Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 35 appraisals
ACS biomaterials science & engineering
A Review of Biomass-Derived Nanomaterials as Functional Interfaces for Advanced Electrochemical Hormone Detection
The study of high-performance sensing platforms is essential to achieve a high sensitivity for hormonal analytes, short response times, and cost-effectiveness. Nevertheless, standard analytical procedures, such as high-performance liquid chromatography, inductively coupled plasma mass spectrometry, and gas chromatography mass spectrometry (HPLC, ICP-MS, and GC-MS), are rather limited because their sensitivity is low at pM/nM concentrations, and the costs of these processes are too high. Moreover, modern sensor technology can be ill-equipped to selectively respond to intricate biological sample types, and there is no single standard used to quantify sensor signals. This review addresses these knowledge gaps by providing a comprehensive discussion on the role of biomass-derived materials as useful interfaces in electrochemical hormone sensing systems. To achieve improved results, we critically investigated the recent advances in the electrode modification process through biomass-based nanomaterials to promote electron transfer kinetics and interfacial engineering. The present article also discusses contemporary issues such as problems with stability, reproducibility, and selectivity in complex matrices. Furthermore, we highlighted some of the new approaches, such as the development of artificial intelligence, Internet of Things, and wearable point-of-care devices, to create the next generation hormone sensors. Overall, this current review is a comprehensive overview of biomass-derived materials, which have the prospect of enhancing surface modification at the advanced phase, AI integration, and the development of point-of-care diagnostics.
14 July 2026
Read appraisal →Analytica chimica acta
Rapid detection of drug-resistant leukemia cell using an optofluidic chip and machine learning
BACKGROUND: Rapid detection of drug-resistant leukemia played a crucial role in formulating appropriate treatment plans for patients and improving their prognosis. In this research, an integrated optofluidic platform was developed to detect and analyze leukemia and drug-resistant leukemia cell. The detection technique was designed by embedding optical fibers coupled with photosensors and a laser source into optofluidic chip. The scattered light signals were detected when the cells pass through the detecting area. RESULTS: The platform was first validated by classifying 1 μm polystyrene microparticles and 1 μm polystyrene microparticles coated with spherical 10 nm Fe3O4 nanoparticles. After validation, the method was applied to classify leukemia and drug-resistant leukemia cells by injecting the testing sample and obtaining. The SVM classifier demonstrated the highest classification accuracy of 91.1% compared with LR, RF, and KNN classifiers for analyzing leukemia cells and drug-resistant leukemia cells. The proposed method can perform detection within 10 min with a total experimental timeframe of 20 min. SIGNIFICANCE: The presented results demonstrate the feasibility of applying microfluidics and machine learning approaches to detect and classify biological entities with slight variations based on scattered light signals. This platform holds significant potential for clinical diagnostics, offering a rapid, cost-effective, and efficient method for detecting drug-resistant leukemia cells, potentially aiding in personalized treatment strategies.
16 June 2026
Read appraisal →Blood advances
FDG-PET medullary total tumor volume highlights high-risk patients with newly diagnosed multiple myeloma in CASSIOPEIA trial.
This study aimed to assess the prognostic value of medullary total metabolic tumor volume (mTMTV) derived from fluorodeoxyglucose-positron emission tomography/computed tomography ([18F]FDG-PET/CT) compared with conventional PET-derived features and biological/chromosomal abnormalities in patients with newly diagnosed multiple myeloma (NDMM) treated with daratumumab for induction/consolidation and/or maintenance and enrolled in CASSIOPET, a companion study of CASSIOPEIA, with long-term follow-up. Automated bone/liver CT-based segmentation were applied to the baseline [18F]FDG-PET images, with mTMTV being defined using the median liver background as the cut-off, including focal lesions and diffuse bone marrow (BM) involvement. Both univariate/multivariate Cox and machine learning (ML)-based survival models were performed. A total of 195 patients were included, 81% of them PET-positive. Multivariate analysis demonstrated independent prognostic value of mTMTV for progression-free survival (PFS) (P< .001) and overall survival (OS) (P< .001), complementary to Revised International Staging System (R-ISS) (P = .008 and P< .001, respectively). The ML model confirmed these findings, achieving concordance index of 0.609 and 0.659 and identifying mTMTV as the most informative feature for PFS and OS. Adding R-ISS, BM maximum standardized uptake value (SUVmax) and anemia to mTMTV accounted for >60% of the ML model explanation for PFS and adding R-ISS, the number of focal lesions and BM SUVmax for >60% of the model for OS. Combining R-ISS and mTMTV enabled the creation of 2 new-risk subgroups. In conclusion, this prospective study demonstrated the prognostic relevance of [18F]FDG-PET/CT-based parameters in the initial workup of patients with NDMM in the era of anti-CD38-based therapy. mTMTV was found to have strong independent prognostic value, complementary to R-ISS and refining risk stratification. This trial was registered at www.clinicaltrials.gov as #NCT02541383.
9 June 2026
Read appraisal →Gene
Utilization of next generation sequencing from glioma patient liquid biopsy to determine survival prognostics: A systematic review
Glioma is a malignant cancer that affects the central nervous system. Early detection of glioma is still difficult due to the hard-to-reach location of the cancer. The use of next generation sequencing is one of the new developments that can be used for diagnosis, however the use of this modality for prognosis is still rarely discussed. This systematic review aims to determine the benefits of using next generation sequencing (NGS) on the assessment of ctDNA or cfDNA concentration, progression free survival (PFS), and overall survival (OS). This systematic review was made using data from six databases, namely PubMed, PMC, ProQuest, Google Schoolar, ScienceDirect, and SCOPUS, and was compiled based on guidelines from PRISMA. Fourteen studies were included in this systematic review with a total of 663 glioma patients. The results of ctDNA measurements generated through the NGS method with liquid biopsy samples show that the higher the grading and sampling when the tumor progresses can increase the ctDNA value. When assessing ctDNA concentrations at baseline when samples were taken, most studies showed a worse prognosis for PFS and OS survival time. However, the grouping of patients in the current studies is still highly variable so further studies with standardized ctDNA cut off values are needed for more precise determination of prognosis.
7 June 2026
Read appraisal →Medicine
Prevalence of GJB2 gene mutations in nonsyndromic hearing impairments: A systematic review and meta-analysis
BACKGROUND: Nonsyndromic hearing impairment (NSHI) is the most common type of inherited hearing loss. Mutations in GJB2, which encodes connexin 26, are a major cause worldwide, but their prevalence and variant distribution vary across populations. To systematically evaluate the global prevalence of GJB2 mutations and 5 hotspot variants (c.35delG, c.176_191del16, c.235delC, c.299_300delAT, and c.109G > A) in NSHI patients, and assess their diagnostic relevance. METHODS: A meta-analysis was conducted on studies published between January 2019 and June 2024 retrieved from PubMed, Scopus, Web of Science, and Cochrane Library. Only high-quality studies (Newcastle-Ottawa Scale ≥ 7) with molecular diagnostic data were included. Pooled prevalence, 95% confidence intervals, and heterogeneity (I2) were calculated. Subgroup and meta-regression analyses were performed according to geographic region. RESULTS: Fifty-one studies from 24 countries covering 18,968 patients with NSHI were included. The overall prevalence of GJB2 mutations was 25.9% (95% confidence intervals: 21.7-30.1%; I2 = 96%). The variant-specific rates included c.235delC (15.2%), c.35delG (12.0%), c.109G > A (7.6%), c.299_300delAT (3.1%), and c.176_191del16 (0.7%). The highest prevalence occurred in the Middle East (38.5%), whereas Africa had the lowest (11.1%). CONCLUSION: GJB2 mutations show marked geographic and ethnic variability in populations with NSHI. These findings support region-specific genetic screening to improve the early diagnosis, counseling, and targeted interventions for hereditary hearing loss.
