Search Results (2,502)

Japanese encephalitis virus (JEV) is a mosquito-borne zoonotic flavivirus that circulates primarily within animal populations and occasionally spills over to humans, causing severe neurological disease. While humans are terminal hosts, veterinary species such as pigs and birds play essential roles in viral amplification and maintenance, making JEV fundamentally a veterinary infectious disease with zoonotic potential. This review summarizes the current understanding of JEV transmission dynamics from a veterinary and ecological perspective, emphasizing the roles of amplifying hosts and animal surveillance in controlling viral circulation. Recent genotype shifts and viral evolution have raised concerns regarding vaccine effectiveness and regional emergence. National surveillance systems and animal-based monitoring strategies are examined for their predictive value in detecting outbreaks early. Veterinary and human vaccination strategies are also reviewed, highlighting the importance of integrated One Health approaches. Advances in modeling and climate-responsive surveillance further underscore the dynamic and evolving landscape of JEV transmission. By managing the infection in animal reservoirs, veterinary interventions form the foundation of sustainable zoonotic disease control. Full article

Background/Objectives: Periodontitis is a chronic infectious inflammatory disorder that affects the supporting structures of the teeth. The gingival epithelium plays a crucial role as a physical and immunological barrier, producing pro-inflammatory cytokines in response to microbial pathogens. Modulation of gingival epithelial function has been proposed as a therapeutic strategy to prevent the progression of periodontal disease. Houttuynia cordata, a perennial herb traditionally used in Asian medicine, is recognized for its anti-inflammatory properties, with documented benefits in the cardiovascular, respiratory, and gastrointestinal systems. However, its potential therapeutic role in oral pathologies, such as periodontitis, remains underexplored. This study aimed to investigate the anti-inflammatory effects of H. cordata extract on interleukin (IL)-1β-stimulated primary gingival keratinocytes (PGKs) subjected to IL-1β-induced inflammatory stress, simulating the conditions encountered during orthodontic treatment. Methods: Inflammation was induced in PGKs using IL-1β, and the impact of H. cordata extract pretreatment was assessed using quantitative real-time reverse transcription polymerase chain reaction, enzyme-linked immunosorbent assay, and immunoblotting. Results: H. cordata extract significantly downregulated the mRNA and protein expression levels of tumor necrosis factor-alpha, IL-8, and intercellular adhesion molecule-1 in IL-1β-stimulated PGKs without inducing cytotoxicity. Conclusions: These findings suggest that H. cordata holds promise as a preventive agent against periodontitis by attenuating inflammatory responses in gingival epithelial tissues. We believe that our findings will inform the development of prophylactic interventions to reduce periodontitis risk in patients undergoing orthodontic therapy. Full article

Background: Neuropathic pain (NP), resulting from damage to the somatosensory nervous system, affects 7–10% of the global population and remains poorly managed despite available therapies. Smoking has been associated with increased pain severity and disease burden, yet its role in neuropathy/NP has not been systematically reviewed. This systematic review synthesizes the existing literature on smoking status and its relationship with neuropathy/NP incidence, prevalence, and severity. Methods: The review was conducted in accordance with PRISMA guidelines and included studies that assessed smoking consumption, dependency, quantity, and cessation in individuals with neuropathy/NP. Summary estimates were stratified by exposure type, and pooled odds ratios and relative risks were calculated. Results: Across 62 studies comprising cohort, case–control, and cross-sectional designs, smoking was consistently associated with greater NP prevalence and pain severity. Smoking dependency was linked to increased incidence, while cessation was associated with reduced risk of NP. Despite considerable heterogeneity and risk of bias, particularly from subjective exposure measurement and inconsistent classification, this relationship remained statistically significant. Conclusions: Findings support the role of smoking as a modifiable risk factor in various etiologies of neuropathy/NP. Cessation may represent a low-cost, low-risk, low-tech adjunctive therapy; however, further robust cessation interventional trials are needed, particularly for less common infectious causes of chronic NP such as leprosy. Full article

