Biosensors and Bioelectronics: X最新文献

英文中文

Vis-SWNIR spectroscopic and hyperspectral imaging sensor integrated with artificial intelligence for early diagnosis of breast cancer 与人工智能集成的可见光- swnir光谱和高光谱成像传感器用于乳腺癌早期诊断

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2025-12-12 DOI: 10.1016/j.biosx.2025.100729

Maryam Kashi, Farbod Bayat-Afshary, Hadi Parastar

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.

乳腺癌（BC）是世界范围内的一种常见癌症，需要发展快速的早期检测方法。最近，人工智能（AI），特别是机器学习（ML）的进步，促进了高光谱成像（HSI）和便携式光谱传感器在疾病诊断中的应用。本文研究了在400-1000 nm波长范围内，使用便携式可见短波长近红外光谱（Vis-SWNIR）和HSI对143份干燥的血浆斑（DPS）样品（73份对照样品和70份BC患者样品）进行分析，并使用ML进行BC诊断。本研究对血浆样品进行干燥，并利用方差同步成分分析（ASCA）分析样品之间的变异性、干燥方法及其影响因素。Vis-SWNIR光谱和HSI传感器提供了一种安全、快速和具有成本效益的诊断方法，是重复筛选的理想选择。由于HSI数据的复杂性，采用多元曲线分辨率-交替最小二乘（MCR-ALS）算法作为特征提取技术，提取现有成分的纯空间和光谱轮廓。然后，利用类类比的数据驱动-软独立建模（DD-SIMCA）、偏最小二乘判别分析（PLS-DA）、人工神经网络（ANN）、k近邻（kNN）、随机森林（RF）和支持向量机（SVM）对光谱和HSI数据进行多元分类。人工神经网络在HSI数据中区分健康和病变样本的准确率达到86.0%。相比之下，支持向量机模型对便携式光谱仪数据的预测集的准确率为62%。结果显示胆红素、血红蛋白、卟啉、蛋白质和脂质的变化。虽然对BC检测的发现很有希望，但还需要更多的研究。

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引用次数: 0

Advances and challenges in digitally connected point-of-care biosensing 数字连接的护理点生物传感的进展和挑战

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2025-12-11 DOI: 10.1016/j.biosx.2025.100728

Abdellatif Ait Lahcen , Jegan Rajendran , Gymama Slaughter

Point-of-care (POC) biosensors are undergoing a paradigm shift from isolated diagnostic tools to digitally connected, intelligent platforms that enable continuous and decentralized healthcare delivery. This review critically examines recent advances in wearable, implantable, and portable biosensors, highlighting how integration with wireless communication, the Internet of Medical Things (IoMT), and artificial intelligence is transforming their functionality and clinical utility. Particular attention is given to innovations such as smartphone-enabled interfaces, cloud-based analytics, and machine learning-assisted analysis, which collectively enhance sensitivity, specificity, and user accessibility across diverse healthcare settings, from personalized home monitoring and bedside diagnostics to deployment in resource-limited regions. The review also considers regulatory, ethical, interoperability, and cybersecurity challenges that influence their adoption. By synthesizing recent technological breakthroughs with systemic barriers, this article provides a comprehensive perspective on how connected POC biosensors are redefining the future of biosensing and decentralized healthcare.

医疗点（POC）生物传感器正在经历从孤立的诊断工具到数字连接的智能平台的范式转变，从而实现持续和分散的医疗保健服务。本文回顾了可穿戴、植入式和便携式生物传感器的最新进展，重点介绍了与无线通信、医疗物联网（IoMT）和人工智能的集成如何改变其功能和临床用途。特别关注诸如支持智能手机的界面、基于云的分析和机器学习辅助分析等创新，这些创新共同提高了不同医疗保健设置（从个性化家庭监控和床边诊断到资源有限地区的部署）的灵敏度、特异性和用户可访问性。该审查还考虑了影响其采用的监管、道德、互操作性和网络安全挑战。通过综合最近的技术突破和系统障碍，本文提供了一个全面的视角，介绍了连接的POC生物传感器如何重新定义生物传感和分散医疗保健的未来。

