Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 19 appraisals
Bioorganic chemistry
In silico discovery and biological evaluation of a novel selective HDAC8 inhibitor featuring 1H-pyrazolo[3,4-b]pyridine scaffold
Histone deacetylase 8 (HDAC8), a pivotal epigenetic regulator orchestrating chromatin remodeling and transcriptional control, has emerged as a compelling anticancer target owing to its frequent dysregulation in malignancies. The development of isoform-selective inhibitors, however, confronts substantial challenges because of the pronounced structural conservation across HDAC isoforms. This study developed a high-efficiency drug screening pipeline primarily leveraging artificial learning (AI) models and molecular simulation techniques, and further evaluated the biological activity of computationally prioritized compounds through enzymatic inhibition assays and cellular experiments. The structurally novel compound Cmpd.16 was successfully identified, exhibiting promising inhibitory activity against HDAC8 (IC50 = 0.16 ± 0.01 μM) and moderately inhibited HCT116 cell proliferation mainly through the induction of apoptosis and G2/M phase cell cycle arrest. Subsequently, molecular dynamics simulations were employed to investigate the binding conformation of Cmpd.16 within the active pocket, and the key residues identified through free energy decomposition further elucidated the drug-target binding mode. Collectively, Cmpd.16, featuring 1H-pyrazolo[3,4-b]pyridine scaffold, was discovered via an efficient screening workflow, offering valuable insights to guide the rational design of HDAC8-targeted agents.
7 June 2026
Read appraisal →Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Multivariate feature analysis of early-stage laryngeal cancer serum components using surface-enhanced Raman spectroscopy
Laryngeal cancer is a common head-and-neck malignant tumor with geographically variable incidence. Its lack of specific early clinical symptoms often causes missed diagnosis, leading to most cases being identified at intermediate/advanced stages and thus poor treatment efficacy and prognosis. Early, accurate screening for high-risk populations is therefore critical to improving survival. This study systematically investigates the clinical application value of surface-enhanced Raman spectroscopy (SERS) combined with deep learning models for serum screening in early laryngeal cancer. It specifically examines the discriminative performance of one-dimensional convolutional neural network (1D-CNN) models, including CNN-attention with a fused attention mechanism and CNN-baseline without an attention mechanism, and compares these with traditional machine learning models.Serum samples were collected from three groups: Early laryngeal cancer, vocal fold polyps, and healthy controls. SERS was used to obtain serum molecular vibration fingerprint spectra. After spectral preprocessing, five models (CNN-attention, CNN-baseline, SVM, AdaBoost, KNN) were established to discriminate the three groups and compare performance. Leave-one-patient-out cross-validation (LOPO-CV) results indicate that the CNN-attention and CNN-baseline models significantly outperform other models in discrimination efficacy. Specifically, the CNN-attention model achieves a patient-level accuracy of 89.33%, while the CNN-baseline model reaches 88.67%. SVM, KNN, and AdaBoost had inferior performance. This study confirms that serum SERS-based 1D-CNN provides an efficient, accurate method for early laryngeal cancer screening, laying a foundation for clinical translation and offering new technical support for early screening.
7 June 2026
Read appraisal →Clinical chemistry and laboratory medicine
Automation in value-based laboratory medicine: driving precision, capacity, and better outcomes
Laboratory medicine lies at the core of modern healthcare, enabling timely diagnosis, effective patient monitoring, and increasingly personalized therapeutic strategies. Over the past decades, automation has profoundly reshaped the role of clinical laboratories, substantially enhancing their contribution to clinical outcomes, operational efficiency, and the overall sustainability of healthcare systems. More recently, laboratory automation has emerged as a cornerstone of value-based laboratory medicine, representing not merely a technological upgrade but a strategic transformation of laboratory practice aimed at delivering measurable value to patients and healthcare stakeholders. Although automation has long been established in clinical chemistry and immunoassays, its scope is now expanding to molecular diagnostics and mass spectrometry - two disciplines that are central to precision medicine. Looking ahead, the convergence of automation, digitalization, and artificial intelligence is driving the emergence of hyperautomation in laboratory medicine. Within this paradigm, laboratories evolve from isolated testing units into integrated diagnostic hubs, in which results from multiple laboratory disciplines are harmonized and contextualized to effectively support clinical decision-making.
