Browse Articles

Characterization of lower urinary tract dysfunction in a mouse model of amyotrophic lateral sclerosis

Experimental study of the effects of diazepam on vasospasm in a subarachnoid rat model through pathological and biochemical analysis

Abstract Subarachnoid hemorrhage (SAH), characterized by bleeding in the subarachnoid space, is associated with high morbidity and mortality, primarily due to cerebral vasospasm. Recent studies suggest oxidative stress and inflammation play crucial roles in vasospasm pathogenesis. This study investigates the effects of diazepam, a benzodiazepine with vasodilatory properties, in a rat SAH model. Three groups of female Sprague Dawley rats were analyzed: a control group, an SAH-induced group without treatment, and an SAH-induced group treated with 3 mg/kg of diazepam. Our findings revealed SAH significantly increased Total Oxidant Status (TOS), Oxidative Stress Index (OSI), and inflammatory markers (IL-1β, IL-6, TNF-α) in both tissue and serum samples. Diazepam treatment mitigated these effects, showing reduced TOS, OSI, and cytokine levels compared to the untreated SAH group. Additionally, diazepam helped maintain thiol-disulfide balance, with higher Total Thiol and Native Thiol levels, indicating a protective effect against oxidative damage. Histopathological examination revealed significant vasospasm and inflammatory infiltration in the SAH group, which was partially alleviated in the diazepam-treated group. Diazepam may serve as an adjunct therapy in SAH management by modulating oxidative stress and inflammation, potentially alleviating vasospasm and related ischemic injuries.

Research and design experience of a 1 million tons/year CO2 capture project in the Shengli oilfield of Qilu petrochemical

Time series AQI forecasting using Kalman-integrated Bi-GRU and Chi-square divergence optimization

Hybrid strategy enhanced crayfish optimization algorithm for breast cancer prediction

Risk factors for prolonged hospitalization in acute decompensated heart failure from the HEROES study

Discovery and genesis mechanism of high content diamondoids in the Gulong shale oil

High-energy X-ray irradiation-induced functionalization of Ni(OH)₂ for enhanced supercapacitor electrodes

Biomechanics of humeral locking plate augmented with fibular strut allograft and intramedullary strut plate using finite element analysis

Abstract A humeral locking plate augmented with fibular strut allograft treated for proximal humeral fracture without internal structural support is satisfactory. While it is better clinically and biomechanically than the locking plate alone, it has disadvantages, including difficulty to obtain and the possibility of infection. Other alternative augmentation approaches are in demand. Therefore, the hypothesis of this study is whether intramedullary strut plate can replace fibular strut allograft as a surgical method while providing similar biomechanical performance. The finite element analysis (FEA) models were established based on three-dimensional computed tomography images. Computer-aided design implants were incorporated into the models. According to different implants, models were divided into four groups: the intact humerus, humeral locking plate alone (LP), humeral locking plate augmented with fibular strut allograft (FA), and humeral locking plate augmented with intramedullary strut plate (IMP). The displacements and von Mises stresses were measured on the models by simulating axial force, oblique force and torsion. Compared with the LP group, the displacements and von Mises stresses on the humerus and humeral locking plate in the FA, and IMP groups were all smaller in axial force, oblique force, and torsion. The biomechanical effects of FA and IMP in proximal humeral fracture without internal structural support were basically similar in terms of axial force, oblique force, and torsion. Findings provide useful new ideas for implant design. Our FEA simulation indicates that both the fibular strut allograft (FA) and intramedullary strut plate (IMP) offer similar biomechanical stability in treating proximal humeral fractures without internal structural support. This supports the hypothesis that the intramedullary strut plate can effectively replace the fibular strut allograft.

The moderating role of aerobic exercise in the relationship between stress and cognitive functions

Abstract This study examines the effect of stress on cognitive failures and the potential moderating role of aerobic exercise. A total of 290 university students participated, and moderation analysis was conducted using Hayes’ PROCESS Model 1 (Version 4.2). Results showed that stress alone did not significantly predict cognitive failures. However, aerobic exercise appeared to play a potential moderating role in this relationship. Specifically, a significant association between stress and cognitive failures was observed among moderate- and high-intensity exercisers, while no such effect was found in low-intensity exercisers. Additionally, sleep duration was negatively associated with cognitive failures. These findings suggest that aerobic exercise may influence the relationship between stress and cognitive failures, although further investigation is needed to establish this effect more conclusively.

Dietary supplement consumption is associated with lower dietary quality in Chinese university students based on a cross-sectional study

Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models

An unexpected tumor-resistant phenotype from floxing PAK1 in a mouse model of colitis associated cancer

Abstract Inflammatory bowel disease (IBD) and colitis-associated cancer are associated with activation of PAK1 (p-21 activated kinase 1). We previously found that total knockout of PAK1 (PAK1KO) reduced tumorigenesis upon AOM/DSS but enhanced tumorigenesis in another model of IBD with total knockout of IL10 (IL10KO). To better understand the specific role of epithelial PAK1, we crossed Pak1 floxed (PAK1fl) with VillinCre mice for a conditional knockout of PAK1 in intestinal epithelia (PAK1CKO). PAK1fl were included as additional controls. Unexpectedly, inflammation and tumorigenesis were greatly reduced in PAK1fl compared to WT or PAK1KO after AOM/DSS treatment. PAK1CKO had higher tumor incidence and counts compared to PAK1fl, but was still lower in comparison to PAK1KO or WT. When crossed with IL10KO mice, PAK1CKO exacerbated the expected hyperproliferative phenotype, resulting in early mouse morbidity. Despite normal Pak1 mRNA expression in PAK1fl colonic lysates, PAK1 protein expression on immunohistochemistry was higher that WT. Both PAK1fl and PAK1CKO mice were more resistant to shifts in microbiome, and remained clustered together compared to WT or PAK1KO. Altogether, our results suggest that floxing itself may have altered Pak1 expression, which conferred protection from AOM/DSS carcinogenesis.

