Search Results (41,816)

Cardiovascular diseases (CVDs), which remain globally one of the most common causes of death, are usually diagnosed based on the electrocardiogram (ECG) signal. To support human experts, modern deep-learning models are used for CVD classification problems as an early warning. This article proposes a novel multi-branch architecture focused on processing various modalities of the ECG signal in parallel branches, replacing typical single-input architectures. Each branch is given separate input in the form of the raw signal, domain knowledge, the wavelet transform of the signal, or the signal with drift removed. The proposed method is based on deep-learning core models that can incorporate various modern neural networks. It was thoroughly tested on N-BEATS, LSTM, and GRU neural networks. The proposed architecture allows the retention of the speed of the neural network. At the same time, the combination of independently computed branches improves model performance, which finally exceeds the performance obtained by classical single-branch architectures. Full article

Deer tick virus (DTV) is a Tick-Borne Orthoflavivirus endemic to the United States, transmitted to humans through bites from the deer tick, Ixodes scapularis, which is also the primary vector of Borrelia burgdorferi s.l., the causative agent of Lyme disease. Human infection with DTV can result in acute febrile illness followed by central nervous system complications, such as encephalitis and meningoencephalitis. Currently, there are mouse models established for investigating the pathogenesis and clinical outcomes of DTV that mimic human infections, but the strains of mice utilized are refractory to infection with B. burgdorferi s.l. Here, we describe the pathogenesis and clinical outcomes of DTV infection in C3H/HeJ mice. Neurological clinical signs, mortality, and weight loss were observed in all DTV-infected mice during the investigation. Infected animals demonstrated consistent viral infection in their organs. Additionally, neuropathology of brain sections indicated the presence of meningoencephalitis throughout the brain. This data, along with the clinical outcomes for the mice, indicates successful infection and showcases the neuroinvasive nature of the virus. This is the first study to identify C3H/HeJ mice as an appropriate model for DTV infection. As C3H/HeJ mice are already an established model for B. burgdorferi s.l. infection, this model could serve as an ideal system for investigating disease progression and pathogenesis of co-infections. Full article

This study proposes CEL-DL-Bagging (Cross-Entropy Loss-optimized Deep Learning Bagging), a multi-model fusion framework that integrates cross-entropy loss-weighted voting with Bootstrap Aggregating (Bagging). First, we develop a lightweight recognition architecture by embedding a salient position attention (SPA) mechanism into four base networks (YOLOv5s-cls, EfficientNet-B0, MobileNetV3, and ShuffleNetV2), significantly enhancing discriminative feature extraction for disease patterns. Our experiments show that these SPA-enhanced models achieve consistent accuracy gains of 0.8–1.7 percentage points, peaking at 97.86%. Building on this, we introduce DB-CEWSV—an ensemble framework combining Deep Bootstrap Aggregating (DB) with adaptive Cross-Entropy Weighted Soft Voting (CEWSV). The system dynamically optimizes model weights based on their cross-entropy performance, using SPA-augmented networks as base learners. The final integrated model attains 98.33% accuracy, outperforming the strongest individual base learner by 0.48 percentage points. Compared with single models, the ensemble learning algorithm proposed in this study led to better generalization and robustness of the ensemble learning model and better identification of rice diseases in the natural background. It provides a technical reference for applying rice disease identification in practical engineering. Full article

Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. To address the issue of insufficient sample data for certain disease categories, we utilize OpenAI’s DALL-E image generation model to synthesize images for low-frequency diseases, which effectively improves the model’s recognition ability and generalization performance for underrepresented classes. To tackle the challenges of large-scale variations and irregular lesion distribution, we design a multi-scale feature aggregation module (MFAM). This module integrates multi-scale semantic information through a lightweight, multi-branch convolutional structure, enhancing the model’s ability to detect small-scale lesions. To further overcome the receptive field limitations of traditional convolution, we propose incorporating a deformable attention transformer (DAT) into the C2PSA module. This allows the model to flexibly focus on lesion areas amidst complex backgrounds, improving feature extraction and robustness. Moreover, we introduce an enhanced efficient multi-dimensional attention mechanism (EEMA), which leverages feature grouping, multi-scale parallel learning, and cross-space interactive learning strategies to further boost the model’s feature expression capabilities. Lastly, we replace the traditional regression loss with the MPDIoU loss function, enhancing bounding box accuracy and accelerating model convergence. Experimental results demonstrate that the proposed DEMM-YOLO model achieves 94.8% precision, 93.1% recall, and 96.7% mAP@0.5 in cotton leaf disease detection, highlighting its strong performance and promising potential for real-world agricultural applications. Full article