26 May 2026
Read appraisal →ACS applied bio materials
Next-Generation MXene-Hydrogel Hybrids: Frontiers in Biosensing and Environmental Surveillance
Hydrogels have emerged as promising soft materials for applications in sensing, energy storage, and wearable electronics due to their tunable physicochemical properties and intrinsic biocompatibility. The integration of two-dimensional transition metal carbides/nitrides (MXenes) into hydrogel matrices has enabled the development of highly conductive, flexible, and electrochemically active composites for advanced sensing platforms. MXene-hydrogel hybrids exhibit enhanced charge transport, mechanical stability, and interfacial functionality, making them particularly attractive for electrochemical sensing applications. This review provides a comprehensive overview of MXene-hydrogel composites, focusing on their design strategies, synthesis approaches, and electrochemical sensing performance. Despite these advantages, critical challenges remain, including susceptibility of MXenes to oxidation, restacking of nanosheets, limited long-term stability, and difficulties in reproducible and scalable synthesis. These limitations significantly impede their translation into practical and commercial devices. Particular emphasis is placed on identifying current bottlenecks and outlining future research directions, including the development of oxidation-resistant MXenes, advanced hybrid architectures, scalable fabrication techniques, and integration into wearable and point-of-care sensing systems. Addressing these challenges is essential for realizing the full potential of MXene-hydrogel systems in next-generation electrochemical sensing technologies.
19 May 2026
Read appraisal →Clinica chimica acta; international journal of clinical chemistry
Artificial intelligence algorithm optimization and application in patient-based real-time quality control (PBRTQC)
Patient-based real-time quality control (PBRTQC) serves as a vital supplement to quality management in clinical laboratories. Its core principle is to monitor the testing process in real time and continuously through patient test data. As artificial intelligence (AI) technology develops rapidly, AI has provided novel pathways for the innovation of PBRTQC algorithms and drives its transition from a traditional statistics-driven model to intelligent monitoring. This review systematically summarizes the progress of AI-driven PBRTQC algorithm optimization. Meanwhile, it provides a detailed account of the clinical applications of the AI-PBRTQC monitoring platform. These applications encompass timely quality control early warning, homogeneous monitoring across multiple settings, precise quality control in complex clinical settings, anomaly traceability and subsequent correction. In addition, this review offers an in-depth analysis of the challenges that arise during the practical implementation of AI-PBRTQC. These include technical limitations, shortage of professional talents, system compatibility barriers, and lagging standardization and regulation. It also explores future development trends and provides valuable references for the intelligent upgrade of PBRTQC.
16 May 2026
Read appraisal →Journal of chromatography. A
Microfluidics in separation science: Fabrication, applications and intelligent systems
The emergence of advanced, innovative microfluidic devices has reshaped analytical separation methodologies and biomolecular diagnostics by introducing highly integrated, miniaturized systems that enable next-generation fluid manipulation, enhanced automation, precision, cost efficiency, and high-throughput capabilities. Advances in fabrication methods, such as soft lithography, photolithography, and 3D printing, have enabled the production of customizable microfluidic platforms from materials such as PDMS, glass, and thermoplastics. These microfluidic systems support a range of analytical functionalities of the technologies, including gas chromatography (GC), high-performance liquid chromatography (HPLC), and electrophoresis, delivering rapid, high-resolution analyses with minimal sample and reagent requirements, quicker analysis, and enhanced uniformity and stability, facilitating high productivity screening, improved sensitivity and portability. Integration of micro-GC systems enables rapid, portable detection of volatile compounds, while microfluidic HPLC and electrophoresis provide precise biomolecular separations and real-time diagnostics. Recent advancements in artificial intelligence (AI) have now brought significant innovations in microfluidic devices, resulting in a new era of intelligent analytical separations, where machine learning algorithms and neural networks are applicable not only for data processing and pattern recognition but also for real-time optimization, anomaly detection, innovative read-out systems, and autonomous system control. The fusion of AI and microfluidics has enabled new approaches to dynamic experimentation, reshaping traditional microfluidics and personalized diagnostics and health care systems, where rapid, reliable analysis is paramount. In this review article, we comprehensively explain the progressive convergence of microfluidics and AI across various electrophoretic and chromatographic analytical separation techniques, elaborating on the latest technological innovations, fundamental fabrication strategies, and emergent applications. The article also presents a forward-looking perspective on how AI-enabled microfluidics enhances automation, scalability, and application-specific customization, which could shape the future of intelligent analytical separation systems.