The emergence of SARS-CoV-2 variants highlights the urgent need for novel therapeutic strategies, particularly entry inhibitors that could efficiently prevent viral infection. Medicinal herbs and herbal combination formulas have long been recognized for their effects in treating infectious diseases and their antiviral properties, thus providing abundant resources for the discovery of antiviral candidates. While many candidates have been suggested to have antiviral activity against SARS-CoV-2 infection, few have been validated for their mechanisms, including possible effects on viral entry. This study aimed to identify SARS-CoV-2 entry inhibitors from medicinal herbs and herbal formulas that are known for heat-clearing and detoxifying properties and/or antiviral activities. A SARS-CoV-2 pseudoparticle (SARS-CoV-2pp) system was used to assess mechanism-specific entry inhibition. Our results showed that the methanol extract of Anemarrhena asphodeloides rhizome, as well as the water extracts of Cimicifuga foetida rhizome, Xiao Chai Hu Tang (XCHT), and Sheng Ma Ge Gen Tang (SMGGT), have substantial inhibitory effects on the entry of SARS-CoV-2pps into host cells. Given the observation that Cimicifuga foetida exhibited the most potent inhibition and is a constituent of SMGGT, we further investigated the major compounds of the herb and identified caffeic acid as a bioactive component for blocking SARS-CoV-2pp entry. Entry inhibition of Cimicifuga foetida and caffeic acid was validated on both wild-type and the currently dominant JN.1 strain SARS-CoV-2pp systems. Moreover, caffeic acid was able to both inactivate the pseudoparticles and prevent their entry into pretreated host cells. The results support the traditional use of these herbal medicines and underscore their potential as valuable resources for identifying active compounds and developing therapeutic entry inhibitors for the management of COVID-19. Full article

This study presents an extended approach to compartmental modeling of infectious disease spread, focusing on regional heterogeneity within affected areas. Using classical SIS, SIR, and SEIR frameworks, we simulate the dynamics of COVID-19 across two major regions of Ukraine—Dnipropetrovsk and Kharkiv—during the period 2020–2024. The proposed mathematical model incorporates regionally distributed subpopulations and applies a system of differential equations solved using the classical fourth-order Runge–Kutta method. The simulations are validated against real-world epidemiological data from national and international sources. The SEIR model demonstrated superior performance, achieving maximum relative errors of 4.81% and 5.60% in the Kharkiv and Dnipropetrovsk regions, respectively, outperforming the SIS and SIR models. Despite limited mobility and social contact data, the regionally adapted models achieved acceptable accuracy for medium-term forecasting. This validates the practical applicability of extended compartmental models in public health planning, particularly in settings with constrained data availability. The results further support the use of these models for estimating critical epidemiological indicators such as infection peaks and hospital resource demands. The proposed framework offers a scalable and computationally efficient tool for regional epidemic forecasting, with potential applications to future outbreaks in geographically heterogeneous environments. Full article

This article describes a new software architecture for the non-invasive detection of canine leishmaniasis disease. The proposed platform combines gas-sensing technologies, artificial intelligence (AI), and modular cloud-based software components to identify disease-specific volatile organic compounds (VOCs) found in dog breath and hair samples. The system, which has a multi-tier architecture that includes data collection, pre-processing, machine learning-based analysis, diagnosis-request processing, and user interfaces for veterinarians, faculty researchers, and dog owners, has been integrated into a Li-ion Power website plug-in. The primary goal of implementing the proposed platform is to detect parasites at any point they are infectious to a host. This includes detecting parasites at all stages of their life cycle, where they can infect a new host. In addition, this is crucial for accurate diagnosis, effective treatment, and preventing further transmission. Full article

