Biosensors and Bioelectronics: X最新文献

{"title":"Vis-SWNIR spectroscopic and hyperspectral imaging sensor integrated with artificial intelligence for early diagnosis of breast cancer","authors":"Maryam Kashi,&nbsp;Farbod Bayat-Afshary,&nbsp;Hadi Parastar","doi":"10.1016/j.biosx.2025.100729","DOIUrl":"10.1016/j.biosx.2025.100729","url":null,"abstract":"<div><div>Breast cancer (BC) is a common cancer worldwide, requiring the development of rapid methods for early detection. Recently, advances in artificial intelligence (AI), especially in machine learning (ML), have facilitated the use of hyperspectral imaging (HSI) and portable spectroscopic sensors in disease diagnosis. This paper investigates the use of portable visible-short wavelength near-infrared (Vis-SWNIR) spectroscopy and HSI in the wavelength range of 400–1000 nm to analyze 143 dried plasma spot (DPS) samples, (73 controls and 70 samples from BC patients), using ML for BC diagnosis. In this study, plasma samples were dried and the variability between samples, drying method and factors affecting it were investigated using analysis of variance-simultaneous component analysis (ASCA). The Vis-SWNIR spectroscopic and HSI sensors offer a safe, rapid and cost-effective diagnostic method that is ideal for repeated screening. Due to the complexity of HSI data, multivariate curve resolution-alternating least squares (MCR-ALS) algorithm was used as a feature extraction technique to extract pure spatial and spectral profiles of the existing components. Then, multivariate classification was performed on spectroscopic and HSI data using data driven-soft independent modeling of class analogy (DD-SIMCA), partial least squares-discriminant analysis (PLS-DA), artificial neural networks (ANN), k-nearest neighbor (kNN), random forest (RF) and support vector machine (SVM). The ANN achieved an accuracy 86.0 % in differentiating healthy and diseased samples in HSI data. In contrast, SVM modeling for portable spectrometer data showed an accuracy of 62 % for prediction set. The results showed changes in bilirubin, hemoglobin, porphyrin, proteins and lipids. While the findings for BC detection are promising, more studies are needed.</div></div>","PeriodicalId":260,"journal":{"name":"Biosensors and Bioelectronics: X","volume":"28 ","pages":"Article 100729"},"PeriodicalIF":10.61,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning framework for advanced analytical detection of CD36 using immunosensors below limit of detection","authors":"Muhammet Cagri Yeke ,&nbsp;Sultan Sacide Gelen ,&nbsp;Hilal Fil ,&nbsp;Muhammet Mustafa Yalcin ,&nbsp;Abdurrahman Gumus ,&nbsp;Idris Yazgan ,&nbsp;Dilek Odaci","doi":"10.1016/j.biosx.2025.100733","DOIUrl":"10.1016/j.biosx.2025.100733","url":null,"abstract":"<div><div>We introduce a machine learning (ML)-based regression framework for quantitative electrochemical analysis, representing a paradigm shift from traditional univariate methods to a multivariate approach. Conventional analysis is constrained by reducing the entire signal to a single peak current feature to define a linear range and calculate a limit of detection (LOD). In contrast, our methodology treats the Differential Pulse Voltammetry (DPV) curve as time-series data, creating a high-dimensional fingerprint by systematically evaluating multiple data windows with varying widths around the main signal peak to identify the most informative segment. To validate this approach, a biosensor was developed by immobilizing Anti-CD36 antibodies on polydopamine-modified screen-printed carbon electrodes for the detection of CD36, a key protein in metabolism and immunity. Measurements were collected across 12 concentrations, including blank samples, spanning a range of 0 to 25 ng/mL. Following data augmentation, nine different regression models were evaluated, with the top-performing models achieving near-perfect prediction accuracy (R<span><math><mrow><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup><mo>&gt;</mo><mn>0</mn><mo>.