12 May 2026
Read appraisal →Analytical chemistry
Quantitative Analysis of Multiple Serum Tumor Biomarkers by an Interpretable Stacked Ensemble Model
Surface-enhanced Raman spectroscopy (SERS) offers exceptional sensitivity and specificity for biomolecular detection, particularly in analyzing serum tumor biomarkers. However, its application is hindered by spectral complexity and matrix interference. Although machine learning (ML) has shown promise in SERS data analysis, existing quantitative models for biomarker detection lack generalizability and interpretability. To address these challenges, we developed an interpretable stacked ensemble model (ISEM) that integrates feature selection, ensemble learning, and explainable artificial intelligence (XAI) for quantifying 12 serum tumor biomarkers. To ensure reliable quantitative analysis, we validated the acquired SERS data using uniform and reproducible Au nanoparticle substrates. Preprocessing, least absolute shrinkage, and selection operator (LASSO) feature selection were employed to establish a foundation for accurate quantification. Subsequently, 12 serum tumor biomarkers were quantified, demonstrating the superior performance of ISEM, which achieved a high predictive accuracy, with R2 values exceeding 0.9 for all biomarkers. Crucially, based on the quantitative results, we provided molecular-level interpretability for structure-spectrum correlations using Shapley additive explanations (SHAP), revealing how glycosylation reactions, matrix interference, and spectral overlap influence prediction accuracy. Furthermore, the capability of ISEM to quantify multiple biomarkers in unseen samples was confirmed through validation of its generalizability. Our study establishes an ensemble-driven, interpretable framework for quantitative biomarker analysis in a complex biological matrix, demonstrating significant potential for early cancer diagnosis and screening.
11 May 2026
Read appraisal →Analytical chemistry
Precise Artificial Intelligence Nanomotor Tracking-Powered Amplification-Free Digital MicroRNA Sensing
Digital assays have become a state-of-the-art technology for the sensing of nucleic acid molecules. Nevertheless, most of the prevalent digital nucleic acid assays heavily rely on sealed microchambers and sophisticated nucleic acid amplification strategies to achieve an end-point digital signal readout. Herein, we proposed an artificial intelligence (AI)-facilitated, digital fluorescence nanomotor tracking-powered assay (AI-dFNA) for one-step, amplification-free, and highly sensitive detection of microRNAs (miRNAs). In this design, even a single target miRNA-templated amplification-free ligation can introduce a sufficient driving force on the surface of a fully open fluorescence nanoparticle to cause remarkable motion behaviors. With the assistance of a self-developed AI algorithm, clear discrimination of positive and negative fluorescent nanomotors can be achieved, enabling real-time digital miRNA sensing. The AI-dFNA avoids the use of enclosed microchambers and intricate signal amplification techniques, which could even identify varying fluorescence colors of nanomotors to allow for the analysis of different kinds of miRNA molecules, pioneering a new direction toward next-generation digital nucleic acid sensing.
11 May 2026
Read appraisal →Annals of laboratory medicine
Evaluation of the Performance of Advanced Large Language Models in Laboratory Medicine Using Residency Examinations
BACKGROUND: Recent advancements in large language models (LLMs) have accelerated their integration into clinical domains, including laboratory medicine. The performance of LLMs in answering board-level laboratory medicine questions has not been comprehensively evaluated. Given the importance of diagnostic accuracy in this field, rigorous and objective evaluations of LLM capabilities are essential. METHODS: We assessed 12 LLMs from OpenAI, Anthropic, and Google using 320 Korean Residency Examination questions (2021-2024) spanning six laboratory medicine subspecialties. Standardized prompts were provided via their application programming interfaces under deterministic settings (temperature=0). Questions were administered thrice to assess response reproducibility. Outputs were compared with validated answers and analyzed for accuracy, reasoning quality, and error typology. RESULTS: Google's Gemini 2.0 Pro achieved the highest accuracy (80.0%), followed by OpenAI's GPT-4.5 (77.2%) and Anthropic's Claude 3.7 Sonnet (74.1%). Accuracy decreased as the difficulty of questions increased (78.0% for easy vs. 45.1% for challenging). Subspecialty performance varied. Al models underperformed on questions on transfusion medicine (mean accuracy: 38.8%), primarily because of limitations in domain-specific and regional knowledge representations. Incorrect answers primarily resulted from reasoning errors. Reproducibility exceeded 95% for most models; however, some residual non-determinism appeared even with greedy decoding (temperature=0). CONCLUSIONS: LLMs demonstrated substantial potential for integration into laboratory medicine, particularly in clinical chemistry and immunology. Performance inconsistencies (particularly for high-difficulty questions) and knowledge gaps (notably for transfusion medicine) highlight the necessity for further development-potentially including domain-specific fine-tuning and retrieval-augmented generation integration-and robust expert oversight before clinical application.