31 May 2026
Read appraisal →Clinical chemistry and laboratory medicine
T-cell receptor beta constant 1 flow cytometry for diagnosing clonality in mature T-cell neoplasms: a systematic review and meta-analysis of diagnostic accuracy
INTRODUCTION: Accurate assessment of T-cell clonality is key for diagnosing mature T-cell neoplasms. TRBC1-based flow cytometry provides a rapid, robust, and cost-efficient approach. This systematic review and meta-analysis assessed the diagnostic accuracy of TRBC1 flow cytometry (TRBC1-FC) for detecting T-cell clonality in mature T-cell neoplasms. CONTENT: We systematically searched Scopus, PubMed (MEDLINE), and Google Scholar for articles on TRBC1-FC diagnostic accuracy published up to July 1, 2025. Pooled sensitivity and specificity were estimated, between-study heterogeneity was evaluated and small-study effects were examined. SUMMARY: This meta-analysis included 10 studies. The pooled sensitivity was 97.6 % (95 % CI, 95.1-99.4 %) and specificity 90.7 % (95 % CI, 76.0-99.3 %). The pooled LR+ was 10.9 (95 % CI, 4.1-28.9), LR-was 0.053 (95 % CI, 0.025-0.12), and DOR 339 (95 % CI, 64-1,788). The HSROC curve demonstrated an AUC of 0.974 (partial AUC=0.970), confirming excellent global discriminatory capacity. Between-study heterogeneity was substantial (I2=83.3 %), mainly affecting specificity and DOR, while sensitivity remained highly consistent. No evidence of a threshold effect was found. Deeks' test showed significant small-study effects (p<0.001), and sensitivity analyses identified one influential study whose exclusion markedly reduced heterogeneity. These results confirm the high diagnostic performance and robustness of TRBC1-FC for T-cell clonality assessment. OUTLOOK: TRBC1-FC demonstrates high sensitivity and low LR-, supporting its role as a rule-out test. Variability in specificity, LR+ and DOR, mainly due to small-study effects, advises caution for rule-in use. Standardized protocols and cost-effectiveness analyses are needed before broad clinical adoption.
26 May 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 →Circulation journal : official journal of the Japanese Circulation Society
Association Between Ventricular-Arterial Coupling and C-Reactive Protein in Patients at Risk for Heart Failure
BACKGROUND: Although ventricular-arterial coupling (VAC) and C-reactive protein (CRP) are associated with the pathogenesis of heart failure (HF), their association in patients at risk of HF remains unclear. METHODS AND RESULTS: VAC was assessed by arterial elastance (Ea) to left ventricular endsystolic elastance (Ees) ratio (Ea/Ees) using the noninvasive single-beat method in 112 hypertensive patients. Log-transformed high-sensitivity CRP (hs-CRP) and categorical hs-CRP were associated with Ea/Ees on univariate analysis. Categorical hs-CRP was independently associated with Ea/Ees after adjusting for atherosclerotic risk factors, cardiac function and vascular function (β=0.27, P=0.003). CONCLUSIONS: hs-CRP was independently associated with VAC assessed by Ea/Ees in patients at risk of HF.
25 May 2026
Read appraisal →Medicine
Machine learning identifies a NETs-related four-gene diagnostic signature for abdominal aortic aneurysm
Abdominal aortic aneurysm (AAA) is a chronic degenerative disease characterized by localized aortic dilation and persistent inflammation. While neutrophil extracellular traps (NETs) are increasingly recognized as key drivers of vascular inflammation and aneurysm progression, the transcriptomic landscape of NETs-related genes (NRGs) in AAA remains inadequately characterized. This study aimed to identify reliable diagnostic biomarkers and explore the immune heterogeneity of AAA to facilitate early risk stratification. We integrated transcriptomic datasets from the Gene Expression Omnibus to elucidate the role of dysregulated NRGs. A comprehensive bioinformatics pipeline was employed, combining weighted gene co-expression network analysis with differential expression profiling to screen for AAA-specific NRGs. Rigorous feature selection was conducted through the intersection of 3 machine learning algorithms - least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest - to derive a diagnostic signature. The model was constructed using the GSE232911 cohort and validated in an independent external cohort. A robust 4-gene diagnostic signature comprising CXCR4, GZMB, ITGA6, and CD47 was identified. This signature demonstrated a favorable diagnostic performance, achieving an area under the curve of 0.920 in the training cohort. The model maintained consistent discriminatory ability in the external validation cohort, primarily driven by the high discriminative ability of CXCR4 and GZMB. Consensus clustering based on these hub genes revealed 2 distinct molecular subtypes, with Cluster 2 characterized by significant enrichment of neutrophils and innate immune pathways, suggesting intense NETosis activity. Furthermore, drug prediction analyses identified candidate therapeutic compounds, including Eugenol and Tretinoin, offering potential avenues for targeting the NETs-associated molecular landscape. Our findings underscore the pivotal role of NETs-mediated inflammation in AAA pathogenesis and validate a robust 4-gene signature for early diagnosis. By delineating immune-related molecular subtypes and identifying potential drug candidates, this study provides a foundational framework for precision risk stratification and the development of targeted nonsurgical therapies for aneurysm management.
25 May 2026
Read appraisal →ACS sensors
Diagnosis of Primary Open-Angle Glaucoma Using Spectral Profiling of Aqueous Humor-Derived Exosomes
Primary open-angle glaucoma (POAG) is one of the most common neurodegenerative diseases that cause irreversible optic nerve damage. POAG diagnosis requires multimodal assessments; however, current methods like intraocular pressure (IOP) and optical coherence tomography (OCT) imaging suffer from low sensitivity and inter-patient variability, respectively. Liquid biopsy provides objective molecular signatures, offering a robust alternative to overcome limitations of conventional clinical hallmarks. Here, we present an efficient and highly sensitive diagnostic platform operating on spectral profiles from aqueous humor (AH)-derived exosomes. Following morphological and compositional validation of exosome presence in AH samples, antibody-functionalized substrates were fabricated for selective capture. Immobilized AH-derived exosomes are then coated with gold nanoparticles to generate molecular fingerprint Raman signals. A total of 7600 spectra are acquired and used to train and evaluate the convolutional neural network (CNN) model for binary classification between healthy controls and glaucoma patients. The trained CNN model achieved an AUC of 0.96 and a prediction accuracy of 91%. The system enables rapid, individualized diagnosis, overcoming sample volume limitations via integrated immunoassay-based isolation and artificial intelligence (AI)-driven classification, highlighting the potential of ocular fluid-derived EVs in the diagnosis of neurodegenerative diseases.
