Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 14 appraisals
Rheumatic diseases clinics of North America
Machine Learning-Enhanced Autoantibody Discovery and Diagnostics in Systemic Autoimmune Rheumatic Diseases
The growing implementation of machine learning (ML) has extended into autoantibody research for the study of systemic autoimmune rheumatic diseases (SARDs). ML methods offer a promising approach for efficiently handling and identifying important signals within the big data generated by modern autoantibody technologies. The novel biomarkers identified through advanced ML techniques show promise in outperforming current clinical tools, bringing us closer to the goal of precision medicine. In this article, we will provide an overview of ML approaches and how they have been applied in autoantibody research to improve the diagnosis and characterization of SARDs.
2 Aug 2026
Read appraisal →Journal of chemical information and modeling
Gradient-Guided Graph Contrastive Learning for Mass Spectrometry-Based Proteomics Clustering
Single-cell proteomic data generated by mass spectrometry-based technologies provide direct insights into cellular functional states and have become increasingly important for revealing cellular heterogeneity in complex biological systems. Accurate clustering of such data is essential for identifying functionally distinct cell subpopulations and understanding biological processes such as immune responses, tumor heterogeneity, and cell fate regulation. However, mass spectrometry-based single-cell proteomic data are often characterized by high dimensionality, measurement noise, technical bias, and complex nonlinear structures, which pose major challenges to conventional clustering methods. To address these issues, this study proposes a gradient-information-guided graph contrastive learning framework for single-cell proteomic clustering. The proposed method adaptively reconstructs intercellular relationship graphs through gradient-guided structure learning and introduces a gradient-weighted contrastive loss to alleviate the influence of false-negative samples. By better preserving similarity among biologically related cells, the framework learns more robust and biologically meaningful representations. Experimental results on multiple data sets demonstrate that the proposed method outperforms conventional clustering approaches and existing graph contrastive learning methods in terms of clustering accuracy, stability, and biological consistency. Overall, this work provides an effective framework for clustering mass spectrometry-based single-cell proteomic data and offers new insights into the application of graph contrastive learning in bioinformatics.
28 July 2026
Read appraisal →Cell
Deep learning of functional perturbations from condensate morphology
Biomolecular condensates compartmentalize the interior of cells to organize complex functions, yet linking molecular interactions within condensates to their mesoscale organization remains a major challenge. To bridge this gap, we developed a neural-network-based framework-Deep-Phase (deep learning of phase-separated condensates)-that uses microscopy images to directly measure condensate morphology changes resulting from pharmacological alterations in associated biochemical processes. We use Deep-Phase to precisely quantify time- and concentration-dependent structural perturbations to the multiphase nucleolus and show that they are tightly coupled to potencies of drugs inhibiting ribosomal RNA (rRNA) transcription and processing. Applying Deep-Phase in a chemical screen, we identify a unique nucleolar morphology and discover a role for a DNA topoisomerase in rRNA processing. Mechanistic studies of this morphology provide insights into how the interfaces between nucleolar sub-compartments are maintained. We demonstrate Deep-Phase's adaptability to diverse cell lines, labeling techniques, and condensates, offering a powerful platform for connecting molecular pathways to cellular mesoscale organization.
25 July 2026
Read appraisal →Cell reports. Medicine
Toward generalizable prediction of cancer signal using a cell-free DNA language model.
Cell-free DNA can be used for early cancer detection, minimal residual disease monitoring, and post-treatment risk stratification. However, current assays are often designed for a single purpose and rely on deep or broad sequencing panels that capture only a small fraction of tumor-derived signals, limiting transferability, increasing cost, and reducing scalability. Fragmentia-AI is an artificial intelligence language model that learns fragment-level sequence patterns in tumor-derived cell-free DNA. Instead of focusing on mutations, it uses the structure of cell-free DNA to detect cancer signals in a partially panel-agnostic manner from ultra-low sequencing input, approximately 0.1%-1% of conventional depth. The model performs well across cancer types and clinical settings, including monitoring after surgery or immunotherapy, and in samples with low variant allele frequencies or no detected mutations. Fragment-level analyses identify shorter fragments and tumor-derived sequence patterns across panels of different sizes and ultra-low-pass whole-genome sequencing in multiple cohorts.
23 July 2026
Read appraisal →Emergency medicine journal : EMJ
Are rapid diagnostic tests reliable for the detection of malaria in the emergency department?
