Search Results (1,949)

Background/Objective: Infectious bursal disease (IBD) is an acute and highly contagious immunosuppressive disease in chickens caused by infectious bursal disease virus (IBDV). In recent years, a novel variant IBDV (nVarIBDV) has emerged and spread widely, inducing severe immunosuppression and posing a substantial threat to the poultry industry. More importantly, owing to antigenic variations, nVarIBDV can escape the immune protection of the existing vaccines. Therefore, it is imperative to develop a new vaccine that is antigenically matched to nVarIBDV. Methods: The major protective antigen gene VP2 of the representative nVarIBDV strain SHG19 was inserted into the eukaryotic expression plasmid pCAGGS to construct the recombinant plasmid pCASHGVP2. Subsequently, pCASHGVP2 was encapsulated in lipid nanoparticles (LNPs) to form pCASHGVP2-LNP nanoparticles. Finally, using the SPF chicken model, the immune efficacy of pCASHGVP2-LNP was preliminarily assessed by administering two vaccine doses (10 and 20 μg) and two immunization regimens (single or double immunization). Results: Efficient VP2 protein expression from pCASHGVP2 was confirmed by in vitro transfection experiments. The prepared pCASHGVP2-LNP nanoparticles exhibited an optimal particle size distribution and acceptable polydispersity index, indicating a homogeneous formulation. Furthermore, animal experiments showed that the candidate DNA vaccine elicited specific neutralizing antibodies after double immunization and protected immunized chickens from disease induced by nVarIBDV challenge. Conclusions: This study reports the first development of an LNP-encapsulated VP2 DNA vaccine (pCASHGVP2-LNP) against nVarIBDV, highlighting its potential application for the prevention of nVarIBDV. Full article

This study aims to explore characteristics and clinical predictors of Lymphogranuloma venereum (LGV) and non-LGV Chlamydia trachomatis (Ct) serovars. We conducted a retrospective study on men who have sex with men (MSM) diagnosed with rectal or urethral Ct between 2015 and 2022 at the Infectious Diseases Unit of San Raffaele Scientific Institute, Milan, Italy. Nucleic acid amplification test with sequencing was used for Ct serovar determination. Individuals’ characteristics were described by median (interquartile, IQR) or frequency (%) and compared using Kruskal–Wallis or Chi-Square tests, as appropriate. Logistic regression model was used to identify predictors of LGV; multinomial logistic regression model, with LGV group as reference category, investigated factors associated with the LGV group (serovars L1, L2B, L2C), specific highly prevalent non-LGV serovars (D, E, G) or the non-amplifiable group. Overall, 211 MSM were included: 29.8% with LGV, 50.2% non-LGV and 19.9% non-amplifiable. Symptomatic cases were 46% of which 48% LGV; rectal infection was the most common (86%), followed by urethral (10%) and both sites (4%). People living with HIV were 91.5%; 31.3% had ≥1 concomitant STI and 65.4% ≥1 previous one. According to logistic regression analysis, after adjustment for the diagnosis of ≥1 concomitant and previous STI, LGV serovars were significantly associated with symptomatic infections (adjusted odds ratio, aOR = 6.05; 95%CI = 2.92, 13.13; p < 0.001) and anorectal site (aOR = 17.12; 95%CI = 3.17–319.17, p = 0.007) compared to non-LGV. Among MSM, almost 30% of Ct infections were due to LGV serovars. Presence of symptoms and anorectal site involvement, identified as clinical predictors of LGV, should guide clinicians during diagnosis. Full article

Feline infectious peritonitis (FIP) is a major disease of cats which, unless promptly diagnosed and treated, is invariably fatal. Although it has long been recognised that the condition is the result of an aberrant immune response to infection with feline coronavirus, there remain significant gaps in our understanding of its pathogenesis. Consequently, diagnosis is complex and relies on the combined interpretation of numerous clinical signs and laboratory biomarkers, many of which are non-specific. In the case of effusive FIP, a commonly encountered acute form of the disease where body cavity effusions develop; the interpretation of fluid analysis results is key to diagnosing the condition. We hypothesised that machine learning could be applied to fluid analysis test data in order to help diagnose effusive FIP. Thus, historical test records from a veterinary laboratory dataset of 718 suspected cases of effusive disease were identified, representing 336 cases of FIP and 382 cases that were determined not to be FIP. This dataset was used to train an ensemble model to predict disease status based on clinical observations and laboratory features. Our model predicts the correct disease state with an accuracy of 96.51%, an area under the receiver operator curve of 96.48%, a sensitivity of 98.85% and a specificity of 94.12%. This study demonstrates that machine learning can be successfully applied to the interpretation of fluid analysis results to accurately detect cases of effusive FIP. Thus, this method has the potential to be utilised in a veterinary diagnostic laboratory setting to standardise and improve service provision. Full article

