Acta microbiologica et immunologica Hungarica最新文献

In recent years, the presence of microbiota in tumors has been discovered through extensive research, overturning the longstanding belief that "tumors are sterile." Advanced techniques such as 16S rRNA gene sequencing, fecal microbiota transplantation, and the construction of mouse models specific to different tumor types have been utilized to validate the existence of microbiota within various tumors. The intratumoral microbiota significantly influences tumor development by modulating immune responses, mediating inflammatory reactions, and interfering with or enhancing immunotherapy or chemotherapy. For instance, Aspergillus sydowii in lung adenocarcinoma promotes immunosuppression via the Dectin-1/CARD9 pathway, while colibactin-producing Escherichia coli in colorectal cancer facilitates tumor progression through lipid metabolism dysregulation. Moreover, intratumoral microbiota can predict patient prognosis and guide personalized cancer treatment strategies, highlighting their potential as therapeutic targets. This review synthesizes current evidence on the roles of intratumoral microbiota across multiple cancer types and discusses their clinical implications.

The emergence of mupirocin-resistant Staphylococcus aureus strains poses a significant challenge to public health due to limited treatment options and the risk of multidrug resistance. This study aims to investigate the antibiotic susceptibility and molecular characteristics of mupirocin resistant S. aureus isolates. A total of 65 mupirocin-resistant isolates were included in the study. The isolates were characterized using antimicrobial susceptibility testing, biofilm formation assay, staphylococcal cassette chromosome mec typing, multilocus sequence typing, and polymerase chain reaction analysis to detect resistance (mecA, mecC, mupA, erm(A), erm(B), erm(C), tet(M), ant (4')-Ia, aac (6')-Ie/aph (2″), and aph (3')-IIIa) and toxin genes (eta, etb, pvl, and tst). Resistance to mupirocin was observed in 12.5% of the S. aureus isolates collected during the study period. Among the 65 mupirocin-resistant MRSA isolates, 75.4% were classified as HLMUPR and 24.6% as LMUPR. cMLSB and iMLSB phenotypes were identified in 41.5 and 36.9% of the isolates. Our results showed that 49.2, 30.8, and 15.4% of isolates were classified as strong, intermediate, and weak biofilm-forming strains, respectively. Our result revealed that about three-quarters of isolates harbored mecA (100%), tet(M) (76.9%), mupA (75.4%) resistance genes. MLST revealed that the 65 isolates belonged to seven clonal complexes, including CC8 (41.5%), followed by CC22 (20%), CC5 (10.8%), CC30 (10.8%), CC15 (7.7%), CC1 (4.6%) and CC80 (4.6%). The vast majority of S. aureus isolates belonged to CC8/ST239-MRSA (21.5%). Among the 32 strong biofilm producers, the majority (28.1%) belonged to CC8/ST8 MRSA clone. Our result revealed that 39.1% of PVL-positive strains belonged to CC/ST22. The fusidic acid resistance isolates belonged to CC/ST8-MRSA (7.7%), CC8/ST239-MRSA (12.3%), CC/ST22-MRSA (7.7%), and CC30/ST80-MRSA (1.5%) lineages. In conclusion, this study provides valuable insights into the characteristics of mupirocin-resistant S. aureus isolates from Tehran, Iran. The results highlight a high prevalence of HLMUPR in this research. Additionally, the study reveals a diverse genetic landscape, with isolates belonging to various clonal complexes, particularly CC8, CC22, and CC5. The high frequency of biofilm formation and resistance to other antibiotics underscores the need for ongoing surveillance and the development of more effective treatment strategies to combat these multidrug-resistant strains.