</mo><mn>99</mn></mrow></math></span>) across this entire range. This high accuracy across the full concentration spectrum quantitatively demonstrates the method’s ability to operate without relying on traditional concepts like linear range or LOD, enabling reliable detection at ultra-low levels. Furthermore, the immunosensor exhibited high selectivity against common interferents and excellent recovery in human serum. This methodology represents a significant advancement in analytical electrochemistry, providing a transferable approach for enhancing sensitivity in biomarker detection with potential applications in clinical diagnostics and biomedical research. The codes and dataset are made publicly available on GitHub to support further research: <span><span>https://github.com/miralab-ai/biosensors-AI</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":260,"journal":{"name":"Biosensors and Bioelectronics: X","volume":"28 ","pages":"Article 100733"},"PeriodicalIF":10.61,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review on the impact of AI-enabled thermal imaging and IoT sensor fusion on early detection of mastitis in dairy cattle","authors":"Arjun Asogan ,&nbsp;Norazlianie Sazali ,&nbsp;Arigela Satya Veerendra ,&nbsp;Lingenthiran Samylingam ,&nbsp;Navid Aslfattahi ,&nbsp;Chee Kuang Kok ,&nbsp;Kumaran Kadirgama","doi":"10.1016/j.biosx.2025.100735","DOIUrl":"10.1016/j.biosx.2025.100735","url":null,"abstract":"<div><div>One of the most emerging economic diseases of dairy cattle, mastitis, reduces animal welfare, produces costly veterinary bills, prevents the animal from reaching its full milk production potential, creates the need to cull cows from the herd, and reduces the overall efficiency of the farm system. Conventional detection methods such as clinical inspection, the California Mastitis Test (CMT), and somatic cell (SC) functions, while useful, can identify subclinical cases at such an early enough stage for intervention. New technologies are turning towards the development of mastitis detection in real time and remotely using IoT devices, software, and complex AI algorithms. AI-driven analytics, coupled with infrared thermal imaging IRT and state of the art diagnostic IoT devices such as milking systems and behavioural collars, have yet to be integrated. IRT devices have the capability to analyze udder temperature and map inflammation. AI algorithms on image classification or data fusion from single tool-based approaches are able to achieve precision above the rest. Market and technological readiness, alongside cost and environmental variability are still fuelling the debate on the practical use of the technologies. This study slams breakthroughs and obstacles on record for detection of mastitis with the help of AI, Image fusion technologies, integrated IRT sensors, and IoT systems, while the rest argue the integration methods for cost-effective, vigilant husbandry of dairy cows in the herd.</div></div>","PeriodicalId":260,"journal":{"name":"Biosensors and Bioelectronics: X","volume":"28 ","pages":"Article 100735"},"PeriodicalIF":10.61,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous monitoring of systemic inflammation through TSLP and Interleukin-13 using the sweat-based AWARE sensor","authors":"Akash Kumar ,&nbsp;Sumana Karmakar ,&nbsp;Santhosh Gowtham Giddaluru ,&nbsp;Preeti Singh ,&nbsp;Kai-Chun Lin ,&nbsp;Sriram Muthukumar ,&nbsp;Shalini Prasad","doi":"10.1016/j.biosx.2025.100715","DOIUrl":"10.1016/j.biosx.2025.100715","url":null,"abstract":"<div><div>Chronic inflammatory conditions are mediated by complex cytokine networks that remain challenging to monitor using conventional methods, which are invasive, time-consuming, and limited to single-point measurements. Continuous and non-invasive detection of the inflammatory mediators can provide earlier insights into the disease and the therapeutics. Here, we report a sweat-based non-invasive biosensor for sensitive and selective detection of thymic stromal lymphopoietin (TSLP) and interleukin-13 (IL-13) using electrochemical impedance spectroscopy (EIS). The sensor platform, functionalized with monoclonal antibodies via DTSSP cross-linking, demonstrated stable immobilization as validated by FTIR, cyclic voltammetry, and zeta potential analysis. To confirm the biological presence of these cytokines in inflammatory human sweat, orthogonal MALDI-TOF MS and LC-MS analyses were performed, detecting intact TSLP and IL-13 masses, as well as their characteristic fragment-ion fingerprints. The AWARE sensor has achieved an ultra-low limit of detection of 0.076 pg/mL for TSLP and 0.018 pg/mL for IL-13 with recovery values within clinical standards and minimal interference from sweat constituents. The spiked absolute sample values in human sweat were strongly correlated with the values from endogenous human saliva samples. Experiments simulating real-time monitoring reveal the distinct temporal dynamics of TSLP and IL-13, as well as their relationship. This study has established a sweat-based, non-invasive biosensor, advancing the development of sensors for real-time inflammation tracking.