Sensors & diagnostics最新文献

{"title":"Adapting antibody–invertase fusion protein immunoassays to multiwell plates for infectious disease antibody quantification","authors":"Elysse Ornelas-Gatdula, Xinran An, Jamie B. Spangler and Netzahualcóyotl Arroyo-Currás","doi":"10.1039/D5SD00117J","DOIUrl":"10.1039/D5SD00117J","url":null,"abstract":"<p >Traditional enzyme-linked immunosorbent assays (ELISAs) rely on horseradish peroxidase (HRP)-conjugated antibodies to generate a colorimetric response proportional to target antibody concentration. However, spectrophotometric quantification requires expensive benchtop equipment, limiting its usability for frequent, population-scale immunity screening. To overcome this barrier, we previously developed LC15, an antibody–invertase fusion protein that catalyzes sucrose-to-glucose conversion in proportion to antibody levels. This fusion protein enabled antibody quantification using handheld glucometers – affordable, widely available devices already integrated with telehealth infrastructure. Unlike commercial ELISAs, which report relative antibody titers, LC15 facilitates absolute antibody quantification (μg mL<small>⁻¹</small>), enhancing applications such as epidemiological monitoring and convalescent plasma dosing. To increase the number of clinical samples processed in a single run of the assay, in this study we transitioned from poly(methyl methacrylate) strips to microwell plates, optimizing pH conditions and reagent concentrations. This adaptation yielded similar sensitivity to the original strip-based assay, but with a 5-fold reduction in reagent consumption and in plasma, as opposed to serum used for the previous study. Using the SARS-CoV-2 receptor binding domain (RBD) as the antigen, we applied LC15 in a 96-well plate format to screen 72 clinical samples in triplicate for anti-RBD antibodies. A blinded comparison with commercial ELISAs demonstrated strong linear correlation (<em>R</em><small>²</small> = 0.85) over four orders of magnitude in concentration. By combining accuracy with accessibility, this approach has the potential to facilitate population-level immunity assessments, supporting rapid public health responses in future outbreaks.</p>","PeriodicalId":74786,"journal":{"name":"Sensors & diagnostics","volume":" 1","pages":" 83-93"},"PeriodicalIF":4.1,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12592976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145483736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Label-free optical detection of miRNA using electrostatic interactions between oppositely charged gold nanoparticles for signal amplification","authors":"Fatemeh Hakimian, Behdad Delavari, Samaneh Hadian-Ghazvini, Mohammad Behnam Rad, Fariba Dashtestani, Vahid Sheikhhassani, Hamideh Fouladiha, Hadi Zare-Zardini and Hedayatollah Ghourchian","doi":"10.1039/D5SD00129C","DOIUrl":"https://doi.org/10.1039/D5SD00129C","url":null,"abstract":"<p >We developed a simple yet innovative biosensing system for the detection of miRNA-155 (miR-155), a promising biomarker for the early diagnosis of breast cancer. This system utilizes two types of gold nanoparticles (Au NPs) with opposing surface charges: (1) negatively charged citrate-stabilized Au NPs (Cit-Au NPs) for probe immobilization and (2) positively charged polyethylenimine-capped Au NPs (PEI-Au NPs) for signal amplification. The DNA probe was covalently attached to Cit-Au NPs <em>via</em> Au–S bonds. In the presence of miR-155, a DNA–miRNA hybrid forms, stabilizing the nanoparticles. The subsequent introduction of PEI-Au NPs enhances the surface plasmon resonance (SPR) signal due to increased nanoparticle dispersion. PEI-Au NPs enhance the diagnostic system's sensitivity by providing a high surface area and improved nanoparticle stability. Upon binding to the DNA–miRNA hybrid, the increased interparticle distance leads to enhanced colloidal stability. This stabilization manifests visually as an intensified red color, indicating the presence of the target when PEI-Au NPs are introduced into the solution. In contrast, in the absence of miR-155, electrostatic interactions cause aggregation of the Au NPs, leading to a measurable SPR shift. This facile method demonstrated a detection limit of approximately 8 pM and a wide linear detection range from 80 pM to 2 μM, making it a promising tool for early diagnostics of breast cancer.