6 May 2026
Read appraisal →Analytical methods : advancing methods and applications
Beyond identification: inferring physical activity from fingerprint lipids using machine learning.
Fingerprints are a widely recognized form of forensic evidence, valued for their ability to link individuals with specific locations. Traditional fingerprint analysis relies on optical imaging to identify a match in a fingerprint database; however, where no match is found, the evidential value of a latent print is limited. Here, we present the first study to integrate matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) with supervised machine learning to infer physical activity from fingerprint chemistry, expanding the utility of fingerprints beyond identification alone. Physical activity labels were derived from a validated questionnaire and converted into binary classes. Supervised machine learning algorithms were trained on the lipid features and evaluated against the survey-derived labels. The top-performing models were an ensemble algorithm based on multiple decision trees and a neural network, which classified physical activity with accuracies of 75 ± 8% and 73 ± 7%, respectively. These results demonstrate that fingerprint lipid chemistry encodes biologically meaningful information related to physical activity and establish a new approach for extracting lifestyle and behavioral indicators from trace evidence, with potential applications in forensic investigations and noninvasive fingerprint-based assessments in medicine.
4 May 2026
Read appraisal →ACS sensors
Nanobiosensors and Artificial Intelligence Strategies for Glycan Profiling in Cancer Progression: A Critical Review
Glycans are complex carbohydrates that are integral to cancer progression and tumor biology. Conventional methods face challenges due to the structural diversity and low abundance of glycans, limiting the detection of cancer-associated glycan signatures. This bottleneck hampers the accurate monitoring of cancer progression and the development of effective diagnostic tools. Nanobiosensor-enabled glycan profiling overcomes conventional limitations by providing highly sensitive detection of low-abundance glycans and precise discrimination of similar forms through nanoscale electrochemical or optical signal amplification and selective biorecognition. This review evaluates emerging nanobiosensors, including optical modalities such as surface plasmon resonance, localized surface plasmon resonance, surface-enhanced Raman scattering, fluorescence, and colorimetric nanobiosensors. It also covers electrochemical systems, including impedimetric, voltammetric, and amperometric sensors that use nanomaterials to enhance the sensitivity, specificity, and reliability of glycan detection. After a critical evaluation of nanobiosensors, this review highlights their applications for glycan analysis in cancer progression and potential of miniaturized point-of-care platforms. In parallel, this review highlights emerging artificial intelligence (AI) and machine learning (ML) approaches that are being explored to analyse complex biosensor outputs. By enabling multivariate pattern recognition, dimensionality reduction, and nonlinear modeling of biosensor responses, these computational frameworks may facilitate the extraction of diagnostically relevant signatures from high-dimensional optical and electrochemical datasets. The review identifies challenges in standardization and clinical scalability of nanobiosensors and explores future directions, including AI integration to advance early cancer detection and personalized patient management.
