Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 47 appraisals
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Utility of Repeating Blood Cultures in Candidemia: Insights From 2 Large Clinical Data Repositories
We evaluated utility of repeating blood culture for candidemia using 2 electronic health record datasets. Among 1386 candidemia episodes, repeated blood culture detected 23% of episodes at Houston Methodist Hospital and 45% in Medical Information Mart for Intensive Care IV after the initial negative cultures. These findings support repeating blood cultures when candidemia is highly suspected.
24 July 2026
Read appraisal →mSystems
MeLSI: Metric Learning for Statistical Inference in microbiome community composition analysis
Microbiome beta diversity analysis relies on distance-based methods, including permutational multivariate analysis of variance (PERMANOVA) combined with fixed ecological distance metrics (Bray-Curtis, Euclidean, Jaccard, and UniFrac), which treat all microbial taxa uniformly, regardless of their biological relevance to community differences. This "one-size-fits-all" approach may miss subtle but biologically meaningful patterns in complex microbiome data. We present Metric Learning for Statistical Inference (MeLSI), a novel machine learning framework that learns data-adaptive distance metrics optimized for detecting community composition differences in multivariate microbiome analyses. MeLSI employs an ensemble of weak learners using bootstrap sampling, feature subsampling, and gradient-based optimization to learn optimal feature weights, combined with rigorous permutation testing for statistical inference. The learned metrics can be used with PERMANOVA for hypothesis testing and with principal coordinates analysis for ordination visualization. Comprehensive validation on synthetic benchmarks and real data sets shows that MeLSI maintains proper type I error control while delivering competitive or superior statistical power for detecting subtle community shifts and, crucially, supplies interpretable feature-weight profiles that clarify which taxa drive group separation. On the DietSwap data set, MeLSI was the only method to achieve significance at α = 0.05, demonstrating that adaptive weighting can detect diet-induced community shifts that fixed metrics miss. Across all data sets, the learned feature weights identified biologically relevant taxa while providing actionable insight that no fixed distance metric can supply. MeLSI therefore offers a statistically rigorous tool that augments beta diversity analysis with transparent, data-driven interpretability.IMPORTANCEUnderstanding which microbes differ between groups of interest could reveal therapeutic targets and diagnostic biomarkers. However, current analysis methods treat all microbes equally (similar to using the same ruler to measure everything, regardless of what matters most). This means subtle but biologically important differences may go undetected, especially when only a few key species drive disease states while hundreds of "bystander" species add noise. Metric Learning for Statistical Inference (MeLSI) solves this by learning which microbes matter most for each specific comparison. In comparing male and female gut microbiomes, MeLSI identified specific bacterial families driving the differences, providing actionable biological insights that standard methods miss. This capability is particularly crucial for detecting early disease biomarkers, where differences are subtle and masked by biological variability. By telling researchers not just whether groups differ, but which specific microbes drive those differences, MeLSI accelerates the path from microbiome data to testable biological hypotheses and clinical applications.
22 July 2026
Read appraisal →Archives of microbiology
SigMine and OPathDb: a literature-mining pipeline and database of potential opportunistic pathogens
Conversion of unstructured biomedical literature into structured knowledge for identifying cross-domain associations between biological entities remains a challenging task. SigMine is an automated pipeline constructed to mine biomedical literature to identify significantly associated biological entities. SigMine performs biomedical entity recognition from PMC articles using the EuropePMC Annotation API. Advanced entity recognition was performed using Python scripting, NCBI E-Utilities, and an n-gram algorithm followed by extensive data cleaning and mapping against standard databases. Statistical evaluation identified significantly co-occurring entities. The entire workflow was automated through a modular framework developed in Python v3.13 with a Tkinter-based Graphical User Interface. SigMine enhances usability while retaining the flexibility to use new dictionaries for annotation. SigMine was used to construct a literature-derived potential human Opportunistic Pathogens Database (OPathDb), housing 5,626 potential opportunistic pathogens significantly co-occurring with 1440 diseases and 7121 genes mined from 25,000 PMC articles. Additional annotation of 598 significantly co-occurring metabolites and 30 affected tissues is available for 3204 and 227 pathogens, respectively. OpathDb has a user-friendly query interface searchable by organism, disease, tissue, gene, protein and metabolite available at https://www.opathdb.cbsblab-nsut.in . Organism-entity associations can be visualized as weighted networks, with color-coded nodes and significance-scaled edges. Significant associations of opportunistic pathogens like Akkermansia mucinifila with colorectal cancer and Segatella copri with glucose intolerance can be identified through OpathDb. Through this database, the SigMine framework demonstrates conversion of unstructured text in vast and heterogenous corpora into standardized and well-organized information. Statistically inferred associations in OPathDb are potential candidates for clinical and experimental validation.
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 →ACS infectious diseases
Machine Learning Approaches Using High-Throughput Profiling Data for Antibiotic Discovery
Antibiotic-resistant bacterial infections continue to increase globally, creating an urgent need for new antibacterials with novel mechanisms of action. Early stages of antibiotic discovery are often limited by the difficulty of identifying compounds that act through previously unrecognized pathways and by challenges in determining their mechanisms. Machine learning (ML) integrated with high-throughput profiling now provides systematic approaches to overcome these barriers. Beyond initial hit discovery, multilayer profiling using morphological phenotyping, transposon sequencing, transcriptomics, proteomics, and metabolomics captures cellular responses that reflect the mechanisms of action. Because profiling data sets are typically high-dimensional and contain defined features and variables, ML can extract complex patterns associated with pathway-level responses and predict mechanisms for unknown compounds. In this review, we summarize current progress in high-throughput profiling and describe how ML applied to each data set can accelerate the identification of antibacterials with new mechanisms. These approaches accelerate the transition from large-scale compound screening to mechanistic validation and enable effective prioritization of lead compounds in early-stage antibiotic discovery.
11 July 2026
Read appraisal →Cell host & microbe
Mining the code of life for new antibiotics.
Antimicrobial resistance (AMR) is outpacing antibiotic development, creating an urgent need for discovery strategies that are faster, broader, and more systematic. Here, we review the transition from classical "dirt mining" and phenotypic screening toward digital discovery approaches that treat chemical structures and biological sequences as searchable, engineerable substrates for antibiotic innovation. Modern extensions of conventional screening, including in situ cultivation, co-culture, and microfluidics, have broadened access to previously uncultured microbes. Computer-aided approaches spanning virtual screening, molecular networking, and deep learning have enabled identification of unconventional antibacterial scaffolds from ultra-large chemical libraries. Mining genomes, proteomes, and metagenomes has uncovered antimicrobial peptides, encrypted peptides, and biosynthetic gene clusters encoding novel small-molecule antibiotics. Generative AI now enables design of peptides and small molecules under multiobjective constraints, including potency, toxicity, stability, and resistance risk. Together, these advances point toward discovery platforms that improve novelty, hit rates, and long-term durability in the face of AMR.
10 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 →The Kurume medical journal
Development of Machine Learning Models to Predict Candidemia in Hospitalized Adult Patients
PURPOSE: We aimed to develop a generalizable machine learning model that leverages electronic medical record (EMR) data to predict candidemia using information available on or before the date of blood culture collection. METHODS: This study included adult patients admitted to Shin Koga Hospital (April 2014-March 2022) who underwent blood cultures at least 14 days after admission. We prepared two datasets: a "Complete case dataset" (13 variables, no missing values) and a "Imputed full-variable dataset" (36 variables, single imputation). We evaluated the discriminative performance of four machine learning models, including extreme gradient boosting, random forest, least absolute shrinkage and selection operator (LASSO) logistic regression, and logistic regression using stratified 5-fold cross-validation by comparing their area under the receiver operating characteristic curve (AUROC). The model with the highest mean AUROC was selected, assessed on the entire dataset, and internally validated using 1,000 bootstrap replicates. RESULTS: A total of 919 patients were included, of whom 47 (5.1%) had candidemia. The LASSO logistic regression demonstrated the best performance and was selected as the final model. The AUROC (95% CI) was 0.880 (0.836-0.924) for the Complete case dataset and 0.913 (0.878-0.948) for the Imputed full-variable dataset. The optimism-corrected AUROCs were 0.851 and 0.876, respectively. CONCLUSION: We developed two versions of the LASSO logistic regression model using only information automatically extracted from EMR, considering real-world data availability. These models have the potential to serve as practical tools to support early diagnosis and therapeutic intervention for candidemia.
3 July 2026
Read appraisal →Life sciences
The interplay of plant polysaccharide structure, gut microbiota metabolism, and host health: Mechanisms and perspectives
Plant polysaccharides, as natural biomolecules, possess the capability to modulate the structure and function of gut microbiota, subsequently influencing host health. Nevertheless, the molecular mechanisms underlying their interactions remain insufficiently understood. This article presents a systematic review of the impact of the chemical structure of plant polysaccharides, including monosaccharide composition and types of glycosidic bonds, on the metabolic preferences of gut microbiota. It emphasizes how structural variations in polysaccharides can promote the proliferation of specific gut microbiota, such as Bacteroidetes and Firmicutes. Additionally, the study analyzes critical enzymatic systems, including glycoside hydrolases (GHs) and polysaccharide lyases (PLs), alongside metabolic pathways associated with the production of short-chain fatty acids (SCFAs) that facilitate the degradation of plant polysaccharides by gut microbiota. Furthermore, it explores the influence of these metabolites on conditions such as obesity, diabetes, inflammatory bowel disease (IBD), and non-alcoholic fatty liver disease. The findings provide a robust theoretical framework for enhancing the utilization efficiency of bioactive polysaccharide components through targeted extraction and characterization techniques, thereby fostering potential advancements in innovative nutritional formulations and pharmacological applications.
