Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 10 appraisals
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Shorter Antitubercular Regimens Versus 9 Months of Isoniazid for Latent Tuberculosis in Children: A Systematic Review and Meta-Analysis
BACKGROUND: We conducted a systematic review and meta-analysis to compare effectiveness and safety of 9 months of isoniazid (9H) versus shorter rifamycin-containing regimens for treating latent tuberculosis infection (TBI) in children. METHODS: We systematically searched MEDLINE, Embase, and Cochrane Central Register of Controlled Trials to June 2025 for randomized, controlled trials (RCTs) and cohort studies that compared regimens that were shorter than 9 months of isoniazid in children aged 1-18 years. Outcomes were development of TB disease, treatment completion, and adverse events. Risk of bias was assessed using RoB 2.0 and the Risk Of Bias In Non-Randomized Studies - of Interventions (ROBINS-I) tool; certainty of evidence was graded using Grading of Recommendations Assessment, Development, and Evaluation (GRADE). RESULTS: Five RCTs and 7 nonrandomized studies that enrolled approximately 2950 children in trials and >25 000 in observational cohorts were included. In pooled analysis of 3 RCTs, shorter rifamycin-containing regimens resulted in little to no difference in development of TB disease compared with 9H (odds ratio [OR], 0.19; 95% confidence interval [CI], .03-1.12; moderate-certainty evidence). Treatment completion was probably higher with shorter regimens (OR, 0.51; 95% CI, .42-0.62; moderate-certainty evidence). Adverse events were similar between groups, but evidence is uncertain (low-certainty evidence). Observational data were consistent with these findings, showing higher completion rates and lower hepatotoxicity with shorter treatments. CONCLUSIONS: Shorter rifamycin-containing regimens for pediatric TBI probably increase treatment completion and have similar safety outcomes, with no important difference in development of TB disease compared with the standard regimen. These findings support current guideline recommendations that favor shorter regimens in children.
25 July 2026
Read appraisal →International journal of cancer
Drug repurposing in oncology: Bridging computational discovery to clinical application.
Drug repurposing, the identification of new therapeutic applications for existing drugs, has emerged as a pragmatic and cost-efficient strategy to accelerate oncology drug discovery. Faced with rising development costs, protracted timelines, and high attrition rates associated with traditional de novo drug development, repurposing leverages known pharmacokinetics, safety profiles, and manufacturing processes to expedite clinical translation. This review synthesizes current advances in computational and experimental methodologies and mechanistic insights that drive drug repurposing for cancer therapy. In silico strategies, including molecular docking, machine learning, transcriptomic-proteomic signature reversal, and network-based modeling, have enabled rapid identification of repurposable agents by mining multi-omics and historical pharmacological data. Experimental pipelines spanning high-throughput screening, phenotypic assays, biochemical validations, and functional animal models remain essential to establish efficacy and delineate mechanisms of action. Notably, repurposed drugs exhibit anticancer activity by modulating key pathways, including phosphatidylinositol 3-kinase/Ak strain transforming/mechanistic target of rapamycin, mitogen-activated protein kinase/extracellular signal-regulated kinase, wingless-related integration site/β-catenin signaling, as well as redox homeostasis and DNA response. Despite their promise, repurposed candidates face barriers including limited intellectual property protections, dose optimization challenges, and regulatory uncertainty. Moreover, clinical translation is often hindered by insufficient mechanistic understanding and a lack of predictive biomarkers. Integration of multi-omics datasets, explainable artificial intelligence, patient-derived organoids, and clustered regularly interspaced short palindromic repeats-based genetic screens now offers unprecedented precision in identifying context-specific drug effects and synthetic lethal interactions. With cancer causing one in six global deaths and marked by therapeutic resistance and molecular heterogeneity, drug repurposing provides a scalable solution. This approach bridged preclinical insight with clinical application, potentially transforming cancer therapeutics through rational, data-driven innovation.