</p>","PeriodicalId":74786,"journal":{"name":"Sensors & diagnostics","volume":" 1","pages":" 56-62"},"PeriodicalIF":4.1,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2026/sd/d5sd00129c?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to ‘Paper-Based Point-of-Care Diagnostics’","authors":"Daniel Citterio, Thiago R. L. C. Paixão and William Reis de Araujo","doi":"10.1039/D5SD90031J","DOIUrl":"https://doi.org/10.1039/D5SD90031J","url":null,"abstract":"<p >A graphical abstract is available for this content</p>","PeriodicalId":74786,"journal":{"name":"Sensors & diagnostics","volume":" 12","pages":" 1045-1046"},"PeriodicalIF":4.1,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/sd/d5sd90031j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-based radiomic signature for MYCN amplification prediction of pediatric abdominal neuroblastoma","authors":"Xuan Jia, Junjie Wen, Jiawei Liang, Xiaohui Ma, Wenqi Wang, Jinhu Wang and Yi Zhang","doi":"10.1039/D5SD00089K","DOIUrl":"https://doi.org/10.1039/D5SD00089K","url":null,"abstract":"<p >MYCN gene amplification critically drives neuroblastoma aggressiveness and poor outcomes, necessitating precise preoperative identification to guide risk-adapted therapies. Current invasive detection methods present substantial challenges for pediatric patients. To address this unmet need, we developed a noninvasive MRI-based radiomic signature for predicting MYCN amplification status in childhood abdominal neuroblastoma. In this prospective study, 99 patients with pathologically confirmed abdominal neuroblastoma underwent preoperative MRI between April 2019 and September 2021. From T2-weighted images, 1409 radiomic features were extracted per subject. Through two-sample statistical testing and least absolute shrinkage and selection operator (LASSO) regression, we constructed an optimized radiomic signature incorporating six highly discriminative features. The signature achieved exceptional performance (AUC = 0.91) in predicting MYCN amplification, significantly outperforming neuron-specific enolase levels (AUC = 0.68, <em>p</em>-value &lt; 0.001) and all individual radiomic features. When integrated with neuron-specific enolase <em>via</em> multivariate logistic regression, the model achieved comparable performance (AUC = 0.91) to the signature only. Our findings establish the clinical viability of this MRI-based approach for noninvasively stratifying MYCN amplification status, offering significant potential to optimize surgical planning and therapeutic strategies for pediatric neuroblastoma.</p>","PeriodicalId":74786,"journal":{"name":"Sensors & diagnostics","volume":" 12","pages":" 1114-1121"},"PeriodicalIF":4.1,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/sd/d5sd00089k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence (Al) in healthcare diagnosis: evidence-based recent advances and clinical implications","authors":"Jay Bhatt, Sweny Jain and Dhiraj Devidas Bhatia","doi":"10.1039/D5SD00146C","DOIUrl":"https://doi.org/10.1039/D5SD00146C","url":null,"abstract":"<p >Artificial intelligence (AI) is increasingly shaping modern healthcare by improving the accuracy and efficiency of disease diagnosis. This review summarises the modern advancements in AI-driven diagnostic technologies, with a focus on machine learning (ML) and deep learning (DL) applications for the detection and characterization of cancer, cardiovascular diseases, diabetes, neurodegenerative disorders, and bone diseases. AI models, particularly those employing convolutional neural networks, have demonstrated expert-level performances in interpreting medical images, genomic profiles, and electronic health records, often surpassing traditional diagnostic methods in terms of sensitivity, specificity, and overall accuracy. Using advanced methods like machine learning and deep learning, AI systems can analyze large and complex medical datasets—including images, electronic health records, and laboratory results—to detect patterns linked to various diseases. While integration of AI into clinical practice has shown significant benefits, challenges remain in ensuring the reliability, interpretability, and broad adoption of these systems. Thus, continued research and careful implementation are needed to maximize the potential of AI in transforming diagnostic processes and improving patient outcomes.