Bronchoalveolar lavage (BAL) is a basic diagnostic method for the detection of fungal infections in lung transplant recipients. Aspergillus species are frequently identified, typically by the presence of septate hyphae; however, the visualization of conidia in cytologic preparations is rare. Aspergillosis caused by Aspergillus niger is an uncommon but recognized infectious complication in this patient population.We report on the case of a 60-year-old lung transplant recipient who underwent routine surveillance bronchoscopy eight weeks post-transplantation in August 2025. A substantial amount of adherent secretion was noted at the medial part of the right bronchial anastomosis. Surveillance BAL was performed from the right S8 segment, and cytospin preparations revealed intracellular Aspergillus conidia within alveolar macrophages. Galactomannan antigen assay was negative; however, fungal culture confirmed A. niger after five days.This case highlights the diagnostic value of identifying fungal conidia in BAL cytology, which may facilitate early recognition of invasive fungal infection or fungal colonization potentially leading to invasive disease or facilitate chronic lung allograft dysfunction (CLAD) development.

The gut microbiota has emerged as a critical determinant of antitumor immunity and a potential modulator of responses to immune checkpoint inhibitors (ICIs). Although pre-clinical and clinical studies suggest that specific bacterial taxa may influence both efficacy and immune-related adverse events (irAEs). However, the magnitude and consistency of these associations remain unclear. A systematic search of PubMed, Embase, Web of Science, and the Cochrane Library was conducted through March 2025. Eligible studies evaluated baseline gut microbiota composition, fecal microbiota transplantation (FMT), probiotic/prebiotic interventions, or antibiotic exposure in cancer patients treated with ICIs. Pooled hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS), and odds ratios (ORs) for response rates and irAEs, were estimated using random-effects models. Across 38 studies involving 5,642 patients were included. Pooled analysis demonstrated that enrichment of Akkermansia muciniphila, Bifidobacterium longum and Faecalibacterium prausnitzii was significantly associated with improved OS (HR 0.62, 95% CI 0.51-0.76) and PFS (HR 0.69, 95% CI 0.55-0.83). Conversely, antibiotic exposure before or during ICI treatment was associated with worse OS (HR 1.84, 95% CI 1.45-2.34). Patients undergoing FMT from responders exhibited higher objective response rates (OR 2.91, 95% CI 1.48-5.73). Microbiota diversity indices were consistently higher in responders than in non-responders. Collectively, gut microbiota composition and its modulation significantly impact the therapeutic efficacy and toxicity profile of ICIs. These findings highlight the translational potential of microbiome-based biomarkers and interventions in optimizing immunotherapy.

This study evaluated the reliability of different laboratory methods for detecting resistance to glycopeptide antibiotics-vancomycin and teicoplanin-in clinical Staphylococcus aureus and coagulase-negative staphylococci (CoNS) isolates. While automated systems are widely used in clinical microbiology laboratories due to their efficiency and ease of use, they may yield inaccurate results when assessing glycopeptide susceptibility. A total of 87 previously collected clinical isolates (22 S. aureus and 65 CoNS), initially identified as resistant to at least one of the vancomycin or teicoplanin by an automated system, were retrospectively analyzed. All isolates were stored at -80 °C and retested using three methods: the same automated system (following the manufacturer's protocol), the gradient diffusion method, and the reference broth microdilution (BMD) method. Interpretations were made according to European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints. Upon re-evaluation, all isolates were found to be susceptible to vancomycin and teicoplanin using the BMD method. The automated system yielded 100% concordance with BMD for vancomycin and 77% for teicoplanin, while the gradient method produced similar findings. Notably, five S. aureus isolates (23%) remained resistant to teicoplanin according to both the automated system and the gradient method but were susceptible by BMD. These results emphasize that automated systems, although practical, may lead to overestimation of glycopeptide resistance. Therefore, when resistance is suspected, especially to teicoplanin, confirmatory testing with the BMD reference method is essential to ensure accurate interpretation and avoid misclassification.