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引用次数: 0

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] “基于液晶和机器学习的快速、无标签和低成本COVID-19诊断试剂盒”的勘误表[Biosens。生物电子：X 12 (2022) 100233]

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2025-12-08 DOI: 10.1016/j.biosx.2025.100724

Mahboube Esmailpour , Mohammad Mohammadimasoudi , Mohammadreza G. Shemirani , Ali Goudarzi , Mohammad-Hossein Heidari Beni , Hosein Shahsavarani , Hamid Aghajan , Parvaneh Mehrbod , Mostafa Salehi-Vaziri , Fatemeh Fotouhi

引用次数: 0

Wearable sensors for health monitoring: Current applications, trends, and future directions 用于健康监测的可穿戴传感器：当前应用、趋势和未来方向

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2025-12-05 DOI: 10.1016/j.biosx.2025.100727

Fatma Kurul , Damla Aydoğan , Sevval Janat , Irem Aydın Kırlangıc , Hüseyin Oğuzhan Kaya , Seda Nur Topkaya

Wearable sensors are redefining health, wellness, and performance monitoring by enabling continuous, non-invasive measurement of biochemical and biophysical signals directly on the body. In this review, we focus on two major classes of wearable technologies: wearable biochemical biosensors, including sweat, tear, saliva, and epidermal-based biochemical sensing, and wearable physical sensors, which monitor pressure, strain, temperature, motion, and other non-biological signals. We summarize recent advances in materials, microfluidics, and electronics that are enabling more practical and versatile wearable sensing platforms. Particular attention is given to multimodal systems that combine chemical and physical measurements on a single device, on-body fluid handling and sampling strategies, and data processing with embedded algorithms. Commercial examples such as continuous glucose monitors, smart patches, and consumer wearables are also highlighted. Key challenges include getting enough biofluid in a reliable way, reducing signal drift and biofouling, dealing with user-to-user variability, keeping data secure, making sensors comfortable to wear, and setting clear on-body or clinical validation protocols. We also highlight recent efforts that aim to address these issues through better surface coatings, more stable sensor designs, new power and energy-harvesting options, and improvements in data management and manufacturing. This review mainly covers studies published between 2018 and 2025, with a particular focus on work from the last 4–5 years.

可穿戴传感器通过直接在身体上进行连续、无创的生化和生物物理信号测量，重新定义了健康、健康和性能监测。在这篇综述中，我们重点介绍了两大类可穿戴技术：可穿戴生化生物传感器，包括汗液、眼泪、唾液和基于表皮的生化传感，以及可穿戴物理传感器，监测压力、应变、温度、运动和其他非生物信号。我们总结了材料、微流体和电子方面的最新进展，这些进展使更实用和通用的可穿戴传感平台成为可能。特别关注多模态系统，将化学和物理测量结合在一个设备上，体内流体处理和采样策略，以及数据处理与嵌入式算法。商业例子，如连续血糖监测仪、智能贴片和消费者可穿戴设备也得到了强调。主要挑战包括以可靠的方式获得足够的生物流体，减少信号漂移和生物污垢，处理用户对用户的可变性，保持数据安全，使传感器佩戴舒适，以及制定明确的身体或临床验证方案。我们还强调了最近的努力，旨在通过更好的表面涂层，更稳定的传感器设计，新的电源和能量收集选项，以及数据管理和制造的改进来解决这些问题。本综述主要涵盖2018年至2025年之间发表的研究，特别关注最近4-5年的工作。

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引用次数: 0

Electrochemical sensing of perfluorooctanoic acid in wastewater: Characterization of a molecularly imprinted polymer-based sensor 废水中全氟辛酸的电化学传感：分子印迹聚合物传感器的表征

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2025-12-04 DOI: 10.1016/j.biosx.2025.100726

Patrick Severin Sfragano , Anna Emanuele , Serena Laschi , Giovanni Ferraro , Emiliano Fratini , Ilaria Palchetti