23 May 2026
Read appraisal →Revista do Instituto de Medicina Tropical de Sao Paulo
Molecular biomarkers associated with ATLL and HAM progression in HTLV-1 infection: a systematic review
Human T-lymphotropic virus type 1 (HTLV-1) infects approximately five to 10 million individuals worldwide, although only a minority develop severe outcomes such as adult T-cell leukemia/lymphoma (ATLL) or HTLV-1-associated myelopathy (HAM). Proviral load (PVL), while widely used, shows limited sensitivity and specificity, reinforcing the need for complementary biomarkers. Omics-based approaches have emerged as promising tools to improve risk prediction. We conducted a systematic review following PRISMA guidelines, searching PubMed, Web of Science, Virtual Health Library (BVS), and CAPES Periodicals databases for studies published between May 2020 and May 2025. Eligible studies included original observational designs investigating genomic, proteomic, and metabolic biomarkers associated with progression to ATLL or HAM. Methodological quality was assessed using tools from the National Heart, Lung, and Blood Institute (NHLBI). In total, 35 studies met the inclusion criteria, most conducted in Brazil, Japan, and Iran. A total of 67 biomarkers were identified: 37 genomic, 27 proteomic, and three metabolic with potential clinical applications in risk stratification, prognosis, and therapeutic monitoring of HTLV-1 infection. PVL remained the most frequently investigated marker but lacked predictive power in isolation. Additional candidates with strong potential included IFN-γ, CXCL10, Neopterin, AnxA1, and sTNFR2. This review highlights the potential of integrated multiparametric panels-combining PVL with omics-derived biomarkers-as a promising strategy to improve risk stratification, prognosis, and therapeutic monitoring in people living with HTLV-1 (PLHTLV-1). However, further longitudinal and clinically validated studies are needed to confirm their applicability and support their translation into early intervention strategies, particularly during the asymptomatic phase.
20 May 2026
Read appraisal →Clinica chimica acta; international journal of clinical chemistry
AI in the Prediction of Hepatic Fibrosis Progression Using Non-Coding RNAs
Hepatic fibrosis is a dynamic and progressive condition that can lead to cirrhosis and hepatocellular carcinoma (HCC) if left untreated. Appropriate assessment of the disease progression of fibrosis is critical for early intervention and individualized treatment regimens. Traditional biopsy techniques are invasive and prone to sampling errors, highlighting the need for less invasive predictive techniques. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as key regulators of hepatic fibrogenesis and as a possible biomarker for disease staging and prognosis. The emergence of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized the comprehensive large-scale analysis of transcriptomic data, enhancing the identification of ncRNA biomarkers and predictive modeling. The AI-based algorithms have been found to be more precise in anticipating fibrosis progression by means of integrating multi-omics data, ncRNA interaction networks, and by improving non-invasive diagnostic tools. This review involves the analysis of AI and ncRNA research in hepatic fibrosis, highlighting recent discoveries, possible challenges, and future opportunities. We address the necessity of standardization of data and clinical validation, as well as discuss the role of AI in identifying biomarkers of ncRNA, predicting the stage of fibrosis and risk stratification. ncRNA analysis with AI has a tremendous potential of transforming the diagnostics and prognostics of hepatic fibrosis, enabling precision hepatology.
17 May 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Circulating Tumor DNA Dynamics and Clinical Outcomes in Patients with Advanced Colorectal Cancer Treated with Cetuximab-Based Induction and Maintenance Treatment
PURPOSE: We investigated whether circulating tumor DNA (ctDNA) changes may be useful to assess clinical outcomes in patients with metastatic colorectal cancer (mCRC) randomized in the TIME-PRODIGE-28 trial comparing biweekly maintenance with cetuximab alone with observation after 4-month fluorouracil, folinic acid, and irinotecan (FOLFIRI) plus cetuximab induction chemotherapy. EXPERIMENTAL DESIGN: ctDNA samples were collected at four time points from baseline until disease progression during the first chemotherapy-free interval and analyzed using next-generation sequencing and methylation marker approaches. Progression-free survival (PFS) and overall survival (OS) from randomization were analyzed according to ctDNA kinetics and EGFR-MAPK pathway alterations. RESULTS: Among 139 randomized patients, 104 (74.8%) had paired samples available. Patients with negative baseline ctDNA remaining negative after 4-month induction chemotherapy had significantly longer PFS from randomization (9.6 months) as compared with patients with a ctDNA decrease of ≥80% (3.4 months) or a ctDNA decrease of <80% (2.1 months; P = 0.013). Patients with EGFR-MAPK pathway alterations identified either in tissue or baseline ctDNA had worse PFS and OS from randomization. Acquired alterations found in 17 of 63 (26.9%) patients at disease progression during the first chemotherapy-free interval were associated with worse OS from reintroduction of the full induction chemotherapy (14.9 vs. 19.4 months; P = 0.025). CONCLUSIONS: Our findings show the prognostic impact of both ctDNA kinetics and EGFR-MAPK pathway alteration dynamics following induction chemotherapy with FOLFIRI-cetuximab in patients with mCRC. Prospective studies evaluating ctDNA-guided treatment strategies are needed to validate the clinical utility of ctDNA monitoring to improve patient selection for first-line treatment de-escalation and anti-EGFR-based maintenance regimens, including treatment adaptation over time.
16 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Integrating liquid biopsies and artificial intelligence for early cancer detection: A systematic review and meta-analysis
INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection. METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated. RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity. CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.
16 May 2026
Read appraisal →Annals of human biology
Machine learning combined with WGCNA reveals potential calcium uptake-associated biomarkers in Parkinson's disease
BACKGROUND: Parkinson's disease (PD) is the second most common neurodegenerative disease, characterised by progressive loss of dopaminergic neurons in the substantia nigra pars compacta. Although previous studies have suggested a correlation between calcium signal disruption and the development of PD, the role of genes associated with store-operated calcium entry (SOCE) in PD remains unclear. AIM: To combine machine learning methods with Weighted Gene Co-expression Network Analysis (WGCNA) to systematically screen and identify SOCE-associated candidate genes and evaluate their potential for assisting in discrimination. SUBJECTS AND METHODS: This study is based on the GSE6613 dataset, using ssGSEA and Weighted Gene Co-expression Network Analysis (WGCNA) to screen for co-expression modules related to SOCE, and combining LASSO, SVM-RFE, and random forest algorithm to identify feature genes. Subsequently, validation was conducted in GSE20163 and GSE22491, and in vitro experiments were carried out in 6-OHDA and MPP+ models of dopaminergic neurons derived from LUHMES. RESULTS: A total of 5 SOCE-associated feature genes (LPCAT3, CLCNKB, TXLNA, etc.) were identified, and their combined model showed moderate discriminative ability in external validation. Immune analysis showed immune dysfunction in PD patients, and in vitro experiments also observed consistent expression trends with patient samples. CONCLUSIONS: In summary, this study identified potential PD feature genes associated with SOCE, providing clues for further exploration of the molecular mechanisms of SOCE-associated calcium signalling in PD.