27 Apr 2026
Read appraisal →ACS sensors
Wearable Multimodal Detection System for Real-Time In Situ Analysis of Exhaled Breath Condensate
Exhaled breath condensate (EBC) analysis, a promising noninvasive respiratory monitoring method, has emerged as a pivotal technique for assessing the health status of patients with respiratory disorders and is widely used in clinical research and daily health management. However, conventional analytical methods face challenges in real-time in situ detection of physicochemical indicators and active EBC collection in a power-free way. Herein, a wearable multimodal detection system (WMDS) with efficient collection and real-time analysis of EBC is developed. Specifically, the WMDS consists of a bio-inspired collector, an electrochemical sensor (EBC analysis), a respiratory sensor (humidity and respiratory rate), a temperature sensor, and a flexible printed circuit board. The miniature-sized collector with a cactus spine-like structure can actively harvest 4.1 μL of EBC within 1 min without power consumption. Leveraging self-developed sensors and wireless data transmission circuitry, the WMDS enables real-time in situ monitoring of multimodal EBC analytes (hydrogen peroxide, nitrite, urea) and respiratory parameters (temperature, humidity, and rate). Remarkably, the WMDS exhibits dual-range detection capability covering both physiological and pathological conditions: the low-concentration range of 0-500 μmol/L is applicable for routine health monitoring and early disease screening, with detection limits (LODs) of 0.209, 0.155, and 0.573 μmol/L and sensitivities of 2.7 × 10-2, 3.4 × 10-2, and 9.0 × 10-3 μA/(μmol/L) for EBC analytes; the high-concentration range exceeding 500 μmol/L is designed for severe pathological condition detection, where LODs and sensitivities are 302.2, 278.7, 325.2 μmol/L and 1.9 × 10-2, 1.3 × 10-2, 3.9 × 10-3nA/(μmol/L) respectively. As a proof-of-concept, the WMDS is applied to on-body respiratory monitoring, validating its potential application in real-time in situ health monitoring.
26 Apr 2026
Read appraisal →ACS sensors
Self-Correction of pH-Induced Signal Variations in Methylene Blue-Labeled Aptamer Electrochemical Biosensors: Wearable Cortisol Detection in Sweat
Common electrochemical aptamer-based (E-AB) biosensors employing methylene blue (MB) redox reporters suffer from significant pH-induced signal variations. Consequently, dynamic pH fluctuations in biofluids, such as in sweat, can greatly distort the measured signal and lead to inaccurate target quantification. Here, we introduce a self-correction strategy that enables accurate analyte quantification by compensating for pH-induced signal variations in MB-based aptasensors using the Nernstian shift of the MB peak potential. The highly reproducible reversible pH dependence of the MB peak potential enables real-time monitoring of the sample pH that provides a continuously synchronized built-in compensation of the pH interference, thereby eliminating the need for external pH sensors or additional measurement steps. This precise yet simple and effective self-correction of pH effects ensures that the Square Wave Voltammetry (SWV) signal reliably reflects real-time analyte variations, as demonstrated using a cortisol MB-labeled E-AB biosensor in both in vitro and on-body settings. In vitro measurements in artificial sweat across pH 5.5-7.5 showed excellent correlation with real-time pH and cortisol changes, confirming the reliability of the peak potential-based correction. On-body measurements using an epidermal wearable patch showed cortisol changes in sweat that would have been missed without this signal-correction method. This approach is broadly applicable to other MB-labeled E-AB biosensors and biofluids, providing a robust strategy for continuous on-body monitoring.
26 Apr 2026
Read appraisal →ACS sensors
Vertically Oriented Assembly of a Few-Layer Ti3C2Tx Grid by Electric Field Inducing and Freeze Drying for Wearable Levodopa Sensing in Sweat
Compared with the parallel assembly, the vertical assembly of MXene nanosheets on the substrate exhibits open three-dimensional porous structure, which is beneficial to the construction of high-performance electrodes. Herein, the vertically oriented assembly of Ti3C2Tx nanosheets is proposed by a novel preparation process of electric field inducing and freeze drying, resulting in the Ti3C2Tx grid with thin-wall and open porous structure. First, the electric field induces the formation of Ti3C2Tx hydrogel layer on the substrate. Second, freeze drying causes the growth of ice crystals for squeezing few-layer Ti3C2Tx nanosheets to stand up. More importantly, such a Ti3C2Tx grid with large surface area and intrinsic conductivity can effectively load electrocatalysts, resulting in the Au-Ti3C2Tx grid as a high-performance working electrode for the electrochemical detection of levodopa. The main parameters include 1-30 μM and 30-160 μM two-stage linear ranges and 37 nM detection limit. The structural advantages of Ti3C2Tx grid-based electrode materials are their three-dimensional conductive network and interconnected porous structure, which contribute to the penetration of the electrolyte, the diffusion of the analyte, and the electrocatalytic reaction. Furthermore, the self-adapting shape of the Ti3C2Tx grid leads to the fan-shaped Au-Ti3C2Tx grid collaborated with a self-designed leaf-like microfluidic. Ultimately, a wearable levodopa sensor composed of Au-Ti3C2Tx grid, microfluidic, flexible electronic system, and mobile phone client is established for detecting levodopa in human sweat, which is expected to help Parkinson's patients control the drug dosage. Our work proposes the vertically assembled Ti3C2Tx nanosheet loaded noble metal electrocatalysts to develop high-performance electrodes for electrochemical sensing.