Stricter guidelines are progressively established to control the presence of perfluorooctanoic acid (PFOA) in water bodies due to its toxicity and persistence in the environment. This study focused on the development of an electrochemical sensor based on a molecularly imprinted poly(o-phenylenediamine) film to detect PFOA in aqueous matrices. A comparative evaluation of sensor fabrication and analytical performance was performed on gold (AuE) and glassy carbon (GCE) electrodes. Experimental conditions influencing electropolymerization behavior, template removal, polymer morphology, and PFOA recognition were studied. Characterizations using electrochemical techniques and Raman spectroscopy were correlated with analytical performance. These were assessed in spiked buffer solution, yielding concentration-dependent trends over a wide dynamic range, with detection limits of 23.0 and 20.2 ppt for the AuE and the GCE, respectively, meeting the sensitivity requirements of current EU regulations. Selectivity was evaluated by studying sensor response in the co-presence of potential interferents. The sensor was then tested on real samples collected from a wastewater treatment plant. A minor matrix effect was registered in the filtered effluent, supporting the applicability of the sensor for rapid on-site PFOA screening near regulatory thresholds.

由于全氟辛酸在环境中的毒性和持久性，正在逐步制定更严格的准则，以控制水体中全氟辛酸（PFOA）的存在。本文研究了一种基于分子印迹聚邻苯二胺薄膜的电化学传感器，用于检测水溶液中的PFOA。在金（AuE）和玻璃碳（GCE）电极上对传感器的制造和分析性能进行了比较评价。研究了影响电聚合行为、模板去除、聚合物形态和PFOA识别的实验条件。电化学技术和拉曼光谱表征与分析性能相关。这些在加标缓冲溶液中进行了评估，在很宽的动态范围内产生了浓度依赖的趋势，AuE和GCE的检测限分别为23.0和20.2 ppt，符合当前欧盟法规的灵敏度要求。通过研究传感器在潜在干扰下的响应来评估选择性。然后，该传感器在从污水处理厂收集的真实样本上进行了测试。在过滤后的流出物中记录了轻微的基质效应，支持传感器在接近监管阈值的地方快速现场筛选PFOA的适用性。

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引用次数: 0

Preface of special issue: Advances in point-of-care technologies and biosensing for diagnostics and treatment surveillance 特刊前言：用于诊断和治疗监测的即时护理技术和生物传感的进展

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2025-12-01 DOI: 10.1016/j.biosx.2025.100707

Fatih Inci (Assoc. Prof. Dr.), Mehmet Toner (Prof. Dr.), Arzum Erdem Gürsan (Prof. Dr.)

引用次数: 0

Preface of special issue: Recent advances in cancer biosensors & diagnostics 特刊前言：癌症生物传感器与诊断的最新进展

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2025-12-01 DOI: 10.1016/j.biosx.2025.100694

Ahu Arslan Yildiz , Onur Parlak , Arzum Erdem Gürsan

引用次数: 0

Performance of transformer-convolutional neural network ensemble for melanoma diagnosis on segmented 3D total body photography data: Cross-Validation stratified K-fold 变换-卷积神经网络集成在分段三维全身摄影数据上诊断黑色素瘤的性能：交叉验证分层K-fold

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2025-12-01 DOI: 10.1016/j.biosx.2025.100714

Eeshan G. Dandamudi , Shakeel Abdulkareem , Mihir Tekal , Khartik Uppalapati , Dhruv Karthik Alamuri , S. Nick Ice , Kalicharan Sharma , Aniket Nandi , Christian L. Lorson , Kamal Singh

Skin cancer is one of the most common cancers worldwide, and its early diagnosis is critical for the survival of cancer patients. Many artificial intelligence models have been developed to support early detection; however, limited progress has been made towards a reliable pre-diagnosis of skin cancer types. We developed a deep learning ensemble integrating ConvNeXt-Base, ResNet-50, and Swin Transformer-Base architectures to detect melanoma using images from 3D total body photography (3D-TBP) data. By combining the complementary strengths of individual models coupled with the Quadruple Stratified Leak-Free 5-Fold Cross-Validation (QSLF-KF-CV) approach, the ensemble achieved an Area Under the Curve (AUC) of 0.9208, which is greater than that of individual models. Our strategy demonstrates potential for accurate melanoma detection, as well as broader applications in medical image diagnostics.