15 May 2026
Read appraisal →Journal of molecular neuroscience : MN
Development and Validation of a SHAP-Interpretable Machine Learning Model for Stroke Risk Prediction Using Circulating MicroRNA Biomarkers
BACKGROUND/OBJECTIVE: Stroke remains a leading cause of morbidity and mortality worldwide. Circulating microRNAs (miRNAs) have emerged as promising non-invasive biomarkers for cardiovascular disease diagnosis and risk stratification. However, their integration into predictive models remains limited by challenges in feature selection, model robustness, and interpretability. This study aims to develop an interpretable machine learning framework for predicting stroke incidence using serum miRNA signatures. METHODS: We analyzed serum miRNA expression profiles from 1,785 human samples (173 stroke patients and 1,612 non-stroke controls) obtained from the GEO dataset GSE117064. Differential expression analysis was performed to identify significantly dysregulated miRNAs using linear modeling and empirical Bayes moderation. LASSO logistic regression was then applied to select predictive miRNA features. Five supervised machine learning classifiers-logistic regression, support vector machine (SVM), random forest, XGBoost, and k-nearest neighbors-were evaluated using 10-fold cross-validation. The best-performing model (SVM) was further interpreted using Shapley Additive exPlanations (SHAP) to assess individual miRNA contributions to prediction. To validate the robustness and clinical generalizability of the identified signatures, quantitative real-time PCR (qPCR) and Next-Generation Sequencing (NGS) were performed on independent sets of serum samples. The pre-trained SVM model was applied to the NGS data to verify its classification performance in an external cohort. RESULTS: A total of 604 miRNAs were differentially expressed between stroke and non-stroke groups, including 206 downregulated and 398 upregulated candidates. LASSO regression identified 66 non-zero-coefficient miRNAs with potential predictive value. Among the classifiers tested, the SVM model achieved the highest average accuracy of 0.9983 and perfect AUC (1.0000), demonstrating superior performance and stability. SHAP analysis revealed that a subset of miRNAs, including hsa-miR-3648, hsa-miR-1290, and hsa-miR-6765-3p, had the greatest impact on classification outcomes, providing mechanistic insights and enhancing model interpretability. qPCR results preliminarily supported the differential expression patterns of key miRNAs. Furthermore, NGS analysis of an independent validation cohort (n = 10) demonstrated distinct hierarchical clustering of the selected biomarkers. The pre-trained SVM model preliminarily supported its cross-platform validity by classifying these external samples with an overall accuracy of 80% (sensitivity 80%, specificity 80%), suggesting the potential generalizability of the identified miRNA panel across different quantification platforms. CONCLUSIONS: This study demonstrates the utility of integrating differential expression analysis, regularized feature selection, and machine learning with SHAP-based interpretation to identify and validate serum miRNA signatures predictive of stroke. The successful external validation using NGS highlights the robustness of the identified biomarkers and their potential for cross-platform application. The results provide a transparent, data-driven framework for biomarker discovery in clinical risk prediction and support the development of non-invasive diagnostic tools for stroke detection.
14 May 2026
Read appraisal →Annals of medicine
A comprehensive review of regulatory mechanisms of novel enterotoxin genes in Staphylococcus aureus from meat sources
BACKGROUND AND AIMS: Meat-borne Staphylococcus aureus (S. aureus) remains a leading global foodborne pathogen harbouring novel enterotoxin genes (NEGs) encoding superantigenic toxins with conditionally enhanced pathogenicity, representing a critical food safety hazard. This review characterizes NEG features, pathogenic mechanisms, multi-layered regulatory networks, and identifies key research challenges. METHODS: We systematically searched PubMed, Embase and the Web of Science Core Collection to synthesize molecular and multi-omics evidence (ChIP-seq, RNA-seq, CRISPR-Cas9) characterizing NEG classification, pathogenic mechanisms, multi-level regulatory networks (transcriptional, post-translational, environmental), and geographical distribution patterns in meat-derived S. aureus. RESULTS: NEGs comprise superantigenic and tissue-targeting subgroups mediating pathogenicity via cytotoxicity, intestinal microenvironment disruption, and immune evasion. Regulation involves a complex network of Agr/σB/SarA/Rot-mediated transcriptional control, phosphorylation/lactylation modifications and environmental sensing, exhibiting marked geographical divergence. Current limitations include technical resolution constraints, insufficient physiological model fidelity and incomplete regulatory crosstalk elucidation. CONCLUSIONS: Future research should prioritize transcription factor interaction mechanisms, growth-toxin correlation prediction models and multi-omics-based network decipherment. This review provides a foundational framework for NEG research to inform food safety risk assessment and targeted contamination control strategies.
10 May 2026
Read appraisal →Biosensors & bioelectronics
Peptide-responsive photonic hydrogels integrated with deep learning assistance for early MMP-9 detection
Matrix metalloproteinase-9 (MMP-9) is crucial for extracellular matrix remodeling, and its dysregulation is associated with inflammatory diseases and different forms of cancer. Conventional MMP-9 detection methods such as enzyme-linked immunosorbent assay (ELISA) are limited by complexity, expensive equipment, and lengthy antibody incubation times. These limitations prevent their use in point-of-care testing. An MMP-9 responsive photonic crystal (PC/PEG-M9SP) hydrogel has been developed to address these challenges. The hydrogel is synthesized from 4-arm polyethylene glycol-acrylate and an MMP-9 sensitive peptide via Michael-type addition reaction. Upon MMP-9-specific enzymatic cleavage, the hydrogel undergoes a structural reconfiguration, resulting in a distinct color shift. Integrated with a deep learning-based smartphone app, this platform enables both visual and quantitative detection within 10 min, achieving high sensitivity (10.60 nm mL/ng) and a detection limit of 0.62 ng/mL. Validation in complex biological fluids demonstrated strong concordance with ELISA, confirming the analytical reliability of the hydrogel. This system provides a rapid, portable, and cost-effective solution for accurate MMP-9 detection, with strong potential for clinical and point-of-care applications.