</p>","PeriodicalId":74786,"journal":{"name":"Sensors & diagnostics","volume":" 12","pages":" 1047-1059"},"PeriodicalIF":4.1,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/sd/d5sd00146c?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A near-infrared fluorescent aptananosensor enables selective detection of the stress hormone cortisol in artificial cerebrospinal fluid","authors":"Jessica Kretli Zanetti, Maria Celina Stefoni, Catarina Ferraz, Amelia Ryan, Atara Israel and Ryan M. Williams","doi":"10.1039/D5SD00085H","DOIUrl":"10.1039/D5SD00085H","url":null,"abstract":"<p >Cortisol is a hormone which regulates the body's response to stressors. Detection and monitoring of cortisol levels can provide information about physical and psychological health, thus it is essential to develop a sensor that can detect it in a sensitive manner. This study presents a biocompatible near-infrared fluorescent sensor, wherein single-walled carbon nanotubes (SWCNT) are functionalized with a cortisol-specific aptamer. We found this sensor was capable of detecting cortisol from 37.5 μg mL<small>⁻¹</small> to 300 μg mL<small>⁻¹</small> and that it was selective for cortisol compared to the similar molecule estrogen. Moreover, SWCNT functionalized with non-specific oligonucleotides did not exhibit a concentration-dependent response to cortisol, demonstrating the specificity provided by the aptamer sequence. The sensor also demonstrated the ability to detect cortisol in artificial cerebrospinal fluid. We anticipate that future optimization of this sensor will enable potential point-of-care or implantable device-based rapid detection of cortisol, with the potential for improving overall patient health and stress.</p>","PeriodicalId":74786,"journal":{"name":"Sensors & diagnostics","volume":" 12","pages":" 1103-1113"},"PeriodicalIF":4.1,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12502572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathogenic bacteria characterization through portable optical scatter device and machine learning","authors":"Ramana Pidaparti, Sanjay Oruganti, Naveen Kurra, Patrick Maffe, Everett Grizzle, Arnab Mondal, Rebecca Johnson, Hitesh Handa and Rao Tatavarti","doi":"10.1039/D5SD00112A","DOIUrl":"https://doi.org/10.1039/D5SD00112A","url":null,"abstract":"<p >Rapid and accurate detection and characterization of pathogenic bacteria is critical for clinical diagnosis. Most selective clinical procedures are limited by their diagnostic speed, accuracy, and sensitivity challenges. In order to overcome these, we introduce a novel photonics-based, point-of-care device designed for rapid and accurate characterization of bacteria. The device is designed to capture optical scatter signatures unique to various pathogenic bacteria, which are analyzed using advanced clustering and machine learning techniques for characterization. Our preliminary results from controlled experiments show that our device successfully distinguishes bacteria genus with reasonable accuracy.</p>","PeriodicalId":74786,"journal":{"name":"Sensors & diagnostics","volume":" 12","pages":" 1122-1133"},"PeriodicalIF":4.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/sd/d5sd00112a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Liquid crystal-based optical platform for the detection of colon and breast cancer cell lines using folic acid-conjugated gold nanoparticles","authors":"Anupama Kadam, Rajendra Patil, Sneha Mahalunkar, Muthupandian Ashokkumar, Ratna Chauhan and Suresh Gosavi","doi":"10.1039/D5SD00111K","DOIUrl":"https://doi.org/10.1039/D5SD00111K","url":null,"abstract":"<p >In the present study, we report for the first time a liquid crystal-based biosensor for the highly sensitive and specific detection of colon and breast cancer cells using folic acid-conjugated gold nanoparticles (FA@GNPs) as the recognition element. FA@GNPs were immobilized on a glass substrate coated with <em>N</em>-dimethyl-<em>N</em>-octadecyl-3-aminopropyltrimethoxysilyl chloride (DMOAP), which induces homeotropic alignment of the liquid crystal molecules. Folate receptors, which are overexpressed in various cancer types, including colon and breast cancer cells, facilitate the selective binding of these cells to FA@GNPs. This binding event disrupts the vertical alignment of the liquid crystal molecules, causing a distinct transition from a dark to a bright state, which is observable <em>via</em> polarizing optical microscopy. Quantitative analysis of the cancer cells was performed by calculating the average grayscale intensity of the optical images, demonstrating that the proposed cell detection platform can sensitively detect individual cancer cells. The proposed liquid crystal biosensor utilizing FA@GNPs as the detection element offers a simple, cost-effective, label-free platform with exceptional specificity and sensitivity for early cancer detection. This novel approach holds significant potential for the development of advanced diagnostic tools in oncological research.