Clinical Microbiology and Infection最新文献

Background: Current tuberculosis (TB) diagnostics have limited sensitivity in children, resulting in undiagnosed and untreated cases; host blood biomarkers have the potential to narrow this diagnostic gap.

Objectives: To conduct a systematic review to identify host blood biomarkers which diagnose childhood TB and to summarize biomarker accuracy.

Methods: Data sources: MEDLINE, Embase, Cochrane Central Register of Controlled Trials, SCOPUS, ClinicalTrials.gov, and World Health Organization (WHO) Global Index Medicus.

Study eligibility criteria: Original studies that reported biomarker concentration and/or diagnostic accuracy for childhood TB.

Participants: Children <15 years undergoing evaluation for TB disease.

Tests: Any host blood biomarker measured prior to anti-TB therapy.Reference standards: Mycobacterial culture, nucleic acid amplification tests, and/or clinical diagnosis.Assessment of risk of bias: Quality Assessment of Diagnostic Accuracy Studies-2.Methods of data synthesis: A hierarchical bivariate model was used for meta-analysis.

Results: Fifty-five studies were included, of which 42 (76.4%) studies were at high risk of bias and/or had applicability concerns. The following biomarker classes were reported: cytokine/protein (n=39 studies), mRNA (n=10 studies), miRNA (n=4 studies), and other (n=11 studies). Twelve biosignatures (seven cytokine, four metalloproteinase, one miRNA) and two individual biomarkers (one cytokine, one metalloproteinase) met the WHO target product profile (TPP) for TB disease diagnosis (sensitivity ≥95%, specificity >98%). Meta-analysis was conducted for the cytokine interferon-γ-inducible protein 10 (IP-10) from seven studies; summary estimates of sensitivity (85.2%, 95% CI, 71.1-93.1%) and specificity (59.3%, 95% CI, 44.7-72.5%) did not meet WHO TPP.

Discussion: Host blood biomarkers were identified that met WHO targets for childhood TB disease diagnosis, however, most were reported from a single centre. Meta-analysis did not support IP-10 as an accurate stand-alone biomarker. High-quality studies are needed to validate host blood biomarkers in larger cohorts, and future work should focus on the development of point-of-care tests suitable for low resource settings.

Background: Treatment duration in invasive pulmonary aspergillosis (IPA) in patients with haematological malignancies is not well defined through clinical trials and is therefore often driven more by experience than evidence, with implications for outome, patient quality-of-life, drug toxicity, costs, and antifungal stewardship.

Objectives: To review current evidence and provide expert considerations regarding cessation of antifungal therapy for IPA, and to propose pragmatic criteria and follow-up strategies to guide safe discontinuation and early detection of recurrence.

Sources: Narrative review of clinical trials, observational and biomarker studies, imaging data, and expert guidance related to IPA treatment response, recurrence, and antifungal toxicity.

Content: Shared definitions of treatment success exist, but standardized criteria for treatment failure, stable disease, and stopping therapy are lacking. Decisions to discontinue antifungals rely on individualized assessment of host immune status and recovery, clinical stability, mycological markers (notably serum galactomannan), and imaging findings, primarily chest CT, with adjunctive roles for [¹⁸F]FDG PET and emerging biomarkers. All of the above have limited sensitivity, require thorough interpretation and are to be used as a bundle, not as stand-alone criterion. Our review outlines prerequisites for stopping treatment, structured surveillance after cessation, triggers for immediate re-evaluation, and scenarios in which treatment should not be stopped. Risks of recurrence, particularly in persistently immunocompromised patients, are weighed against cumulative toxicity and drug-drug interactions of prolonged antifungal use. Management options for antifungal toxicity and potential utility of novel antifungal agents are discussed.

Implications: Stopping antifungal therapy for IPA should be a standardized and patient-specific decision supported by predefined monitoring and rapid response pathways within standardized institutional procedures. Prospective trials comparing cessation strategies are needed to increase knowledge on optimal duration, in specific during reoccurring or ongoing immunosuppression.