5 May 2026
Read appraisal →Journal of proteome research
Stability-Based Machine Learning Identifies a Minimal Two-Protein Serum Signature for Early Silicosis
BACKGROUND: The early diagnosis of silicosis, an irreversible fibrotic lung disease, is challenged by the low sensitivity of current radiological methods in early-stage disease and their susceptibility to interobserver variability. Consequently, a pressing need exists for noninvasive, objective biomarkers to facilitate timely detection and intervention. METHODS: We employed a multistage study design comprising a discovery cohort (57 Stage I silicosis patients, 57 matched controls) and an independent, unmatched validation cohort (40 patients, 40 controls). Serum protein profiles were generated using Olink targeted proteomics. We utilized a rigorous, stability-based machine learning framework, which integrated Lasso, Random Forest, and SVM-RFE algorithms over 100 iterations, to perform feature selection and identify a robust biomarker signature from the discovery cohort. Based on the selected features, a logistic regression model was subsequently constructed, and its performance was evaluated using both internal and external validation. RESULTS: Our discovery strategy identified a two-protein signature comprising IL8 and CCL3. This signature demonstrated excellent diagnostic performance in the discovery cohort, achieving a cross-validation AUC of 0.986 (95% CI: 0.975-1.000). Importantly, the model's robustness was confirmed in the heterogeneous validation cohort, where it achieved an outstanding AUC of 0.973 (95% CI: 0.936-1.000), with 95.0% specificity and 77.5% sensitivity. Bioinformatic analysis revealed that decreased serum levels of IL8 and CCL3 were associated with silicosis, providing novel diagnostic biomarkers and highlighting a complex, paradoxical shift in circulating chemokines during early-stage disease.
4 May 2026
Read appraisal →Journal of chemical information and modeling
TriPDCL: A Tri-Pathway Prototype-Driven Contrastive Learning Framework for Cross-Modality Single-Cell Integration
The rapid development of single-cell sequencing technologies has laid a solid foundation for multiomics data integration. A central objective in multiomics integration is to attenuate cross-omic discrepancies stemming from technical batch effects and inherent biological variability, thereby facilitating a unified latent representation that supports robust downstream analyses. However, the intrinsic high sparsity of single-cell data and pronounced intercellular heterogeneity pose greater challenges for joint analysis. We foreground cellular heterogeneity and present TriPDCL, a prototype-based contrastive learning framework, which not only achieves effective transfer of heterogeneity information through an iterative prototype-learning update mechanism but also, by leveraging learnable prototype centers, enables precise construction of reliable positive-negative sample pairs. It tackles two principal challenges, the alignment of cross-modal heterogeneity and the robust learning, in multiomics analysis. Subsequently, comparative assessments conducted on five data sets in contrast to seven representative methods illustrate the superiority of the proposed approach.
2 May 2026
Read appraisal →PloS one
Biomarkers of mitochondrial dynamics in idiopathic pulmonary fibrosis: Identification and validation through transcriptomic and single-cell analyses
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease that has increasingly been associated with dysregulated mitochondrial quality control and dynamics. However, the molecular mechanisms underlying these alterations remain incompletely understood. This study aimed to systematically identify and validate candidate biomarkers related to mitochondrial dynamics in IPF and to characterize their cell-type specificity and putative regulatory relationships. METHODS: We integrated bulk transcriptomic datasets from the Gene Expression Omnibus (GEO), single-cell RNA sequencing (scRNA-seq) data, and literature-derived mitochondrial dynamics gene sets. Candidate genes were identified through differential expression analysis and consensus clustering, followed by functional enrichment and protein-protein interaction (PPI) network analyses. A total of 101 machine-learning model combinations-including random forest, LASSO, and support vector machine-were constructed to select optimal feature genes. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and further evaluated with artificial neural network (ANN) modeling. Additional analyses included chromosomal localization, immune infiltration profiling, multilayer regulatory network construction (transcription factors, lncRNAs, circRNAs), molecular docking prediction, and single-cell expression and pseudotime trajectory analysis. Key biomarkers were further evaluated by RT-qPCR in an independent clinical cohort. RESULTS: Integrated multi-omics and machine-learning analyses identified CD247, IL7R, and RETN as candidate biomarkers related to mitochondrial dynamics-associated pathways in IPF. Across independent transcriptomic datasets, RETN was upregulated, whereas CD247 and IL7R were downregulated, and each showed diagnostic value (single-gene AUC > 0.7). The ANN model based on these genes achieved encouraging discriminative performance (training AUC = 0.91; validation AUC = 0.82), and expression differences were confirmed by RT-qPCR in a modest independent cohort. Enrichment analyses indicated convergence on spliceosome-related pathways, and regulatory-network analysis highlighted interactions involving transcription factors and non-coding RNAs, including circRNA CDR1as. Molecular docking suggested putative interactions with selected compounds. Single-cell analyses suggested that dysregulation was most evident in monocyte-associated compartments in one publicly available scRNA-seq dataset, and pseudotime analysis indicated dynamic expression patterns, with early transient increases in CD247 and IL7R and progressive elevation of RETN. CONCLUSION: Through multi-omics integration and machine-learning approaches, we identified and preliminarily validated CD247, IL7R, and RETN as candidate biomarkers related to mitochondrial dynamics-associated pathways in IPF. These findings provide transcriptomic and cell-type-specific evidence suggesting potential immune-mitochondrial associations in IPF and may inform future biomarker validation and mechanistic hypothesis generation.
26 Apr 2026
Read appraisal →Clinical chemistry and laboratory medicine
Serum GFAP as a biomarker for progression in multiple sclerosis: assay comparison and a large reference database of healthy controls
OBJECTIVES: Compare Elecsys (Roche) and Simoa (Quanterix) immunoassays for serum glial fibrillary acidic protein (GFAP) using our reference database and Z scores, and evaluate their prognostic value for progression independent of relapse activity (PIRA) in multiple sclerosis (MS). METHODS: Platform correlation was assessed in 612 samples from healthy controls (n=188; median [interquartile range, IQR] age 45.1 [36.4-61.7] years) and people with MS (n=424; 45.3 [35.2-53.9] years). Elecsys values were converted to Z scores via Passing-Bablok-derived regression and validated in fingolimod (n=414), and B-cell depleting therapy (BCDT; n=353) cohorts. Z scores and hazard ratios (HRs) for time-to-PIRA were compared using Cox regression. RESULTS: GFAPSimoa and GFAPElecsys measurements were correlated (r=0.94), with Elecsys values ∼54 % lower (GFAPElecsys, ng/L=2.847 [95 % confidence interval, CI: 1.335 - 4.98] + 0.457 [0.434 - 0.478] * GFAPSimoa, ng/L). In univariable Cox models, GFAPSimoa and GFAPElecsys Z scores were associated with time-to-PIRA in both validation cohorts. In multivariable Cox models, higher GFAPSimoa Z scores were associated with shorter time-to-PIRA in fingolimod cohort (HR: 1.27 [95 % CI 1.08 - 1.50], p=0.0031) and trended toward significance in BCDT (1.18 [0.99 - 1.41, p=0.0693). In contrast, GFAPElecsys Z scores were associated with time-to-PIRA in both cohorts (fingolimod: 1.27 [1.09 - 1.48], p=0.0023; BCDT: (1.19 [1.00 - 1.40], p=0.0487). CONCLUSIONS: Serum GFAP measured by Elecsys shows a comparable association with time-to-PIRA as Simoa, and GFAPSimoa Z scores can be successfully bridged to GFAPElecsys Z scores, supporting Elecsys`s potential for clinical implementation.
