Biosensors and Bioelectronics: X最新文献_第4页

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 : 2026-03-01 Epub 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

A machine learning framework for advanced analytical detection of CD36 using immunosensors below limit of detection 使用低于检测极限的免疫传感器进行CD36高级分析检测的机器学习框架

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2026-03-01 Epub Date: 2026-01-05 DOI: 10.1016/j.biosx.2025.100733

Muhammet Cagri Yeke , Sultan Sacide Gelen , Hilal Fil , Muhammet Mustafa Yalcin , Abdurrahman Gumus , Idris Yazgan , Dilek Odaci

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

^{2} > 0.99

) 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: https://github.com/miralab-ai/biosensors-AI.

我们介绍了一种基于机器学习（ML）的定量电化学分析回归框架，代表了从传统的单变量方法到多变量方法的范式转变。传统的分析受限于将整个信号减少到单个峰值电流特征，以定义线性范围并计算检测限（LOD）。相比之下，我们的方法将差分脉冲伏安法（DPV）曲线视为时间序列数据，通过系统地评估主信号峰值周围不同宽度的多个数据窗口来创建高维指纹，以识别信息最丰富的部分。为了验证这一方法，通过将抗CD36抗体固定在聚多巴胺修饰的丝网印刷碳电极上，开发了一种生物传感器，用于检测CD36， CD36是代谢和免疫的关键蛋白质。测量收集了12种浓度，包括空白样品，范围为0至25 ng/mL。在数据扩充之后，对9种不同的回归模型进行了评估，表现最好的模型在整个范围内实现了近乎完美的预测精度（R2>0.99）。在整个浓度谱上的高精度定量证明了该方法的操作能力，而不依赖于传统的概念，如线性范围或LOD，可以在超低水平下进行可靠的检测。此外，该免疫传感器对常见干扰素具有高选择性，在人血清中具有良好的回收率。这种方法代表了分析电化学的重大进步，为提高生物标志物检测的敏感性提供了一种可转移的方法，在临床诊断和生物医学研究中具有潜在的应用。代码和数据集在GitHub上公开提供，以支持进一步的研究：https://github.com/miralab-ai/biosensors-AI。

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引用次数: 0

A review on the impact of AI-enabled thermal imaging and IoT sensor fusion on early detection of mastitis in dairy cattle 人工智能热成像和物联网传感器融合对奶牛乳腺炎早期检测的影响

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.biosx.2025.100735

Arjun Asogan , Norazlianie Sazali , Arigela Satya Veerendra , Lingenthiran Samylingam , Navid Aslfattahi , Chee Kuang Kok , Kumaran Kadirgama

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.

乳腺炎是奶牛最新兴的经济疾病之一，它降低了动物福利，产生了昂贵的兽医费用，阻碍了动物充分发挥其产奶潜力，导致需要从牛群中剔除奶牛，并降低了农场系统的整体效率。传统的检测方法，如临床检查、加州乳腺炎试验（CMT）和体细胞（SC）功能，虽然有用，但可以在足够早的阶段识别亚临床病例，以便进行干预。新技术正在转向使用物联网设备、软件和复杂的人工智能算法进行实时和远程乳腺炎检测的发展。人工智能驱动的分析，加上红外热成像IRT和最先进的诊断物联网设备，如挤奶系统和行为项圈，尚未整合。IRT设备具有分析乳房温度和绘制炎症图的能力。基于单一工具的图像分类或数据融合的人工智能算法能够达到高于其他方法的精度。市场和技术的准备程度，以及成本和环境的可变性，仍在推动有关这些技术实际应用的辩论。这项研究对人工智能、图像融合技术、集成IRT传感器和物联网系统在乳腺炎检测方面的突破和障碍进行了抨击，而其他研究则认为，集成方法可以提高牛群中奶牛的成本效益和警惕性。

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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 : 2026-03-01 Epub 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 : 2026-03-01 Epub 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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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 : 2026-03-01 Epub 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

Recent advances in smart contact lenses 智能隐形眼镜的最新进展

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.biosx.2025.100734

Zahra Adibag , Mahsa Ghanbarzadeh , Mohammad Amin Salati , Monireh Esmaeili Rad , Rais Ansari , Christopher N. Ta , Daddi Fadel , Mohammad Mofidfar , Farhang Abbasi

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.