16 June 2026
Read appraisal →International immunopharmacology
Antimicrobial peptides in malaria and tuberculosis management: a systematic review of emerging evidence
AMPs (Antimicrobial peptides) are small molecules that are crucial components of biological activities, viz wound healing, angiogenesis, antimicrobial activity, immune regulation, and exhibit anticancer properties. This systematic review summarizes the origin, sequence, efficacy, and structure-activity mechanisms of AMPs against tuberculosis and malaria, the world's leading killer infectious diseases, employing the AMP database (APD3) (till June 2025) and PubMed search (January 1965 to June 2025). The comprehension is well addressed by covering the inhibitory activity, SAR (structure-activity relationships), and action mechanisms of AMPs (natural and synthetic) against Mycobacterium tuberculosis and Plasmodium spp., as both pathogens exhibit multidrug resistance. The review also illustrates how these specific motifs, along with their properties such as hydrophobicity, charge, or amphipathicity, influence the AMPs activity against the pathogens. Moreover, challenges in the AMPs use and the way forward, including the recent breakthroughs employing nanocarriers, inhalable formulations for TB, and targeted erythrocyte delivery for malaria, are also highlighted. AMPs have illustrated promising anti-TB and antimalarial efficacy owing to their unique structure and novel mode of action, despite exhibiting varying extents of toxicity. Anti-malarial AMPs are grouped in defensins and cecropins, while the majority of natural anti-TB AMPs are classified as bacteriocins, defensins, and cathelicidins. Synthetic AMPs, in contrast, are designed to enhance activity and selectivity while reducing toxicity and the development of resistance. AMPs have α-helical, β-sheet, or random-coil structures and are cationic, amphipathic, and highly hydrophobic, attributes that contribute to observed anti-TB and anti-malarial potential. A comprehensive understanding of AMP's structure and function, along with the use of artificial intelligence (AI) and machine learning, can provide impetus for AMP therapy as an alternative to tackle tuberculosis and malaria.
15 June 2026
Read appraisal →Archives of microbiology
AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding
The ability to reliably predict protective immunity against infectious diseases remains a central challenge in immunology, vaccine development, and public health. Despite major advances in immunological measurement, immunity prediction has historically relied on simplified correlates most notably antibody titers that often fail to generalize across pathogens, populations, and time. The emergence of large-scale, high-dimensional immune datasets spanning multi-omics, longitudinal cohort studies, electronic health records, and pathogen genomics has created new opportunities for artificial intelligence (AI)-driven modeling. However, early applications reveal that predictive accuracy alone is insufficient and is frequently undermined by spurious correlations, limited generalizability, and a lack of biological interpretability. This review synthesizes current advances and limitations in AI-based immunity prediction, arguing that meaningful progress requires embedding machine learning within immune biology, causal reasoning, and rigorous validation frameworks. We examine the biological foundations of immunity relevant to modeling including innate immune programming, trained immunity, adaptive immune dynamics, and immune memory and map these processes to appropriate data modalities and model classes. We critically evaluate predictive tasks across individual and population scales, from early infection outcomes and vaccine responsiveness to immunity waning and herd-level protection. Emphasis is placed on validation beyond internal cross-validation, on interpretability grounded in immune mechanisms, and on robustness under demographic diversity and pathogen evolution. We conclude that immunity is best understood and predicted as a dynamic, probabilistic, and multiscale system rather than as a static trait. AI has substantial potential to improve infectious disease preparedness and clinical decision-making.
1 June 2026
Read appraisal →JAMA
Fast Antimicrobial Susceptibility Testing for Gram-Negative Bacteremia: The FAST Randomized Clinical Trial
IMPORTANCE: Novel blood culture diagnostics provide rapid antimicrobial susceptibility testing (AST) results for bacteria causing bloodstream infections (BSIs) but have unclear clinical impact. OBJECTIVE: To evaluate clinical outcomes of blood culture testing using a rapid AST method compared with standard AST in patients with BSIs caused by gram-negative bacilli in regions with high prevalence of antimicrobial resistance. DESIGN, SETTING, AND PARTICIPANTS: Open-label randomized clinical trial that enrolled participants from December 2023 to May 2025 (final follow-up, June 18, 2025) at 7 medical centers in Greece (n = 2), India (n = 1), Israel (n = 3), and Spain (n = 1). Hospitalized patients (adults and children) with BSIs caused by gram-negative bacilli were eligible. Statistical analysis was conducted from August 2025 to January 2026. INTERVENTION: Patients were randomized to undergo blood culture evaluation using rapid, phenotypic AST directly from positive blood cultures plus standard susceptibility testing (n = 413) vs standard susceptibility testing alone (n = 437). Local antimicrobial stewardship teams reviewed all patients and provided treatment recommendations. MAIN OUTCOMES AND MEASURES: The primary outcome was a desirability of outcome ranking (DOOR) at day 30, with 3 categories (alive without deleterious events, alive with deleterious events, and death). The difference between rapid and standard testing was summarized as the probability that the DOOR outcomes were more desirable in the rapid testing group. Superiority was concluded if the lower limit of the 95% CI exceeded 50%. Secondary outcomes included 30-day mortality, length of hospitalization, intensive care unit admission, acquisition of hospital-acquired infections, time to effective antibiotic therapy within 3 days, and time to antibiotic escalation or deescalation within 3 days. RESULTS: Of the 899 patients randomized, 850 were included in the primary outcome analysis (median [IQR] age, 72 [21] years; 43% female). The probability that DOOR outcomes were more favorable in the rapid testing group was 48.8% (95% CI, 45.3%-52.4%). Median time to effective antibiotic therapy was not different between the groups in the as-randomized population. Median time to antibiotic escalation or deescalation was faster in the rapid testing group by 14 hours (95% CI, 6-22) in the as-randomized population. There were no differences between the groups in other secondary outcomes. In the prespecified subgroup with carbapenem-resistant infections, median time to effective therapy was 9.5 hours in the rapid testing group vs 28 hours in the standard testing group (difference, -18 hours [95% CI, -42 to 6]). CONCLUSIONS AND RELEVANCE: Among patients with gram-negative bacilli BSIs, rapid blood culture AST was not superior to standard testing by DOOR. When considered with other efficacy and safety outcomes, these findings may help inform the use of rapid susceptibility testing in clinical practice. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06174649.
29 May 2026
Read appraisal →Cell reports
Artificial intelligence-driven donor-recipient gut microbiome matching for optimized fecal microbiota transplantation
Fecal microbiota transplantation (FMT) has emerged as a promising therapy for gastrointestinal diseases, yet its clinical efficacy remains individually variable. Here, we analyze multi-kingdom and functional profiles in pre- and post-FMT metagenomes from 515 FMTs across 30 cohorts and 12 diseases, in which 94 metagenomes from 44 FMTs are newly collected. We reveal a robust association between clinical efficacy and post-FMT microbiome convergence of recipients toward donors, across diseases. To predict post-FMT microbial convergence, we develop MOZAIC (Microbiome Matching Optimization via Artificial Intelligence), a framework that integrates multi-dimensional donor-recipient microbiota features. MOZAIC achieves an average area under the curve (AUC) of 0.88 and accuracy/recall >0.80 in forecasting microbiome convergence, with 78.7% accuracy in predicting clinical outcomes, and retrospectively simulates a 1.44-fold improvement (from 49.4% to 71.0%) in clinical response rates over baseline. This study establishes microbiome convergence as a key mediator of FMT and provides a scalable tool for precision matching in microbiota-based therapies.
28 May 2026
Read appraisal →PloS one
Computational models for the classification of antibody specificity using heavy chain features
BACKGROUND: Antibodies play a critical role in immune defense, with their antigen specificity primarily governed by the unique sequences of their heavy chains, rendering them invaluable tools in research and diagnostics. High-throughput sequencing technologies have facilitated comprehensive profiling of the immune repertoire, generating vast antibody sequence datasets that necessitate advanced analytical methods. METHODS: In this study, we utilized curated antibody sequences from NCBI databases to develop computational classification models for categorizing antibodies into predefined antigen classes. We extracted multifaceted features from the heavy chain sequences, encompassing physicochemical properties, structural composition, sequence order, and evolutionary information. These features were input into machine-learning classifiers to predict antigen specificity across five classes of antibodies: anti-dengue virus, anti-influenza virus, anti-tetanus bacillus, anti-SARS-CoV-2, and anti-Mycobacterium tuberculosis. RESULTS: Five tree-based machine-learning models were employed, with CatBoost achieving the highest accuracy of 0.7713. To further enhance predictive performance, we developed a stacking model leveraging multiple algorithms, resulting in an improved accuracy of 0.7803. Additionally, a Feature-Based Transformer deep-learning architecture was implemented, yielding an accuracy of 0.7399 and an F1-score of 0.6761. To elucidate the key determinants of antibody-antigen interactions, we applied the SHAP framework to assess feature importance. Among the top 30 contributing features, those representing sequence order and evolutionary information predominated, with amino acids such as cysteine (C), isoleucine (I), histidine (H), and phenylalanine (F) exhibiting notable SHAP values. Notably, cysteine (Cys) emerged as the most influential feature, underscoring its critical role in antibody structure and function. Specific antibodies contributed variably to these key features; for instance, the anti-tuberculosis antibody accounted for approximately 11% of a sequence order feature associated with alanine (A), while the anti-SARS-CoV-2 antibody contributed about 9.26% to a feature associated with isoleucine (I). CONCLUSIONS: Our study demonstrates the efficacy of machine-learning and deep-learning approaches in classifying antibodies into specific antigen categories, providing sequence-based insights into features associated with antibody specificity. These findings have significant implications for the mechanistic understanding, isolation, and development of potential therapeutic antibodies.
