Search Results (726)

Background: Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental condition influenced by both genetic and non-genetic factors, although the underlying pathomechanisms remain unclear. We systematically analyzed microRNA (miRNA) expression and associated functional pathways in ASD to evaluate their potential as prenatal/postnatal, diagnostic, and prognostic biomarkers. Methods: Peripheral blood mononuclear cells from 12 Sicilian patients with ASD (eight with normal cognitive function) and 15 healthy controls were analyzed using small RNA sequencing. Differential expression analysis was performed with DESeq2 (|fold change| ≥ 1.5; adjusted p ≤ 0.05). Functional enrichment and network analyses were conducted using Ingenuity Pathway Analysis, focusing on Diseases and Biofunctions. Results: 998 miRNAs were differentially expressed in ASD, 424 upregulated and 553 downregulated. Enriched pathways were primarily associated with psychological and neurological disorders. Network analysis highlighted three principal interaction clusters related to inflammation, cell survival and mechanotransduction, synaptic plasticity, and neuronal excitability. Four miRNAs (miR-296-3p, miR-27a, miR-146a-5p, and miR-29b-3p) emerged as key regulatory candidates. Conclusions: The marked divergence in miRNA expression between ASD and controls suggests distinct regulatory patterns, thus reinforcing the central involvement of inflammatory, autoimmune, and infectious mechanisms in ASD, mediated by miRNAs regulating S100 family genes, neuronal migration, and synaptic communication. However, rather than defining a predictive biomarker panel, this study identified candidate miRNAs and regulatory networks that may be relevant to ASD pathophysiology. As such, further validation in appropriately powered cohorts with predictive modeling frameworks are warranted before any biomarker or diagnostic implications can be inferred. Full article

Background/objectives: Metabolomics has emerged as a powerful systems-biology tool for deciphering dynamic metabolic alterations occurring during infectious diseases and following vaccination. While genomics and proteomics provide extensive molecular and regulatory information, metabolomics uniquely reflects the biochemical phenotype associated with infection, immune activation, and immunometabolic reprogramming. The objective of this review is to provide an integrated analysis of metabolomics applications across both neglected tropical diseases (NTDs) and non-NTD pathogens, highlighting its dual role in biomarker discovery and vaccine response evaluation. Methods: A comprehensive literature-based synthesis was conducted to examine metabolomic studies in infectious diseases and vaccinology. Metabolic perturbations associated with specific pathogens, as well as vaccine-induced metabolic changes and correlates of immune responses, were systematically analyzed and compared across NTD and non-NTD contexts. Results: Distinct pathogen- and vaccine-associated metabolic signatures were identified, reflecting alterations in glycolysis, amino acid metabolism, lipid remodeling, and immunoregulatory pathways. Comparative analysis revealed both shared and disease-specific metabolic biomarkers across NTDs and non-NTD infections. Importantly, vaccine-related metabolic correlates were shown to mirror immune activation states and, in some cases, predict immunogenicity and response durability. Conclusions: This review bridges metabolomics research in infectious disease pathogenesis and vaccine immunology across the NTD and non-NTD spectrum. By integrating these domains, it introduces the concept of “metabolic immuno-signatures” as predictive and translational tools for evaluating vaccine efficacy and immune response outcomes. Full article

Background: Cardiovascular risk models, such as the Framingham and atherosclerotic cardiovascular disease (ASCVD) calculators, have improved risk prediction but often fail to identify individuals who experience ASCVD events despite low or intermediate predicted risk. This suggests that underrecognized, non-traditional risk factors may contribute significantly to the development of atherosclerosis. Objective: This narrative review synthesizes and summarizes recent evidence on high-yield non-traditional risk factors for atherosclerosis, with a focus on clinically significant, emerging, and applicable contributors beyond conventional frameworks. This review is distinct in that it aggregates a wide array of non-traditional risk factors while also consolidating recent data on ASCVD in more vulnerable populations. Unlike the existing literature, this manuscript integrates in a single comprehensive review various domains of non-traditional atherosclerotic risk factors, including inflammatory, metabolic, behavioral, environmental, and physical pathways. An additional unique highlight in the same manuscript is the discussion of non-traditional risk factors for atherosclerosis in more vulnerable populations, specifically South Asians. We also focus on clinically actionable factors that can guide treatment decisions for clinicians. Results: Key non-traditional risk factors identified include inflammation and biomarker-based risk factors such as C-reactive protein or interleukin-6 levels, metabolic and microbial risk factors, behavioral factors such as E-cigarette use, and environmental or infectious risk factors such as air and noise pollution. We explore certain physical exam findings associated with atherosclerotic burden, such as Frank’s sign and Achilles tendon thickness. Conclusions: Atherosclerosis is a multifactorial process influenced by diverse and often overlooked factors. Integrating non-traditional risks into clinical assessment may improve early detection, guide prevention and personalize care. Future risk prediction models should incorporate molecular, behavioral, and environmental data to reflect the complex nature of cardiovascular disease. Full article