A short systematic review assessed the diagnostic accuracy of malaria rapid diagnostic tests in febrile adults from malaria-endemic regions. A database search of MEDLINE, EMBASE, Cochrane and Google Scholar returned 138 papers. Six papers met the inclusion criteria and were included in our analysis.
23 July 2026
Read appraisal →Cell systems
BulkFormer: A large-scale foundation model for bulk transcriptomes
Foundation models are transforming transcriptome analysis, yet most RNA sequencing (RNA-seq) foundation models are pretrained on sparse single-cell RNA-seq data, which typically captures only ∼3,000 genes per cell. This creates a need for models tailored to bulk transcriptomes, a modality that profiles ∼16,000 genes per sample and supports clinical and tissue-level analyses. Here, we present BulkFormer, a foundation model for bulk transcriptome analysis. BulkFormer contains ∼150 million parameters, covers 20,010 protein-coding genes, and is pretrained on 581,503 human bulk RNA-seq profiles. Its hybrid encoder combines a graph neural network to model explicit gene-gene relationships with a Performer module to capture global expression dependencies. Across five downstream tasks, BulkFormer outperformed existing single-cell foundation models while requiring substantially lower training cost. These results identify pretraining data modality as a key determinant of foundation model performance and establish BulkFormer as a framework for bulk transcriptome modeling. A record of this paper's transparent peer review process is included in the supplemental information.
17 July 2026
Read appraisal →ACS applied materials & interfaces
The Evolving Landscape of Terahertz Biosensing: From Sensitivity to Precision
Terahertz (THz) spectroscopy has recently gained significant attention as a powerful tool for biomacromolecule detection due to its exceptional sensitivity in capturing molecular fingerprints, attributed to its unique wavelength range. THz biosensing platforms offer distinct advantages in identifying molecular rotational and vibrational states, making them effective for high-sensitivity, high-precision biomacromolecule analysis. This review highlights recent advancements in THz biosensing platforms, focusing on their spectroscopy interpretation and practical applications. We begin by introducing commonly used THz equipment and various metamaterials, followed by an overview of the key spectroscopy interpretation strategies for THz spectral analysis, including refractive index sensing and molecular fingerprint recognition. We also summarize the machine learning (ML) methods applied to enhance spectral analysis in THz biosensing platforms. Lastly, we provide an integrated perspective on the spectroscopy interpretation, clinical applications, and recent technological advancements in THz biosensing platforms, with an emphasis on how to construct a standardized THz-based biosensing workflow. We believe THz biosensing platforms hold immense potential to revolutionize molecular detection by enabling precise, label-free, and highly sensitive analysis of biomacromolecule interactions, paving the way for breakthroughs in medical diagnostics, environmental monitoring, and biochemical research.
17 July 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis
BACKGROUND: Since 1992, the incidence of lung cancer has shown an overall downward trend, with survival rates significantly improving in recent years due to the application of immune checkpoint inhibitors (ICIs). Assessing programmed death ligand 1 (PD-L1) expression status in lung cancer patients is crucial for predicting the efficacy of ICIs and formulating treatment plans. Therefore, there is an urgent need to assess PD-L1 expression status in lung cancer patients, particularly those with nonsmall cell lung cancer (NSCLC). While numerous studies have demonstrated promising results using artificial intelligence (AI) models based on radiomics, current understanding in this area remains incomplete. METHOD: We conducted a search of medical databases and screened studies that meet the criteria. We performed feature extraction and data extraction from these studies, analysed them using R software, and generated forest plots. We conducted subgroup analyses based on imaging modality and algorithm model. RESULTS: A total of 35 studies were included in the analysis. For the Tumor Proportion Score (TPS)1 group, the pooled area under the curve (AUC) for the validation set was 0.782. For the TPS50 group, the pooled AUC for the validation set was 0.798. Subgroup analysis revealed that machine learning performed better when predicting TPS50, while deep learning was superior for predicting TPS1. When predicting TPS, prediction models constructed using 2-fluoro-2-deoxy-d-glucose (18F) positron emission tomography/computed tomography (CT) and contrast-enhanced CT outperformed those based on noncontrast CT. Conversely, when predicting a TPS, the noncontrast-CT-based prediction model significantly outperformed the other two groups. CONCLUSIONS: Imaging modality and algorithm model are key factors influencing AI prediction models for PD-L1 expression in NSCLC patients.
