Hypervirulent Klebsiella pneumoniae is a pathotype capable of causing invasive infections with high morbidity and mortality rates. In this study, we conducted a surveillance analysis of hypervirulent isolates circulating in Mexico to characterize their phenotypic and genomic features. Presumptive hypervirulent isolates were identified at a frequency of 6.48 % (19/293), comprising 17 K. pneumoniae sensu stricto and two K. quasipneumoniae subsp. similipneumoniae. Isolates were predominantly recovered from male patients (12/19, 63 %). Clinical samples were obtained from lower respiratory tract (15/19, 78.9 %), blood (3/19, 15.7 %), and pleural fluid (1/19, 5.2 %). Further genetic and phenotypic analyses revealed substantial heterogeneity among these strains, including significant phenotype-genotype discordance. Notably, this cohort includes the first identified convergent hypervirulent K. pneumoniae strain in Mexico, as well as two hypervirulent K. quasipneumoniae isolates, a phenomenon that is less frequent in K.quasipneumoniae than in K. pneumoniae. These discrepancies prompted us to propose a local classification scheme based on the presence of virulence-associated genes, lethality in mice and antimicrobial susceptibility. Phylogenetic and pangenome analysis revealed clustering patterns associated with sequence types and capsule serotypes. The data generated in this study contribute to a deeper understanding of Hypervirulent K. pneumoniae species complex biology and provide valuable insights into the diversity of strains currently circulating in Mexico.
Background:Pediatric lupus nephritis (LN) remains a major cause of morbidity and mortality, yet data from Latin American populations are limited. This study aimed to describe the clinical, laboratory, and histopathological characteristics of pediatric LN and identify prognostic factors associated with renal replacement therapy (RRT). Methods:We conducted a retrospective cross-sectional study including patients <18 years of age with LN diagnosed between 2020 and 2024 at a national referral center in Mexico. Demographic, clinical, immunological, histopathological, and therapeutic variables at diagnosis were analyzed. Multivariable logistic regression was performed to identify predictors of RRT. Results:Eighty patients were included (83% female; mean age 15.1 ± 2.8 years). Median proteinuria was 41 mg/m²/h; hematuria and leukocyturia were present in 46% and 26% of patients, respectively. All patients were ANA positive, with frequent hypocomplementemia and elevated anti-double-stranded DNA titers. Among biopsied patients, class IV was the most common histological subtype (60%). Proliferative forms were associated with reduced glomerular filtration rate (<90 mL/min/1.73 m²; p = 0.012) and higher activity index scores (p = 0.04), while chronicity indices were low. Fifteen patients (18.8%) required RRT, and mortality was 6.25%. In multivariable analysis, hypoalbuminemia (<2.5 g/dL) was independently associated with RRT (OR 6.04; 95% CI 1.33-27.50; p = 0.020). Conclusions:This study represents one of the largest pediatric LN cohorts reported from Mexico. Proliferative forms were associated with greater inflammatory activity and impaired renal function at diagnosis. Hypoalbuminemia emerged as a simple and accessible biomarker for early risk stratification of severe renal outcomes.
Idiopathic pulmonary fibrosis (IPF) is a progressive disease of unknown aetiology, characterised by a radiological and/or morphological pattern of usual interstitial pneumonia. Its diagnosis is challenging, and disease progression is often variable and unpredictable. In recent years the introduction of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) models, has shown the potential to improve the diagnosis, prognosis and therapeutic strategies for IPF. As part of DL, convolutional neural networks enhance the accuracy of high-resolution computed tomography analysis, facilitating early and precise diagnosis. Likewise, predictive ML and DL models are being developed using clinical, morphological, transcriptional and imaging data to assess disease progression and stratify patients by risk, thereby improving prognosis evaluation. Furthermore, AI-driven drug discovery may optimise treatment strategies by identifying novel therapeutic targets, as recently demonstrated with the discovery of an NCK-interacting kinase inhibitor with strong antifibrotic properties. However, several challenges hamper widespread clinical integration and real-life implementation, including data heterogeneity, model interpretability and the need for robust validation through large-scale, multicentre studies. Future research should prioritise the development of standardised models of AI in large cohorts of IPF patients, combining clinical, imaging, morphological, multi-omics and other data, and enhance model transparency to strengthen clinical confidence. With continued advancements, AI holds potential to improve IPF management, enabling early diagnosis, individualised prognosis and targeted therapy, all aimed at improving patient outcomes. In this review, we explore the evolving role of AI in IPF management, its potential to support clinical decisions and the challenges to its clinical integration.
Heart rate variability (HRV) reflects autonomic regulation and has emerged as a promising noninvasive marker for risk stratification in critical illness. In HIV-positive intensive care unit (ICU) patients, autonomic dysfunction may influence survival, yet its prognostic potential remains underexplored. We analyzed HRV and physiological data from 145 HIV-positive ICU patients to develop machine-learning models for in-hospital survival prediction. Three feature selection techniques—correlation analysis, mutual information, and random forest importance—were systematically compared using the top 5, 10, and 15 ranked variables. Artificial neural networks (ANNs) were trained on each subset, and the most discriminative features were further evaluated through logistic regression for interpretable probability estimation. A graphical user interface (GUI) was implemented to facilitate clinical use. The correlation-based top-15 model achieved the best ANN performance (AUC = 0.90), identifying SOFA score, platelet count, and maximum heart rate as consistent predictors of survival. Random forest and mutual information approaches yielded complementary but lower discriminative power. The developed GUI integrates HRV extraction and individualized mortality prediction through a dual-tab interface. Correlation-driven feature selection produced the most accurate and parsimonious HRV-based survival models, supporting its clinical utility for real-time prognostication in HIV-positive ICU patients. The integrated ANN–logistic regression framework and GUI enhance interpretability and potential bedside deployment. Retrospective analysis; no prospective enrollment or interventions.
Epstein–Barr virus (EBV) is a key oncogenic pathogen implicated in the development of lymphomas, particularly among HIV-positive and immunocompromised individuals. While the association between EBV and lymphoma is well established, the mechanisms underlying progression from infection to malignancy—especially in the head and neck region—remain incompletely understood. This review offers a comprehensive analysis of the pathophysiological pathways by which EBV and HIV contribute to lymphomagenesis, with an emphasis on latency patterns, immune evasion, and epigenetic “hit and run” oncogenesis. Notably, it integrates novel findings on the diagnostic implications of EBV latency proteins, explores HIV-mediated B-cell dysregulation, and evaluates the emerging landscape of targeted therapies, including monoclonal antibodies and lytic cycle inducers. By focusing specifically on head and neck lymphomas, this review underscores a clinically underrepresented domain and offers insights that may guide future diagnostics, surveillance, and treatment strategies in vulnerable patient populations. This review also highlights the pressing need for improved animal models and continued research into EBV-specific therapeutic targets.