Recurrent pregnancy loss (RPL) is defined as the occurrence of two or more pregnancy losses before 20 weeks of gestation. RPL is a common medical condition among reproductive-age women, with approximately 23 million cases reported annually worldwide. Up to 5% of pregnant women may experience two or more consecutive pregnancy losses. Previous studies have investigated risk factors for RPL, including maternal age, uterine pathology, genetic anomalies, infectious agents, endocrine disorders, thrombophilia, and immune dysfunction. However, RPL is a disease caused by a complex interaction of genetic factors, environmental factors (e.g., diet, lifestyle, and stress), epigenetic factors, and the immune system. In addition, due to the lack of research on genetics research related to RPL, the etiology remains unclear in up to 50% of cases. Platelets play a critical role in pregnancy maintenance. This study examined the associations of platelet receptor and ligand gene variants, including integrin subunit beta 3 (ITGB3) rs2317676 A > G, rs3809865 A > T; fibrinogen gamma chain (FGG) rs1049636 T > C, rs2066865 T > C; glycoprotein 1b subunit alpha (GP1BA) rs2243093 T > C, rs6065 C > T; platelet endothelial cell adhesion molecule 1 (PECAM1) rs2812 C > T; and platelet endothelial aggregation receptor 1 (PEAR1) rs822442 C > A, rs12137505 G > A, with RPL prevalence. In total, 389 RPL patients and 375 healthy controls (all Korean women) were enrolled. Genotyping of each single nucleotide polymorphism was performed using polymerase chain reaction–restriction fragment length polymorphism and the TaqMan genotyping assay. All samples were collected with approval from the Institutional Review Board at Bundang CHA Medical Center. The ITGB3 rs3809865 A > T genotype was strongly associated with RPL prevalence (pregnancy loss [PL] ≥ 2: adjusted odds ratio [AOR] = 2.505, 95% confidence interval [CI] = 1.262–4.969, p = 0.009; PL ≥ 3: AOR = 3.255, 95% CI = 1.551–6.830, p = 0.002; PL ≥ 4: AOR = 3.613, 95% CI = 1.403–9.307, p = 0.008). The FGG rs1049636 T > C polymorphism was associated with a decreased risk in women who had three or more pregnancy losses (PL ≥ 3: AOR = 0.673, 95% CI = 0.460–0.987, p = 0.043; PL ≥ 4: AOR = 0.556, 95% CI = 0.310–0.997, p = 0.049). These findings indicate significant associations of the ITGB3 rs3809865 A > T and FGG rs1049636 T > C polymorphisms with RPL, suggesting that platelet function influences RPL in Korean women. Full article

The current proceedings summarize the presentations delivered during the third International Electronic Conference on Microbiology (ECM 2025), which was held online from 1 to 3 April 2025, via the SciForum platform. This virtual event brought together researchers from around the world to share recent advances in microbiological sciences. The ECM 2025 highlighted recent developments across a broad spectrum of microbiological research, including antimicrobial resistance, gut microbiota, infectious diseases, and environmental microbiomes. Participants shared their work through online presentations and abstracts, with selected submissions invited for full publication. The event fostered global collaboration, promoted open-access science, and showcased innovative tools for studying and managing microbial systems in health, agriculture, and industry. The multidisciplinary program was organized into several thematic sessions: S1. Gut Microbiota and Health Disease. S2. Foodborne Pathogens and Food Safety. S3. Antimicrobial Agents and Resistance. S4. Emerging Infectious Diseases. S5. Microbiome and Soil Science. S6. Microbial Characterization and Bioprocess. S7. Microbe–Plant Interactions. This conference report presents summaries of the contributions made by participating authors over the three-day event. Full article

Background/Objectives: Mucosal vaccines, delivered intranasally or via inhalation, are being studied for respiratory infectious diseases like COVID-19 and influenza. These vaccines aim to provide non-invasive administration and strong immune responses at infection sites, making them a promising area of research. This systematic review and meta-analysis assessed their immunogenicity, safety, and protective efficacy. Methods: The study design was a systematic review and meta-analysis, searching PubMed and Cochrane databases up to 30 May 2025. Inclusion criteria followed the PICOS framework, focusing on mucosal vaccines for COVID-19, influenza, RSV, pertussis, and tuberculosis. Results: A total of 65 studies with 229,614 participants were included in the final analysis. Mucosal COVID-19 vaccines elicited higher neutralizing antibodies compared to intramuscular vaccines (SMD = 2.48, 95% CI: 2.17–2.78 for wild-type; SMD = 1.95, 95% CI: 1.32–2.58 for Omicron), with varying efficacy by route (inhaled VE = 47%, 95% CI: 22–74%; intranasal vaccine VE = 17%, 95% CI: 0–31%). Mucosal influenza vaccines protected children well (VE = 62%, 95% CI: 30–46%, I² = 17.1%), but seroconversion rates were lower than those of intramuscular vaccines. RSV and pertussis vaccines had high seroconversion rates (73% and 52%, respectively). Tuberculosis vaccines were reviewed systemically, exhibiting robust cellular immunogenicity. Safety was comparable to intramuscular vaccines or placebo, with no publication bias detected. Conclusions: Current evidence suggests mucosal vaccines are immunogenic, safe, and protective, particularly for respiratory diseases. This review provides insights for future research and vaccination strategies, though limitations include varying efficacy by route and study heterogeneity. Full article