The 16S rRNA sequencing technology was used to investigate changes in the abundance of intestinal microbiota, metabolites of blood and fecal samples were analyzed and their relationships with neurotransmitters were evaluated in patients with liver cirrhosis after hepatitis B infection. The liver function phenotypes correlated with Phylum Proteobacteria, Class Clostridia and Gamma Proteobacteria, Family Enterobacteriaceae, Ruminococcaceae, Streptococcaceae, Lachnospiraceae and Lactobacillaceae, Genus Faecalibacterium, Streptococcus, species Escherichia coli, etc. Genus Streptococcus has a good diagnostic value for patients with liver cirrhosis in the COM (Compensated liver disease) group, with an AUC of 0.81 (95% CI: 0.70-0.92), while Genus Streptococcus, Veillonella, Faecalibacterium, Blautia, and Bacteroides have a better diagnostic value for patients with liver cirrhosis in the DECOM (Decompensated liver disease) group (including DECOM1 and DECOM2), with the combined AUC reaching 0.93 (95% CI: 0.88-0.98). The level of ammonia in the DECOM2 group was significantly higher than that of the COM group (P < 0.01). Patients with post-hepatitis B cirrhosis have intestinal flora disorder, which leads to abnormal amino acid metabolism and further leads to neurotransmitter disorder in patients with cirrhosis and accelerates the disease progression. Probiotics can reduce the serum ammonia level in patients with cirrhosis and may prevent the occurrence of hepatic encephalopathy.

Invasive fungal infections caused by resistant Candida species are a global public health problem. Increasing antifungal resistance makes antifungal susceptibility tests (AFST) crucial, necessitating rapid methods. This study aims to determine the fluconazole and anidulafungin susceptibility profiles of clinical Candida strains using matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and compare the results with the European Committee on Antimicrobial Susceptibility Testing (EUCAST) reference broth microdilution method to assess the accuracy and reproducibility of MALDI-TOF MS in evaluating in vitro antifungal susceptibility. The susceptibilities of 40 Candida glabrata isolates for anidulafungin and fluconazole, and 40 Candida albicans and 40 Candida parapsilosis isolates for fluconazole were tested. Candida isolates were incubated for 3 h at two different antifungal concentrations ("maximum" and "breakpoint" concentrations) and a drug-free control (Anidulafungin: 16, 0.06, and 0 mg L-1; Fluconazole: 256, 16, and 0 mg L-1). MALDI-TOF MS spectra from these concentrations were used to create composite correlation index (CCI) matrices for each isolate. The strains with the "mean CCI of the breakpoint/maximum concentration" of which was higher than the "mean CCI of the breakpoint/null concentration" were classified as susceptible. Classifications defined by the MS-AFST method were compared to those based on the EUCAST broth microdilution method. The overall agreement between MS-AFST and EUCAST AFST ranged from 60% to 85%, highest for C. glabrata and anidulafungin. The reproducibility of the MS-AFST assay ranged from 45% to 75%, highest for C. parapsilosis and fluconazole. The study suggests that the MALDI-TOF MS method for assessing antifungal susceptibility in Candida strains is promising but requires further improvements for enhancing the accuracy and reproducibility.

The human gut microbiota plays a pivotal role in maintaining host immunity, regulating metabolism, and sustaining neurophysiological homeostasis. Increasing evidence implicates gut dysbiosis in the onset and progression of neurodegenerative disorders (NDDs), including Alzheimer's and Parkinson's disease, primarily through the gut-brain axis. Recent advances in high-throughput sequencing and multi-omics technologies, such as metagenomics, metabolomics, and metaproteomics have generated vast datasets, yet their clinical translation remains hindered by data heterogeneity, analytical complexity, and the absence of standardized workflows. Disjointed findings across studies underscore the urgent need for reproducible pipelines and integrative computational strategies. This review presents a comprehensive framework that leverages artificial intelligence (AI) and machine learning (ML) for systematic microbiome investigation in NDDs. We highlight how multi-omics integration with AI improves the resolution of host-microbiome interactions, while standardized preprocessing workflows ensure reproducibility and comparability across datasets. The role of explainable AI is emphasized in enhancing interpretability, improving biomarker discovery, and fostering trust in predictive models. We further examine the emerging field of pharmacomicrobiomics, where ML-driven approaches support the development of precision therapies tailored to microbiome-drug interactions in neurodegeneration. Sophisticated models, including random forests (RF), neural networks, and transfer learning, are critically assessed for predictive diagnostics, therapeutic target identification, and cross-cohort generalizability. Finally, the review proposes a roadmap to address current barriers, particularly challenges of heterogeneity and reproducibility, and advocates for validated pipelines and interdisciplinary collaboration. Collectively, AI-driven multi-omics strategies hold transformative potential for advancing microbiome-based precision medicine in NDDs.