10 July 2026
Read appraisal →Medical image analysis
Medical hierarchical image classification via dual-geometry image-text learning
Hierarchical image classification is a fundamental challenge in medical image analysis, as tree-structured taxonomies inherently reflect biological and clinical relationships, spanning the general categorisation of disease entities and fine-grained cellular distinctions. Existing approaches primarily rely on multi-task learning and fine-grained detection, often requiring intricate model design and complex training strategies. In this paper, we aim to exploit the negative curvature property of hyperbolic space, which allows efficient representation of hierarchical structures. We propose a dual-geometry image-text framework, termed H2CL. Specifically, we introduce a lightweight classifier head on top of image backbones to extract both Euclidean and hyperbolic features, which are then combined to simultaneously preserve taxonomic consistency from an etiological perspective and enhance instance discrimination from a morphological perspective. Furthermore, a text branch is incorporated to integrate label semantics, where an entailment loss is employed to jointly model image-text alignment and inter-sample relationships. Extensive experiments on cervical cell, skin lesion, and gallbladder disease datasets demonstrate that our framework consistently outperforms advanced methods. Compared to the standard Swin Transformer, H2CL achieves an average accuracy improvement of 7% across all three datasets at the fine-grained level, with similarly consistent gains observed when integrated with other backbone models. The source code is publicly available at https://github.com/MCPathology/H2CL.
4 July 2026
Read appraisal →Journal of advanced research
scMapNet: Marker-based cell type annotation of scRNA-seq data via vision transfer learning with tabular-to-image transformations
INTRODUCTION: Identifying cell types is a key step in single-cell RNA sequencing data analysis that aids in understanding cellular heterogeneity and facilitates downstream analyses such as those concerning cell-cell interactions and data integration. Cell-type annotation methods often rely on unsupervised clustering algorithms, followed by manual or automatic annotation via marker genes, which are prone to inefficiency and inconsistency. Supervised methods, while more automated and consistent, have gained remarkable attention because of the rapid growth of large-scale, high-quality single-cell datasets. However, these methods cannot effectively leverage cellular marker knowledge and much unlabelled data. OBJECTIVES: This study aims to introduce a novel deep learning method, scMapNet, which can sufficiently learn cellular marker knowledge and information from unlabelled data. METHODS: scMapNet is a self-supervised deep learning model based on masked autoencoders (MAE) and vision transformer (ViT), which adopts treemap transformations to leverage cell marker information and capture information by pretraining on large amounts of unlabelled data. RESULTS: scMapNet outperforms six competing methods across diverse datasets, excelling in accuracy and batch insensitivity. Moreover, scMapNet can effectively extract attention gene information, providing strong support for cell type identification. The models and codes are available at https://github.com/Yuz7/scMapNet. CONCLUSION: scMapNet shows significant performance in annotation accuracy and batch insensitivity. Besides, this method also has good interpretability and can provide biological insights for researchers.
3 July 2026
Read appraisal →Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and Workflow Implications
Prostate MRI has become a cornerstone of contemporary prostate cancer diagnosis, enabling improved detection of clinically significant disease while reducing unnecessary biopsies and overtreatment. However, prostate MRI remains technically demanding, time-consuming, and subject to inter-reader variability, particularly as healthcare systems move toward abbreviated protocols such as non-contrast MRI (biparametric MRI). In this context, artificial intelligence (AI) has emerged as a promising tool to enhance image quality, diagnostic consistency, and workflow efficiency across the prostate MRI pathway. This non-systematic narrative review provides a comprehensive overview of the technical foundations, clinical applications, and workflow implications of AI integration into prostate MRI. It summarizes key concepts in machine learning and deep learning relevant to prostate imaging and reviews current evidence supporting AI-based solutions for image quality assessment and reconstruction, automated prostate segmentation, lesion detection, and risk stratification. Particular attention is given to human-AI collaboration models, the role of AI in supporting equivocal lesions, and the integration of imaging with clinical variables for personalized risk estimation. In addition, it discusses the impact of AI on reporting efficiency, training, and standardization, as well as the current landscape of commercially available AI tools. Despite encouraging results from large multicenter studies, important challenges remain, including heterogeneity in study design, limited prospective validation, generalizability across institutions, and ethical and regulatory considerations. Overall, AI should be regarded as a complementary decision-support technology rather than a replacement for radiologists. Thoughtful implementation, robust validation, and appropriate user training are essential to ensure that AI meaningfully enhances the quality, efficiency, and reliability of prostate MRI-based care.