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

End-stage chronic kidney disease markedly increases susceptibility to infections due to compromised immune function and other physiological alterations. Bacteremia is responsible for higher mortality rates in hemodialysis patients compared to the general population. Our study aimed to investigate the incidence and clinical outcomes among patients with end-stage CKD and associated infections. The study retrospectively analyzed admitted patients between 1 January 2023 and 31 May 2025. Among 56 hospitalized patients with CKD and infection (30 hemodialysis [HD], 26 non-HD), baseline comorbidity profiles were broadly comparable. Microbiology was frequently positive (46/56, 82.1%), dominated by Staphylococcus aureus (25/98, 25.5%), Klebsiella pneumoniae (19.98, 19.4%), and Escherichia coli (15/98, 15.3%). Crude in-hospital mortality was higher in HD (46.7% vs. 15.4%; p = 0.012; RR 3.03). In multivariable logistic regression, HD remained independently associated with death (adjusted OR 38.22, 95% CI 1.55–940.53; p = 0.026), alongside hypotension (OR 17.55, 1.46–210.92; p = 0.024) and male sex (OR 4.41, 1.29–15.11; p = 0.018); model performance was strong (AUC 0.867). In this single-center cohort of infected patients with end-stage CKD, maintenance hemodialysis was independently associated with higher in-hospital mortality, even after adjustment for age, sex, comorbidity burden, hypotension, and length of stay; hypotension and male sex were additional risk factors. LOS and most presenting features did not differ meaningfully by dialysis status. Our findings also emphasize the urgent necessity for heightened surveillance of local antimicrobial resistance patterns and underscore the profound vulnerability of hemodialysis patients to severe infectious outcomes, which is exacerbated by immunosuppressive conditions and the limited efficacy of available therapeutic options against resistant pathogens. Full article

Mathematical models for infectious diseases, particularly autonomous ODE models, are generally known to possess simple dynamics, often converging to stable disease-free or endemic equilibria. This paper investigates the dynamic consequences of a crucial, yet often overlooked, component of pandemic response: the saturation of public health testing. We extend the standard SIR model to include compartments for ‘Confirmed’ (C) and ‘Monitored’ (M) individuals, resulting in a new SICMR model. By fitting the model to U.S. COVID-19 pandemic data (specifically the Omicron wave of late 2021), we demonstrate that capacity constraints in testing destabilize the testing-free endemic equilibrium ($E_1$). This equilibrium becomes an unstable saddle-focus. The instability is driven by a sociological feedback loop, where the rise in confirmed cases drive testing effort, modeled by a nonlinear Holling Type II functional response. We explicitly verify that the eigenvalues for the best-fit model satisfy the Shilnikov condition (${\lambda }_u>{\lambda }_s$), demonstrating the system possesses the necessary ingredients for complex, chaotic-like dynamics. Furthermore, we employ Stochastic Differential Equations (SDEs) to show that intrinsic noise interacts with this instability to generate ’noise-induced bursting,’ replicating the complex wave-like patterns observed in empirical data. Our results suggest that public health interventions, such as testing, are not merely passive controls but active dynamical variables that can fundamentally alter the qualitative stability of an epidemic. Full article

Infectious diseases remain a persistent global health threat, intensified by the rapid emergence of antibiotic-resistant pathogens. Despite the transformative impact of antibiotics, the escalating resistance crisis underscores the urgent need for alternative therapeutic approaches. Antimicrobial peptides (AMPs) have emerged as promising candidates due to their broad-spectrum antimicrobial and immunomodulatory activities. The present study investigated 82 honey bee antimicrobial peptides (BAMPs) representing seven families: abaecin, apamin, apisimin, apidaecin, defensin, hymenoptaecin, and melittin among eight honey bee species. Immunoinformatics analyses identified five peptides (P15450, A0A2A3EK62, Q86BU7, C7AHW3, and I3RJI9A) with high antigenicity and non-allergenic profiles. Structural modeling, molecular docking with TLR3 and TLR4-MD2, and molecular dynamics simulations revealed stable receptor-peptide interactions and favorable binding energetics, further supported by silico immune simulations. Overall, these findings suggest that the selected BAMPs exhibit strong immunogenic potential and may serve as effective adjuvants or carrier molecules in subunit vaccine design against drug-resistant pathogens; however, further experimental validation is essential to confirm their safety and immunological efficacy. Full article