22 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Respiratory Syncytial Virus Vaccination in Solid Organ Transplant Recipients: Interim Findings From a Phase 3 Trial of mRNA-1345
BACKGROUND: Solid organ transplant (SOT) recipients are at increased risk for severe respiratory syncytial virus (RSV) disease due to chronic immunosuppression. METHODS: In this ongoing, open-label, phase 3 trial, adults ≥18 years with a history of liver, kidney, or lung transplant received 2 doses of mRNA-1345 RSV vaccine (50 µg) 56 days apart. Primary endpoints were tolerability, safety, and RSV-A/RSV-B neutralizing antibody (nAb) responses on Day 85; secondary endpoints included immunogenicity on Days 29 and 181. Cell-mediated immunity was an exploratory endpoint assessed in a subset of participants. RESULTS: 146/150 participants (median: age, 57 years; time since transplant, 4.7 years) received both doses. Reactogenicity was mild to moderate and transient. No vaccine-related discontinuations, deaths, adverse events of special interest (AESIs), or events of transplant rejection were reported within 28 days after any dose. One dose was immunogenic across all SOT types, with 4.9- and 3.4-fold increases in RSV-A/RSV-B nAb GMTs by Day 29, respectively. A second dose resulted in modest additional increases over baseline (RSV-A, 7.1-fold; RSV-B, 5.2-fold). Added benefit of the second dose was more apparent in participants with kidney and lung transplant, <2 years post-transplant, and on mycophenolate. Responses remained above baseline through Day 181. Polyfunctional CD4⁺ T-cell responses were robust and sustained; CD8⁺ responses were also observed. CONCLUSIONS: mRNA-1345 was well tolerated and immunogenic in SOT recipients. A single dose induced nAb responses across subgroups, with potential additional benefit from a second dose in specific groups. Durable antibody and cellular responses support mRNA-1345 as a preventive strategy for RSV in this vulnerable population. CLINICAL TRIALS REGISTRATION: NCT06067230.
22 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Cephalosporins Versus Non-cephalosporin Antibiotics for Perioperative Prophylaxis in Primary Arthroplasty: A Systematic Review and Meta-Analysis
BACKGROUND: The number of arthroplasty procedures and infection-related complications, including prosthetic joint infections (PJIs) and surgical site infections (SSIs), continues to rise. Although current guidelines recommend cefazolin or cefuroxime as the first-line perioperative prophylactic antibiotic, substitutions with non-cephalosporins remain common, especially among patients with reported β-lactam allergies. These substitutions may increase infection risk and healthcare costs. Evidence comparing outcomes across antibiotic classes remains variable. METHODS: We conducted a systematic review and meta-analysis of studies comparing single-agent cephalosporin versus single-agent non-cephalosporin prophylaxis in adults undergoing primary arthroplasty. Cochrane, Embase, Medline, Scopus, and Web of Science were searched from database inception through January 2025. Random-effect or fixed-effect models were used to estimate pooled odds ratios (OR) and 95% confidence intervals (CI). Subgroup analyses stratified results by cephalosporin type, study era, and risk of bias. RESULTS: Twenty-three studies (7 randomized controlled trials and 16 observational studies) were included, encompassing 191 527 arthroplasties in the cephalosporin arm and 20 058 in the non-cephalosporin arm. The odds of PJI were lower with cephalosporins (OR 0.59; 95% CI, .46-.75). No overall difference was observed for SSI (OR 0.70; 95% CI, .36-1.36), though post-2013 studies and the cefazolin subgroup demonstrated a significant protective effect. Randomized trial estimates were limited and heterogeneous and did not reach statistical significance. Certainty of evidence was graded as moderate. CONCLUSIONS: Cephalosporins, particularly cefazolin or cefuroxime, remain the preferred prophylactic agents for primary arthroplasty. This quantitative synthesis reinforces their protective association against prosthetic joint infection and supports adherence to guideline-endorsed prophylaxis.
22 May 2026
Read appraisal →Biochemistry
Antimycobacterial Peptides: From Natural Product Discovery to AI Guided Design
Mycobacterial pathogens remain major global health threats, exacerbated by both rapid acquisition of antibiotic resistance and the formidable drug diffusion barrier presented by the rigid mycomembrane. These challenges have renewed interest in antimycobacterial peptides (AMyPs), a diverse class of short amphiphilic sequences capable of rapidly killing both drug-sensitive and drug-resistant mycobacteria. Beyond their intrinsic potency, AMyPs can synergize with existing antibiotics and exhibit markedly slower resistance development relative to conventional small molecules. In this review, we synthesize recent advances spanning natural bioprospecting, mechanism-guided rational design, and chemical optimization strategies that have yielded increasingly potent and selective AMyP candidates. We further highlight the rapid emergence of artificial intelligence-driven discovery platforms, which leverage machine-learning models trained on curated, mycobacteria-specific data sets to predict and refine novel AMyPs with growing accuracy. Together, these technologic, biologic, and computational advances outline a rapidly expanding landscape for AMyP-based therapeutic development and establish a foundation for next-generation antimycobacterial drug design.
21 May 2026
Read appraisal →Revista do Instituto de Medicina Tropical de Sao Paulo
Treatment of brucellosis in pregnant women: a systematic review and analysis of current gaps and future perspectives
Human brucellosis is a globally prevalent bacterial disease with significant implications during pregnancy due to obstetric complications. Despite the importance of early diagnosis and treatment, the lack of therapeutic consensus and robust evidence underscores the need for systematic evaluation to guide clinical practice. This study systematically reviewed the literature to identify and summarize available drug therapy options for brucellosis in pregnant women, focusing on efficacy, safety, and obstetric outcomes. The review followed PRISMA and Cochrane Handbook guidelines. Searches were conducted in MEDLINE, Embase, Cochrane Library, LILACS, and gray literature for references indexed up to September 15, 2025. Eligible studies reported therapeutic interventions in pregnant women with brucellosis. Risk of bias was assessed using the JBI Critical Appraisal tool, and data on participants, interventions, and outcomes were extracted. Of 737 records screened, six studies met the inclusion criteria, comprising retrospective and prospective reports published between 1991 and 2020, totaling 403 pregnant women. Therapeutic regimens varied, with sulfamethoxazole-trimethoprim (SMX-TMP) plus rifampicin being the most frequently used combination. Adverse obstetric outcomes associated with this regimen occurred in 16% (95%CI: 8%-30%) of cases. Overall, abortion occurred in 94 (23.3%) women, 20 preterm births were reported, and 10 newborns had low birth weight. Recurrence was observed in only 3% of cases. Heterogeneity in outcomes and definitions limited comparability. In conclusion, there is no standardized treatment for brucellosis during pregnancy. Adverse obstetric outcomes remain frequent, highlighting the need for controlled studies to establish safe and effective therapeutic protocols.
20 May 2026
Read appraisal →Emerging microbes & infections
Clinical outcomes of carbapenem therapy in OXA-48-producing Enterobacterales infections: a French multicentre cohort, systematic review, and meta-analysis
OXA-48-producing Enterobacterales (OXA-48-PE) represent a growing global health threat. Most of OXA-48-PE remain categorized susceptible to carbapenems, which might encourage their use despite uncertain clinical efficacy. This study evaluated clinical outcomes of infections caused by OXA-48-PE treated with carbapenem compared with alternative active therapies. The analyses were performed at three levels: a descriptive analysis of the French cohort, a comparative descriptive analysis of all published studies, and a meta-analysis restricted to studies providing direct comparisons between carbapenem-based and alternative active regimens. Between September 2021 and March 2023, 59 patients with monomicrobial OXA-48-PE infections were included in a French multicenter retrospective cohort. In parallel, a systematic review was conducted to identify clinical studies published through 31 December 2024, reporting outcomes of OXA-48-PE infections. In the French cohort, the overall 30-day mortality was 49.1%. Clinical failure occurred in 57.1% of patients receiving meropenem monotherapy, including patients infected with isolates exhibiting meropenem MICs within the susceptible range. Across 12 clinical studies (817 patients), carbapenem therapy was associated with high and variable crude mortality (52%), whereas newer agents active against OXA-48-PE, particularly ceftazidime-avibactam, were associated with lower and more consistent crude mortality (30.7%). In the primary meta-analysis of 6 human comparative studies, carbapenem therapy was associated with an increased risk of clinical failure compared with alternative active regimens (OR = 2.02; 95% CI = 1.05-3.88). Despite apparent in vitro susceptibility, carbapenem therapy was consistently associated with unfavourable clinical outcomes in OXA-48-PE infections, supporting the prioritization of alternative active agents whenever available.
20 May 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of cefepime-nacubactam and aztreonam-nacubactam compared with imipenem-cilastatin for complicated urinary tract infection or acute uncomplicated pyelonephritis (Integral-1): a double-blind, randomised phase 3 trial
BACKGROUND: Nacubactam (OP0595) is a newly developed diazabicyclooctane β-lactamase inhibitor used in combination with cefepime or aztreonam. We assessed the efficacy and safety of cefepime-nacubactam and aztreonam-nacubactam versus imipenem-cilastatin in complicated urinary tract infection (cUTI) or acute uncomplicated pyelonephritis. METHODS: The Integral-1 global, phase 3, multicentre, randomised, double-blind study recruited adults (aged ≥18 years) with cUTI or acute uncomplicated pyelonephritis at 79 sites in Bulgaria, China, Czech Republic, Estonia, Georgia, Japan, Latvia, Lithuania, and Slovakia. Patients were randomly assigned (2:1:1) to receive intravenous cefepime (2 g) plus nacubactam (1 g), aztreonam (2 g) plus nacubactam (1 g), or imipenem (1 g) plus cilastatin (1 g) every 8 h for 5-14 days. Randomisation was stratified by diagnosis and geographical region. The primary endpoint was the proportion of patients achieving composite clinical and microbiological success at test of cure in the microbiological modified intention-to-treat population-all patients who were randomly assigned, received any amount of the study drug, and had a baseline qualifying pathogen that was susceptible to imipenem and meropenem. The prespecified non-inferiority margin was more than 15 percentage points difference; the superiority margin was more than zero percentage points difference, for the lower bound of the two-sided 95% CI for imipenem-cilastatin. Safety was assessed in all patients who received at least one dose of study drug. This study is registered with ClinicalTrials.gov, NCT05887908. FINDINGS: Between May 22, 2023, and Nov 26, 2024, 614 patients were randomly assigned and 431 were included in the primary efficacy analysis (cefepime-nacubactam [n=214], aztreonam-nacubactam [n=112], or imipenem-cilastatin [n=105]); 228 patients (53%) were male and 203 (47%) were female. The primary endpoint was achieved by 176 (82%) of 214, 81 (72%) of 112, and 64 (61%) of 105 patients in the cefepime-nacubactam, aztreonam-nacubactam, and imipenem-cilastatin groups, respectively. The percentage difference in the success rate versus imipenem-cilastatin was 21·3% (95% CI 10·9 to 32·0) for cefepime-nacubactam (non-inferior and superior), and 11·4% (-1·2 to 23·7) for aztreonam-nacubactam (non-inferior). Treatment-emergent adverse events were reported in 100 (33%) of 306, 45 (30%) of 152, and 65 (43%) of 150 patients in the cefepime-nacubactam, aztreonam-nacubactam, and imipenem-cilastatin groups, respectively. No treatment-related deaths occurred. INTERPRETATION: Cefepime-nacubactam and aztreonam-nacubactam are potential treatment options for Gram-negative cUTI and acute uncomplicated pyelonephritis, including infections caused by antimicrobial-resistant strains. FUNDING: Meiji Seika Pharma and Japan Agency for Medical Research and Development.