皮肤癌是世界上最常见的癌症之一，其早期诊断对癌症患者的生存至关重要。已经开发了许多人工智能模型来支持早期检测；然而，在皮肤癌类型的可靠预诊断方面取得的进展有限。我们开发了一个深度学习集成，集成了ConvNeXt-Base、ResNet-50和Swin Transformer-Base架构，利用3D全身摄影（3D- tbp）数据中的图像检测黑色素瘤。通过将个体模型的互补优势与四重分层无泄漏5重交叉验证（QSLF-KF-CV）方法相结合，该集合的曲线下面积（AUC）为0.9208，大于个体模型。我们的策略展示了准确检测黑色素瘤的潜力，以及在医学图像诊断方面更广泛的应用。

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引用次数: 0

Continuous monitoring of systemic inflammation through TSLP and Interleukin-13 using the sweat-based AWARE sensor 使用基于汗液的AWARE传感器通过TSLP和白细胞介素-13持续监测全身炎症

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2025-12-01 DOI: 10.1016/j.biosx.2025.100715

Akash Kumar , Sumana Karmakar , Santhosh Gowtham Giddaluru , Preeti Singh , Kai-Chun Lin , Sriram Muthukumar , Shalini Prasad

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.

慢性炎症是由复杂的细胞因子网络介导的，使用传统方法进行监测仍然具有挑战性，这些方法具有侵入性，耗时且仅限于单点测量。对炎症介质的持续和非侵入性检测可以提供对疾病和治疗方法的早期见解。在这里，我们报道了一种基于汗液的无创生物传感器，用于使用电化学阻抗谱（EIS）灵敏和选择性地检测胸腺基质淋巴生成素（TSLP）和白细胞介素-13 （IL-13）。该传感器平台通过DTSSP交联单克隆抗体功能化，经FTIR、循环伏安法和zeta电位分析验证，具有稳定的固定化能力。为了证实这些细胞因子在炎症性人体汗液中的生物学存在，我们进行了正交MALDI-TOF MS和LC-MS分析，检测了完整的TSLP和IL-13块，以及它们的特征片段离子指纹图谱。AWARE传感器对TSLP和IL-13的超低检出限分别为0.076 pg/mL和0.018 pg/mL，回收率在临床标准范围内，且受汗液成分的干扰最小。人汗液中添加的绝对样本值与内源性人唾液样本的值密切相关。模拟实时监测的实验揭示了TSLP和IL-13的不同时间动态，以及它们之间的关系。本研究建立了一种基于汗液的非侵入性生物传感器，推动了实时炎症跟踪传感器的发展。

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引用次数: 0

Batteries for wearable and implantable biomedical devices: a comprehensive review 可穿戴和植入式生物医学设备电池：综合综述

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2025-12-01 DOI: 10.1016/j.biosx.2025.100723

Pasquale Gargiulo , Maria F. Gaele , Aniello Costantini , Tonia M. Di Palma

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.

可穿戴和植入式医疗设备通过对患者的持续监测和治疗，为医疗保健系统的进步做出了巨大贡献。这些设备有助于发现症状、诊断疾病和对病人进行远程监测，成为发展一种创新型保健即所谓“远程医疗”的关键因素。这种医疗设备必须保证高安全性、优异的生物相容性和可生物降解性，以避免可能严重伤害患者的意外事故（即释放有毒物质、火灾、爆炸），因此必须仔细选择材料以尽量减少这种风险。这些设备中的大多数都需要一种电源，既要满足安全性、生物相容性和生物降解性的要求，又要长期提供足够的功率。在这方面，电池是生物医学设备最常见的电源，更有效的电化学电池的发展有助于生产具有创新特征的更先进的医疗设备。本文讨论了可穿戴和植入式生物医学设备中最重要的电池，从早期的技术开始，如镍镉电池和锌汞电池，到现代医疗设备中广泛使用的锂电池。此外，本文还重点介绍了正在开发的新型后锂电池，与锂电池相比，其电化学性能优越，安全性和可持续性更高，可能成为新一代智能医疗设备的未来电源。

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