomedical Research and Diagnostics)

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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 - 12 Sep 2026

Viewed by 480

Abstract

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

Viewed by 472

Abstract

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

Viewed by 585

Abstract

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

Viewed by 450

Abstract

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

Viewed by 471

Abstract

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

Viewed by 498

Abstract

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, 1612 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 - 10 Sep 2026

Viewed by 323

Abstract

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)

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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

Viewed by 341

Abstract

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

Viewed by 363

Abstract

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

Viewed by 672

Abstract

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

Viewed by 387

Abstract

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

Viewed by 443

Abstract

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

Viewed by 409

Abstract

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

Viewed by 402

Abstract

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

Viewed by 387

Abstract

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

Viewed by 278

Abstract

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

Viewed by 393

Abstract

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

Viewed by 438

Abstract

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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