18 May 2026
Read appraisal →Talanta
SERS technology helps bacterial inactivation in vivo: including detection, identification, and evaluation.
The problem of bacterial infection and antimicrobial resistance has become a major challenge threatening to human health. Detection, identification, and real-time evaluation of bacteria during the sterilization process are of great significance for interpreting the sterilization mechanism and ensuring the effectiveness of sterilization. With the advantages of high sensitivity, fast detection speed, and real-time monitoring, Surface-enhanced Raman Scattering (SERS) technology provides a revolutionary solution to in vivo bacterial detection, identification, and antibacterial effect evaluation through functional substrate optimization, microfluidic chip integration, and deep collaboration of machine learning algorithms. This review focuses on the prominent applications of SERS technology in chemical, biological, and physical sterilization processes. By analyzing the changes in bacterial SERS fingerprint patterns, precise detection and identification of sensitive/resistant bacteria have been achieved, deepening the interpretation of drug resistance mechanisms at the gene, cell, and biofilm levels. Meanwhile, SERS technology provides an ideal tool for monitoring and evaluating the biological sterilization process. In the process of physical sterilization, enhancing the substrate material not only helps to improve SERS signals, but also promotes bacterial inactivation. By monitoring real-time changes in bacterial metabolism, the sterilization process can be precisely regulated. This review summarizes the innovative application of SERS technology in accurately guiding the sterilization process in vivo, providing important support for optimizing antibacterial treatment strategies.
18 May 2026
Read appraisal →The New England journal of medicine
Ensitrelvir for Covid-19 Postexposure Prophylaxis in Household Contacts
BACKGROUND: Ensitrelvir, an oral inhibitor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 3C-like protease, is approved in Japan for the treatment of mild-to-moderate coronavirus disease 2019 (Covid-19). Previously, no antiviral agents were approved for postexposure prophylaxis in household contacts of patients with Covid-19. METHODS: In this double-blind, randomized, placebo-controlled trial, we randomly assigned persons who were SARS-CoV-2-negative on local diagnostic testing but were household contacts of a patient with Covid-19 (the index patient) to receive either ensitrelvir (375 mg on day 1 and 125 mg daily on days 2 through 5) or placebo within 72 hours after symptom onset in the index patient. The primary end point was Covid-19 (defined by a central laboratory-confirmed positive reverse-transcriptase-polymerase-chain-reaction assay and the presence of ≥1 of 14 prespecified Covid-19 symptoms lasting ≥48 hours) by day 10 in a household contact in the modified intention-to-treat population (all the participants who underwent randomization, had a central laboratory-confirmed negative RT-PCR test for SARS-CoV-2 at baseline, and received at least one dose of the trial drug or placebo). RESULTS: The modified intention-to-treat population included 1030 participants in the ensitrelvir group and 1011 in the placebo group. The mean age of the participants was 42.4 years; 71.1% had undergone randomization within 48 hours after symptom onset in the index patient, and 37.0% had at least one risk factor for severe Covid-19. The incidence of Covid-19 was lower in the ensitrelvir group than in the placebo group (2.9% vs. 9.0%; risk ratio, 0.33; 95% confidence interval [CI], 0.22 to 0.49; P<0.001). The incidence of adverse events during the trial was similar in the two groups (15.1% in the ensitrelvir group and 15.5% in the placebo group), as was the incidence of serious adverse events (0.2% in each group). No Covid-19-related hospitalizations or deaths were reported. CONCLUSIONS: Ensitrelvir administered to household contacts of a patient with Covid-19 within 72 hours after symptom onset in the index patient was effective in preventing Covid-19 in the contacts. (Funded by Shionogi; SCORPIO-PEP Japan Registry for Clinical Trials number, jRCT2031230124; ClinicalTrials.gov number, NCT05897541.).
15 May 2026
Read appraisal →Journal of clinical microbiology
Diagnostic performance of β-(1→3)-D-glucan, two Candida antigen, and five anti-Candida antibody assays in ICU patients with sepsis and high risk for invasive candidiasis: a secondary endpoint of the CandiSep randomized clinical trial
Invasive Candida infection (ICI) is the most common fungal infection in critically ill patients. This study analyzed the performance of various biomarkers in sera from the CandiSep trial, a randomized, multicenter trial including 342 sepsis patients at high risk for ICI across 18 German intensive care units. ICI and candidemia were diagnosed in 48 (14.0%) and 14 (4.1%) patients, respectively. Sera collected on 2 consecutive days at sepsis onset were analyzed for β-(1→3)-D-glucan (BDG; Fungitell), mannan (Platelia-Candida-Ag-Plus [Platelia-Mn] and Serion-ELISA-antigen-Candida [Serion-Mn]), and anti-Candida antibodies (Platelia-Candida-Ab-Plus, Serion-ELISA-Candida albicans-IgA/IgM/IgG, and Virclia-Candida albicans-germ-tube-antibody-IgG-Monotest). Only antigen levels (BDG, Platelia-Mn, Serion-Mn), but not anti-Candida antibody levels, were significantly elevated in ICI patients. Antigen levels were unaffected by Candida colonization, while antibody levels were significantly increased. Sensitivity and specificity at the manufacturer's cutoffs were unsatisfactory. Performance improved by adjusting cutoffs: BDG required a 3-fold increase, while all others required lowering. At 80% specificity, sensitivities (95% confidence intervals) for the diagnosis of ICI and candidemia were as follows: BDG (cutoff >280 pg/mL), 46% (31.4-60.8) and 64% (35.1-87.2); Platelia-Mn (cutoff >50 pg/mL), 38% (24.0-52.6) and 64% (35.1-87.2); Serion-Mn (cutoff >0.7 U/mL), 38% (24.0-52.6) and 50% (23.0-77.0), and below 28% and 43% for all antibody assays. Area under the receiver operating characteristic curve comparisons showed no significant differences between antigen assays. Combining biomarkers, either simultaneously or sequentially, offered no diagnostic advantage over single-biomarker use. In conclusion, anti-Candida antibody assays are likely influenced by colonization and have limited diagnostic utility. Antigen assays offer similar diagnostic value but require cutoff optimization. Combining biomarkers did not enhance diagnostic yield in our cohort.IMPORTANCEOur main findings based on our specific ICU cohort were as follows: (i) only Candida antigen levels, not anti-Candida antibody levels, were significantly elevated in ICI and candidemia. (ii) In patients without ICI, Candida colonization was not associated with altered antigen levels, but with elevated antibody levels. (iii) Sensitivity and specificity at the manufacturer's cutoffs were unsatisfactory, and cutoffs should be significantly adjusted. Potential optimal cutoff values are proposed by us. (iv) The evaluated antigen assays demonstrated overall comparable diagnostic performance in this study. (v) Anti-Candida antibody assays did not provide a meaningful diagnostic contribution. (vi) The combination of biomarkers offered no diagnostic advantage over the use of individual biomarkers, neither simultaneously nor sequentially. Our results suggest that the use of a single Candida antigen test with a cohort-adapted cutoff value may be sufficient. Furthermore, avoiding biomarker combinations could potentially reduce healthcare costs. The clinical implications of these findings should be interpreted with caution, given the limited data with associated large confidence intervals for candidemia and the cohort-specific nature of the study. Our results should therefore be confirmed in larger studies and additionally with other patient groups.
15 May 2026
Read appraisal →Journal of clinical microbiology
Prediction of KPC-producing Klebsiella pneumoniae by MALDI-TOF MS, ensemble learning, and spectral peak annotation
The increasing prevalence of carbapenem-resistant Klebsiella pneumoniae represents a serious global health challenge. Rapid and accurate detection methods are essential to inform early and correct use of antimicrobial therapy to idealize patient outcomes and limit the spread of resistant isolates. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been evaluated for resistance detection, but its clinical application remains limited. In this study, MALDI-TOF MS was integrated with machine learning (ML) to improve detection of Klebsiella pneumoniae carbapenemase (KPC) among K. pneumoniae isolates harboring KPC carbapenemase. Ensemble learning strategies were applied to 435 clinical isolates from different geographic locations (Spain, United States, South America, and Asia), with validation performed using external data sets. By constructing two peak matrices and applying four ML algorithms, as well as combinations of two and three in ensemble models, 92 different classifiers were tested. Ensemble combinations increased the specificity of classifiers to over 95%, while sensitivity reached 72%, being significantly higher than that of the MALDI Biotyper KPC module. Low sensitivity may be affected by technical variability during spectral acquisition. The first annotated MALDI-TOF MS spectrum for K. pneumoniae by in silico prediction of protein masses was developed to enable peak identification, including peaks potentially related to antimicrobial resistance. Overall, the results of this study show that combining MALDI-TOF MS with ensemble learning can enhance KPC detection performance.IMPORTANCERapid and accurate detection of Klebsiella pneumoniae carbapenemase (KPC)-producing Klebsiella pneumoniae informs early and correct use of antimicrobial therapy to idealize patient outcomes and limit the spread of antimicrobial resistance. In this study, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and ensemble machine learning were employed to detect KPC-producing isolates, using diverse isolate collections, obtaining excellent specificity. An annotated K. pneumoniae MALDI-TOF MS profile is provided with peak annotation, providing a resource for peak analysis.