Salvia officinalis is an aromatic plant of Mediterranean origin traditionally used to treat inflammatory, cardiovascular, endocrine, and digestive diseases. In this work, the ability of the Salvia officinalis extract in the treatment of gastric ulcers was evaluated, and an innovative administration system was proposed to increase the therapeutic effect of this plant. Salvia officinalis ethanolic extract was prepared and analyzed by HPLC/UV-DAD and encapsulated in a matrix based on gelatin and pectin using an emulsion–coacervation process. The prepared microcapsules were analyzed by laser particle size, optical microscopy, in vitro dissolution kinetics, and ex vivo bioadhesion. In order to determine the action mechanism of Salvia officinalis extract, in the treatment of gastric ulcer, the in vivo anti-ulcerogenic activity in rats, using the ulcer model induced by ethanol; the in vivo anti-Helicobacter pylori activity; and in vitro inhibitory activity of H⁺/K⁺-ATPase were carried out. These three biological activities were evaluated for ethanolic extract and microcapsules to determine the effect of formulation on biological activities. Ethanolic extract of Salvia officinalis was mainly composed of polyphenols (chlorogenic acid 7.43%, rutin 21.74%, rosmarinic acid 5.88%, and quercitrin 14.39%). Microencapsulation of this extract allowed us to obtain microcapsules of 104.2 ± 7.5 µm in diameter, an encapsulation rate of 96.57 ± 3.05%, and adequate bioadhesion. The kinetics of in vitro dissolution of the extract increase significantly after its microencapsulation. Percentages of ulcer inhibition for 100 mg/kg of extract increase from 71.71 ± 2.43% to 89.67 ± 2.54% after microencapsulation. In vitro H⁺/K⁺-ATPase-inhibiting activity resulted in an IC50 of 86.08 ± 8.69 µM/h/mg protein for free extract and 57.43 ± 5.78 µM/h/mg protein for encapsulated extract. Anti-Helicobacter pylori activity showed a similar Minimum Inhibitory Concentration (MIC) of 50 µg/mL for the extract and microcapsules. Salvia officinalis ethanolic extract has a significant efficacy for the treatment of gastric ulcer; its mechanism of action is based on its gastroprotective effect, anti-Helicobacter pylori, and H⁺/K⁺-ATPase inhibitor. Moreover, the microencapsulation of this extract increases its gastroprotective and H⁺/K⁺-ATPase-inhibiting activities significantly. Full article

Congenital portosystemic shunts (CPSSs) are often associated with life-threatening systemic complications, which may be detected by identifying hypergalactosemia in newborn screening (NBS). However, diagnosing CPSS at an early stage is not easy. The purpose of this study was to predict CPSS early using screening values and general blood tests. The medical records of 153 patients with hypergalactosemia who underwent NBS in Saitama Prefecture between 1 December 1997 and 31 October 2023 were retrospectively analyzed. We provided the final diagnosis of the analyzed patients. Of the 153 patients, 44 (29%) were in the CPSS group and 83 (54%) were in the transient galactosemia group. Using the initial screening items and the six blood test items, we attempted to extract a CPSS group from the transient galactosemia group. Finally, a model for CPSS prediction was established. From multiple logistic regression analysis, filtered blood galactose-1 phosphate/galactose, serum total bile acid, and ammonia were adopted as explanatory variables for the prediction model. If the cut-off value for predicted disease probability value (P) was >0.357, CPSS was identified with 86.4% sensitivity (95%CI 72.6–94.8%) and 81.9% specificity (95%CI 72.0–89.5%). This predictive model might allow prediction of CPSS and early intervention. Full article