15 July 2026
Read appraisal →ACS biomaterials science & engineering
AI-Driven Design and Clinical Translation of Nucleotide-Peptide and Peptide-Drug Conjugates
While nucleotide-peptide conjugates and peptide-drug conjugates demonstrate a considerable evolution as next-generation therapeutics, the successful translation of these complex agents relies on advances in bioconjugation strategies. By anchoring nucleotides or pharmaceutical payloads to multifaceted peptide scaffolds, researchers have engineered molecular platforms with dual mechanisms of action. However, to fully realize the potential of these hybrid systems, further refinements are required to ensure both in vivo stability and retention of the desired biological activities over time. This comprehensive review examines ongoing progress toward optimizing conjugation chemistry for the development of stable and efficacious NPC and PDC candidates. Through evaluation of fundamental design principles and their clinical relevance, it aims to accelerate the journey of these multilayered biomolecular constructs from theory to practice. It details various strategies to mitigate challenges such as enzymatic degradation and rapid systemic clearance, including covalent linkages (e.g., aminoacylation and thiol oxidation) and noncovalent interactions (e.g., ligand-receptor binding). Central to this emerging field is the integration of artificial intelligence and deep learning, which are rapidly transforming the design and development of NPCs and PDCs. AI-driven tools enable predictive modeling of peptide-drug interactions, intelligent linker optimization, and in silico screening of bioactive scaffolds-thus redefining the pipeline for precision therapeutics with improved selectivity and translatability. The potential of hybrid nanostructures formed by peptide-DNA conjugates for advanced drug delivery and diagnostic applications is also discussed. Furthermore, the clinical translation of PDCs demonstrates their promise in treating conditions such as cancer, neurodegenerative diseases, and inflammatory disorders, alongside the ongoing challenges regarding peptide availability, linker optimization, and scalable synthesis. The review concludes by discussing future directions of NPCs and PDCs, highlighting the role of interdisciplinary approaches in advancing precision medicine and addressing complex therapeutic needs.
14 July 2026
Read appraisal →Cardiovascular toxicology
Mechanisms of Cardiovascular Toxicity Induced by Silver Nanoparticles: A Systematic Review of Preclinical Evidence
Silver nanoparticles (AgNPs) have emerged as one of the most widely used nanomaterials in medical and consumer products, yet their cardiovascular safety remains inadequately characterized. While their antimicrobial properties are well-documented, accumulating evidence reveals that AgNPs pose a significant threat to the cardiovascular system. Exposure to AgNPs through inhalation, ingestion, or dermal contact enables these particles to enter systemic circulation, where they accumulate in cardiac and vascular tissues in a dose-, time-, and size-dependent manner. Once deposited, AgNPs initiate a cascade of pathological events, including oxidative stress, mitochondrial dysfunction, DNA damage, and disruption of calcium and sodium channel signaling. These molecular disturbances translate into physiological alterations such as bradycardia, atrioventricular conduction block, impaired contractility, and exacerbated ischemia-reperfusion injury. AgNPs also trigger endothelial activation, leading to upregulation of adhesion molecules, namely vascular cell adhesion molecule-1 (VCAM-1), intracellular adhesion molecule-1 (ICAM-1) and recruitment of inflammatory cells, creating a proinflammatory and prothrombotic environment. Elevated cardiac biomarkers, including creatine kinase-MB fraction (CK-MB), brain natriuretic peptide (BNP), lactate dehydrogenase (LDH), and histopathological changes, including pericardial edema, myofibril disorganization, fibrosis, and inflammatory infiltration are consistently observed. Vascular dysfunction manifests as impaired vasorelaxation, enhanced vasoconstriction via endothelin-1 upregulation, thrombosis, and abnormal angiogenesis driven by vascular endothelial growth factor (VEGF) dysregulation. Although plant-based AgNPs have shown cardioprotective potential in limited studies, the overall evidence indicates significant cardiovascular risk. This systematic review synthesizes findings from 38 preclinical studies (35 in vivo, 3 in vitro), providing a comprehensive analysis of AgNP-induced cardiotoxicity with emphasis on oxidative stress, inflammation, and molecular dysregulation as core mechanistic drivers.
13 July 2026
Read appraisal →PloS one
Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis
Tuberculosis is a deadly airborne disease caused by Mycobacterium tuberculosis. Drug-resistant tuberculosis presents significant challenges for treatment and control of the disease. Resistant strains of Mycobacterium tuberculosis arise from specific mutations in the bacterium. Identification and characterization of resistance-associated mutations are crucial for effective treatment strategies because the first- and second-line drugs for the disease target distinct genes in the bacterium and serve different purposes. Our study developed a machine learning prediction model to analyze mutations across multiple drug-resistance types. The proposed framework predicts drug-resistance mutations across four drug-resistance types, including Rifampicin Resistance, Isoniazid Resistance, Multidrug Resistance, and Pre-extensively Drug-Resistant tuberculosis. The NIAID-NIH TB portal is a publicly available dataset of tuberculosis patients, including drug-resistance information. Our study analyzed 3,065 cases of drug-resistant TB. Eight supervised ML algorithms were implemented for the study. A Random Forest classifier with 10-fold cross-validation shows higher predictive performance than the other seven algorithms considered for further analysis. Significant drug resistance mutations were identified using SHapley Additive exPlanations feature importance. The World Health Organisation mutation catalogues, considered the gold standard for drug-resistant mutations, were used to evaluate prediction results. Mutations not reported in the WHO catalogues were identified during the post-prediction comparative analysis stage, as they may represent potential resistance-conferring markers warranting further investigation, including structural and functional validation or experimental validation. The mutations are rpoB-I480T, rpoC-G332R, L527V, gyrA-D94V, KatG-G99E, A106V, W191R, W328C, T380I, and M420T. The study further checks the stability and pathogenicity of the mutations using computational tools, including I-Mutant 2.0 and PredictSNP. The findings added more clarity and further evidence for the significance of the mutation, based on its contribution to drug resistance.