Recently, extracellular vesicles (EVs) have emerged as pivotal mediators of intercellular communication that reflect physiological homeostasis and pathological alterations. By encapsulating diverse biomolecules, including proteins, nucleic acids, and lipids, EVs mirror the molecular signatures of their parent cells, thereby positioning EV-based biosensing as a transformative platform for noninvasive diagnostics, prognostic prediction, and therapeutic monitoring. This review provides a comprehensive overview of the current state and clinical translation of EV biosensing technologies. Herein, we have discussed ongoing efforts toward standardization and analytical validation (e.g., MISEV2023 and EV-TRACK) and evaluated advances in sensing modalities such as surface plasmon resonance (SPR), electrochemical, fluorescence, and magnetic detection systems, which have significantly improved analytical performance in terms of sensitivity and specificity. Furthermore, we highlight recent developments in multiplexed and multiomics integration at the single-EV level and the application of machine learning to enhance diagnostic accuracy and interpret biological heterogeneity. The clinical relevance of EV biosensing has been explored across multiple disease domains, including oncology, neurology, and cardiometabolic and infectious diseases, with an emphasis on translational progress toward standardized, regulatory-compliant, and scalable platforms. Finally, this review identifies key challenges in manufacturing scale-up, quality control, and point-of-care deployment and proposes a unified framework to accelerate the adoption of EV biosensing as a cornerstone of next-generation precision diagnostics and personalized medicine. Full article

Background: Zoonotic spillover events with pandemic potential are increasingly associated with environmental change, ecosystem disruption, and intensified human–animal interactions. Although the specific origin and timing of future pandemics remain uncertain, there is a clear need to complement traditional preparedness strategies with approaches that support earlier anticipation and prevention. Objectives: This study aims to propose a conceptual approach to reframe pandemic preparedness toward proactive surveillance and spillover prevention. Methods: We introduce a tachyon-inspired conceptual approach, using a thought experiment based on hypothetical faster-than-light particles to illustrate anticipatory observation of pandemic emergence. The framework is informed by interdisciplinary literature on emerging infectious diseases, One Health surveillance, predictive epidemiology, and public-health preparedness. Results: The proposed approach highlights the importance of proactive, integrated surveillance systems that combine human, animal, and environmental data. Key elements include the use of advanced analytical tools such as neural networks, early characterization of population risk profiles, strengthened public-health infrastructure, coordinated governance, adaptable financial resources, and a resilient healthcare workforce. The integration of animal welfare considerations, translational research, and planetary health principles is emphasized as central to reducing spillover risk. Conclusions: Tachyon-inspired thinking offers a conceptual tool to support a shift from reactive pandemic response toward proactive anticipation and prevention. Embedding integrated surveillance and One Health principles into public-health systems may enhance early detection capacity and contribute to mitigating the impact of future pandemics. Full article