25 Apr 2026
Read appraisal →European heart journal
Pre-treatment lysis time of plasma-derived fibrin clots and bleeding in patients on oral anticoagulants for atrial fibrillation in the ARISTOTLE trial
BACKGROUND AND AIMS: Oral anticoagulation reduces stroke risk in patients with atrial fibrillation (AF) but increases bleeding. Longer fibrin clot lysis time has been shown to predict adverse cardiovascular outcomes in acute coronary syndromes. This study explored relationships between fibrin clot lysis time at randomization and clinical outcomes in patients with AF enrolled in the Apixaban for Reduction in Stroke and Other Thromboembolic Events in AF (ARISTOTLE) trial. METHODS: Plasma samples were obtained from anticoagulation-naïve participants, before initiation of study medication (n = 1841). Fibrin clot turbidimetry was performed, and lysis time determined. Associations between lysis time and characteristics, biomarkers, and on-treatment bleeding and cardiovascular events were assessed by lysis time quartile (Q1-4, shortest to longest). RESULTS: A shorter lysis time was associated with being older, male, permanent AF, lower body mass index, estimated glomerular filtration rate and C-reactive protein, and higher N-terminal pro-B-type natriuretic peptide. Major and clinically relevant non-major bleeding was significantly more frequent in lysis time Q1 vs. Q4 [6.3%/yr vs. 2.1%/yr; HR, 2.99 (95% CI, 1.75-5.12); P = .001], including after multifactorial adjustment [HR, 2.61 (1.45-4.69); P = .016]. Those in Q2 and Q3 had intermediate bleeding risk vs. Q4 [HR, 2.21 (1.27-3.87); 2.08 (1.18-3.66) respectively], suggesting a graduated effect. Treatment allocation to apixaban vs. warfarin did not affect the relationship between lysis time and bleeding (interaction-P = .80). There was no significant association between lysis time and a composite of cardiovascular death, stroke, systemic embolism or myocardial infarction. CONCLUSIONS: Shorter pre-treatment fibrin clot lysis time independently predicted higher bleeding risk in patients receiving oral anticoagulation for AF.
24 Apr 2026
Read appraisal →European heart journal
Venous thromboembolism and bleeding in cancer patients: role of inflammatory and cardiac biomarkers.
BACKGROUND AND AIMS: Patients with cancer have increased risk of venous thromboembolism (VTE) and bleeding. Inflammatory and cardiac biomarkers may predict these complications, but their role remains unclear. This study examined associations between two inflammatory-related markers (C-reactive protein and growth differentiation factor-15) and two cardiac markers [N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T (hs-TnT)] with VTE and clinically relevant bleeding in cancer patients. METHODS: A post hoc analysis of the AVERT trial, which evaluated apixaban for VTE prevention in ambulatory cancer patients with a Khorana score of ≥2, was performed. Biomarkers were measured at baseline and 1 month, with C-reactive protein also at 3 months. Fine and Gray regression, accounting for competing risk of death and adjusted for age and advanced cancer, estimated subdistribution hazard ratios (SHRs) for VTE and clinically relevant bleeding. RESULTS: Of 574 patients, 514 provided baseline samples. One- and 3-month samples were available from 454 and 447, and 378 and 364, patients without prior VTE and bleeding events, respectively. Elevated baseline growth differentiation factor-15 was associated with increased VTE risk [SHR 1.36, 95% confidence interval (CI) 1.01-1.84]. N-terminal pro-B-type natriuretic peptide (SHR 1.44, 95% CI 1.08-1.92) and C-reactive protein (SHR 1.38, 95% CI 1.07-1.76) were linked to bleeding risk. Increasing high-sensitivity troponin T from baseline to 1 month was associated with higher VTE risk (SHR 1.89, 95% CI 1.14-3.16). Nomograms were developed to estimate VTE and clinically relevant bleeding risks. CONCLUSIONS: Select inflammatory-related and cardiac markers were associated with VTE and bleeding risks in cancer patients, which can be determined using developed nomograms. Prospective research is needed to confirm these findings.
23 Apr 2026
Read appraisal →PloS one
A hybrid CNN-XLSTM-GRU deep learning model with autoencoder-based feature selection for hypothyroidism diagnosis.
Hypothyroidism, caused by reduced thyroid-hormone production, is often difficult to diagnose because its symptoms are non-specific and overlap with other disorders. We developed FusionNet-CXG, a hybrid deep-learning model that couples CNN, eXtended LSTM, and GRU components for hypothyroidism prediction. Experiments were performed on a public dataset (3,772 records; 30 features). Class imbalance was mitigated using SMOTE-NC, and performance was estimated using 10 repetitions of 5-fold stratified cross-validation, yielding 50 fold-level evaluations to obtain stable results. Across folds, FusionNet-CXG achieved 0.9394 mean accuracy, F1 = 0.906, and AUC-ROC = 0.94, and it exceeded CNN+LSTM and CNN + BiLSTM baselines under the same protocol. The results indicate that combining local feature extraction with recurrent modelling is effective for this task. To support interpretability, SHAP was used to quantify feature influence on the predictions. Future work will focus on external validation using more diverse real-world cohorts and incorporating temporal clinical data to improve clinical relevance.
23 Apr 2026
Read appraisal →International journal of biometeorology
Random forest-based medical geography: geographical distribution of reference values for LP-PLA2 in healthy adults in China
Lipoprotein-associated phospholipase A2 (LP-PLA2), a vascular-specific inflammatory marker, is associated with coronary heart disease (CHD), a highly prevalent condition among middle-aged and older adults. It explores the relationship between environmental factors in environmentscience, geography, statistics and medicine and the reference value of LP-PLA2 in China. A geographic distribution prediction model for Lp-PLA2 medical reference values was developed using optimized machine learning approaches. The support vector machine (SVM) model employed a polynomial kernel function, with hyperparameters tuned via grid search and cross-validation to determine the optimal classifier configuration. Concurrently, the random forest algorithm was applied to mitigate overfitting and perform regression analysis on the dataset. Further considering the influence of geographical factors on the 100 LP-PLA2 medical reference values in city and county units from 2000 to 2024, using 24 geographical factors as independent variables, to fit 2317 LP-PLA2 medical reference values, and then explore the spatial variation characteristics of LP-PLA2 medical reference values. Further spatial interpolation was visualized on the national map. The medical reference value of LP-PLA2 was significantly correlated with longitude, altitude, annual average wind speed, topsoil clay percentage, topsoil reference capacity, topsoil organic matter content, and total convertible amount of topsoil, respectively. The geospatial distribution map shows that the whole value was high in the east and low in the west, with significant differences in longitude. The geographical characteristics of any given region, when analyzed through either the SVM prediction model or the derived geographical distribution map, enable precise determination of region-specific Lp-PLA2 reference values. This establishes an evidence-based foundation for clinical decision-making and research design in healthcare institutions and medical research organizations.