11 July 2026
Read appraisal →Cell host & microbe
Predicting antimicrobial resistance for precision medicine
Antibiotics are among medicine's greatest successes, but resistance evolution threatens their continued efficacy. Decades of research have deepened our understanding of the mechanisms and evolutionary dynamics of antimicrobial resistance. More recently, advances in machine learning (ML) and artificial intelligence (AI) show promise in predicting antimicrobial resistance in pathogens based on rapid whole-genome sequencing and other accessible data. In this perspective, we highlight advances in understanding the mechanisms and spread of antimicrobial resistance. We discuss how this knowledge, coupled with ML- and AI-based approaches, can inform the prediction of resistance and a precision-medicine strategy that targets pathogenic bacteria specifically, thereby limiting resistance evolution and collateral damage to the microbiome. These accurate predictions of bacterial vulnerabilities will enable the adaptation of classical antimicrobial treatments with adjuvants, as well as the use of novel, narrow-spectrum therapeutics. Implementing these strategies, while also identifying key challenges, will help bring this strategy into clinical practice.
10 July 2026
Read appraisal →Infectious diseases of poverty
Broadly neutralizing antibodies for HIV therapy in clinical trials: a systematic review
BACKGROUND: Lifelong daily antiretroviral therapy (ART) effectively suppresses human immunodeficiency virus type 1 (HIV-1) replication but does not eradicate the virus, underscoring the urgent need for long-acting antivirals and functional cure strategies. Broadly neutralizing antibodies (bNAbs) have emerged as a promising approach for achieving durable HIV-1 remission. In this systematic review, we summarize recent advances in the development of bNAbs for HIV-1 treatment. METHODS: We searched PubMed, Embase, and Web of Science for clinical trials published up to March 22, 2026. We included data evaluating intravenously administered bNAbs, with or without concomitant conventional ART, and comparing them with placebo, ART or no intervention. These data were used to evaluate the pharmacokinetics, antiviral efficacy, resistance profiles, immunologic effects, and safety of intravenously administered bNAbs. RESULTS: LS-modified bNAbs extended half-life by 2- to 5-fold relative to their parental counterparts (e.g., VRC01LS: 71 vs 15 days), although viremia reduced half-life by 20-40%. In viremic participants harboring bNAb-sensitive virus, monotherapy achieved viral load (VL) reductions of 0.93-1.8 log₁₀ copies/ml, with rebound occurring within approximately 4-8 weeks, whereas combinations regimens achieved declines of up to 2.04 log₁₀ copies/ml and delayed rebound to 15-33 weeks. No consistent reduction in total reservoir size was observed, although early intervention and baseline viral sensitivity appeared to limit reservoir expansion. Resistance emerged through epitope-proximal mutations, with cross-resistance observed mainly among antibodies targeting shared epitope classes. Overall, bNAbs were well tolerated, with rare discontinuations (0.4%) and low immunogenicity. CONCLUSIONS: LS-engineered bNAbs exhibit a longer half-life than their parental antibodies. Combination regimens achieve greater VL reductions and a longer delayed rebound compared with monotherapy, indicating that LS-modified multi-epitope bNAb cocktails are promising for long-acting HIV-1 remission. To become clinically competitive, larger resistance-guided trials are needed to extend dosing intervals, improve resistance mitigation, and define optimal integration with other long-acting therapies.