2 July 2026
Read appraisal →Open biology
Deep learning in tumour genomics: from multi-omics integration to precision oncology
Cancer remains a leading cause of death globally, with nearly 10 million deaths in 2020. Advances in genomic technologies have revolutionized cancer research, shifting focus towards precision medicine based on comprehensive tumour genomic profiling. Concurrently, deep learning (DL) has emerged as a powerful paradigm for complex biological data. This review critically assesses recent advances in DL applications for tumour genomics, emphasizing four key domains: DNA sequencing analysis for mutation detection, gene expression profiling for cancer subtype classification, methylation function prediction for epigenetic characterization and integrative multi-omics approaches for comprehensive tumour profiling. We systematically analyse how different DL architectures-including convolutional neural networks, recurrent neural networks, graph neural networks, autoencoders and transformers-address specific challenges in cancer genomics. Our review highlights how these approaches significantly enhance detection sensitivity for genomic alterations, improve cancer subtype stratification, identify novel biomarkers and optimize therapeutic target selection. We examine technical challenges in DL implementation, including model interpretability, data scarcity, computational requirements and integration issues, alongside emerging solutions such as explainable AI, federated learning, and multi-modal frameworks. By synthesizing methodological innovations and identifying research directions, this review provides bioinformaticians and cancer researchers with a roadmap for leveraging DL to advance precision oncology.
2 July 2026
Read appraisal →Human genetics
AI in variant analysis: fast track to genetic diagnoses
While falling costs have expanded access to genomic sequencing, clinical utility is frequently hindered by the challenge of interpreting complex genetic data. Variant analysis for rare disease patients especially requires significant time and expertise, creating a bottleneck that delays diagnostics. Although advances in genetic variant classification have improved diagnostic precision, they have also increased the identification of variants of uncertain significance (VUSs), widening the interpretation gap between data generation and clinical actionability. The high prevalence of VUSs can lead to false reassurance or psychological distress by misinterpretting inconclusive results. We propose that artificial intelligence (AI) is a critical clinical decision-support tool for bridging this gap, offering a scalable framework to optimize variant interpretation and shorten the diagnostic odyssey. While reclassification ultimately requires biological evidence that AI cannot replace, these tools serve as essential aggregators and prioritizers, especially as guidelines transition toward the upcoming quantitative ACMG v4 framework. We advocate integrating AI throughout the genetic diagnostic workflow-from initial phenotyping to variant prioritization-to facilitate data-driven, personalized treatment. We outline current AI-assisted approaches and discuss anticipated challenges in this pursuit, such as privacy, training data bias and quality, model explainability, and the necessity of a total product life cycle for validation. To address these challenges, we provide recommendations for "human-in-the-loop" design and intuitive workflow integration to ensure AI tools meet the highest standards of precision, reproducibility, and transparency to maximize adoption. By standardizing AI across the variant analysis pipeline, we can fast-track the path to genetic diagnoses, effectively bridging the interpretation gap and enabling rapid delivery of personalized medical interventions.
28 June 2026
Read appraisal →PloS one
Few-shot skin lesion classification with Adaptive Multi-Scale Convolutional Attention Network
Computer-aided diagnosis of skin lesions faces core challenges, including scale diversity, blurred boundaries, intra-class morphological variations, and sparse data. Existing methods often rely on fixed receptive fields or generic attention mechanisms, struggling to fully adapt to the unique characteristics of skin lesions. To address this, we propose an Adaptive Multi-scale Convolutional Attention Network (AMCANet), which aims to achieve accurate and robust classification of skin lesions with limited data. AMCANet comprises three core modules: the adaptive multi-scale convolution module dynamically adjusts the receptive field to accommodate lesions of varying sizes; the hierarchical channel attention module integrates multi-level semantic information across different resolutions; and the skin spatial attention module leverages image gradient information to enhance lesion boundaries and local texture features. Extensive few-shot experiments on the HAM10000 and PAD-UFES-20 public datasets demonstrate that AMCANet significantly outperforms existing baseline models across multiple metrics, exhibiting promising generalization capabilities on the evaluated datasets. Qualitative and visual analyses further validate the model's ability to extract discriminative features and effectively focus on lesion regions. This study proposes a deep learning model, which demonstrates certain effectiveness in classifying skin lesions even with a small number of samples, providing a potential direction for future research.
27 June 2026
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