An investigation on a discrete-time infectious disease model that incorporating vertical transmission is presented in this paper. Departing from prior research centered on continuous-time frameworks, our study adopts a discrete-time formulation to better capture the complex epidemiological dynamics. We establish a model and conduct a bifurcation analysis of its equilibrium points. In particular, sufficient conditions for the local stability and the emergence of Neimark–Sacker and flip bifurcations are rigorously derived and analytically verified. As anticipated, variations in the bifurcation parameter give rise to distinct periodic regimes in the system response. To mitigate the instabilities and chaotic behaviors resulting from these bifurcations, we propose and validate two control strategies, which are Hybrid Control Method and State Feedback Control. Numerical simulations futher substantiated the analytical results, demonstrating that appropriate parameter adjustments can shift the system behavior from chaotic attractors and limit cycles toward stable equilibria. Our results show that by dynamically adjusting the intensity of prevention and control measures to mitigate unstable factors such as vertical transmission and high infection rates, or reducing the frequency of system updates to slow down the growth of infections, the epidemic can be transitioned from repeated outbreaks to a stable and manageable state. 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

Infectious diseases caused by bacterial and viral pathogens remain a major global threat, particularly in areas with limited diagnostic resources. Conventional optical techniques are time-consuming, prone to operator errors, and require sophisticated instruments. Colorimetric biosensors, which convert biorecognitive processes into visible color changes, enable simple and low-cost point-of-care testing. Artificial intelligence (AI) enhances decision-making by enabling learning, training, and pattern recognition. Machine learning (ML) and deep learning (DL) improve diagnostic accuracy, but they do not autonomously adapt and are pre-trained on complex color variation, whereas traditional computer-based methods lack analysis ability. This review summarizes major pathogens in terms of their types, toxicity, and infection-related mortality, while highlighting research gaps between conventional optical biosensors and emerging AI-assisted colorimetric approaches. Recent advances in AI models, such as ML and DL algorithms, are discussed with a focus on their applications to clinical samples over the past five years. Finally, we propose a prospective direction for developing robust, explainable, and smartphone-compatible AI-assisted assays to support rapid, accurate, and user-friendly pathogen detection for health and clinical applications. This review provides a comprehensive overview of the AI models available to assist physicians and researchers in selecting the most effective method for pathogen detection. Full article

Coxsackievirus B3 (CVB3) is a picornavirus that causes systemic inflammatory diseases including myocarditis, pericarditis, pancreatitis, and meningoencephalitis. We have previously reported that CVB3 induces mitochondrial fission and mitophagy while inhibiting lysosomal degradation by blocking autophagosome-lysosome fusion. This promotes the release of virus-laden mitophagosomes from host cells as infectious extracellular vesicles (EVs), enabling non-lytic viral egress. Transient receptor potential vanilloid 1 (TRPV1), a heat and capsaicin-sensitive cation channel, regulates mitochondrial dynamics by inducing mitochondrial membrane depolarization and fission. In this study, we found that TRPV1 activation by capsaicin dramatically enhances CVB3 egress from host cells via EVs. Released EVs revealed increased levels of viral capsid protein VP1, mitochondrial protein TOM70, and fission protein phospho-DRP1. Moreover, these EVs were enriched in heat shock protein HSP70, suggesting its role in facilitating infectious EV release from cells. Furthermore, TRPV1 inhibition with capsazepine and SB-366791 significantly reduced viral infection in vitro. Our in vivo studies also found that SB-366791 significantly mitigates pancreatic damage and reduces viral titers in a mouse model of CVB3 pancreatitis. Given the lack of understanding regarding factors that contribute to diverse clinical manifestations of CVB3, our study highlights capsaicin and TRPV1 as potential exacerbating factors that facilitate CVB3 dissemination via mitophagy-derived EVs. Full article