20 Apr 2026
Read appraisal →JMIR research protocols
Integrating Sperm Microscopy, Environmental Exposures, and Lifestyle Factors for Male Fertility Analysis: Protocol for the Nippon Semen and Environmental Exposure Database (N-SEED) Cross-Sectional Study
BACKGROUND: Conventional semen analysis does not fully capture male reproductive potential. The sperm DNA fragmentation index (DFI) may detect latent infertility, although the sperm chromatin structure assay (SCSA) is costly and technically demanding. Image-based analysis of semen microscopy, including artificial intelligence (AI), may enable lower-cost noninvasive assessment. However, progress is limited by a lack of standardized multimodal datasets linking sperm images with the DFI and relevant covariates. OBJECTIVE: This protocol describes the initial phase of the Nippon Semen and Environmental Exposure Database (N-SEED) initiative. The study aims to (1) establish standardized acquisition and quality control procedures for sperm microscopy/video and DFI measurement; (2) evaluate the feasibility and magnitude of associations between predefined image-/video-derived variables and the DFI; and (3) characterize clinical, environmental, physical, and lifestyle factors that may act as candidate correlates or confounders for subsequent image-based fertility research. METHODS: This multi-institutional cross-sectional study centralized clinical assessment and specimen collection at a single site in Japan. The prespecified group-specific enrollment targets were 25 for group 1 (a relatively homogeneous low-risk cohort for DFI quality control) and 100 for group 2 (apparently healthy community-based adult volunteers for exploratory association analyses). Microscopic sperm videos, automated semen parameters, and sperm oxidative stress data were obtained at collection. SCSA-based DFI assays are currently being performed sequentially. Group 2 participants additionally provided fasting blood and early-morning urine samples for endocrine, metabolic, environmental chemical, and elemental assays. They also underwent physical measurements and completed structured questionnaires/interviews on lifestyle, nutrition, sleep, psychological status, sexual function, and collection conditions. The primary outcome measure is the continuous DFI assessed with SCSA. Group 1 data will be used to evaluate intraday, interday, and interfacility variability and to fix flow cytometry gating settings. In groups 1 and 2, predefined image-/video-derived summary variables after standardized preprocessing will be analyzed against the continuous DFI using correlation analyses and simple linear regression to assess whether sperm microscopy data contain information relevant to sperm DNA integrity. Exploratory categorical DFI analyses will use simple logistic regression. Secondary exploratory analyses will examine candidate covariates and data completeness, including sensitivity analyses. RESULTS: The study was registered in the University Hospital Medical Information Network Clinical Trials Registry on January 18, 2026. Between January 18 and February 21, 2026, 124 unique participants were recruited (group 1: n=25; group 2: n=103; both groups: n=4). Sample and data collection were completed on February 21, 2026. Biospecimen assays and primary association analyses are expected to be completed by March 2028. CONCLUSIONS: This initial N-SEED phase will deliver a standardized, quality-controlled multimodal resource linking sperm microscopy/video data with the DFI and broader physiological and environmental measures. Its immediate contribution is not to provide an immediately deployable clinical AI tool but rather to support feasibility assessment, confounder identification, and planning for subsequent database expansion and external validation of image-based male fertility assessment methods. TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000060395; https://tinyurl.com/mryfymw5. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/93803.
19 Apr 2026
Read appraisal →Medicine
Integration of the ISO 15189 quality management system in the undergraduate internship teaching of medical laboratory technology
BACKGROUND: The International Organization for Standardization (ISO) 15189 standard is essential for quality management in clinical laboratories, yet its integration into undergraduate medical laboratory technology education remains limited. This study aimed to evaluate the impact of a simulated laboratory teaching model based on the ISO 15189 standard on the quality management competencies of undergraduate interns and to compare it with traditional teaching methods. METHODS: A parallel-group, randomized controlled trial was conducted in the Department of Clinical Laboratory at the Second People's Hospital of Hunan Province (Brain Hospital of Hunan Province). A total of 60 undergraduate medical laboratory technology students scheduled to commence their clinical clerkship between July 2023 and May 2025 were enrolled and randomly allocated to an intervention group (n = 30) or a control group (n = 30). The intervention group received simulated laboratory teaching based on the ISO 15189 standard, including the construction of a quality management system and full-process simulated accreditation practice. The control group received traditional clinical clerkship teaching. Primary outcome measures were comprehensive assessment scores at the end of the internship (covering pre-examination, intra-examination, and post-examination quality control knowledge) and problem-solving ability scores in quality management roles. Secondary outcome measures included understanding of the quality management system, quality improvement awareness, and teaching satisfaction. RESULTS: Compared with the control group, the intervention group showed significantly higher scores in pre-examination quality control, intra-examination quality control, post-examination quality control, understanding of the quality management system, quality improvement awareness, and problem-solving ability in quality management roles (P < .01). Teaching feedback questionnaires also indicated higher satisfaction with the teaching model in the intervention group. CONCLUSION: For undergraduate medical laboratory technology interns, the simulated laboratory teaching model based on the ISO 15189 standard is significantly more effective than traditional teaching methods in enhancing their knowledge, skills, and competency in quality management.
19 Apr 2026
Read appraisal →PloS one
The association between RDW-to-platelet ratio and in-hospital mortality in critically ill stroke patients: A retrospective cohort study based on the eICU database
OBJECTIVE: The red cell distribution width-to-platelet ratio (RDW-to-platelet ratio, RPR) is a potential biomarker of inflammation and bone marrow function. This study aimed to investigate the association between RPR and in-hospital mortality in critically ill stroke patients in the eICU, and to evaluate mediating role of APACHE-IV score. METHOD: This retrospective cohort study utilized data from the 2014-2015 US multicenter eICU database. Of 200,859 initially included patients, 9,736 critically ill stroke patients were analyzed after excluding non-stroke cases and those with missing key variables. Multivariable logistic regression assessed the relationship between RPR and in-hospital mortality, adjusting for age, sex, comorbidities, and laboratory parameters, with multiple imputation for missing data. Subgroup analyses, mediation analysis, and restricted cubic spline modeling were performed. RESULTS: Patients with higher RPR exhibited significantly elevated in-hospital mortality (17.7% vs. 11.6%, P < 0.001). After adjustment, each unit increase in RPR was associated with 4.6% higher odds of death (adjusted OR=1.046, 95% CI: 1.032-1.061, P < 0.001), with consistent findings across subgroups. Restricted cubic spline analysis indicated a linear relationship. Mediation analysis showed that APACHE-IV score mediated 20.15% of the total effect (P < 0.001). CONCLUSIONS: Elevated RPR is independently associated in-hospital mortality in critically ill stroke patients, partly mediated by disease severity. As a simple and accessible marker, RPR shows promise for clinical prognostic risk stratification.