PDIA3 rs2788, a risk factor for metabolic syndrome, interacted negatively with antihypertensive medications

Experimental study on the effects of low pressure and acoustic characteristics on heart rate and acoustic comfort

Species richness is an important mediator of multifunctionality changes in Hobq desert shrub ecosystem

Optimization of roof slope, design and wood strength classes in timber Fink type truss

High coffee consumption may increase aortic diameter and risk of abdominal aortic aneurysm in smokers

Abstract An association of coffee consumption with a risk of abdominal aortic aneurysm (AAA) is unknown. We hypothesized that coffee consumption influences aortic diameter and AAA risk, with smoking status as a modifier. The study included 42,723 Swedish men and 34,921 women (age 45–83 years) with infrarenal aortic diameter (IAD) measured in 8,109 men. Over 18.7 years, 1863 AAA cases (1585 non-ruptured, 278 ruptured) were identified. Among participants with coffee consumption ≤ 5 cups/day, current smokers versus never smokers had a 3-fold higher risk of non-ruptured and ruptured AAA (HR = 3.12, 95%CI = 2.62–3.71 and HR = 2.90, 95%CI = 1.95–4.31, respectively); the risk increased with coffee consumption > 5 cups/day and was a 4-fold higher (HR = 3.89, 95%CI = 3.12–4.85) for non-ruptured and a 4.6-fold higher (HR = 4.61, 95%CI = 2.72–7.86) for ruptured AAA (P-value- multiplicative-interaction = 0.009). 160 (2.0%) screened men had an IAD ≥ 30 mm. In men drinking daily ≤ 3 cups of coffee, current smokers versus never smokers had a 4-fold (OR = 4.09, 95%CI = 1.81–9.22) higher risk of IAD ≥ 30 mm; in men with higher coffee consumption (> 3 cups/day), the risk increased 6.6-fold (OR = 6.58, 95%CI = 2.98–14.6). In ex-smokers, the corresponding ORs were 1.67 (95%CI = 0.62–4.49) and 3.27 (95%CI = 1.27–8.40), respectively. In conclusion, high coffee consumption may increase risk of AAA and infrarenal aortic diameter in smokers.

Evaluation of antiarrhythmia drug through QSPR modeling and multi criteria decision analysis

Abstract This study explores how topological indices (TIs), which are mathematical descriptors of a drug’s molecular structure, can support to predict vital properties and biological activities. This understanding is a key for more effective drug design. We focused on drugs used to treat several arrhythmia conditions, including tachycardias, bradycardias, and premature beats. Our approach combines molecular modeling with decision-making techniques to offer a cost-effective way to understand how these drug molecules behave. Our procedure started with calculating topological indices for the chemical structures of these medications to extract information about their features. We then established quantitative structure-property relationship (QSPR) models using quadratic regression, training and validating them. We concentrated on TIs that showed a strong correlation $$(> 0.7)$$ with physicochemical properties. Each property was also weighted, based on its correlation with the topological indices. As a final point, to aid in informed decision-making, we employed multiple-criteria decision-making approaches Technique for Order Preference by Similarity to Ideal Solution TOPSIS and Simple Additive Weighting SAW to rank the anti- arrhythmia medications. Drug Amiodarone ranked highest due to strong correlation with boiling point and polarizability. The study also highlights the potential of machine learning to analyze large datasets, allowing for accurate predictions of chemical behavior. This comprehensive method can facilitate the detection of new drugs with valuable qualities and improve our understanding of how chemical structures affect drug effectiveness.

DDoS classification of network traffic in software defined networking SDN using a hybrid convolutional and gated recurrent neural network

Abstract Deep learning (DL) has emerged as a powerful tool for intelligent cyberattack detection, especially Distributed Denial-of-Service (DDoS) in Software-Defined Networking (SDN), where rapid and accurate traffic classification is essential for ensuring security. This paper presents a comprehensive evaluation of six deep learning models (Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and a proposed hybrid CNN-GRU model) for binary classification of network traffic into benign or attack classes. The experiments were conducted on an SDN traffic dataset initially exhibiting class imbalance. To address this, Synthetic Minority Over-sampling Technique (SMOTE) was applied, resulting in a balanced dataset of 24,500 samples (12,250 benign and 12,250 attacks). A robust preprocessing pipeline followed, including missing value verification (no missing values were found), feature normalization using StandardScaler to standardize numerical values, reshaping the data into 3D format to fit temporal models like CNN and GRU, and stratified train-test split (80% training, 20% testing) to maintain class distribution. The CNN-GRU model integrates a 1D convolutional layer for spatial pattern extraction and a GRU layer for temporal sequence learning, followed by dense layers with dropout regularization. The model was trained using the Adam optimizer with early stopping to prevent overfitting. Among all models, the CNN-GRU hybrid achieved perfect test performance, with 100% accuracy, 1.0000 precision, recall, and F1-score, and an ROC AUC of 1.0000. It also demonstrated exceptional generalization, achieving a mean cross-validation (CV) accuracy of 99.70% ± 0.09% and a mean AUC of 1.0000 ± 0.0000 across 5-fold stratified cross-validation. While individual models such as GRU, 1D-CNN, and LSTM also showed strong performance, the CNN-GRU hybrid consistently outperformed them in both accuracy and stability. These results validate the effectiveness of combining convolutional and recurrent architectures, augmented with data balancing via SMOTE, for highly accurate SDN-based intrusion detection.