Objectives: To quantify the antibacterial effects and pharmacodynamic interactions of meropenem and fosfomycin combinations against multidrug-resistant (MDR) Klebsiella pneumoniae through pharmacometric analysis, and to support an evidence-based rationale for combined clinical breakpoints.

Methods: In vitro data from 12 clinical Klebsiella pneumoniae strains (carrying genes such as KPC, NDM, OXA-48, VIM, CTX-M, SHV, etc.), generated in dynamic in vitro infection model experiments mimicking human pharmacokinetics of meropenem and fosfomycin, were analysed using pharmacodynamic modelling. A nonlinear mixed-effects model, incorporating MICs as covariates, was developed. Simulations using published population pharmacokinetic models evaluated 9 mono and combination dosing regimens (IV meropenem 2 g q8h; IV fosfomycin 6-24 g total daily dose q6h, q8h, or continuous infusion) and calculated probability of target attainment (PTA) (for bacteriostasis, 1-log, and 2-log killing after 24 h) for each dosing regimen across wide meropenem and fosfomycin MICs.

Results: Meropenem and fosfomycin monotherapies achieved PTA ≥90% only for low MICs (≤4 mg/L for meropenem, ≤4 mg/L for fosfomycin). In contrast, combination therapy enabled PTA ≥90% for bacteriostasis at MICs up to 32 mg/L (meropenem) and 512 mg/L (fosfomycin), and for 1-log killing at 32 mg/L | 256 mg/L, using low-dose regimens (meropenem 2 g + fosfomycin 2 g q8h). For high-dose combinations (meropenem 2 g + fosfomycin 8 g q8h), PTA ≥90% was achieved for 1-log and 2-log killing at even higher MICs. In total, the model captured time-kill dynamics for all 12 strains and 9 distinct drug regimens.

Conclusions: This study demonstrates that meropenem-fosfomycin combination therapy produces robust synergy against MDR Klebsiella pneumoniae strains, and expands attainable clinical breakpoints compared to monotherapy. These findings provide evidence for reintroducing a fosfomycin breakpoint in combination with meropenem and supports further clinical validation of the proposed dosing regimens in patients with severe infections.

Background: There is an increasing amount of evidence on microbiome-drug interactions in several clinical settings, including in immunocompromised patients. The gut microbiome has been shown to directly and indirectly influence drug efficacy and toxicity, offering high potential for clinical translation.

Objectives: This narrative review aims to provide an up-to-date overview of the relationship between gut microbes and drugs, with a focus on immu1no-chemotherapy in immunocompromised hosts, including oncological and transplant patients.

Sources: We searched PubMed to identify relevant literature in English up to February 2026, as well as included articles known to the authors (prioritising clinical studies wherever possible).

Content: For commonly used anticancer drugs in untargeted conventional chemotherapy, gut microbes may directly activate prodrugs, inactivate biologically active drugs, and/or interfere with their toxicity. Furthermore, indirect mechanisms of immune system modulation have been shown to enhance or worsen therapeutic outcomes, including in targeted immunotherapy. For immunosuppressants in transplant recipients, there is less available evidence overall. Nevertheless, existing studies support the role of the gut microbiome in influencing pharmacokinetics, including enterohepatic recirculation, also through modulation of host drug-metabolising enzymes. Notably, some studies have demonstrated the potential of targeted microbiome manipulation to improve therapeutic outcomes. However, most of this information derives from small, heterogeneous studies, including animal models and in vitro studies.

Implications: The translational implications of microbiome research in pharmacology are of paramount importance. Well-designed clinical studies and the integration of in vivo and ex vivo models will be essential for advancing knowledge and providing mechanistic insights into microbiome-drug interactions. In parallel, advanced computational approaches such as artificial intelligence and machine learning tools will facilitate the analysis of complex microbiome data. These approaches will help identify clinically relevant microbial signatures, including high-risk microbiome-drug interactions. This will enable the development of personalised precision strategies to improve clinical outcomes and prolong disease-free survival.