Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
Journal of ovarian research
Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma
BACKGROUND: High-grade serous ovarian cancer (HGSOC) is the most lethal ovarian cancer subtype, responsible for ~ 70% of ovarian cancer-related deaths and marked by late-stage diagnosis and frequent platinum resistance. Although transcriptomic profiling enables molecular stratification and prediction of therapeutic response; routine clinical use of this approach is limited by cost and logistical constraints. Computational pathology analysis offers a scalable alternative by inferring transcriptional states directly from routine hematoxylin and eosin (H&E) whole-slide images (WSIs). METHODS: Paired H&E WSIs and RNA-sequencing data from the TCGA-OV cohort, including 1,371 diagnostic H&E WSIs retrieved for preprocessing and quality control, were used to develop a self-supervised virtual-transcriptomics framework based on Momentum Contrast v2 (MoCo v2) and multi-output Random Forest regression. Model performance was assessed using patient-level five-fold cross-validation. Candidate genes were evaluated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in an independent cohort of 10 HGSOC tumors, including 4 platinum responders and 6 non-responders. RESULTS: The model predicted expression of approximately 6,400 protein-coding genes, achieving a genome-wide mean Pearson correlation of r = 0.36, with more than 300 genes showing stronger image-expression coupling (r > 0.44). RT-qPCR analysis of 18 candidate genes revealed substantial inter-patient heterogeneity. NR5A1 exhibited the highest expression variability (coefficient of variation [CV] = 1.486) and significantly higher expression in platinum-responsive tumors than in non-responders (mean 2⁻ΔCt = 0.263 vs. 0.013; p < 0.05). Exploratory in silico docking and molecular dynamics (MD) analyses suggested structurally stable binding interactions between Steroidogenic Factor-1 (SF-1/NR5A1) and the natural plant compound cubebin. CONCLUSION: This study demonstrates that histological architecture contains measurable transcriptomic information that can support scalable biomarker prioritization from routine diagnostic histology in HGSOC. NR5A1 represents a hypothesis-generating candidate biomarker and structurally tractable target for future experimental studies. Future validation in larger, multi-center cohorts will be essential to confirm model robustness, biological relevance, and potential clinical utility.
14 June 2026
Read appraisal →Medicine
Common biomarkers of Alzheimer disease and postoperative delirium
Alzheimer disease (AD) and Postoperative delirium (POD) may share a common mechanism, but their shared genes and potential novel therapeutic targets remains unknown. We selected datasets GSE48350, GSE4226, and GSE16759 containing AD and control samples from the GEO database for AD model development, and use the GSE63061 dataset for AD model validation. The GSE163943 dataset containing PODs and controls was used to identify common genes. Differentially expressed genes (DEGs) associated with POD and AD respectively, as well as common DEGs between 2 diseases were analyzed. Shared DEGs were assessed using gene ontology and KEGG enrichment and PPI interaction networks. Machine learning algorithms, nomogram and decision tree analyses were used to interpret top 5 genes and visualize the models. 259 DEGs in POD with 148 being up-regulated and 111 down-regulated, and 199 DEGs in AD with 25 up-regulated and 174 down-regulated were found, of which 23 were common to POD and AD. Gene ontology analysis indicated that DEGs were primarily concentrated in 9 distinct biological processes, while KEGG results revealed 3 major metabolic pathways. Five hub genes emerged as potential diagnostic biomarkers for both diseases, Bruton tyrosine kinase (BTK), NCF2, CRH, FCGR3A, and SERPINA3. The LASSO model determined that the 23 common genes could serve as biomarkers for both POD and AD patients, and the Random Forest model is the the most suitable model. POD or AD decision trees showed BTK and NCF2 were sufficient to distinguish between POD or AD patients and healthy controls. BTK and NCF2 were identified common biomarkers of POD and AD. Further investigations are required to verify correlations between gene expression levels and pathological characteristics.
31 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 →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 →Psychological medicine
Inflammatory cytokine alterations in genetic and clinical high risk groups of psychosis: a systematic review and network meta-analysis
BACKGROUND: Inflammation has been implicated in psychosis, but its role in individuals at clinical (CHR) and genetic (GHR) high-risk remains unclear. We therefore conducted a network meta-analysis (NMA) to compare circulating cytokine levels across CHR, GHR, and healthy control (HC) groups. METHODS: We systematically searched multiple databases up to February 2025, extracting cytokine levels (plasma/serum) from CHR, GHR, and HC groups. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were estimated using random-effects models. Given that no direct head-to-head comparisons between CHR and GHR were available, indirect comparisons were performed through the common comparator (HC). The transitivity assumption was assessed by comparing key study and participant characteristics across comparisons. RESULTS: Thirty studies were included (CHR: 1601, GHR: 675, HC: 1980). NMA estimates indicated higher IL-6 levels in CHR compared with GHR, while IL-6 and IL-1β levels were lower in GHR compared with HC. In pairwise subgroup analyses, CHR converters showed higher IL-13 levels than non-converters. The evidence network was sparse and star-shaped, with all CHR-GHR estimates relying exclusively on indirect comparisons. CONCLUSIONS: This study represents the first NMA to synthesize cytokine alterations in individuals at high risk for psychosis using indirect evidence. Elevated IL-6 in CHR individuals suggests immune activation, whereas reduced IL-6 in GHR may reflect a distinct immune profile. Increased IL-13 levels in converters highlight potential involvement of Th2-related pathways during transition to psychosis. However, the sparse nature of the evidence network necessitates cautious interpretation of the findings, and larger, standardized multi-center studies are required for confirmation.
11 Apr 2026
Read appraisal →Nature reviews. Clinical oncology
MicroRNAs in oncology: a translational perspective in the era of AI
Over the past three decades, knowledge of microRNA (miRNA) biology has advanced from the initial discovery of their regulatory functions to the finding of abnormal activity in leukaemias, and then to a comprehensive understanding of the roles of miRNAs in both normal physiology and most diseases, with cancer being extensively studied. miRNA dysregulation contributes to tumorigenesis, with certain miRNAs acting as either tumour suppressors or oncogenic factors in a context-dependent manner. A subset of miRNAs have shown promise as tumour biomarkers and therapeutic targets in preclinical studies, with several miRNA-based diagnostic tools and treatments progressing to clinical trials. Artificial intelligence (AI) and machine learning techniques began to be introduced into cancer research and oncology a decade ago and are now on the verge of revolutionizing biomarker identification and clinical trials. In this Review, we highlight important roles of miRNAs in cancer biology and their potential as diagnostic tools and therapeutic targets. In particular, we discuss emerging challenges and opportunities presented by AI-driven data analysis and combinatorial strategies, and how advances in these areas have addressed previous doubts on the clinical translation of miRNA-based biomarkers and therapeutics.
4 Apr 2026
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