4 July 2026
Read appraisal →Current allergy and asthma reports
Harnessing Machine Learning and Electronic Health Record Data to Improve Asthma Management
PURPOSE OF REVIEW: The review examines the application of machine learning (ML) and large language models (LLMs) to asthma management. We sought to identify clinically relevant applications, and particularly those that harness electronic health record data. We review methodological challenges and future directions for translating these tools into meaningful improvements in asthma care. RECENT FINDINGS: ML applied to electronic health record data has been utilized across several domains of asthma management: predicting medication response to inhaled corticosteroids and biologics, improving inhaler adherence through digital inhaler systems, and predicting exacerbation risk with moderate-to-high accuracy. Tools for patient education include clinician-guided chatbots, as well as publicly available LLMs. Limitations include accuracy, hallucinations, and patient health literacy. ML and LLMs offer promising pathways towards personalized, data-driven asthma management. However, harnessing this potential will require rigorous external validation, transparent model design, equitable implementation, and adaptive clinician oversight. Collaboration among clinicians, data scientists, and policymakers will be essential to implement these tools for patient-centered asthma care.
30 June 2026
Read appraisal →Medicine
Efficacy and safety of traditional Chinese medicine decoctions as an adjuvant treatment for rheumatoid arthritis: A systematic review and meta-analysis
BACKGROUND: To assess the effectiveness and safety of traditional Chinese medicine (TCM) decoctions as an additional therapy for rheumatoid arthritis (RA) via a systematic review and meta-analysis. METHODS: Databases including PubMed, Web of Science, Cochrane Library, Embase, China National Knowledge Infrastructure, and Wanfang were searched up to June 2024. Randomized controlled trials (RCTs) on TCM decoctions as adjunctive RA treatment were included. Primary outcomes were efficacy and safety. Effect sizes were measured using standardized mean difference, risk ratio, and 95% confidence intervals. Sensitivity and subgroup analyses were conducted to test result robustness and identify heterogeneity sources. Data were analyzed using RevMan 5.4 and Stata 15. In addition, the quality of evidence was assessed using grading of recommendations assessment, development and evaluation grading. RESULTS: Thirty-six RCTs with 6934 participants showed that TCM decoctions significantly improved overall efficacy versus control (risk ratio = 1.25, 95% confidence interval [1.21-1.29], P < .00001). They also reduced erythrocyte sedimentation rate, C-reactive protein, rheumatoid factor, swollen joint count, Disease Activity Score, and morning stiffness duration compared to placebo. Subgroup analysis suggested treatment duration, disease duration, and age may affect efficacy. Among them, the quality of evidence was high for Visual Analog Scale, and moderate for overall efficacy, C-reactive protein, Disease Activity Score, Traditional Chinese Medicine Symptom Score, and interleukin-1. CONCLUSION: TCM decoctions as adjunctive RA treatment significantly enhance efficacy. However, due to study limitations, further large-scale, multicenter RCTs are needed to confirm these findings.
28 June 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Mucoactive agents in bronchiectasis: a systematic review and meta-analysis
BACKGROUND: Mucoactive agents aim to improve mucociliary clearance in bronchiectasis, disrupting the cycle of impaired clearance, infection and inflammation. Registry data show 28% of patients use these agents, but prescribing varies due to limited evidence. We aimed to update evidence on mucoactive agents' effects in adults with bronchiectasis. METHODS: We conducted a systematic review and meta-analysis, searching Medline, Embase, CENTRAL and trial registries to 1 October 2025. Eligible studies included adults with bronchiectasis, evaluating any mucoactive agent versus placebo or control. Cystic fibrosis and paediatric studies were excluded. The primary outcome was exacerbation frequency; secondary outcomes included lung function, quality of life and adverse events. FINDINGS: From 1512 records, 24 studies (20 randomised, four observational: n=7051) evaluated eight mucoactive agents (ambroxol, bromhexine, carbocisteine, erdosteine, hypertonic saline, mannitol, N-acetylcysteine, rhDNase). Most trials had high or some risk of bias. Seven randomised studies (n=1259) reported exacerbations; pooled analysis did not demonstrate a statistically significant difference in annualised exacerbation incidence (mean difference -0.40 per patient per year, 95% CI -1.04-0.24; p=0.22; I2=91%; very low certainty). Nine studies (n=767) reported percent predicted forced expiratory volume in 1 s (FEV1 % pred), showing a small mean increase of 3.23% (95% CI 0.31-6.15; p=0.03; I2=69.9%; very low certainty). Pooled analyses did not demonstrate significant differences for other spirometry measures, quality of life or adverse events. INTERPRETATION: Pooled evidence did not show a reduction in exacerbations and FEV1 % pred improvements were small. Evidence certainty was low/very low, meaning overall clinical benefit remains uncertain, highlighting the need for targeted trials of specific agents and subgroups.
25 June 2026
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