Uveitis constitutes a heterogeneous group of intraocular inflammatory pathologies, including both infectious and non-infectious aetiologies, often leading to substantial morbidity and permanent loss of vision in up to 20% of the affected cases. Visual impairment is most prominent in intermediate, posterior, or panuveitis and is commonly associated with cystoid macular oedema, epiretinal membranes, macular holes, and retinal detachment. In the context of uveitis, these complications arise as a result of recurrent flare-ups or chronic inflammation, contributing to cumulative ocular damage. Pars plana vitrectomy (PPV) has an evolving role in the diagnostic and therapeutic approach to uveitis. Diagnostic PPV allows for the analysis of vitreous fluid and tissue using techniques such as PCR, flow cytometry, cytology, and cultures, providing further insights into intraocular immune responses. Therapeutic PPV can be employed for the management of structural complications associated with uveitis, in a wide spectrum of inflammatory clinical entities such as Adamantiades–Behçet disease, juvenile idiopathic arthritis, acute retinal necrosis, or ocular toxoplasmosis. Modern small-gauge and minimally invasive techniques improve visual outcomes, reduce intraocular inflammation, and may decrease reliance on systemic immunosuppression. Emerging technologies, including robot-assisted systems, are expected to enhance surgical precision and safety in the future. Despite these advances, PPV outcomes remain variable due to heterogeneity in indications, surgical techniques, and postoperative management. Prospective studies with standardized protocols, detailed subgroup analyses, and the integration of immunological profiling are needed to define which patients benefit most, optimize therapeutic strategies, and establish predictive biomarkers in uveitis management. Full article

In recent years, the complexity and suddenness of infectious disease transmission have posed significant limitations for traditional time-series forecasting methods when dealing with the nonlinearity, non-stationarity, and multi-peak distributions of epidemic scale variations. To address this challenge, this paper proposes a forecasting framework based on diffusion models, called CNL-Diff, aimed at tackling the prediction challenges in complex dynamics, nonlinearity, and non-stationary distributions. Traditional epidemic forecasting models often rely on fixed linear assumptions, which limit their ability to accurately predict the incidence scale of infectious diseases. The CNL-Diff framework integrates a forward–backward consistent conditioning mechanism and nonlinear data transformations, enabling it to capture the intricate temporal and feature dependencies inherent in epidemic data. The results show that this method outperforms baseline models in metrics such as Mean Absolute Error (MAE), Continuous Ranked Probability Score (CRPS), and Prediction Interval Coverage Probability (PICP). This study demonstrates the potential of diffusion models in complex-distribution time-series modeling, providing a more reliable probabilistic forecasting tool for public health monitoring, epidemic early warning, and risk decision making. Full article

Aim: To explore the association between urinary stone cultures and infectious complications following PCNL. Materials and Methods: An observational case–control study was conducted in patients undergoing PCNL. The assessment included demographic parameters, medical history, urinalysis, and urine culture and blood testing. Pre-operatively, urinary stone samples were collected for cultures. Post-operatively, patients were observed for infectious complications such as fever and/or SIRS. Patients were divided into two groups based on the presence or absence of infected renal calculi. Patient characteristics, stone factors, and intra-operative and post-operative findings were studied in relation to stone culture. Descriptive statistics was used to present the data and the SPSS software was used for analysis. Results: From December 2023 to March 2025, a total of 126 patients were included in the study. A total of 16 patients (12.6%) had a positive stone culture. Statistical significance was found upon the comparison of stone culture with gender (p = 0.046), chronic kidney disease (p = 0.002), pre-operative urine culture (p = 0.001), pre-operative haemoglobin (g/dL) (<0.001), pre-operative S. creatinine (mg/dL) (p = 0.038), stone volume (mm³) (p = 0.012), CROES score (p = 0.023), SIRS (p = 0.001), and AKI (p = 0.021). Conclusions: Infected renal calculi identified by positive stone cultures were strongly associated with infective complications such as fever and SIRS following PCNL. E. Coli was the dominant bacteria present in both bladder urine and renal stone culture. The occurrence of infectious complications despite the administration of pre-operative antibiotics highlights the antibiotic resistance patterns noted among the cultured bacteria. The pre-operative factors identified to be associated with a positive stone culture could potentially be used for predicting infected stones, thereby improving outcomes. Full article