14 May 2026
Read appraisal →Annals of medicine
Ceftriaxone for methicillin-susceptible Staphylococcus aureus bloodstream infections is associated with increased short-term mortality: a systematic review and meta-analysis
BACKGROUND: Bloodstream infections (BSIs) by methicillin-susceptible Staphylococcus aureus (MSSA) are a significant cause of morbidity and mortality, traditionally treated with antistaphylococcal penicillins (ASPs) or cefazolin. Ceftriaxone has emerged as an alternative due to its once-daily dosing regimen and favourable safety profile; however, its efficacy compared to the standard of care (SoC) remains controversial. This evidence synthesis aimed to assess the role of ceftriaxone in treating MSSA-BSIs. METHODS: A systematic literature search was conducted in PubMed, Embase, and Scopus up to December 31, 2025 (PROSPERO protocol CRD42024595748). Studies comparing ceftriaxone to ASPs or cefazolin for MSSA-BSIs were included. Primary outcomes were 30-day and 90-day all-cause mortality . Pooled effect sizes with their 95% confidence intervals (CIs), were calculated using random-effects models, odds ratios (ORs) and mean differences (MDs) according to the type of outcome. RESULTS: Eleven studies totalling 2,568 patients were included. Ceftriaxone was associated with significantly increased 30-day mortality (OR 3.33; 95% CI: 2.17-5.10), although differences at 90 days were not significant (OR 1.71; 95% CI: 0.75-3.90). No significant differences were noted for clinical success (OR 0.49; 95% CI: 0.19-1.26), microbiological clearance (OR 1.66; 95% CI: 0.73-3.82). Adverse event rates were similar between groups. CONCLUSION: Given the availability of various alternatives and the consistent short-term mortality signal observed, routine use of ceftriaxone for MSSA-BSIs, especially as initial therapy, is not supported by current evidence. Only novel findings from randomized studies may change the place in therapy of the drug in this context.
13 May 2026
Read appraisal →Immunologic research
An integrated AI-driven vaccine design process: a systematic review of workflows from generative design to translational prediction
Traditional vaccine development faced significant hurdles, including lengthy timelines and high costs, which hindered rapid responses to pathogens. Although the emergence of AI offered transformative potential, the necessity for a fully integrated workflow was often overlooked in studies focusing on individual tools. This review addressed a critical gap by synthesizing AI technologies across the vaccine design process, focusing on the integrated workflow from antigen discovery to clinical translation. A systematic framework was required to connect disparate tools and ensure seamless transitions. Consequently, this study provided a comprehensive roadmap for pandemic preparedness and vaccine discovery. A systematic analysis based on the PRISMA framework (2015-2024) was conducted, and 19 landmark articles were reviewed.It was demonstrated that the paradigm shift from predictive to generative AI offered unprecedented opportunities for developing novel antigens and adjuvants with superior immunogenicity. Synthesis of the literature revealed rapid progress toward sophisticated deep learning. Transformer models and Protein Language Models emerged as dominant for epitope prediction, while AlphaFold2 became the standard for structural modeling. The advent of generative AI for de novo antigen design represented the leading edge of the discipline. Additionally, AI-enhanced molecular dynamics and digital twin simulations accelerated clinical validation and manufacturing scalability. The "Integrated AI Workflow for Vaccine Design and Development" was emphasized as a comprehensive system and a prerequisite for sustainable innovation. Overall, this analysis served as a strategic roadmap for utilizing AI as a transformative framework for next-generation vaccine discovery and pandemic preparedness.
12 May 2026
Read appraisal →Pancreas
Whole Metagenomic Profiling Identifies a Gut Microbial Signature for Chronic Pancreatitis via Machine Learning
BACKGROUND: Pancreatitis significantly alters the microbial composition of the oral and intestinal compartments, causing dysbiosis that may contribute to disease mechanisms and potentially serve as a basis for diagnosis or treatment. OBJECTIVE: To determine whether the oral or gut microbial signature can classify chronic pancreatitis (CP). METHODS: Stool samples (n=707) were collected from participants in the Prospective Evaluation of Chronic Pancreatitis for Epidemiologic and Translational Studies (PROCEED). Samples were distributed among 200 healthy (HC), 310 CP, 49 acute pancreatitis (AP), and 148 recurrent acute pancreatitis (RAP). In addition, saliva samples were collected for a subset of participants (n=156). Whole genome sequencing was performed to assess microbiome composition. Machine learning algorithms were utilized to identify a signature with microbial features predictive of CP. RESULTS: Gut alpha diversity was significantly decreased in AP, RAP, and CP compared with HC, with CP exhibiting the lowest diversity. In contrast, oral microbial diversity showed no significant variation across groups. Beta diversity analysis revealed distinct gut microbiome compositions between HC and pancreatitis subtypes, with CP showing the most pronounced differences. Random forest models using gut microbial species demonstrated robust predictive performance for CP using a minimum of 10 species (Area under the curve-AUC: 0.834; accuracy: 0.774). Despite similarities in gut microbiome composition across pancreatitis subtypes, a unique gut microbial signature for CP was identified highlighting the microbiome's potential in CP diagnosis. CONCLUSION: Our study reveals a gut microbial signature predictive of CP using machine learning models in a large US multi-institutional cohort.
6 May 2026
Read appraisal →Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Bacterial quantitation in clinical infection: a systematic review
Pivotal clinical studies of new anti-infective drugs enrol patients with strictly defined clinical syndromes, with clinical and laboratory endpoints used to define treatment success. Assessing bacterial killing in the host by serial quantitation has potential as a more efficient pharmacometric approach to assess antibiotic efficacy. We conducted a systematic review of the use of bacterial quantitation in clinical infection. The main syndromes studied in the 222 included articles were bacteraemia, chronic respiratory disease, diarrhoea, pneumonia and sexually transmitted infections. Most reports (135 articles, 61%) quantified bacterial load in different specimen types using quantitative polymerase chain reaction (qPCR). For bacteraemias, bacterial DNA load measured in whole blood by qPCR at clinical presentation was typically 2-3 log10 copies ml-1, usually substantially higher than the estimates from quantitative culture. Higher bacterial loads were associated with increased mortality in 28 of 35 studies. Faster bacterial clearance was correlated with appropriate antibiotic therapy and improved outcome in the majority of studies. Most studies sampled too infrequently for accurate characterization of bacterial clearance rates. The rate of bacterial clearance from blood or other compartments is an informative pharmacodynamic endpoint in the assessment of antibacterial therapeutic effects, but standardized approaches to assessment are needed based on optimal study design. This article is part of the Theo Murphy meeting issue 'Evaluating anti-infective drugs'.
3 May 2026
Read appraisal →mSystems
Short-chain fatty acid-producing microbes differentiate non-infectious and infectious neutropenic fever in leukemia.
UNLABELLED: Neutropenic fever (NF) is often the first sign of infection in patients with hematologic malignancies, but its cause is frequently unknown, leading to broad-spectrum antibiotic use without confirmed infections. Although research links gut microbiome disruptions to treatment-related infections, it typically examines NF as the outcome, leaving a gap in understanding how the microbiome and metabolic factors distinguish infectious from non-infectious cases. Stool samples from acute myeloid leukemia patients were analyzed to characterize gut microbiome composition and fecal metabolites at baseline and at fever onset. Machine learning models, network analyses, and functional profiling were used to differentiate infectious NF vs non-infectious NF at baseline and at fever onset. The baseline model (area under the receiver operating characteristic [AUROC] = 0.769) identified higher levels of Akkermansia, Enterobacter, Escherichia-Shigella, and Flavonifractor as predictors of infectious NF, while Collinsella, Lachnospiraceae, Coprococcus, and acetic acid were linked to non-infectious cases. At fever onset, Enterococcus was enriched in infectious NF, whereas Gemella, Butyrate, Lachnospiraceae, Ruminococcaceae, and Eisenbergiella abundances favored non-infectious NF outcomes (AUROC = 0.752). Network analyses also revealed greater functional diversity and microbiome-metabolome connectivity in non-infectious cases at fever onset. This study suggests that gut microbiota and metabolites may serve as biomarkers for distinguishing infectious from non-infectious neutropenic fever, warranting further validation in larger cohorts. IMPORTANCE: Our study tackles the challenge of managing neutropenic fever (NF) in immunocompromised patients whose numbers have increased due to various immunodeficiencies and treatments that suppress immune function. Fever is often the only sign of a serious infection in these patients, yet there are neither clear patterns linking risk factors to infection nor biomarkers reliable for ruling out non-infectious causes. As a result, febrile patients are typically empirically treated for major pathogens, even in the absence of confirmed infections, which propagates antimicrobial resistance and gut dysbiosis. Our research utilizes gut microbiome and targeted metabolomic profiling from two cohorts of patients with acute myeloid leukemia undergoing chemotherapy and employs a machine learning framework to distinguish between infectious and non-infectious NFs at baseline and upon fever onset.
23 Apr 2026
Read appraisal →PloS one
Prevalence of antimicrobial resistance in Tanzania: A systematic review and meta-analysis
Antimicrobial resistance (AMR) threatens global health, and understanding resistance patterns aids in effective treatment and promotes responsible antimicrobial use. Despite the urgency of resistant pathogens, systematic reviews focusing specifically on Tanzania are limited, and while several studies report resistance patterns for individual pathogens, a consolidated analysis of overall prevalence is needed to inform policymaking and public health interventions. Therefore, this review and meta-analysis assessed the prevalence of antimicrobial resistance among clinically relevant pathogens in Tanzania, providing a comprehensive overview to support surveillance, infection control, and stewardship efforts. A total of 1865 studies identified from Google Scholar (1600), PubMed (13), and Science Direct (252) underwent screening and full article review. Finally, 28 studies were included. A subgroup analysis was performed to evaluate the resistance patterns within antibiotic classes for specific pathogens. Descriptive statistics were used to describe the characteristics of the studies, while the prevalence of antimicrobial resistance was estimated through Meta-analysis. Inconsistency and heterogeneity between studies were quantified by the I2 index. Among the included studies, most isolates (25.0%) were obtained from urine samples. Of these studies, 75% were cross-sectional studies and 92.9% were conducted in hospital settings. The analysis revealed high resistance to penicillin, particularly amoxicillin-clavulanic and ampicillin, with Klebsiella pneumoniae (0.96 [0.83-0.99]), Acinetobacter baumannii (0.94 [0.67-0.99]) and Escherichia coli (0.90 [0.81-0.95]). Similarly, erythromycin resistance was most prevalent in Campylobacter spp. (0.85 [0.80-0.89]). Ciprofloxacin resistance was highest in Acinetobacter baumannii (0.54 [0.33-0.73]), whereas amikacin resistance was highest in Proteus spp. (0.86 [0.35-0.99]). Ceftriaxone resistance was particularly high in Acinetobacter baumannii (0.91 [0.70-0.98]) and Pseudomonas aeruginosa (0.85 [0.74-0.92]). Meropenem resistance was lowest among Escherichia coli (0.04 [0.01-0.10]) and Klebsiella spp. (0.07 [0.03-0.15]), while the pooled resistance across ESKAPE-E pathogens was (0.11[0.06-0.19]). Imipenem and clindamycin each had an overall pooled resistance of (0.06[0.02-0.14]) against both Escherichia coli and Klebsiella pneumoniae. The findings highlight widespread resistance among bacterial pathogens, ESKAPE-E, particularly in the Access and Watch groups of antibiotics. The variability in resistance patterns underscores the need for the Ministry of Health to re-evaluate empirical treatment protocols (STG/NEMLIT) to ensure effective treatment regimens, strengthen antimicrobial stewardship, enhance surveillance systems, and promote rational antibiotic use.