18 Apr 2026
Read appraisal →Platelets
Neural network reveals platelet age from fluorescence microscopy images
Platelets are small, anucleate cells with a primary physiological role in vascular damage repair (hemostasis) and initiation of thrombus formation in response to vascular injury. Platelets circulate approximately 7-10 days, slowly undergoing age-related changes in molecular composition, morphology, activation capacity, function, and surface receptor density. As older platelets are associated with poor clinical outcome, no in vitro tests are available to predict platelet age, or to determine the fitness of platelet transfusion products. In this study, we developed a convolutional neural network model that could determine platelets' chronological age from confocal microscopic images. The model was trained using platelets stored in platelet-rich plasma up to 8 hours and using routine platelet concentrates up to 10 days. The model predicted chronological age of stored platelets with >97% accuracy. To test our model in vivo, we analyzed a cohort of patients with acute myeloid leukemia, experiencing thrombocytopenia due to chemotherapy. Our model could reliably distinguish in vivo between samples with younger and older platelets during the course of treatment. This study demonstrates the ability to predict platelets' chronological age both in vitro during storage and in vivo, which may impact clinical transfusion medicine and the diagnosis and treatment of patients with platelet disorders. What is the context?Platelets circulate approximately 7–10 days, slowly undergoing age-related changes in molecular composition, morphology, activation capacity, function, and surface receptor density. During storage, proinflammatory changes occur in platelets over time and platelet age may be associated with poorer clinical outcomes. Therefore, understanding platelet aging and developing novel tools to interrogate platelet age will be useful. However, no platelet tests are currently available that can predict platelet age accurately based on platelet characteristics without keeping record of the (storage) time.What is new?We developed a convolutional neural network model that could determine platelets’ chronological age from confocal microscopic images. The model was trained using platelets stored in platelet-rich plasma up to 8 hours and using routine platelet concentrates up to 10 days. The model predicted chronological age of stored platelets with very high accuracy. Furthermore, our model was in vivo tested in a cohort of patients with acute myeloid leukemia, experiencing thrombocytopenia due to chemotherapy. Our model could reliably distinguish between in vivo samples with younger and older platelets during the course of treatment.What is the impact?Our study demonstrates the utility of a convolutional neural network model using confocal images of platelets to determine storage time-associated platelet phenotype in vitro as well as physiologic platelets’ chronological age in vivo. This model may be useful in several clinical settings, including clinical transfusion medicine, testing the quality of stored platelet concentrates, and diagnosing and treatment of patients with platelet disorders, thrombocytopenia, or thrombosis.
18 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 →Medicine
Association of blood urea nitrogen to serum albumin ratio with mortality in ICU patients with sepsis-induced coagulopathy: A Retrospective Cohort Study from MIMIC-IV database
The blood urea nitrogen to serum albumin ratio (BAR) has shown promise as a prognostic marker in sepsis, yet its specific role in sepsis-induced coagulopathy (SIC) remains unclear. We aimed to clarify whether BAR informs mortality risk in critically ill intensive care unit (ICU) patients with SIC. Using the Medical Information Mart for Intensive Care IV database, we identified consecutive ICU patients admitted between 2008 and 2022 who fulfilled Sepsis-3 criteria and had an SIC score ≥4. BAR was calculated from the 1st available laboratory values. Multivariable Cox regression and restricted cubic splines were used to estimate mortality associations after adjustment for demographics, comorbidities, interventions, severity scores (Sequential Organ Failure Assessment, Simplified Acute Physiology Score II), and laboratory parameters. The primary end-point was 30-day mortality; 90- and 365-day mortality were secondary. Among 4244 patients, higher admission BAR independently predicted increased mortality at 30, 90, and 365 days. Each 10-unit rise in BAR conferred a 30% higher hazard of death within 30 days (unadjusted hazard ratio (HR) 1.30, 95% confidence interval (CI) 1.25-1.35; P < .001). After full adjustment, the association persisted (HR 1.18, 95% CI 1.12-1.24; P < .001). Similar patterns were seen for longer-term outcomes. A clear dose-response relationship was observed across BAR tertiles; patients in the highest tertile (BAR ≥ 10 mg/g) had markedly higher mortality risk (adjusted HR 1.69, 95% CI 1.43-2.00; P < .001) than those in the lowest. Nonlinear analysis revealed a curvilinear BAR-mortality relationship, and subgroup analyses showed consistent findings, with significant effect modification by age. In critically ill patients with SIC, an elevated admission BAR independently predicts short- and long-term mortality and may aid risk stratification and early intervention planning.
12 Apr 2026
Read appraisal →PloS one
Integrative bioinformatics and machine learning identify shared molecular mechanisms and diagnostic biomarkers between Helicobacter pylori infection and atrial fibrillation
BACKGROUND: Helicobacter pylori (H. pylori) infection and atrial fibrillation(AF) are major global health concerns. Emerging evidence has suggested a potentially chronic inflammation-mediated link between them, but the shared genetic mechanisms remain unclear. METHODS: We analyzed multidataset gene expression profiles from the Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), functional enrichment, and machine learning were employed to identify common genes, pathways, and diagnostic biomarkers. Protein-protein interaction (PPI) networks, drug-gene analysis, and molecular docking were used to identify hub genes and potential therapeutics. RESULTS: We identified 73 common differentially expressed genes (DEGs) between H. pylori infection and AF, which were predominantly enriched in immune-related processes including leukocyte activation, neutrophil migration, and myeloid cell-mediated immunity. Machine learning identified 15 and 23 key feature genes for H. pylori and AF, respectively, with S100A8 emerging as a shared diagnostic biomarker. Ten hub genes including TYROBP, ITGB2, and SPI1, were identified from the PPI network. Drug repositioning analysis suggested retinoic acid, indirubin, and ropivacaine as candidate therapeutics targeting these key hub genes. CONCLUSION: Our integrative analysis highlights the central role of immune-inflammatory pathways in linking H. pylori infection to AF. We propose S100A8 and other identified hub genes as potential biomarkers and therapeutic targets. The predicted candidate therapeutics, particularly retinoic acid, may offer novel avenues for intervention, warranting further experimental validation.
12 Apr 2026
Read appraisal →Clinical chemistry
Discovery of an Artificial Intelligence Label Feedback Loop: How the Success of a Clinically Implemented Artificial Intelligence Algorithm Has Created an Unforeseen Challenge to Algorithm Retraining
BACKGROUND: Artificial intelligence (AI) augmented laboratory tests can improve quality, efficiency, cost-effectiveness, and staff satisfaction. However, the clinical success of these tests can introduce unforeseen challenges for model retraining. This study describes the discovery of an "AI label feedback loop" in a clinically implemented AI-augmented kidney stone composition test. METHODS: An AI-augmented kidney stone composition test (V1) has been previously deployed for clinical kidney stone characterization. After several years of clinical use, a retrained model (V2) was developed using 6 times more data. Model performance of both V1 and V2 were evaluated across 3 datasets: a recent production validation (hold-out) set (mostly AI-influenced labels), the original V1 validation set (pre-AI, entirely human-labeled), and a subset of recent cases with exclusively human-generated or human-corrected labels. RESULTS: V2 demonstrated a 10% lower concordance rate than V1 when evaluated on the recent production hold-out set, despite a much larger training dataset. Performance between V1 and V2 was similar when applied to the pre-AI validation set. Notably, V2 outperformed V1 on the recent subset of cases with human-only or human-corrected labels, particularly for less-common stone types. These findings revealed an AI label feedback loop, confounding retraining and evaluation. CONCLUSIONS: The integration of AI into clinical practice can potentially influence reported test results, complicating the development and evaluation of future models. To mitigate AI label feedback loops, ongoing human annotation and careful validation set construction are essential. These strategies can ensure reliable performance assessment and support the safe evolution of clinical AI systems.
4 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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