Background: Varicella remains a prevalent vaccine-preventable disease, but breakthrough infections are increasingly reported. However, long-term, population-based studies investigating the temporal and demographic characteristics of breakthrough varicella remain limited. Methods: This retrospective study analyzed surveillance data from Jinhua City, China, from 2016 to 2024. Varicella case records were obtained from the China Information System for Disease Control and Prevention (CISDCP), while vaccination data were retrieved from the Zhejiang Provincial Immunization Program Information System (ISIS). Breakthrough cases were defined as infections occurring more than 42 days after administration of the varicella vaccine. Differences in breakthrough interval were analyzed across subgroups defined by dose, sex, age, population category, and vaccination type. A bivariate cubic regression model was used to assess the combined effect of initial vaccination age and dose interval on the breakthrough interval. Results: Among 28,778 reported varicella cases, 7373 (25.62%) were classified as breakthrough infections, with a significant upward trend over the 9-year period (p < 0.001). Most cases occurred in school-aged children, especially those aged 6–15 years. One-dose recipients consistently showed shorter breakthrough intervals than two-dose recipients (M = 62.10 vs. 120.10 months, p < 0.001). Breakthrough intervals also differed significantly by sex, age group, population category, and vaccination type (p < 0.05). Regression analysis revealed a negative correlation between the initial vaccination age, the dose interval, and the breakthrough interval (R² = 0.964, p < 0.001), with earlier and closely spaced vaccinations associated with longer protection. Conclusions: The present study demonstrates that a two-dose varicella vaccination schedule, when initiated at an earlier age and administered with a shorter interval between doses, provides more robust and longer-lasting protection. These results offer strong support for incorporating varicella vaccination into China’s National Immunization Program to enhance vaccine coverage and reduce the public health burden associated with breakthrough infections. Full article

Cardiovascular diseases remain a leading cause of mortality in the United States, driving the need for advanced cardiovascular devices and pharmaceuticals. Mock Circulatory Loops (MCLs) have emerged as essential tools for in vitro testing, replicating pulsatile pressure and flow to simulate various physiological and pathological conditions. While many studies focus on custom MCL designs tailored to specific applications, few have systematically reviewed their use in device testing, and none have assessed their broader utility across diverse biomedical domains. This comprehensive review categorizes MCL designs into three types: mechanical, computational, and hybrid. Applications are classified into four major areas: Cardiovascular Devices Testing, Clinical Training and Education, Hemodynamics and Blood Flow Studies, and Disease Modeling. Most existing MCLs are complex, highly specialized, and difficult to reproduce, highlighting the need for simplified, standardized, and programmable hybrid systems. Improved validation and waveform fidelity—particularly through incorporation of the dicrotic notch and other waveform parameters—are critical for advancing MCL reliability. Furthermore, integration of machine learning and artificial intelligence holds significant promise for enhancing waveform analysis, diagnostics, predictive modeling, and personalized care. In conclusion, the development of MCLs should prioritize standardization, simplification, and broader accessibility to expand their impact across biomedical research and clinical translation. Full article

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: Dermatitis is frequently treated with dexamethasone cutaneous application, which causes adverse effects mainly when it is chronically administered. Sesamol is a phytochemical compound known for its anti-inflammatory activity and low toxicity. Therefore, this study reports the optimization of a guar gum hydrogel with enhanced physicochemical and microbiological stability, providing an effective dosage form for topical application of sesamol nanocapsules to treat irritant contact dermatitis. Methods: Nano-based hydrogel containing 1 mg/g sesamol was prepared by adding the nanocapsule suspension to form a 2.5% (w/v) guar gum dispersion. Dynamic rheological analysis indicates that the formulations exhibit a non-Newtonian flow with pseudoplastic behavior. Hydrogels were evaluated by Fourier-transformed infrared (FTIR) spectroscopy, and, following spectrum acquisition, an unsupervised chemometrics model was developed to identify crucial variables. Additionally, the physicochemical and microbiological stability of the hydrogel was evaluated over a 60-day period. Results: ATR-FTIR spectra of all hydrogels evaluated are very similar after preparation and 60 days of storage. However, it showed a slight increase in average diameter and PDI and decreased pH values after 60 days. Microbiological assessment demonstrated that the hydrogel met the requirements for the microbial count over 60 days. The dermatitis model was induced by repeated applications of croton oil in the right ears of mice. The effectiveness of the hydrogels was evaluated by assessing ear edema and migration of polymorphonuclear cells. The nano-based hydrogel exhibited anti-inflammatory properties similar to those of dexamethasone. Conclusions: Therefore, the nano-based hydrogel containing sesamol exhibits therapeutic potential for treating cutaneous inflammatory diseases. Full article

