Browse Articles

HIV heart inflammation is mediated by HIV infected myeloid cells, HIV-tat secretion, and aberrant function of Connexin43-containing channels

Dual coordinate attention (DCA) network for accurate cerebral vascular endothelium segmentation in OCT images

Assessing the impact of groundwater abstraction and concrete dam fractures on saltwater intrusion using numerical modeling and interpretable machine learning

Awareness and knowledge of obstetricians about prenatal findings of inherited metabolic disorders

Correction: Engineering dielectric properties and charge transport in PANI/CuO nanocomposites via microstructural control

Comprehensive enhancement of blasting performance and dust suppression in open-pit mines using a modified mudstone–fly ash geopolymer stemming material

Scope of the grain biofortification in relation to food security

An ensemble hybrid and explainable AI (XAI) framework for zero false-positive islanding detection in distribution networks

Association of COVID-19 vaccines and antibody response in individuals with prior Coronavirus infection

Abstract Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) was one of the worst pandemics and viral infections affecting humans across the globe. Many non-pharmaceutical and pharmacological interventions were initiated to prevent the spread of infection and control the disease transmission. Covishield and Covaxin were among the most common vaccines given in India to control the spread of the coronavirus and its variants among the public. To evaluate the efficacy of COVID-19 vaccines in increasing serum and salivary IgA antibody levels and how they are correlated to patients’ demographics, medical conditions, and previous history of coronavirus infection in the Udupi district, Karnataka, India. 127 participants who received two doses of the COVID-19 vaccine were recruited. Anti-SARS-CoV-2 IgA antibodies specific to COVID-19 in serum and saliva were measured by ELISA. The mean serum IgA levels were compared at 0–6 months, 6–12 months, and > 12 months. The IgA levels were compared with age, gender, history of COVID-19 infection, timing of vaccination, body mass index, and comorbidities. The mean serum IgA levels in individuals with a history of COVID-19 (12.59 ± 5.67 μg/ml) were higher than those without a history of infection (8.5 ± 7.20 μg/ml). Among those with a history of COVID-19 infection, 8.1% were infected before the vaccination, and 91.9% were infected post-vaccination. Serum IgA levels were lower in participants under 30 years of age (5.27 ± 3.14 μg/mL) compared to participants above 30 years of age (8.93 ± 4.56 μg/mL) (P = 0.001). Antibody levels were influenced by age, presence of comorbidities, and history of coronavirus infection. Individuals with prior COVID-19 infection showed higher serum IgA antibody levels. Serum and salivary IgA levels were even detected in a group of participants with more than 12 months post-vaccination period.

Water hazard prevention technology for confined mining beneath dual extremely thin aquicludes in roof and floor

Long-term associations between animal-source food consumption and breast and prostate cancer incidence based on cointegration and ARIMAX models

A multi-step short-term photovoltaic power prediction model based on an improved whale migration algorithm

Seismological analysis of the tectonic evolution of the Laji Shan fault from the 2023 Jishishan MS 6.2 earthquake

Effect of biopolymer and plant fiber on soil-water characteristics of sandy soil

Deep optimization-guided hybrid neural network for accurate detection and segmentation of white matter hyperintensities in clinical MRI images

Abstract White matter hyperintensities (WMHs) are common radiological findings in brain magnetic resonance imaging (MRI) and are strongly associated with neurological disorders such as stroke, dementia, and multiple sclerosis. Accurate detection and segmentation of WMHs are crucial for early diagnosis, disease progression analysis, and treatment planning. However, manual delineation of WMHs is labour-intensive, time-consuming, and prone to inter-observer variability, which limits its practicality in large-scale clinical and research settings. Deep learning has shown promise in automating WMH analysis; however, challenges remain due to heterogeneous lesion sizes, low contrast boundaries, and imaging noise. We propose a Deep Optimization-Guided Hybrid Neural Network (DOGHNN) that combines Inception-v3, ResNet-50, and Practical Swarm Optimization (PSO) for enhanced WMH segmentation. Inception-v3 is employed to capture multi-scale lesion features, enabling the detection of both small punctate and large confluent WMHs. ResNet-50 is integrated to extract deep contextual representations, leveraging residual learning to distinguish true lesions from surrounding tissue and artifacts. Finally, PSO is incorporated as an optimization strategy to iteratively refine fusion weights, segmentation thresholds, and key parameters, minimizing segmentation loss and improving boundary delineation. This hybrid approach ensures both fine-grained lesion sensitivity and robust global feature learning. The DOGHNN framework was evaluated on benchmark WMH MRI datasets with diverse lesion loads and anatomical complexities. Comparative experiments showed superior performance over baseline deep learning models. Quantitative evaluation yielded a maximum precision of 93.2%, recall of 91.5%, dice score 91.1%, and f1-score of 90.5% were achieved by the suggested DOGHNN, and Hausdorff distance of 6.5, confirming its robustness and reliability. By combining multi-scale learning, residual contextual modelling, and optimization-driven refinement, the DOGHNN framework delivers accurate and efficient WMH segmentation. This approach holds strong potential for clinical integration, supporting automated neuroimaging workflows and improving diagnostic decision-making in neurological care.