Background: Climate change is increasingly altering the distribution and burden of infectious diseases in Sub-Saharan Africa, where ecological diversity, fragile health systems, and widespread poverty heighten vulnerability. The One Health approach, which integrates human, animal, and environmental health, provides a useful framework for addressing these climate-sensitive health challenges; its application in the region remains limited. Methods: This review was conducted in accordance with PRISMA-ScR guidelines and synthesized evidence from 30 peer-reviewed studies published between 2019 and 2025, identified through PubMed, Scopus, Web of Science, and the Cochrane Library. Results: Studies consistently showed that rising temperatures, altered rainfall patterns, and extreme weather events shifted malaria transmission into highland zones, modified schistosomiasis risk through changes in snail habitats, and drove diarrheal outbreaks following flooding. While One Health initiatives such as Ghana’s Climate-Smart One Health framework and university-led programmes in East Africa demonstrated promise, their impact remained constrained by donor dependence, institutional silos, and limited policy integration. Conclusions: To enhance climate resilience, national strategies need to integrate climate-informed surveillance, predictive modelling, and One Health governance. Future research should extend beyond malaria and schistosomiasis, incorporate longitudinal data, and establish standardized metrics for assessing One Health interventions. Full article

Epidemic modeling plays a crucial role in understanding disease transmission and informing public health strategies. This study presents a fractional Susceptible-Exposed-Infected-Quarantined-Recovered (SEIQR) model incorporating Atangana–Baleanu-Caputo (ABC) fractional derivatives to capture memory effects in disease dynamics. The model extends classical ordinary differential equation-based frameworks by integrating a fractional approach, enhancing its applicability to real-world epidemic scenarios. A key feature of our model is the inclusion of mortality rates across all disease compartments, providing a refined representation of influenza-like infections with pandemic potential. We conduct a detailed stability analysis to assess equilibrium states and derive conditions for disease control. Numerical simulations further validate the theoretical findings, offering insights into epidemic progression and intervention strategies. Our results highlight the significance of fractional calculus in epidemiological modeling and its potential to improve predictive accuracy for infectious disease outbreaks. Full article

Human leukocyte antigen (HLA)-E, a non-classical class I molecule with limited polymorphism, bridges innate and adaptive immunity. Traditionally, the role of HLA-E had been associated with regulating natural killer (NK) cell activity via CD94/NKG2 receptors, by presenting self-peptides derived from the leader sequence of HLA-I. Recent findings reveal its ability to present pathogen-derived peptides to CD8⁺ T cells, eliciting unconventional cytotoxic responses. This review examines the expanding role of HLA-E-restricted T cells in viral and bacterial infections and their capacity to recognize diverse microbial peptides and enhance immune response when classical HLA pathways are impaired. We also highlight key advances in immunotherapy and vaccine development, including CMV-vectored platforms, donor-unrestricted TCR-based strategies, and peptide prediction algorithms. The minimal polymorphism of HLA-E, its resistance to viral immune evasion, and its ability to present conserved pathogen peptides position it as a promising target for universal vaccines and next-generation immunotherapies. Understanding these unconventional roles may pave the way for broadly applicable immunotherapies and vaccines against infectious diseases. Full article

Accurate and generalizable prediction of infectious disease incidence is essential for proactive public health response. This study proposes a novel hybrid VLGA-Transformer model to address this challenge, validated through tuberculosis (TB) and hepatitis B case studies. Utilizing monthly TB data from Zhejiang Province (2013–2023), raw sequences were first decomposed via Variational Mode Decomposition (VMD) to extract intrinsic temporal patterns. To overcome Transformer parameter optimization difficulties, we innovatively integrated the Lorenz attractor into a Genetic Algorithm (GA), creating a Lorenz-attractor-enhanced GA (LGA) that dynamically balances exploration and exploitation. The resulting VLGA-Transformer framework demonstrated superior performance, achieving R² values of 0.96 for TB and 0.93 for hepatitis B prediction, significantly outperforming benchmark models in both accuracy and stability. When tested on hepatitis B data, the model confirmed its robust cross-disease generalizability. These findings highlight the framework’s dual strengths—high-precision forecasting and robust generalization—providing actionable insights for public health authorities to optimize resource allocation and intervention strategies, thereby advancing data-driven infectious disease control systems. Full article