26 Apr 2026
Read appraisal →ACS applied materials & interfaces
Flexible, Scalable SERS Substrate with High Sensitivity and Stability for On-Site Detection and Wearable Applications
A key challenge in the commercialization of surface-enhanced Raman scattering (SERS) technology is the scalable and controllable preparation of substrates that simultaneously achieve high sensitivity, uniform signal distribution, excellent reproducibility, and long-term stability. In this study, we report a flexible SERS substrate based on a porous, high-surface-energy nanofilm, designed to serve as a spatially confined template for the uniform growth of silver nanoparticles (AgNPs). This substrate exhibits outstanding SERS performance with an enhancement factor (EF) of 2.05 × 1010 and a detection limit (DL) as low as 1.74 × 10-15 M for rhodamine 6G (R6G). The preparation process is mild, highly reproducible (RSD = 8.53% over 60 consecutive batches), and scalable to 25 cm2 areas. Moreover, the substrate maintains its SERS activity for over three months within a vacuum bag at room temperature. Owing to its excellent adhesion and good flexibility, the substrate can adhere effectively to complex surfaces. The substrate was successfully applied to sensitive in situ detection of trace thiram residues on grape surfaces, achieving a detection limit (DL) of 8.51 × 10-10 g cm-2. Additionally, a wearable sensor was developed based on the substrate and used for the noninvasive monitoring of uric acid levels in human sweat, with a DL of 2.95 × 10-7 M. This work provides a practical strategy for constructing high-performance, scalable, and flexible SERS substrates, advancing their potential for real-time, on-site analysis.
23 Apr 2026
Read appraisal →The Analyst
Recent progress in wearable electrochemical sensors based on MXene-conductive hydrogels
MXene-based conductive hydrogels (MCHs) have emerged as highly promising materials for next-generation wearable electrochemical sensors, owing to their exceptional electrical conductivity, mechanical flexibility, and biocompatibility. This review provides a comprehensive and up-to-date overview of recent advances in the development of wearable sensors that incorporate Ti3C2Tx MXene hydrogels for the detection of a wide range of analytes in various biofluids. Diverse hydrogel matrices have been integrated with MXenes to fabricate highly sensitive platforms capable of monitoring key biomarkers such as glucose, dopamine, uric acid, lactate, norepinephrine, sodium, creatinine, and pH. These sensors have been successfully deployed at various locations in the body, including the forearm, chest, wrist, and head, using flexible formats such as skin patches, microfluidic devices, pantyliners, wearable caps, and attachable body accessories. Notably, several configurations demonstrate ultralow detection limits, reaching the nanomolar level, enabling real-time, noninvasive analysis of sweat, urine, and other physiological fluids. The wide range of functional additives and customizable design approaches underscores the modularity and tunability of MCH-based systems for specific applications. This review critically evaluates the design principles, sensing mechanisms, performance metrics, and practical limitations of current MCH-based wearable platforms, providing insights that can guide future innovations in smart wearable healthcare technologies.