23 Apr 2026
Read appraisal →The Analyst
Third-generation nanopore sequencing: advancing real-time pathogen detection and antimicrobial resistance surveillance in food safety applications
Foodborne diseases impact 600 million people annually, causing 420 000 deaths and over $110 billion in economic losses. Conventional pathogen detection methods take 3-7 days and lack sensitivity for low-abundance pathogens and antimicrobial resistance (AMR) genes. This review evaluates nanopore sequencing's potential in food microbiology, analyzing real-time pathogen detection, AMR surveillance, and performance versus traditional methods. A systematic review of 80 studies (2015-2025) revealed nanopore sequencing reduces detection time to <24 hours with 95-98% species identification accuracy and >90% AMR gene sensitivity, at $50-200 per sample versus $300-800 for traditional whole-genome sequencing. It enables multiplex pathogen detection, viable but non-culturable organism identification, and real-time AMR profiling. However, challenges include error rates of 8-15% in homopolymeric regions and the need for bioinformatics expertise. Addressing challenges, including error rates of 8-15% in homopolymeric regions and the need for bioinformatics expertise, requires strategic mitigation approaches. Despite limitations, advances in base-calling and workflows make nanopore sequencing a transformative tool for next-generation food safety surveillance.
23 Apr 2026
Read appraisal →PloS one
Comparison of OneChoice AI-based clinical decision support recommendations with infectious disease specialists and non-specialists for bacteremia treatment in Lima, Peru
Bacteremia is a major contributor to global morbidity and mortality, particularly in low- and middle-income countries where diagnostic delays and empirical antimicrobial misuse exacerbate resistance. This study assessed the accuracy of OneChoice®, an artificial intelligence (AI)-based Clinical Decision Support System (CDSS), in guiding antimicrobial therapy for bloodstream infections (BSIs) in Lima, Peru. A cross-sectional, observational design was used, comparing therapeutic recommendations generated by OneChoice®-based on molecular (FilmArray®) and phenotypic (MALDI-TOF MS, VITEK2) data-with the clinical decisions of 94 physicians (35 infectious disease [ID] specialists and 59 non-specialists) across 366 survey-based evaluations of bacteremia cases. Concordance between CDSS and physician decisions was analyzed using Cohen's Kappa and logistic regression. The overall concordance rate was 96.1% when considering any suggested treatment, and 74.6% for the top recommendation, with a substantial agreement (κ = 0.70). ID specialists showed significantly higher concordance (κ = 0.78) than non-ID physicians (κ = 0.61), and specialization was the strongest predictor of agreement (OR = 2.26, p = 0.001). Escherichia coli cases had the highest concordance, while Pseudomonas aeruginosa showed the lowest. The CDSS reduced inappropriate antibiotic use, particularly unnecessary carbapenem prescriptions. These findings support the utility of AI-CDSS tools in enhancing antimicrobial stewardship and standardizing care, especially in resource-limited healthcare settings.
20 Apr 2026
Read appraisal →Gut microbes
Re-establishing bile acid composition after treatment of recurrent Clostridioides difficile infection with fecal microbiota transplantation compared with oral vancomycin or a 12-strain bacterial mixture
Patients with Clostridioides difficile infection have high colonic levels of primary bile acids, which are potent germinators of Clostridioides difficile. Several studies have suggested that re-establishing a normal bile acid composition is a key factor in fecal microbiota transplantation (FMT) for recurrent C. difficile infection, yet former studies supporting this lacked controls. In a subgroup from a randomized controlled trial, we compared the bile acid composition in patients with recurrent C. difficile infection treated with either FMT, a bacterial mixture, or vancomycin. The fecal bile acid content was analyzed several times before and after treatments. Furthermore, we used 16S rDNA gene sequencing to analyze the presence of some bacterial species involved in bile acid metabolism. Stool donors served as healthy controls. We observed a higher proportion of primary bile acids in patients with recurrent C. difficile infection than in donors, yet a donor-like dominance of secondary bile acids was observed after successful treatment in all groups. The shift seemed to occur earliest in the FMT group, followed by the vancomycin group, and the latest in the bacterial mixture group. In approximately half of the participants, the rise in secondary bile acids was timely associated with the detection of bile acid-transforming bacteria that were absent before treatment. Our findings indicate that FMT re-establishes the bile acid composition faster than vancomycin, reducing the time of susceptibility to recurrences of C. difficile infection. Hence, bacterial mixtures developed as an alternative to donor stool for treating recurrent C. difficile infection might benefit from including bile acid-metabolizing bacteria.
19 Apr 2026
Read appraisal →Emerging microbes & infections
Optimizing machine learning-based diagnosis of neurosyphilis in HIV-negative patients: a multicenter, real-world comparison of international diagnostic criteria
Neurosyphilis continues to rise globally, yet diagnosis remains challenging, often requiring multidisciplinary expertise and multiple CSF assays that are difficult to access in resource-limited settings. We developed and compared machine-learning (ML) models tailored to six international diagnostic guidelines and built a free, web-based tool for guideline-adapted decision support. We assembled 1,648 suspected neurosyphilis cases from four centers, using Guangzhou as the training cohort and Beijing, Xiamen (China) and Seattle (USA) as external validation cohorts. Five algorithms (Random Forest, AdaBoost, SVC, NuSVC, XGBoost) were trained with randomized search and three-fold cross-validation; performance was assessed by AUC, PRAUC, calibration, decision-curve net benefit, Brier score, and SHAP for model explainability. Across models and guidelines, neurological symptoms, CSF protein, and CSF white blood cell count consistently ranked as the strongest predictors. All models achieved excellent discrimination (AUC and PRAUC >0.90) with good calibration, reliability, and positive clinical utility, though performance varied modestly by guideline. These findings indicate that the optimal ML approach depends on the diagnostic definition applied. Our freely available online tool operationalizes these models to provide clinicians worldwide with context-adapted support aligned to local criteria.
18 Apr 2026
Read appraisal →Computers in biology and medicine
A systematic review and benchmarking of modern metagenomic tools for taxonomic classification
Advancements in metagenomics have been driven by the continuous development of bioinformatic tools, particularly taxonomic classification software, which are central to the accurate characterization of microbial communities. However, establishing direct comparisons between these tools remains challenging due to variations in evaluation metrics, reference databases, and input data types. In this study, we present a systematic review of recently developed metagenomic taxonomic classification tools. Of the 31 identified tools, nine satisfied all functional and methodological criteria for the benchmark analysis. We evaluated their accuracy and computational performance using a standardized dataset derived from the NCBI RefSeq database. Our analysis revealed that most of these tools are domain-specific, each excelling in particular areas. Tools like TAMA, CAMITAX and PhyloFlash achieved higher accuracy for prokaryotic organisms, while ViWrap and PhaBOX achieved higher accuracy for viral classifications. SqueezeMeta achieved high F1 scores across most domains, though its assembly-based architecture limits effectiveness on highly diverse samples. MegaPath-Nano was least affected by increased mutation rates. The execution time varied widely among the tools, with domain-specific and machine learning-based tools generally being faster, while tools like BASTA had longer runtimes and lower accuracy. This review synthesizes performance results for current tools, providing an overview of their strengths and computational methodologies.
17 Apr 2026
Read appraisal →European journal of pediatrics
7-days versus 14-days antibiotic therapy in uncomplicated culture proven neonatal sepsis: a randomized control assessor-blinded trial
UNLABELLED: The trial aimed to establish the non-inferiority of a 7-day antibiotic therapy for uncomplicated neonatal sepsis when compared to the standard 14-day therapy. This study was a parallel-group, randomized non-inferiority assessor-blinded trial conducted in a tertiary Neonatal Intensive Care Unit in Central India. Neonates weighing ≥ 1000 g with suspected sepsis were screened and those meeting criteria were enrolled. Exclusions included babies with CNS (central nervous system) infections, septic arthritis, and life-threatening congenital malformations. Participants were observed for 7 days on antibiotics and re-evaluated; those with positive blood cultures were then randomized to receive either 7 or 14 days of antibiotics. The primary outcome was the relapse of sepsis, and a sample size of 70 in each arm was calculated based on a non-inferiority margin. Follow-ups were conducted for 48 h post-antibiotic treatment and weekly for 35 days to monitor any recurrence of illness. During the study, 917 babies with suspected sepsis were admitted, of which 256 had culture-positive sepsis. After excluding those with meningitis, staphylococcus, and fungal infections, 140 babies showed improvement at day 5 and were randomized into two groups: one receiving antibiotics for 7 days and the other for 14 days, each consisting of 70 babies. Klebsiella pneumoniae was the prevalent organism. The 7-day group had a shorter hospital stay (p < 0.05) and less respiratory support (p < 0.05). Outcomes revealed a low incidence of probable relapse in both groups, with no fatalities or definitive relapses recorded. CONCLUSIONS: A 7-day antibiotic regimen for uncomplicated neonatal sepsis is not inferior to a 14-day regimen. WHAT IS KNOWN: • Unregulated use of antibiotics can lead to a myriad of problems, especially in neonates. • There is some evidence that uncomplicated neonatal sepsis can be treated with short-course antibiotics. WHAT IS NEW: • Data is lacking-especially from Central India. • This trial checks if it is possible to reduce the duration of antibiotic therapy in uncomplicated neonatal sepsis.