</p>","PeriodicalId":74786,"journal":{"name":"Sensors & diagnostics","volume":" 11","pages":" 1024-1036"},"PeriodicalIF":4.1,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/sd/d5sd00111k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145442780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simple and fast detection of CD4 and CD8 cells: integration of image flow system and acoustophoresis","authors":"Enjian Jin, Anna Go and Min-Ho Lee","doi":"10.1039/D5SD00108K","DOIUrl":"https://doi.org/10.1039/D5SD00108K","url":null,"abstract":"<p >We developed a compact image-based flow cytometry system that combines acoustic focusing with machine learning-driven image analysis. The system enables rapid detection of stained CD4 and CD8 cells flowing on an acoustically defined focal plane, without the need for hydrodynamic focusing. This design allows for a compact configuration while maintaining reliable performance and reduced complexity, making the system suitable for both clinical diagnostics and point-of-care applications.</p>","PeriodicalId":74786,"journal":{"name":"Sensors & diagnostics","volume":" 12","pages":" 1086-1090"},"PeriodicalIF":4.1,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/sd/d5sd00108k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-wavenumber Raman spectroscopy for the detection of Mycobacterium tuberculosis in saliva","authors":"Alec B. Walter, Luke Whitehead, Amelia L. Taylor and Andrea K. Locke","doi":"10.1039/D5SD00092K","DOIUrl":"https://doi.org/10.1039/D5SD00092K","url":null,"abstract":"<p >Despite the increased availability of low-cost and effective treatments for tuberculosis (TB), ∼1 million people continue to die from TB-related symptoms annually. One major challenge limiting the effectiveness of TB treatment is delays in diagnosis, largely due to current detection methods requiring either weeks of culture time or complex processing steps that cannot be performed at the point-of-care. Thus, there is a need for alternative methods that are easier to use yet still effective in providing an accurate TB diagnosis. This work investigates the feasibility of using high-wavenumber Raman spectroscopy to detect the presence of the causative agent of TB, <em>Mycobacterium tuberculosis</em>, in human saliva. To accomplish this, raw saliva was collected from healthy participants and inoculated with a fixed, physiologically relevant, concentration of bacteria (10<small>⁶</small> CFU mL<small>⁻¹</small>) and concentrated into a pellet. The samples were measured using Raman spectroscopy and analyzed with a spectral unmixing approach to determine the relative biochemical composition. The presence of <em>M. tuberculosis</em> resulted in a significant increase in the lipid signal of saliva pellets containing the spiked bacteria, with a median percent increase of 423.6% as compared to the control samples. Control experiments using <em>Streptococcus mutans</em>, a common oral bacterium, only resulted in a slight increase of 9.8%. Additionally, using linear regression analysis, a predictive relationship was found between the Raman lipid fractions of the raw saliva and the control saliva pellets. Using the 95% prediction interval of this relationship as a classification threshold, the presence of <em>M. tuberculosis</em> was accurately determined for all samples with an overall training accuracy of 98.5% and a cross-validation accuracy of 100%. These results showcase the potential of high-wavenumber Raman spectroscopy as a reagent-free method of detecting <em>M. tuberculosis</em> in saliva samples.</p>","PeriodicalId":74786,"journal":{"name":"Sensors & diagnostics","volume":" 12","pages":" 1091-1102"},"PeriodicalIF":4.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/sd/d5sd00092k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}