Vibrio harveyi (Vh) and Vibrio vulnificus (Vv) are major bacterial pathogens affecting farmed eels, but their comparative virulence mechanisms remain poorly characterized. This study combined histopathology and transcriptomic profiling to investigate organ-specific damage and host responses in American eels (Anguilla rostrata, 20 g per fish, for a total of 60 fish) following experimental infection with LD₅₀ doses of Vh (strain HA_1, 7.5 × 10⁴ CFU/fish) and Vv (strain FJ_4, 5.0 × 10⁵ CFU/fish). Tissue samples from liver, kidney, and spleen were collected at 0, 36, and 60 h post-infection (hpi). Histopathological analysis revealed distinct injury patterns: Vh induced severe hepatic edema and necrosis, whereas Vv caused vacuolar degeneration and vascular congestion in the liver. In the kidney, Vv triggered acute necrosis and vacuolization by 36 hpi, while Vh-induced renal damage was delayed until 60 hpi. Transcriptomic analysis of spleen tissue identified 4779 and 1215 differentially expressed genes (DEGs) in the Vh_36 vs. Vv_36 and Vh_60 vs. Vv_60 comparisons, respectively. Functional enrichment analysis associated these DEGs with 109 Gene Ontology (GO) terms—mainly catalytic activity, biological regulation, and binding—and 51 KEGG pathways, including “tuberculosis” and “pathways in cancer”. Differential alternative splicing (DAS) analysis further uncovered 1579 and 1214 DAS events originating from 12,482 and 12,316 splicing genes in the two comparisons. These were enriched in GO categories such as “binding”, “cellular process”, and “cell part”, as well as KEGG pathways related to “signal transduction”, “infectious diseases”, and “immune system.” Protein–protein interaction network analysis identified 119 cross-DAS-encoded proteins, including 8 that were predicted as key regulators of virulence differences. In summary, this work presents the first integrative study comparing the pathogenicity and host transcriptional dynamics of Vh and Vv in American eels, providing new molecular insights into their distinct virulence strategies. Full article

The basic reproduction number ($R_0$) is an epidemiological metric that represents the average number of new infections caused by a single infectious individual in a completely susceptible population. The methodology for calculating this metric is well-defined for numerous model types, including, most prominently, Ordinary Differential Equations (ODEs). The basic reproduction number is used in disease modeling to predict the potential of an outbreak and the transmissibility of a disease, as well as by governments to inform public health interventions and resource allocation for controlling the spread of diseases. A Petri Net (PN) is a directed bipartite graph where places, transitions, arcs, and the firing of the arcs determine the dynamic behavior of the system. Petri Net models have been an increasingly used tool within the epidemiology community. However, no generalized method for calculating $R_0$ directly from PN models has been established. Thus, in this paper, we establish a generalized computational framework for calculating $R_0$ directly from Petri Net models. We adapt the next-generation matrix method to be compatible with multiple Petri Net formalisms, including both deterministic Variable Arc Weight Petri Nets (VAPNs) and stochastic continuous-time Petri Nets (SPNs). We demonstrate the method’s versatility on a range of complex epidemiological models, including those with multiple strains, asymptomatic states, and nonlinear dynamics. Crucially, we numerically validate our framework by demonstrating that the analytically derived $R_0$ values are in strong agreement with those estimated from simulation data, thereby confirming the method’s accuracy and practical utility. Full article

Surgical site infections (SSIs) are among the most frequent healthcare-associated infections, leading to prolonged hospitalization, increased costs, and impaired recovery. This retrospective cohort study aimed to identify the determinants and microbial patterns of SSIs following hepatobiliary and pancreatic (HPB) surgery to inform preventive strategies and optimize clinical outcomes. The patients undergoing hepatobiliary and pancreatic surgery from 2014 to 2024 in a tertiary university hospital are reviewed. SSI was defined according to Centers for Disease Control and Prevention (CDC) criteria, and microbiological isolates were identified through routine culture methods and susceptibility testing. Clinical, operative, and microbiological data of patients who underwent hepatobiliary and pancreatic surgery were extracted, including demographics, comorbidities, operative characteristics, and postoperative outcomes. Among 553 hepatobiliary and pancreatic surgery patients, SSI occurred in 48.6%. Gram-negative bacteria predominated, with E. coli as the leading pathogen. SSI was linked to open surgery, longer operative time, and higher ASA scores; malignancy, renal insufficiency, anemia, and COPD were independent risk factors. Age by itself was not a reliable predictor of infection, while operative duration demonstrated moderate predictive performance, with a sensitivity of 66%. These findings underscore the multifactorial pathogenesis of SSIs and emphasize the importance of refined perioperative strategies to mitigate postoperative infectious complications. Full article