23 Apr 2026
Read appraisal →Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
SERS-based deep learning approach for early detection of gestational diabetes mellitus
Early and precise diagnosis of gestational diabetes mellitus (GDM) is crucial for improving maternal and neonatal outcomes and reducing the risk of adverse pregnancy events. However, current clinical screening methods for GDM still exhibit limitations in detection speed, sensitivity and convenience, making it difficult to meet the clinical demand for rapid early-pregnancy screening. To address this, we propose a novel strategy for early GDM diagnosis based on surface-enhanced Raman spectroscopy (SERS) combined with deep learning, aiming to achieve rapid and accurate early screening. Characteristic SERS spectra of serum were obtained using a substrate based on silver nanoparticles (Ag NPs). A fused PCA-CNN model integrating principal component analysis (PCA) for dimensionality reduction and a one-dimensional convolutional neural network (1D-CNN) for feature learning was developed. The PCA-CNN model effectively extracts potential biomarker features from serum SERS spectra, achieving a diagnostic accuracy of 93.7%, with sensitivity and specificity of 0.95 and 0.93, respectively. Moreover, the entire detection process can be completed within 30 min, requires about 2.5 μL of serum per sample, and involves minimal preprocessing, highlighting both efficiency and practicality. This study provides a novel method for early GDM screening that combines high diagnostic performance with clinical applicability, offering promising technical support for early intervention and clinical management of GDM.
16 Apr 2026
Read appraisal →Analytical chemistry
Multicomponent Simultaneous Identification Network (MSINet): An Advanced Deep Learning Model for Boosting Multiplex SERS Detection in Untreated Real Samples
Surface-enhanced Raman spectroscopy (SERS) represents unique advantages for on-site analysis due to its high sensitivity, high specificity, and applicability to gaseous, liquid, and solid samples. However, the complex and unknown components in matrices, as well as competitive adsorptions between targets, will cause signal fluctuation and spectral overlap, which make it challenging to detect multiple targets in untreated real samples. Herein, we designed a deep learning model named the multicomponent simultaneous identification network (MSINet) for analyzing the SERS spectra of untreated samples and output qualitative and content evaluation results of multiple inside targets. The hierarchical feature extraction and multiple feature integration made MSINet robust to signal fluctuations and spectral overlaps while requiring only a small amount of training to quantify target contents in any samples due to its threshold judgment rule. Using the MSINet-assisted SERS detection scheme, the presence and content levels of multiple UV absorbers in seawater, food additives in ready-to-drink cocktails, and biomarkers in human serum could all be determined with ≥0.90 accuracy when facing testing databases. But in contrast, the accuracy in evaluating the target contents in the above untreated samples by the standard curve method was as low as 0.05. The powerful ability to process spectral data, an easy-to-use graphical user interface, and "zero" extra cost make MSINet a universal tool to assist arbitrary SERS sensors in performing high-throughput detection of real samples, leading to great potential in on-site analysis.
8 Apr 2026
Read appraisal →Clinical chemistry
Towards Clinical Integration of Deep Learning-Based Classification of Urinary Sediment Particles from Digital Microscopy Images: A Prospective Study
BACKGROUND: Urinalysis is a standard clinical test that includes the microscopic examination of urinary sediment to identify formed elements. Manual evaluation by laboratory technicians is time-intensive and subject to human error. Automated analysis using digital microscopy images presents a potential alternative. This study evaluates the integration of a deep learning approach to automatically classify urinary sediment images in the clinical laboratory, including independent prospective validation of its performance. METHODS: An annotated data set comprising 13 classes of urinary sediment elements was created from a database of Sysmex UD-10 digital microscope images. An EfficientNet-based model was trained and tested across three experimental scenarios to evaluate the effects of data collection strategies on performance. Uncertainty calibration was examined. The model's robustness and interpretability were examined using gradient-weighted class activation mapping (Grad-CAM) to visualize influential image regions and t-distributed stochastic neighbor embedding (t-SNE) to analyze learned feature embeddings. Lastly, a graphical user interface was developed for a prospective evaluation in the laboratory. RESULTS: The model achieved approximately 97% overall accuracy on the test set. Experiments revealed sensitivity to data set variability, suggesting that performance may improve by integrating additional training examples. Confidence scores aligned with accuracy, and interpretability analyses showed that the model focused on relevant image regions and learned embeddings demonstrated clear class separation. In the prospective evaluation, top 1 and top 3 accuracies decreased to approximately 78% and 92%, respectively. CONCLUSIONS: Our results indicate that a lightweight deep learning model can achieve high performance in classifying urine particles. Analysis of discrepancies between retrospective and prospective evaluations provides important insights toward reliable clinical application.
4 Apr 2026
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