Background: The COVID-19 pandemic has put other infectious diseases, especially in children, into a new perspective. Our study focuses on two important viral infections: COVID-19 and influenza, which often present with similar clinical symptoms. Taking into consideration that the pathophysiology and systemic impact of the two viruses are distinct, which can lead to measurable differences in laboratory values, this study aimed to analyze laboratory features that differentiate between COVID-19 and influenza virus infections in pediatric patients. Methods: We statistically analyzed the routinely available laboratory data of 98 patients with influenza virus and 78 patients with COVID-19. Afterwards, the classification and regression tree (CART) method was performed to identify specific clinical scenarios, based on multilevel interactions of different features that could assist clinicians in evidence-based differentiation. Results: Significant differences between the two groups were observed in ALT, eosinophils, hemoglobin, and creatinine. Influenza-infected infants presented significantly higher leukocyte, neutrophil, and basophil counts compared to infants infected with COVID-19. Regarding children (over 12 months), significantly lower levels of ALT and eosinophil counts were observed in those with influenza compared to those with COVID-19. Furthermore, the CART decision tree model identified distinct profiles based on a combination of features such as age, leukocytes, lymphocytes, platelets, and neutrophils. Conclusions: After further refinement and application, such machine learning-based, evidence-driven models, considering the large scale of clinical and laboratory variables, might help to improve, support, and sustain healthcare practices. The differential decision tree may contribute to enhanced clinical risk assessment and decision making. Full article

The importance of GIS models to monitor the spread of infectious diseases and support emergency response has been underlined by a large body of literature and strengthened by the COVID-19 pandemic to identify possible solutions able to recognise spatio-temporal clusters and patterns, evaluate the presence of acceleration factors and define specific actions. In the field of applied research on health geography and geography of safety, this work briefly displays the main aims of the project “Integrated revealing GIS models to monitor, understand and foresee the spread of diseases and support emergency response” and shows some illustrative applications. The basic assumption of the project is to test revealing models regarding key objectives of social utility, and one of its main aims is to elaborate GIS applications able to understand the spread of COVID-19, relating the geocalisations of the cases with specific variables. In order to provide targeted evidence able to better highlight local differences, a number of elaborations derived from (Arc)GIS models and based on data regarding COVID-19 according to sex, age and healthcare facilities in the Rome municipality (Italy) are presented and contextualised as examples, also replicable for precision preparedness. Full article

Background: This study seeks to evaluate the 23 year experience of the İnonu University Liver Transplantation Institute from a public health perspective by examining demographic patterns, etiological factors, and transplantation trends between 2002 and 2025. Aims: This analysis aims to provide insights into the epidemiological landscape of liver transplantation in Türkiye from a public health perspective. Methods: In this retrospective cross sectional study, we analyzed 4011 liver transplant procedures performed between March 2002 and March 2025. Recipient demographics, disease etiologies, donor characteristics, and patients geographic distribution were assessed to delineate regional health needs and service utilization patterns. Results: A total of 4011 patients were included. The cohort comprised 2618 males (65.3%) and 1393 females (34.7%). Recipients were classified as adult (n = 3232, 80.9%) or pediatric (n = 779, 19.1%). Among adults, infectious etiologies were the most prevalent (35.5%), followed by cryptogenic liver cirrhosis (24.7%). In contrast, pediatric patients most commonly presented with toxic etiologies (29.4%), metabolic disorders (22.6%) and bile duct diseases (15.9%). Most liver transplantations were performed using living donors (n = 3481, 86.8%), while deceased donors accounted for 530 procedures (13.2%). Additionally, 244 living donor liver transplantations were performed via liver paired exchange (LPE). Conclusions: These findings may inform resource allocation, health policy development, and the optimization of transplantation services. This center-based model offers a useful framework for characterizing regional health needs and strengthening community health, particularly through prevention, screening, and early intervention strategies for liver diseases. Full article

Leishmaniasis, a widespread, neglected infectious disease with limited effective treatments and increasing drug resistance, demands innovative therapeutic approaches. In this study, we report the fabrication of pentamidine (PTM)-loaded polycaprolactone (PCL) nanofibers using solution blow spinning (SBS) as a potential topical delivery system for cutaneous leishmaniasis (CL). Homogeneous PCL fiber mats were produced using a simple SBS set-up with a commercial airbrush after optimizing several working parameters. Drug release studies demonstrated sustained PTM release profile over time, which was mechanistically modeled by utilizing the complete nanofiber diameter distribution, obtained from SEM analysis of the blow-spun material. FTIR and XRD analyses were performed to investigate the drug–polymer interactions, revealing molecularly dispersed PTM at low-proportion drug/polymers and partial crystallinity at high loadings. The released PTM exhibited significant leishmanicidal activity against Leishmania major promastigotes. Biological investigations showed that SBS-formulated PTM treatment was consistent with the downregulation of parasite genes involved in cell division and DNA replication (cycA, cyc6, pcna, top2, mcm4) and upregulation of the drug response gene (prp1). The controlled delivery of PTM within SBS-fabricated PCL nanofibers provides an effective therapeutic approach to tackle CL and, through the incorporation of additional drugs, could be extended to address a broader range of cutaneous infections. Full article