16 Apr 2026
Read appraisal →The New England journal of medicine
Selective Decontamination of the Digestive Tract during Ventilation in the ICU
BACKGROUND: Whether selective decontamination of the digestive tract (SDD) reduces mortality among patients undergoing mechanical ventilation and whether it adversely affects microbial ecology in the intensive care unit (ICU) remain unclear. In an earlier analysis of data from Australia, SDD did not result in a lower incidence of in-hospital death than standard care, but data from the full international trial are needed. METHODS: We randomly assigned ICUs in Australia and Canada to use SDD or to continue standard care for two 12-month periods in patients undergoing mechanical ventilation. Patients in the SDD group received specific oral and gastric antimicrobial interventions for the duration of ventilation and an intravenous antibiotic agent for the first 4 days after enrollment. All other patients in the ICU were included in an observational ecologic assessment. Previously reported data from Australia are now combined with data from Canada. The primary outcome was in-hospital death from any cause at 90 days. The secondary clinical outcomes, assessed at 90 days, were death in the ICU and the number of days alive and free of mechanical ventilation, ICU admission, and hospitalization. Microbiologic secondary outcomes included new positive cultures for bloodstream infections and antibiotic-resistant organisms. For the ecologic assessment, the microbiologic outcomes were tested for noninferiority (noninferiority margin, 2 percentage points). RESULTS: In this trial involving 20,000 patients in 26 ICUs, 9289 patients were enrolled in the randomized trial and 10,711 were included in the ecologic assessment. At 90 days, 1175 of 4215 patients (27.9%) in the SDD group and 1494 of 5065 (29.5%) in the standard-care group had died before hospital discharge (odds ratio, 0.94; 95% confidence interval [CI], 0.84 to 1.05; P = 0.27). New bloodstream infections occurred in 4.9% of the patients in the SDD group and in 6.8% of those in the standard-care group (adjusted mean difference, -1.30 percentage points; 95% CI, -2.55 to -0.05); antibiotic-resistant organisms were cultured in 16.8% and 26.8%, respectively (adjusted mean difference, -9.60 percentage points; 95% CI, -12.40 to -6.80). In the ecologic assessment, noninferiority of SDD was not confirmed for the development of new antibiotic-resistant organisms. Adverse events considered to be related to SDD or standard care were reported in 12 patients (0.3%) in the SDD group and in no patients in the standard-care group. Serious adverse events occurred in 47 patients (1.1%) and 59 patients (1.2%), respectively. CONCLUSIONS: Among critically ill patients undergoing mechanical ventilation, SDD did not result in a lower incidence of in-hospital death than standard care. (Funded by the National Health and Medical Research Council of Australia and the Canadian Institutes of Health Research; ClinicalTrials.gov number, NCT02389036.).
16 Apr 2026
Read appraisal →European journal of pediatrics
Clinical response and risk factors of fecal microbiota transplantation in children: a systematic review and meta-analysis
UNLABELLED: The objective of this study is to investigate the clinical response and incidence of adverse events (AEs) following fecal microbiota transplantation (FMT) in children, across various diseases, populations, and treatment protocols. A systematic search was conducted across eight major Chinese and English databases, identifying 47 studies up to August 28, 2025, for inclusion. Study quality was assessed using the Quality Assessment with Diverse Studies (QuADS) tool. Single-arm rates were pooled via meta-analysis employing the Freeman-Tukey double arcsine transformation, followed by extensive subgroup comparisons to identify influencing factors. FMT demonstrated efficacy in pediatric recurrent Clostridium difficile infection (rCDI), inflammatory bowel disease (IBD), and autism spectrum disorder (ASD), although a higher incidence of AEs was observed in children with IBD. Subgroup analyses revealed that the use of donor feces from relatives or friends was associated with a higher clinical response rate in rCDI. The presence of comorbidities such as IBD diminished the response rate in rCDI patients. Younger age in rCDI and IBD patients showed a trend towards higher clinical response rates, though this did not reach statistical significance. No statistically or clinically significant differences were found in other subgroup comparisons. Meta-regression suggested IBD to be a risk factor for FMT-related AEs. CONCLUSION: This study innovatively delineates the efficacy-safety profile of pediatric FMT and outlines a pathway for optimizing individualized treatment regimens, providing crucial evidence-based guidance for clinical practice. TRIAL REGISTRATION: This study has been registered on the PROSPERO database (CRD42024614196). WHAT IS KNOWN: • Fecal Microbiota Transplantation (FMT) demonstrates preliminary therapeutic potential in several pediatric diseases. • Existing evidence remains fragmented, with limited systematic data on factors modifying efficacy and safety in children. WHAT IS NEW: • The study revealed FMT's high efficacy across rCDI, IBD, and ASD, and identified IBD as a risk factor for elevated FMT-related adverse events in pediatric patients. • Notably, related/friend donors improved rCDI response rates, while comorbidities like IBD reduced rCDI treatment efficacy.
15 Apr 2026
Read appraisal →PloS one
Predicted meta-omics: A potential solution to multi-omics data scarcity in microbiome studies
Imbalances in the gut microbiome have been linked to conditions such as inflammatory bowel disease, diabetes, and cancer. While metagenomics and amplicon sequencing are commonly used to study the microbiome, they do not capture all layers of microbial functions. Other meta-omics data can provide more insights, but these are more costly and laborious to procure. The growing availability of paired meta-omics data offers an opportunity to develop machine learning models that can infer connections between metagenomics data and other forms of meta-omics data, enabling the prediction of these other forms of meta-omics data from metagenomics. We evaluated several machine learning models for predicting meta-omics features from various meta-omics inputs. Simpler architectures such as elastic net regression and random forests generated reliable predictions of transcript and metabolite abundances, with correlations of up to 0.77 and 0.74, respectively, but predicting protein profiles was more challenging. We also identified a core set of well-predicted features for each meta-omics output type, and showed that multi-output regression neural networks performed similarly when trained using fewer output features. Lastly, our experiments demonstrated that predicted features can be used for the downstream task of inflammatory bowel disease classification, with performance comparable to that of experimental data.
11 Apr 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Prevalence of respiratory viruses in stable and acute asthma: a systematic review and meta-analysis
BACKGROUND: Respiratory viruses, frequently detected in asthma, are associated with worse outcomes. This meta-analysis systematically quantifies the prevalence of respiratory viruses in stable and acute asthma, across children and adults, and explores factors associated with increased viral burden through meta-regression. METHODS: This prospectively registered meta-analysis (PROSPERO-CRD42023375108) included studies employing molecular techniques to assess respiratory virus prevalence in asthma. Three databases were searched in August 2024. Risk of bias and certainty of evidence were assessed. We performed random-effects meta-analysis of proportions. RESULTS: We included 111 eligible studies. Moderate-certainty evidence indicated a pooled prevalence of any respiratory virus of 33.9% (95% confidence interval 24.8-43.7%) in children and 23.0% (12.9-35.0%) in adults with stable asthma. In acute asthma, prevalence increased to 58.8% (52.5-65.0%) in children and 49.9% (41.2-58.5%) in adults (moderate certainty). Rhinovirus was the most frequently identified virus, especially in acute asthma (45.0% in children versus 21.2% in adults). Respiratory syncytial virus and bocavirus were more common in younger children, while coronavirus and influenza were more frequently detected in adults; respiratory syncytial virus peaked in older adults too. A higher prevalence of influenza virus B and adenovirus in children, and of influenza virus A and parainfluenza 2 in adults with severe versus non-severe acute asthma suggests a potential association with more severe acute attacks. CONCLUSION: Respiratory viruses are common in both stable and acute asthma. This suggests that the diagnostic value of a positive viral test during acute episodes may be limited and could benefit from complementary biomarkers to improve interpretation.
10 Apr 2026
Read appraisal →Journal of clinical microbiology
Etiologies of community-acquired febrile illness identified by TaqMan Array Card qPCR on blood samples: a systematic review and meta-analysis
UNLABELLED: Community-acquired febrile illness presents a significant diagnostic challenge. The TaqMan Array Card (TAC) enables simultaneous detection of many bacterial, viral, parasitic, and fungal pathogens. This systematic review aimed to characterize the etiologies of community-acquired fever diagnosed by TAC directly on blood specimens. A systematic search of PubMed, Scopus, Embase, and Web of Science (from inception to January 2026 in English) identified studies using TAC to detect pathogens directly in human blood to identify the etiology of community-acquired febrile illness. Eligible studies were appraised using a modified Joanna Briggs Institute (JBI) checklist. Pooled proportions and meta-analyses were conducted using a random-effects model. A total of 16 studies comprising 8,937 participants from 12 African and Asian countries met inclusion criteria. TAC detected at least 1 pathogen in 0.43 (95% CI [0.32; 0.54]), with high between-study heterogeneity (I² = 99%). Parasitic infections were most common (56%), nearly all due to Plasmodium spp., followed by bacterial (26%), viral (22%), and fungal (0.5%) etiologies. Plasmodium spp., Rickettsia spp., dengue virus, Lassa fever virus, Streptococcus pneumoniae, Orientia tsutsugamushi, Mycobacterium tuberculosis complex, and Salmonella spp. were the most frequently identified pathogens. Culture-negative bacteria accounted for 56% of bacterial detections. Positivity with more than 1 organism occurred in 6% of cases. TAC showed strong concordance with blood culture for culturable organisms (positive agreement: 73%, 95%CI [0.53; 0.89]; negative: 98%, 95%CI [0.95; 1.00]). TAC provides culture-independent pathogen detection and reveals overlooked causes of febrile illness, such as culture-negative bacteria. Its application may improve clinical diagnosis and surveillance programs. IMPORTANCE: Accurate identification of the causes of community-acquired febrile illness is essential for guiding clinical decision-making, surveillance programs, and public health response. This systematic review and meta-analysis synthesizes data from more than 8,937 patients across Africa and Asia tested with blood-based TaqMan Array Card (TAC) assays. TAC enabled broad, culture-independent detection of bacterial, viral, parasitic, and fungal pathogens-revealing both expected causes, such as Plasmodium spp. and dengue virus, and neglected culture-negative bacteria including Orientia tsutsugamushi, Coxiella burnetii, and Rickettsia spp. By uncovering these underrecognized etiologies, TAC provides valuable pathogen-level data to strengthen fever surveillance, inform outbreak detection, and refine regional disease prioritization frameworks. These findings highlight the potential of high-throughput molecular diagnostics to enhance infectious disease control, particularly in resource-limited settings.