Objective—To identify effective data analytics and machine learning solutions that can help in the decision-making process in the medical domain and contribute to the understanding of COVID-19 disease. In this study, we analyze data from anonymized electronic medical records of 4711 patients with COVID-19 disease admitted to hospital in Atlanta. Methods—We used random forest, LightGBM, XGBoost, CatBoost, KNN, SVM, logistic regression, and MLP neural network models in this work. The models are evaluated depending on the type of prediction by relevant metrics, especially accuracy, F1-score, and ROC AUC score. Another aim of the work was to find out which factors most affected severity and mortality risk among the patients. To identify the important features, different statistical methods were used, as well as LASSO regression, and explainable artificial intelligence (XAI) method SHAP values for model explainability. The best models were implemented in a web application and tested by medical experts. The model for prediction of mortality risk was tested on a validation cohort of 45 patients from the Department of Infectiology and Travel Medicine, L. Pasteur University Hospital in Košice (UNLP). Results—Our study shows that the best model for predicting COVID-19 disease severity was the LightGBM model with accuracy of 88.4% using all features and 89.5% using the eight most important features. The best model for predicting mortality risk was also the LightGBM model, which achieved a ROC AUC score of 83.7% and a classification accuracy of 81.2% using all features. Using a simplified model trained on the 15 most important features, the ROC AUC score was 83.6% and the classification accuracy was 80.5%. We deployed the simplified models for predicting COVID-19 disease severity and for predicting the risk of COVID-19-related death in a web-based application and tested them with medical experts. This test resulted in a ROC AUC score of 83.6% and an overall prediction accuracy of 73.3%. Full article

Background/Objectives: Oral diseases are highly prevalent in Hungary and driven in part by unhealthy beverage consumption, smoking, and other behaviors. No prior study has examined the impact of beverage consumption patterns on oral health in a representative Hungarian population. This study investigated the association between beverage intake, lifestyle factors, and oral health outcomes among Hungarian adults. Methods: Data were drawn from the 2019 Hungarian European Health Interview Survey, a nationally representative cross-sectional study. Oral health outcomes and key exposures, including beverage consumption, smoking, alcohol use, and sociodemographic variables, were self-reported. Associations were assessed using multiple logistic regression models. Results: Among 5425 adults, higher dairy intake was linked to less gum bleeding (odds ratio = 0.76; 95% confidence intervals [0.59–0.96]) and lower odds of teeth missing (0.63 [0.47–0.86]). Weekly juice intake reduced gum bleeding (0.62 [0.51–0.76]) and missing teeth (0.83 [0.71–0.96]). Daily soda was associated with more gum bleeding (1.94 [1.53–2.47]), caries (1.57 [1.27–1.94]), and poor self-perceived oral health (1.32 [1.10–1.59]). Alcohol (1–4 times/week) increased gum bleeding (1.38 [1.07–1.77]) and tooth mobility (1.47 [1.02–2.11]). Smoking raised odds for caries (1.42 [1.21–1.66]) and missing teeth (1.81 [1.55–2.10]). Conclusions: Increasing dairy and fresh juice intake while reducing sugar-sweetened and acidic beverages, alongside tobacco and alcohol control and routine oral health screening, are effective strategies for improving population oral health across all sociodemographic groups. Full article