</div></div>","PeriodicalId":260,"journal":{"name":"Biosensors and Bioelectronics: X","volume":"28 ","pages":"Article 100715"},"PeriodicalIF":10.61,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Batteries for wearable and implantable biomedical devices: a comprehensive review","authors":"Pasquale Gargiulo ,&nbsp;Maria F. Gaele ,&nbsp;Aniello Costantini ,&nbsp;Tonia M. Di Palma","doi":"10.1016/j.biosx.2025.100723","DOIUrl":"10.1016/j.biosx.2025.100723","url":null,"abstract":"<div><div>Wearable and implantable medical devices have been strongly contributing to the progress of the health care system through the continuous monitoring and treatment of the patient. These devices can facilitate the detection of symptoms, diagnosis of illnesses and remote monitoring of the patient, becoming a key factor in the development of an innovative type of health care, the so called “telemedicine”. This medical equipment must guarantee high safety, excellent biocompatibility and biodegradability to avoid undesired accidents (i.e. release of toxic substances, fires, explosions) that can severely hurt the patient, so a careful choice of materials is necessary to minimize this risk. Most of these devices require a power source that should satisfy the same requirements of safety, biocompatibility and biodegradability but also provide sufficient power for a long period of time. In this context, batteries are the most common power sources for biomedical devices and the development of more efficient electrochemical cells has contributed to the production of more advanced medical devices with innovative features. This review discusses the most important batteries employed in wearable and implantable biomedical devices starting from the early technologies, such as nickel-cadmium and zinc-mercury batteries, to the widespread lithium batteries currently employed in modern medical equipment. Moreover, the paper also provides a focus on the new post-lithium batteries under development with superior electrochemical performance and higher safety and sustainability with respect to lithium ones, which could become the future power sources for a new generation of smart medical devices.</div></div>","PeriodicalId":260,"journal":{"name":"Biosensors and Bioelectronics: X","volume":"28 ","pages":"Article 100723"},"PeriodicalIF":10.61,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Rapid, label-free and low-cost diagnostic kit for COVID-19 based on liquid crystals and machine learning” [Biosens. Bioelectron: X 12 (2022) 100233]","authors":"Mahboube Esmailpour ,&nbsp;Mohammad Mohammadimasoudi ,&nbsp;Mohammadreza G. Shemirani ,&nbsp;Ali Goudarzi ,&nbsp;Mohammad-Hossein Heidari Beni ,&nbsp;Hosein Shahsavarani ,&nbsp;Hamid Aghajan ,&nbsp;Parvaneh Mehrbod ,&nbsp;Mostafa Salehi-Vaziri ,&nbsp;Fatemeh Fotouhi","doi":"10.1016/j.biosx.2025.100724","DOIUrl":"10.1016/j.biosx.2025.100724","url":null,"abstract":"","PeriodicalId":260,"journal":{"name":"Biosensors and Bioelectronics: X","volume":"28 ","pages":"Article 100724"},"PeriodicalIF":10.61,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances in smart contact lenses","authors":"Zahra Adibag ,&nbsp;Mahsa Ghanbarzadeh ,&nbsp;Mohammad Amin Salati ,&nbsp;Monireh Esmaeili Rad ,&nbsp;Rais Ansari ,&nbsp;Christopher N. Ta ,&nbsp;Daddi Fadel ,&nbsp;Mohammad Mofidfar ,&nbsp;Farhang Abbasi","doi":"10.1016/j.biosx.2025.100734","DOIUrl":"10.1016/j.biosx.2025.100734","url":null,"abstract":"<div><div>The growing demand for healthcare services and recent advances in materials and biosensing technologies have accelerated the development of point-of-care (POC) diagnostics, with smart contact lenses (SmCLs) emerging as a promising platform. This review systematically evaluates the role of tear fluid as a noninvasive diagnostic medium, highlighting its biochemical composition and challenges in sample collection. We examine the design parameters essential for functional SmCLs, including biocompatibility, oxygen permeability, wettability, and mechanical properties, which collectively determine long-term comfort and device performance. SmCLs can significantly improve the bioavailability of drug delivery while addressing the limitations of traditional ocular treatments, such as the rapid dissipation of eye medications through the nasolacrimal duct. Furthermore, the potential of SmCLs in disease diagnosis through chemical and physical biomarker detection is highlighted, showcasing their ability to monitor glucose levels and intraocular pressure in real-time. Overall, current evidence supports SmCLs as multifunctional devices capable of combining diagnostics and therapy in real time. However, large-scale validation studies are required to establish clinical accuracy, patient adherence, and cost-effectiveness. This review concludes that SmCLs represents an innovative direction in personalized healthcare, integrating materials science, biosensing, and drug delivery for noninvasive, continuous health monitoring of ocular and system diseases.