The distribution of antimicrobial resistance in major pathogens was analyzed in a tertiary care hospital of Lodhran, Pakistan. Altogether, 1910 patients diagnosed and treated at Shahida Islam Medical Complex Hospital from December 2023 to August 2025 were selected. The antimicrobial resistance of major bacterial and fungal pathogens was quantified, and logistic regression analysis was used to identify the risk factors for infection. Methicillin-resistant Staphylococcus aureus (MRSA) isolates retained susceptibility to vancomycin (58.3%), while ceftazidime/avibactam showed activity against Escherichia coli (80%), Klebsiella pneumoniae (82%), and Pseudomonas aeruginosa (78%). Vancomycin-resistant Enterococcus (VRE) demonstrated resistance to nearly all antibiotics. PCR confirmed TEM and SHV in 23/51 (45%) of E. coli isolates, while in K. pneumoniae TEM and SHV were each detected in 20/35 (56%). Among P. aeruginosa isolates, VIM, NDM, and OXA-48 were each present in 14/37 (37%). The mecA was found in 47/49 (95%) of S. aureus isolates (MRSA), vanA in 34/49 (70%) of S. aureus (VRSA), and vanA in 34/43 (80%) of Enterococcus isolates (VRE). MLST analysis of representative multidrug-resistant isolates identified ST131 (1/3, 33%) among E. coli, ST11 (1/3, 33%) and ST258 (1/3, 33%) among K. pneumoniae, ST175 (1/3, 33%) and ST233 (1/3, 33%) among P. aeruginosa, ST5 (1/3, 33%) and ST22 (1/3, 33%) among S. aureus, and ST17 (1/3, 33%) among Enterococcus spp. PCA revealed distinct clustering of species, with Gram-negatives overlapping, Gram-positives forming separate groups, and fungi clustering independently. Logistic regression identified age ≥65, ICU admission, comorbidities, prior antibiotic exposure, invasive procedures, and immunosuppressive therapy as significant AMR risk factors, while infection control and stewardship reduced risk (P < 0.05). This study demonstrates a high burden of antimicrobial resistance, primarily mediated by TEM and SHV β-lactamases in E. coli and K. pneumoniae, and by VIM, NDM, and OXA-48 carbapenemases in P. aeruginosa. Additionally, MRSA, VRSA, and VRE showed multidrug resistance. Effective infection control and antibiotic stewardship remain critical to limit the spread of resistant pathogens and to reduce hospital-acquired AMR risk.

Antibiotic-associated diarrhea (AAD) is a prevalent iatrogenic complication of antibiotic therapy, primarily triggered by dysbiosis and loss of intestinal homeostasis. The traditional interventions, such as empirical probiotic use, have shown a modest and a heterogeneous efficacy. This review integrates the current mechanistic understanding of AAD through the lens of the microbiota-mucosal-immune axis and provides a comprehensive overview of emerging therapeutic strategies. By integrating evidence from metagenomics, metabolomics, and immunology, we highlight next-generation approaches, including rationally engineered probiotics, standardized fecal microbiota transplantation (FMT), and synthetic-biology-derived interventions. Recent progress in multi-omics technologies and machine learning has enabled patient-stratified modulation of the gut microbiota, moving beyond empirical supplementation toward precision ecological reprogramming. These advanced therapies demonstrate superior outcomes in restoring microbial diversity, strengthening epithelial barrier function, and re-establishing immunological homeostasis. Ultimately, the management of AAD requires a systems-biology strategy that leverages real-time microbiome analytics for targeted, accurate, and sustainable restoration of gut health.