Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 4 appraisals
Orphanet journal of rare diseases
Clinical outcomes in alpha-mannosidosis: a systematic review of therapeutic approaches
BACKGROUND: Alpha mannosidosis (AM) is a rare lysosomal storage disorder caused by a deficiency in the α-mannosidase enzyme, resulting in impaired glycoprotein metabolism within lysosomes. Enzyme dysfunction is attributed to an autosomal recessive mutation in the MAN2B1 gene. Affected individuals present with a broad spectrum of manifestations, including developmental delays, cognitive decline, musculoskeletal abnormalities, hearing difficulties, and recurrent infections. Current therapeutic options are limited to hematopoietic stem cell transplantation and the more recently developed enzyme replacement therapy. OBJECTIVE: The aim of this review was to evaluate and compare the therapeutic outcomes, benefits and challenges associated with Hematopoietic stem cell transplantation (HSCT) and enzyme replacement therapy (ERT) in the treatment of AM. METHODS: A systematic search across PubMed, MEDLINE, EMBASE, the Cochrane Library, OMIM, and ScienceDirect identified 12 original studies from 307 records. The data are presented narratively due to the scarcity of literature and the heterogeneity of study designs and interventions. RESULTS: A total of 28 patients who received hematopoietic stem cell transplantation showed improvements in preserving neurocognitive function and skeletal stabilization and reduced infection rates, especially when performed at relatively young ages. However, this treatment carries significant risks, including infections, graft-versus-host disease, and increased morbidity and mortality, particularly in older patients. Conversely, enzyme replacement therapy was administered to 75 patients, who demonstrated a favorable safety profile, enhanced respiratory function, reduced skeletal abnormalities, and improved overall quality of life. However, enzyme replacement therapy has limited efficacy in preventing neurocognitive decline and requires lifelong administration. CONCLUSION: Both interventions yield better outcomes when initiated early, particularly before cognitive deterioration becomes significant. This review emphasizes the importance of a timely diagnosis to optimize treatment outcomes and prevent severe complications.
23 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 →Medicine
Molecular profiling of coronary stent restenosis: A systematic review and functional analysis of implicated genes
BACKGROUND: Coronary stent restenosis occurs in approximately 5% of patients treated with drug-eluting stents (DES) and is associated with adverse clinical outcomes. Elucidating the genetic mechanisms underlying restenosis may support precision medicine approaches to improve patient management.This systematic review aimed to synthesize evidence on genes and biological pathways associated with DES-related restenosis and to perform functional analysis of the implicated genes using bioinformatics tools. METHODS: The review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. A systematic search of PubMed, Scopus, and Web of Science was performed for human studies investigating genetic or genomic factors in coronary restenosis, with the last search conducted in March 2024. Eligibility criteria included original studies reporting genetic associations with DES restenosis. Screening and data extraction were performed by a single reviewer. Identified genes underwent gene set enrichment analysis using Enrichr (Ma'ayan Laboratory, Computational Systems Biology) and ClueGo extension on Cytoscape (National Resource for Network Biology). RESULTS: Seventeen studies met the inclusion criteria. The studies highlighted multiple genes involved in extracellular matrix remodeling, inflammatory signaling, and the renin-angiotensin system. Gene enrichment analysis confirmed the overrepresentation of these biological pathways in DES-associated restenosis. CONCLUSIONS: This systematic review synthesizes the genetic and molecular contributors to DES-associated restenosis and identifies potential targets for future research and personalized therapies. No external funding was received, and the protocol was not registered.
28 June 2026
Read appraisal →Nucleic acids research
SpliceSelectNet: a hierarchical Transformer-based deep learning model for splice site prediction
Accurate RNA splicing is essential for gene expression and protein function, yet the mechanisms governing splice site recognition remain incompletely understood. Aberrant splicing caused by mutations can lead to severe diseases, including cancer and genetic disorders, underscoring the need for accurate computational tools to predict splice sites and detect disruptions. Existing methods have made significant advances in splice site prediction but are often limited in handling long-range dependencies due to high computational costs, a factor critical to splicing regulation. Moreover, many models lack interpretability, hindering efforts to elucidate the underlying biological mechanisms. Here, we present SpliceSelectNet (SSNet), a hierarchical Transformer-based deep learning model that predicts splice sites from DNA sequences spanning up to 100 kb. By integrating local and global attention mechanisms, SSNet efficiently captures both proximal and distal regulatory signals while maintaining single-nucleotide resolution. Across multiple benchmark datasets, SSNet achieves state-of-the-art performance in splice site prediction and aberrant splicing detection. Systematic in silico mutagenesis demonstrates that attention scores reflect functional sequence importance, supporting their biological relevance. Long-range sequence perturbation experiments further show that SSNet captures distal regulatory effects beyond conventional receptive fields. Together, these results establish SSNet as a biologically interpretable framework for modeling long-range splicing regulation from genomic sequence.
23 June 2026
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