Integrated UPLC, bioinformatics, and in vitro analyses reveal Yiqihuoxue decoction (GSC) alleviates vascular aging by promoting autophagy

Abstract Vascular aging constitutes a predominant risk factor for cardiovascular pathologies. Traditional Chinese Medicine (TCM) employs various formulations to mitigate age-related vascular dysfunction, among which Yiqihuoxue decoction (GSC) is clinically utilized for managing cardiovascular conditions in elderly patients. Our prior work demonstrated GSC’ s capacity to delay vascular aging in mice models and attenuate senescence in vascular endothelial cells, though its mechanistic basis remained unresolved. To address this, we conducted multi investigation combining Ultra-Performance Liquid Chromatography (UPLC), network pharmacology, and molecular dynamics (MD) simulations. UPLC identified 130 bioactive compounds in GSC, while integrative analyses (mass spectrometry, literature mining, and target prediction) revealed 792 putative targets. Intersection with 2,539 vascular aging-associated targets yielded 422 shared candidates, suggesting GSC’ s polypharmacological potential. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses highlighted autophagy-related pathways, notably PI3K/Akt and SIRT1 signaling. Cellular validation experiments in senescent human umbilical vein endothelial cells (HUVECs) demonstrated that GSC restored cell morphology and reduced SA-β-gal activity ( p  < 0.05). GSC alleviated G0/G1 phase cell cycle arrest, restored mitochondrial membrane potential, and suppressed ROS accumulation. Autophagy profiling indicated that GSC promoted autophagic flux, as evidenced by increased LC3B puncta formation in immunofluorescence assays and autophagosome accumulation observed via transmission electron microscopy. Mechanistically, GSC exerted anti-senescence effects via coordinated regulation of the SIRT1-autophagy axis and PI3K/AKT pathway inhibition. Western blotting confirmed dose-dependent upregulation of SIRT1 and downregulation of p-PI3K/p-AKT ( p  < 0.05), consistent with network pharmacology predictions. This study established GSC as a multi-component, multi-target intervention against vascular aging, with autophagy modulation serving as a central mechanism. These findings will provide a theoretical foundation for developing GSC-based therapies targeting age-related cardiovascular diseases.

A time-frequency cross-attention network model for epileptic seizure detection

Pediatric foot anthropometry and its correlation with growth assessment

Abstract Foot dimensions, particularly length and width, are essential anthropometric parameters often used in health, ergonomics, and footwear design. This study aimed to investigate the correlation between foot dimensions, age, and height among children aged 4 to 12 years in Ekpoma, Edo State, Nigeria. A cross-sectional descriptive survey was conducted with 389 children, randomly selected from schools and community centres. Data were collected using standardised anthropometric measurements, including foot length, foot width, and height, ensuring accuracy and consistency. Pearson’s correlation and independent t-tests were used to examine relationships among variables and to identify sex-based differences. Descriptive statistics revealed variations in foot dimensions across the age groups, with a mean foot length of 19.49 cm and a mean foot width of 6.87 cm. Foot length showed moderate-to-strong correlations with age in younger children (overall r  = 0.549, p  < 0.001) and a strong correlation with height ( r  = 0.652, p  < 0.001), while foot width exhibited weaker positive correlations with age (r range 0.254–0.513) and height ( r  = 0.233, p  < 0.001). No significant sex differences were observed (all p  > 0.05). The findings highlight the progressive changes in foot dimensions with age and height and their potential applications in pediatric health, footwear design, and ergonomic planning.

Suppression of transgenerational lipid provisioning inhibits desiccation resistance, but not diapause, in the vector mosquito, Aedes albopictus

A probabilistic framework for effective battery energy storage sizing in microgrids with demand response

Abstract Microgrids (MGs) are increasingly integrating Battery Energy Storage Systems (BESSs) to improve operational flexibility and minimize overall costs. However, probabilistic BESS sizing remains computationally demanding due to uncertainties associated with renewable energy generation, load demand, and market price volatility. This paper presents a hybrid probabilistic sizing framework that integrates the 2m + 1 Point Estimation Method (PEM) with the Equilibrium Optimizer (EO), referred to as the EO–PEM approach. Unlike conventional Monte Carlo simulation–based formulations, the presented method embeds EO within the PEM uncertainty evaluation loop, enabling accurate results with substantially reduced computational effort. Additionally, an incentive-based Demand Response (IDR) model is integrated into the Energy Management (EM) framework. The main objective of the EM is to minimize operational costs and maximize the MG operator’s benefits while ensuring customer satisfaction. Simulation results from the test MG system confirm the superiority of the EO over other applied optimization techniques in solving the deterministic EM problem without BESS. Under uncertainties, the EO–PEM method identifies an optimal BESS capacity of 1 kWh, achieving a reduction in the expected operational cost while maintaining high computational efficiency and robustness. Overall, the results demonstrate the effectiveness of the EO–PEM framework for probabilistic BESS sizing under multi-source uncertainties.