9 Apr 2026
Read appraisal →Journal of clinical microbiology
Identification of Aspergillus at section and species levels by artificial intelligence-based microscopic morphology image recognition
Rapid and accurate identification of Aspergillus species in clinical microbiology laboratories is crucial for aspergillosis diagnosis and antifungal therapy. However, traditional methods still face challenges in distinguishing phylogenetically related species due to their morphological similarities. This study presents FungalNet, a deep learning model integrating ResNet-50 architecture with Focal Loss algorithm, specifically designed to enhance feature extraction for Aspergillus identification. A total of 12,000 high-resolution images were obtained from lactophenol cotton blue-stained slide preparations under a 100× oil immersion objective, among which 311 images were excluded through a novel quality control approach combining fivefold cross-validation and expert manual review. The performance of four deep learning models (FungalNet and three established models) for identifying Aspergillus species and sections was evaluated using the remaining 11,689 qualified images. FungalNet demonstrated superior classification performance, achieving overall accuracies of 98.45% and 97.85% at the section and species levels, respectively. These results indicate that FungalNet shows significant promise for rapid and accurate identification of Aspergillus species. With further optimization and multicenter validation, this tool could potentially be integrated into routine diagnostic workflows to enhance the efficiency and reliability of fungal identification in clinical settings.IMPORTANCEThis study integrates microscopic morphology identification with deep learning to address the challenge of accurate Aspergillus species identification. Twelve clinically isolated Aspergillus species belonging to eight different sections were included. From touch-tape slide preparations with lactophenol cotton blue staining under a 100× oil immersion objective, 11,689 qualified images were collected and analyzed using FungalNet (our proposed model) along with three established models (GoogLeNet, ResNet-50, and Xception). The results showed that FungalNet demonstrated superior performance in Aspergillus identification, achieving the highest classification accuracy at both section (98.45%) and species (97.85%) levels. Given its rapid turnaround time and cost-effectiveness, this AI-based image analysis approach shows promising potential for the rapid and accurate identification of Aspergillus species in clinical microbiology laboratories.
9 Apr 2026
Read appraisal →Nanoscale
A machine learning-assisted Ag-TiO2 SERS platform for intraoperative osteomyelitis diagnosis
Osteomyelitis is a progressive bone infection with high disability and recurrence rates. However, current diagnostic approaches, including bacterial culture, imaging, and serological assays, suffer from poor sensitivity, low specificity, and delayed turnaround, making intraoperative evaluation particularly challenging. Here, we present an Ag-TiO2-based surface-enhanced Raman scattering (SERS) platform for rapid, label-free detection of osteomyelitis using minimal volumes of wound saline irrigation fluid (WSIF). Benefiting from the synergistic interplay between electromagnetic field amplification and enhanced interfacial charge-transfer interactions, the Ag-TiO2 substrate provides ultrasensitive detection with high spectral reproducibility, enabling reliable identification of subtle biochemical changes in complex clinical samples. Quantitative analysis indicates that strengthened charge-transfer processes significantly enhance molecular Raman responses, while plasmonic electromagnetic effects remain essential for overall signal amplification. Distinct Raman fingerprints of infected, non-infected, and recovered patients were successfully classified using artificial intelligence (AI) models, revealing metabolic alterations in amino acids, purines, proteins, and lipids associated with bacterial infections and immune response. This integration of SERS with AI not only elucidates the mechanistic basis of spectral enhancement but also demonstrates clinical feasibility for intraoperative osteomyelitis diagnosis, offering a promising strategy to improve surgical precision and reduce recurrence risk.
5 Apr 2026
Read appraisal →Cell
Generation of antigen-specific paired-chain antibodies using large language models.
The traditional process of antibody discovery is limited by inefficiency, high costs, and low success rates. Recent approaches employing artificial intelligence (AI) have been developed to optimize existing antibodies and generate antibody sequences in a target-agnostic manner. In this work, we present MAGE (monoclonal antibody generator), a sequence-based protein language model (PLM) fine-tuned for the task of generating paired human variable heavy- and light-chain antibody sequences against targets of interest. We show that MAGE can generate novel and diverse antibody sequences with experimentally validated binding specificity against SARS-CoV-2, an emerging avian influenza H5N1, and respiratory syncytial virus A (RSV-A). MAGE represents a first-in-class model capable of designing human antibodies against multiple targets with no starting template.
1 Dec 2025
Read appraisal →Gut Microbes
Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease
Diabetic kidney disease (DKD) is a serious healthcare dilemma. Nonetheless, the interplay between the functional capacity of gut microbiota and their host remains elusive for DKD. This study aims to elucidate the functional capability of gut microbiota to affect kidney function of DKD patients. A total of 990 subjects were enrolled consisting of a control group (n = 455), a type 2 diabetes mellitus group (DM, n = 204), a DKD group (n = 182) and a chronic kidney disease group (CKD, n = 149). Full-length sequencing of 16S rRNA genes from stool DNA was conducted. Three findings are pinpointed. Firstly, new types of microbiota biomarkers have been created using a machine-learning (ML) method, namely relative abundance of a microbe, presence or absence of a microbe, and the hierarchy ratio between two different taxonomies. Four different panels of features were selected to be analyzed: (i) DM vs. Control, (ii) DKD vs. DM, (iii) DKD vs. CKD, and (iv) CKD vs. Control. These had accuracy rates between 0.72 and 0.78 and areas under curve between 0.79 and 0.86. Secondly, 13 gut microbiota biomarkers, which are strongly correlated with anthropometric, metabolic and/or renal indexes, concomitantly identified by the ML algorithm and the differential abundance method were highly discriminatory. Finally, the predicted functional capability of a DKD-specific biomarker, Gemmiger spp. is enriched in carbohydrate metabolism and branched-chain amino acid (BCAA) biosynthesis. Coincidentally, the circulating levels of various BCAAs (L-valine, L-leucine and L-isoleucine) and their precursor, L-glutamate, are significantly increased in DM and DKD patients, which suggests that, when hyperglycemia is present, there has been alterations in various interconnected pathways associated with glycolysis, pyruvate fermentation and BCAA biosynthesis. Our findings demonstrate that there is a link involving the gut-kidney axis in DKD patients. Furthermore, our findings highlight specific gut bacteria that can acts as useful biomarkers; these could have mechanistic and diagnostic implications.
1 Dec 2025
Read appraisal →Intensive Care Med
Invasive candidiasis in intensive care medicine: shaping the future of diagnosis and therapy
BACKGROUND: Invasive candidiasis (IC) remains one of the most challenging infections in critical care, contributing significantly to morbidity, prolonged organ support, and mortality among intensive care unit (ICU) patients. The clinical landscape of IC is evolving, with increasing recognition of Candida non-albicans species and other yeasts formerly classified as Candida spp., alongside emerging multidrug resistance and growing complexity in host immune profiles. OBJECTIVES: This contemporary, multidisciplinary narrative review-authored by intensivists, infectious diseases specialists, clinical microbiologists, and pharmacologists-aims to provide ICU clinicians with practical, up-to-date insights into the diagnosis and management of IC. To maintain clinical focus, the review excludes non-IC fungal infections and non-ICU patient populations. METHODS: Relevant literature and expert consensus were critically reviewed to summarize current diagnostic and therapeutic approaches for IC in critically ill patients. Emphasis was placed on pragmatic clinical application, diagnostic limitations, antifungal stewardship, and personalized therapeutic decision-making. RESULTS: Despite the availability of novel antifungal agents with improved pharmacokinetic properties, treatment success in IC depends equally on timely and accurate diagnosis and individualized, context-aware therapy. Blood cultures continue to demonstrate limited sensitivity (~40%). Non-culture assays, including β-D-glucan and molecular diagnostics, provide faster detection and high negative predictive value but suffer from low positive predictive value and inconsistent adoption in clinical practice. The absence of validated host-derived biomarkers further limits risk stratification, antifungal discontinuation decisions, and personalized care. CONCLUSIONS: Emerging antifungal agents, stewardship strategies, and multidisciplinary care models are essential to improve clinical outcomes and reduce antifungal resistance. This review underscores the need for integrated, team-based diagnostic and therapeutic approaches to close persistent gaps in IC management, ultimately promoting more effective, timely, and individualized care for critically ill patients with Candida spp.
1 Nov 2025
Read appraisal →Cell
A generative deep learning approach to de novo antibiotic design
The antimicrobial resistance crisis necessitates structurally distinct antibiotics. While deep learning approaches can identify antibacterial compounds from existing libraries, structural novelty remains limited. Here, we developed a generative artificial intelligence framework for designing de novo antibiotics through two approaches: a fragment-based method to comprehensively screen >107 chemical fragments in silico against Neisseria gonorrhoeae or Staphylococcus aureus, subsequently expanding promising fragments, and an unconstrained de novo compound generation, each using genetic algorithms and variational autoencoders. Of 24 synthesized compounds, seven demonstrated selective antibacterial activity. Two lead compounds exhibited bactericidal efficacy against multidrug-resistant isolates with distinct mechanisms of action and reduced bacterial burden in vivo in mouse models of N. gonorrhoeae vaginal infection and methicillin-resistant S. aureus skin infection. We further validated structural analogs for both compound classes as antibacterial. Our approach enables the generative deep-learning-guided design of de novo antibiotics, providing a platform for mapping uncharted regions of chemical space.
1 Oct 2025
Read appraisal →