This protocol paper outlines a robust and reproducible framework for a ¹H nuclear magnetic resonance (NMR) metabolomics analysis of rodent plasma, designed to facilitate preclinical biomarker discovery. The protocol details optimised steps for plasma collection in a preclinical rodent model, sample preparation, and NMR data acquisition using presaturation Carr–Purcell–Meiboom–Gill (PRESAT-CPMG) pulse sequences, ensuring high-quality spectral data and effective suppression of macromolecule signals. Comprehensive spectral processing and metabolite assignment are described, with guidance on multivariate and univariate statistical analyses to identify metabolic changes and potential biomarkers. The framework emphasises methodological rigour and reproducibility, enabling accurate quantification and interpretation of metabolites relevant to disease mechanisms or therapeutic interventions. By providing a standardised approach, this protocol supports longitudinal and translational studies, bridging findings from rodent models to clinical applications and advancing the reliability of metabolomics-based biomarker discovery in preclinical research. Full article

Microtubules play a key role in cell division and cell migration. Thus, microtubule-targeting agents (MTAs) are pivotal in cancer therapy due to their ability to disrupt cell division microtubule dynamics. Traditionally divided into stabilizers and destabilizers, MTAs are increasingly being repurposed for central nervous system (CNS) applications, including brain malignancies such as gliomas and neurodegenerative diseases like Alzheimer’s and Parkinson’s. Microtubule-stabilizing agents, such as taxanes and epothilones, promote microtubule assembly and have shown efficacy in both tumour suppression and neuronal repair, though their CNS use is hindered by blood–brain barrier (BBB) permeability and neurotoxicity. Destabilizing agents, including colchicine-site and vinca domain binders, offer potent anticancer effects but pose greater risks for neuronal toxicity. This review highlights the mapping of nine distinct tubulin binding pockets—including classical (taxane, vinca, colchicine) and emerging (tumabulin, pironetin) sites—that offer new pharmacological entry points. We summarize the recent advances in structural biology and drug design, enabling MTAs to move beyond anti-mitotic roles, unlocking applications in both cancer and neurodegeneration for next-generation MTAs with enhanced specificity and BBB penetration. We further discuss the therapeutic potential of combination strategies, including MTAs with radiation, histone deacetylase (HDAC) inhibitors, or antibody–drug conjugates, that show synergistic effects in glioblastoma models. Furthermore, innovative delivery systems like nanoparticles and liposomes are enhancing CNS drug delivery. Overall, MTAs continue to evolve as multifunctional tools with expanding applications across oncology and neurology, with future therapies focusing on optimizing efficacy, reducing toxicity, and overcoming therapeutic resistance in brain-related diseases. Full article

Atherosclerosis is a progressive, multifactorial disease driven by the interplay of lipid dysregulation, chronic inflammation, oxidative stress, and maladaptive vascular remodeling. Despite advances in systemic lipid-lowering and anti-inflammatory therapies, residual cardiovascular risk persists, highlighting the need for more precise interventions. Targeted drug delivery represents a transformative strategy, offering the potential to modulate key pathogenic processes within atherosclerotic plaques while minimizing systemic exposure and off-target effects. Recent innovations span a diverse array of platforms, including nanoparticles, liposomes, exosomes, polymeric carriers, and metal–organic frameworks (MOFs), engineered to engage distinct pathological features such as inflamed endothelium, dysfunctional macrophages, oxidative microenvironments, and aberrant lipid metabolism. Ligand-based, biomimetic, and stimuli-responsive delivery systems further enhance spatial and temporal precision. In parallel, advances in in-silico modeling and imaging-guided approaches are accelerating the rational design of multifunctional nanotherapeutics with theranostic capabilities. Beyond targeting lipids and inflammation, emerging strategies seek to modulate immune checkpoints, restore endothelial homeostasis, and reprogram plaque-resident macrophages. This review provides an integrated overview of the mechanistic underpinnings of atherogenesis and highlights state-of-the-art targeted delivery systems under preclinical and clinical investigation. By synthesizing recent advances, we aim to elucidate how precision-guided drug delivery is reshaping the therapeutic landscape of atherosclerosis and to chart future directions toward clinical translation and personalized vascular medicine. Full article