Cancer disparities in low- and middle-income countries (LMICs) arise from multifaceted interactions between environmental exposures, infectious agents, and systemic inequities, such as limited access to care. The exposome, a framework encompassing the totality of non-genetic exposures throughout life, offers a powerful lens for understanding these disparities. In LMICs, populations are disproportionately affected by air and water pollution, occupational hazards, and oncogenic infections, including human papillomavirus (HPV), hepatitis B virus (HBV), Helicobacter pylori (H. pylori), human immunodeficiency virus (HIV), and neglected tropical diseases, such as schistosomiasis. These infectious agents contribute to increased cancer susceptibility and poor outcomes, particularly in immunocompromised individuals. Moreover, climate change, food insecurity, and barriers to healthcare access exacerbate these risks. This review adopts a population-level exposome approach to explore how environmental and infectious exposures intersect with genetic, epigenetic, and immune mechanisms to influence cancer incidence and progression in LMICs. We highlight the critical pathways linking chronic exposure and inflammation to tumor development and evaluate strategies such as HPV and HBV vaccination, antiretroviral therapy, and environmental regulation. Special attention is given to tools such as exposome-wide association studies (ExWASs), which offer promise for exposure surveillance, early detection, and public health policy. By integrating exposomic insights into national health systems, especially in regions such as sub-Saharan Africa (SSA) and South Asia, LMICs can advance equitable cancer prevention and control strategies. A holistic, exposome-informed strategy is essential for reducing global cancer disparities and improving outcomes in vulnerable populations. Full article

Background: Since the election of Hamas in 2006, Gaza has endured eight major military conflicts, culminating in the ongoing 2023–2025 war, now surpassing 520 days. This protracted violence, compounded by a 17-year blockade, has resulted in the near-total collapse of Gaza’s health system. Over 49,000 deaths, widespread displacement, and the destruction of more than 60% of health infrastructure have overwhelmed both local capacity and international humanitarian response. Objectives: This narrative review aims to examine and synthesize the current literature (October 2023–April 2025) on the health crisis in Gaza, with a specific focus on identifying key themes and knowledge gaps relevant to rebuilding a sustainable health system. The review also seeks to outline strategic pathways for recovery in the context of ongoing conflict and systemic deprivation. Methods: Given the urgency and limitations of empirical data from conflict zones, a narrative review approach was adopted. Fifty-two sources—including peer-reviewed articles, editorials, reports, and correspondence—were selected through targeted searches using Medline and Google Scholar. The analysis was framed within a public health and political economy perspective, also taking health system building blocks into consideration. Results: The reviewed literature emphasizes emergency needs: trauma care, infectious disease control, and supply chain restoration. Innovations such as mobile clinics and telemedicine offer interim solutions. Gaps include limited attention to mental health (including that of health workers), local governance, and sustainable planning frameworks. Conclusions: Sustainable reconstruction requires a durable ceasefire; international stewardship aligned with local ownership; and a phased, equity-driven strategy emphasizing primary care, mental health, trauma management, and community engagement. Full article

Nanotechnology is revolutionizing medicine by enabling highly precise diagnostics, targeted therapies, and personalized healthcare solutions. This review explores the multifaceted applications of nanotechnology across medical fields such as oncology and infectious disease control. Engineered nanoparticles (NPs), such as liposomes, polymeric carriers, and carbon-based nanomaterials, enhance drug solubility, protect therapeutic agents from degradation, and enable site-specific delivery, thereby reducing toxicity to healthy tissues. In diagnostics, nanosensors and contrast agents provide ultra-sensitive detection of biomarkers, supporting early diagnosis and real-time monitoring. Nanotechnology also contributes to regenerative medicine, antimicrobial therapies, wearable devices, and theranostics, which integrate treatment and diagnosis into unified systems. Advanced innovations such as nanobots and smart nanosystems further extend these capabilities, enabling responsive drug delivery and minimally invasive interventions. Despite its immense potential, nanomedicine faces challenges, including biocompatibility, environmental safety, manufacturing scalability, and regulatory oversight. Addressing these issues is essential for clinical translation and public acceptance. In summary, nanotechnology offers transformative tools that are reshaping medical diagnostics, therapeutics, and disease prevention. Through continued research and interdisciplinary collaboration, it holds the potential to significantly enhance treatment outcomes, reduce healthcare costs, and usher in a new era of precise and personalized medicine. Full article

