Biosensors-Basel最新文献

{"title":"A Rapid and Sensitive Loop-Mediated Isothermal Amplification Assay for the Detection of Mulberry Mosaic Dwarf-Associated Virus.","authors":"Shaoshuang Sun, Xueping Zhou, Xiuling Yang","doi":"10.3390/bios16090509","DOIUrl":"10.3390/bios16090509","url":null,"abstract":"<p><p>Mulberry mosaic dwarf-associated virus (MMDaV), a member of the genus <i>Mulcrilevirus</i> in the family <i>Geminiviridae</i>, 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 <i>V2</i> 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.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":"16 9","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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 <i>Mycobacterium tuberculosis</i>.","authors":"Usanee Wattananandkul, Sukanya Saikaew, Sirikwan Sangboonruang, Rodjana Pongsararuk, Prapaporn Srilohasin, Bordin Butr-Indr, Sorasak Intorasoot, Chayada Sitthidet Tharinjaroen, Natthawat Semakul, Surachet Arunothong, Angkana Chaiprasert, Khajornsak Tragoolpua","doi":"10.3390/bios16090506","DOIUrl":"10.3390/bios16090506","url":null,"abstract":"<p><p>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 <i>Mycobacterium tuberculosis</i>. 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 <i>rpoB</i> codons 516, 526, and 531, <i>katG</i> codon 315, and position -15 of the <i>fabG1-inhA</i> promoter. Evaluation using DNA from 50 <i>M. tuberculosis</i> 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 <i>inhA</i> promoter and <i>katG</i> 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.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":"16 9","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retention-Based, Dissociation-Sensitive Pre-Screening of Aptamers Using a Centrifugal Microspin Filter.","authors":"Cheeyoon Ahn, Hwayeon Jeong, Kyungjin Jeon, Cheulhee Jung","doi":"10.3390/bios16090510","DOIUrl":"10.3390/bios16090510","url":null,"abstract":"<p><p>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-<i>k</i>_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 <i>k</i>_off. A preliminary analysis of streptavidin aptamers likewise showed a W0-NRT-based ranking consistent with the literature-reported <i>k</i>_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 <i>k</i>_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-<i>k</i>_off assay therefore provides a simple post-SELEX approach for prioritizing candidates with higher retention under dissociation-sensitive washing before detailed kinetic characterization.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":"16 9","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI-Assisted Label-Free Raman Biosensing Reveals Therapy-Associated Spectral Signatures in Melanoma Tumors.","authors":"Muhammad Nouman Khan, Qingsong Zhou, Jiaqing Guo, Asif Khalid, Rui Hu","doi":"10.3390/bios16090501","DOIUrl":"10.3390/bios16090501","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":"16 9","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Polysaccharide Markers of Fungal Infections by Surface-Enhanced Raman Scattering and Machine Learning Methods.","authors":"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, Andrey N Lagarkov","doi":"10.3390/bios16090502","DOIUrl":"10.3390/bios16090502","url":null,"abstract":"<p><p>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 <i>Candida albicans</i> and galactomannan of <i>Aspergillus fumigatus</i>. 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.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":"16 9","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Entropy-Based Analysis of Olfactory EEG as a Candidate Biomarker for Early Mild Cognitive Impairment Detection: A Proof-of-Concept Study.","authors":"Sabatina Criscuolo, Andrea De Maria, Annarita Tedesco, Pasquale Arpaia, Egidio De Benedetto","doi":"10.3390/bios16090504","DOIUrl":"10.3390/bios16090504","url":null,"abstract":"<p><p>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 <i>group</i> and <i>stimulus</i>, 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, Hedges'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.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":"16 9","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid Bacterial Detection on Surfaces by Field-Deployable Respirometric Sensor Sachets.","authors":"Valeria Ferraro, Loris Pinto, Liang Li, Federico Baruzzi, Dmitri B Papkovsky, Elisa Santovito","doi":"10.3390/bios16090503","DOIUrl":"10.3390/bios16090503","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":"16 9","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous Measurement of Spatially Resolved Red Blood Cell Aggregation Using Multiple Side-Branch Channels.","authors":"Minjae Kim, Yang Jun Kang","doi":"10.3390/bios16090505","DOIUrl":"10.3390/bios16090505","url":null,"abstract":"<p><p>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 (<i>C_dex</i>). At the selected flow rate of 1 mL/h and <i>C_dex</i> = 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.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":"16 9","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microfluidic Light-Scattering Imaging Coupled with Deep Learning for Label-Free Single-Cell Classification of Lymphoma Cells.","authors":"Linyan Xie, Mengfei Wang, Xijia Luo, Shuoxian Xia, Qiongqiong Ren, Xuezhi Zhou","doi":"10.3390/bios16090500","DOIUrl":"10.3390/bios16090500","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":"16 9","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604312/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid Diagnostics for Distinguishing Bacterial and Viral Infections: A Review of Technologies, Clinical Utility, and Stewardship Implications.","authors":"Mohammad Javanmard, Rohan Vellanki, Ali Fardoost, Mehdi Javanmard","doi":"10.3390/bios16090499","DOIUrl":"10.3390/bios16090499","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":"16 9","pages":""},"PeriodicalIF":6.2,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}