对医疗保健服务日益增长的需求，以及材料和生物传感技术的最新进展，加速了护理点（POC）诊断的发展，智能隐形眼镜（smcl）正在成为一个有前途的平台。这篇综述系统地评估了泪液作为一种无创诊断介质的作用，强调了其生化成分和样本收集的挑战。我们研究了功能性smcl的基本设计参数，包括生物相容性、氧渗透性、润湿性和机械性能，这些参数共同决定了长期舒适性和设备性能。SmCLs可以显著提高药物递送的生物利用度，同时解决传统眼部治疗的局限性，例如眼部药物通过鼻泪管快速消散。此外，通过化学和物理生物标志物检测，强调了SmCLs在疾病诊断中的潜力，展示了它们实时监测血糖水平和眼压的能力。总的来说，目前的证据支持smcl作为多功能设备，能够实时结合诊断和治疗。然而，需要大规模的验证研究来建立临床准确性、患者依从性和成本效益。本综述认为，smcl代表了个性化医疗保健的创新方向，将材料科学、生物传感和药物输送结合起来，用于眼部和系统疾病的无创、连续健康监测。

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引用次数: 0

Near field communication integrated lateral flow assays 近场通信集成横向流分析

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.biosx.2025.100731

Sevda Hasanova , Eda Gumus , Erhan Zor

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.

横向流动测定法（LFAs）由于其易于使用、成本低、无需复杂的实验室基础设施就能提供快速结果的能力，在现代诊断中已成为不可或缺的技术。lfa在护理点环境中以及在获得集中医疗设施的机会有限的地区尤其有价值。它们的多功能性允许检测广泛的生物标志物，使其适用于传染病筛查，妊娠检测和环境监测。同时，近场通信（NFC）技术已成为生物医学工程中的强大工具，提供无线数据交换，低能耗和设备之间的无缝连接。NFC通过短距离电磁场工作，实现传感器与智能手机或平板电脑等移动平台之间的安全高效通信。将其集成到诊断系统中可以实现自动数据记录、实时结果共享和增强的用户交互，从而减少人为错误并提高工作流程效率。将NFC技术集成到LFA平台导致智能诊断系统的发展，具有更高的灵敏度，实时数据传输和增强的用户交互。这篇综述讨论了关键的创新，并比较了已报道的方法，强调了nfc支持的LFA系统对便携式诊断未来的影响。

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引用次数: 0

Protein biosensors of heart failure biomarker S100A7 心力衰竭生物标志物S100A7的蛋白质生物传感器

IF 10.61 Q3 Biochemistry, Genetics and Molecular Biology

Biosensors and Bioelectronics: X

Pub Date : 2025-12-01 Epub Date: 2025-10-14 DOI: 10.1016/j.biosx.2025.100700

Roxane Mutschler , Alessandro T. Caputo , Zhong Guo , Yi Jin Liew , Maria Micaela Fiorito , Sophia Newton , Xi Zhang , Nuwan Karunathilaka , Wandy Chan , Karam Kostner , Dariusz Korczyk , John J. Atherton , Andrew J.S. Coates , Chamindie Punyadeera , Kirill Alexandrov , Zhenling Cui

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.

人工变构蛋白开关（生物传感器）有望提供颠覆性的分析和诊断应用。然而，由于选择性受体结构域的有限可用性，它们的构建变得复杂。在这里，我们报告了使用mRNA显示快速选择基于fn3con的S100A7蛋白结合域-心力衰竭的生物标志物。FN3con结合域与S100A7二聚体配合物的晶体结构表明，二聚体的结合界面是由相似但不完全相同的侧链相互作用网络形成的。使用中等通量功能筛选，我们测试了选择的结合域与两种蛋白质生物传感器结构的兼容性。最佳生物传感器动态范围为57倍，检测限为1 nM，可用于临床唾液样品中S100A7的快速均相定量分析。该试验能够将心力衰竭患者的样本与健康捐赠者的样本区分开来。我们的研究结果表明，mRNA结合域的开发和生物传感器原型管道可以为潜在的任何目标提供实际有用的生物传感器。

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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 Epub Date: 2025-10-22 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