Banana Fusarium wilt (BFW, also known as Panama disease) is a highly infectious and destructive disease that threatens global banana production, requiring early recognition for timely prevention and control. Current monitoring methods primarily rely on continuous variable features—such as band reflectances (BRs) and vegetation indices (VIs)—collectively referred to as basic features (BFs)—which are prone to noise during the early stages of infection and struggle to capture subtle spectral variations, thus limiting the recognition accuracy. To address this limitation, this study proposes a discretized enhanced feature (EF) construction method, the automated kernel density segmentation-based feature construction algorithm (AutoKDFC). By analyzing the differences in the kernel density distributions between healthy and diseased samples, the AutoKDFC automatically determines the optimal segmentation threshold, converting continuous BFs into binary features with higher discriminative power for early-stage recognition. Using UAV-based multi-spectral imagery, BFW recognition models are developed and tested with the random forest (RF), support vector machine (SVM), and Gaussian naïve Bayes (GNB) algorithms. The results show that EFs exhibit significantly stronger correlations with BFW’s presence than original BFs. Feature importance analysis via RF further confirms that EFs contribute more to the model performance, with VI-derived features outperforming BR-based ones. The integration of EFs results in average performance gains of 0.88%, 2.61%, and 3.07% for RF, SVM, and GNB, respectively, with SVM achieving the best performance, averaging over 90%. Additionally, the generated BFW distribution map closely aligns with ground observations and captures spectral changes linked to disease progression, validating the method’s practical utility. Overall, the proposed AutoKDFC method demonstrates high effectiveness and generalizability for BFW recognition. Its core concept of “automatic feature enhancement” has strong potential for broader applications in crop disease monitoring and supports the development of intelligent early warning systems in plant health management. Full article

In recent years, the persistent emergence of novel infectious pathogens (epitomized by the global coronavirus disease-2019 (COVID-2019) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) has propelled nucleic acid testing (NAT) into an unprecedented phase of rapid development. As a key technology in modern molecular diagnostics, NAT achieves precise pathogen identification through specific nucleic acid sequence recognition, establishing itself as an indispensable diagnostic tool across diverse scenarios, including public health surveillance, clinical decision-making, and food safety control. The reliability of NAT systems fundamentally depends on reference materials (RMs) that authentically mimic the biological characteristics of natural viruses. This critical requirement reveals significant limitations of current RMs in the NAT area: naked nucleic acids lack the structural authenticity of viral particles and exhibit restricted applicability due to stability deficiencies, while inactivated viruses have biosafety risks and inter-batch heterogeneity. Notably, pseudovirus has emerged as a novel RM that integrates non-replicative viral vectors with target nucleic acid sequences. Demonstrating superior performance in mimicking authentic viral structure, biosafety, and stability compared to conventional RMs, the pseudovirus has garnered substantial attention. In this comprehensive review, we critically summarize the engineering strategies of pseudovirus platforms and their emerging role in ensuring the reliability of NAT systems. We also discuss future prospects for standardized pseudovirus RMs, addressing key challenges in scalability, stability, and clinical validation, aiming to provide guidance for optimizing pseudovirus design and practical implementation, thereby facilitating the continuous improvement and innovation of NAT technologies. Full article

This study presents a next-generation clinical decision-support architecture for Clinical Decision Support Systems (CDSS) focused on emergency triage. By integrating Large Language Models (LLMs), Federated Learning (FL), and low-latency streaming analytics within a modular, privacy-preserving framework, the system addresses key deployment challenges in high-stakes clinical settings. Unlike traditional models, the architecture processes both structured (vitals, labs) and unstructured (clinical notes) data to enable context-aware reasoning with clinically acceptable latency at the point of care. It leverages big data infrastructure for large-scale EHR management and incorporates digital twin concepts for live patient monitoring. Federated training allows institutions to collaboratively improve models without sharing raw data, ensuring compliance with GDPR/HIPAA, and FAIR principles. Privacy is further protected through differential privacy, secure aggregation, and inference isolation. We evaluate the system through two studies: (1) a benchmark of 750+ USMLE-style questions validating the medical reasoning of fine-tuned LLMs; and (2) a real-world case study (n = 132, 75.8% first-pass agreement) using de-identified MIMIC-III data to assess triage accuracy and responsiveness. The system demonstrated clinically acceptable latency and promising alignment with expert judgment on reviewed cases. The infectious disease triage case demonstrates low-latency recognition of sepsis-like presentations in the ED. This work offers a scalable, audit-compliant, and clinician-validated blueprint for CDSS, enabling low-latency triage and extensibility across specialties. Full article