</div></div>","PeriodicalId":260,"journal":{"name":"Biosensors and Bioelectronics: X","volume":"28 ","pages":"Article 100734"},"PeriodicalIF":10.61,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near field communication integrated lateral flow assays","authors":"Sevda Hasanova ,&nbsp;Eda Gumus ,&nbsp;Erhan Zor","doi":"10.1016/j.biosx.2025.100731","DOIUrl":"10.1016/j.biosx.2025.100731","url":null,"abstract":"<div><div>Lateral Flow Assays (LFAs) have become indispensable in modern diagnostics due to their ease of use, low cost, and ability to deliver rapid results without the need for complex laboratory infrastructure. LFAs are particularly valuable in point-of-care settings and in regions with limited access to centralized healthcare facilities. Their versatility allows for the detection of a wide range of biomarkers, making them suitable for applications in infectious disease screening, pregnancy testing, and environmental monitoring. Concurrently, Near Field Communication (NFC) technology has emerged as a powerful tool in biomedical engineering, offering wireless data exchange, low energy consumption, and seamless connectivity between devices. NFC operates through short-range electromagnetic fields, enabling secure and efficient communication between sensors and mobile platforms such as smartphones or tablets. Its integration into diagnostic systems allows for automated data logging, real-time result sharing, and enhanced user interaction, thereby reducing human error and improving workflow efficiency. The integration of NFC technology into LFA platforms has led to the development of smart diagnostic systems with improved sensitivity, real-time data transmission, and enhanced user interaction. This review discusses key innovations and compares the reported approaches, emphasizing the impact of NFC-enabled LFA systems on the future of portable diagnostics.</div></div>","PeriodicalId":260,"journal":{"name":"Biosensors and Bioelectronics: X","volume":"28 ","pages":"Article 100731"},"PeriodicalIF":10.61,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protein biosensors of heart failure biomarker S100A7","authors":"Roxane Mutschler ,&nbsp;Alessandro T. Caputo ,&nbsp;Zhong Guo ,&nbsp;Yi Jin Liew ,&nbsp;Maria Micaela Fiorito ,&nbsp;Sophia Newton ,&nbsp;Xi Zhang ,&nbsp;Nuwan Karunathilaka ,&nbsp;Wandy Chan ,&nbsp;Karam Kostner ,&nbsp;Dariusz Korczyk ,&nbsp;John J. Atherton ,&nbsp;Andrew J.S. Coates ,&nbsp;Chamindie Punyadeera ,&nbsp;Kirill Alexandrov ,&nbsp;Zhenling Cui","doi":"10.1016/j.biosx.2025.100700","DOIUrl":"10.1016/j.biosx.2025.100700","url":null,"abstract":"<div><div>Artificial allosteric protein switches (biosensors) hold the promise to deliver disruptive analytical and diagnostic applications. However, their construction is complicated by the limited availability of selective receptor domains. Here, we report the use of mRNA display to rapidly select FN3con-based binding domains to S100A7 protein - a biomarker of heart failure. The crystal structure of the resulting FN3con binding domain in complex with S100A7 dimer revealed that the binding interface of the dimer is formed by similar, but not identical, side-chain interaction networks. Using medium-throughput functional screening, we tested selected binding domains for compatibility with two protein biosensor architectures. The best biosensor demonstrated a dynamic range of 57-fold and a 1 nM limit of detection and was used to establish a rapid homogeneous assay for quantification of S100A7 in clinical saliva samples. The assay was able to distinguish heart failure patient samples from those of healthy donors. Our results demonstrate that mRNA binding domain development and biosensor prototyping pipelines can deliver practically useful biosensors to potentially any target.</div></div>","PeriodicalId":260,"journal":{"name":"Biosensors and Bioelectronics: X","volume":"27 ","pages":"Article 100700"},"PeriodicalIF":10.61,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preface of special issue: Advances in point-of-care technologies and biosensing for diagnostics and treatment surveillance","authors":"Fatih Inci (Assoc. Prof. Dr.),&nbsp;Mehmet Toner (Prof. Dr.),&nbsp;Arzum Erdem Gürsan (Prof. Dr.)","doi":"10.1016/j.biosx.2025.100707","DOIUrl":"10.1016/j.biosx.2025.100707","url":null,"abstract":"","PeriodicalId":260,"journal":{"name":"Biosensors and Bioelectronics: X","volume":"27 ","pages":"Article 100707"},"PeriodicalIF":10.61,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}