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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.
Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction
The effect of aging in thin films in the picosecond sonar experiment
In vivo anti-inflammatory evaluation of oxadiazole derivatives bearing flurbiprofen moiety using experimental and computational approaches
Premarital intervention based on attachment and differentiation improves communication and conflict management in Iranian couples
A blockchain-based deep learning approach for student course recommendation and secure digital certification
Abstract Over the past decade, the student course recommendation process with secure certificate issuance has remained a critical research area due to the rise of e-learning and personalized learning. The recommendation system enhances the recommended educational resources to improve the students’ learning process. The previous conventional research works shared hybrid content and collaborative filtering techniques, which boosted academic performance, personalized learning, and secure certification for students. However, the existing techniques faced several difficulties in handling the syllabus updates based on evolving recommendations, complexity, and security issues related to certificate issuance. To address the challenges in the existing techniques, the research introduces the Deep Certifier-DX509 model for secure certificate issuance and student course recommendation. The proposed approach exploits the Modified Attention-Enabled Deep Long Short-Term Memory (MA-DLSTM) Model as a recommendation system to suggest the most suitable courses based on users’ prior academic performance, and integrates X509 as the Certificate generation algorithm. Specifically, the incorporation of the X509 Blockchain with Proof-of-Work (PoW) in the certificate sub-system serves as a major contribution to enhance the security with Two-step authentication and generates accurate course recommendations. Experimental results demonstrate that the proposed Deep Certifier-DX509 model shows superior performance, achieving a high Genuine User Rate (GUR) of 0.73, Memory Usage of 453.81KB, Transaction time of 1.03 s, Responsiveness of 2.39s and Throughput of 119.52bps, outperforming the other existing techniques.
Dynamics analysis of a cam with flat-bottomed follower system
A novel method for assessing postmortem interval using radon radioisotopic decay – an internal radon ‘time of death clock’
Prevalence and associated factors of overweight, obesity, and central obesity among adults in Northern Sudan: a community-based cross-sectional study
Mathematical modeling of blood flow with copper and graphene nanoparticles in inclined stenotic arteries
Therapeutic potential of human mesenchymal stromal cell-derived mitochondria in a rat model of surgical digestive fistula
Abstract Mitochondria are central to cellular energy metabolism and play a critical role in tissue regeneration. Mitochondrial dysfunction contributes to a range of degenerative conditions and impaired wound healing, driving increasing interest in mitochondrial transplantation as a novel therapeutic strategy. Gastrointestinal wound healing is particularly susceptible to failure, with complications such as post-surgical fistula formation commonly occurring after procedures like sleeve gastrectomy. Mitochondria derived from human mesenchymal stromal/stem cells (hMSCs) have shown promise in restoring tissue bioenergetics and promoting repair across various disease models. In this study, we evaluated the therapeutic potential of hMSC-derived mitochondria as a nano-biotherapy for gastrointestinal wound healing using a rat model of post-operative fistula. Structurally intact mitochondria were isolated from hMSCs and either applied to human colonic epithelial cells (HCEC-1CT) in vitro or transplanted locally into fistula-bearing rats. Mitochondrial treatment led to a dose-dependent increase in cellular metabolic activity, intracellular ATP levels, and mitochondrial uptake by recipient cells. In vivo, mitochondrial transplantation significantly accelerated fistula closure and tissue regeneration compared to controls. These findings underscore the translational promise of mitochondria-based, cell-free therapies and lay the groundwork for future regenerative strategies targeting gastrointestinal wound repair.
Critical risks of haemoadsorption for COVID-19 patients and directions for future evaluations: a nationwide propensity score matched cohort study
Abstract Haemoadsorption has been suggested as treatment adjunct for sepsis and septic shock, cardiac surgery, acute respiratory distress syndrome, and coronavirus disease 2019 (COVID-19). Randomised clinical trials did not provide conclusive evidence for benefits and even suggest risks in COVID-19 patients. Retrospective observational cohort study based on hospital remuneration data from all COVID-19 patients treated in intensive care units in Germany between 01/01/2020 and 12/31/2021. Regression modelling was performed for 1:1 propensity score matching of 2058 patients. Two-sided probability values for group comparisons and regression models with spline functions controlling for non-linear relationships and medically relevant interaction variables were calculated. In-hospital mortality of patients supported with haemoadsorption was significantly higher compared to matched control patients (74.6% vs. 70.3%, p = 0.0299). Haemoadsorption was associated with coagulopathy (68.0% vs. 54.9%, p < 0.0001), cardiac arrhythmia (49.2% vs. 44.2%, p = 0.0272), and cardiopulmonary resuscitation (CPR, 19.3% vs. 13.1%, p = 0.0002). Further, haemoadsorption increased the chance of death for COVID-19 patients without septic shock (odds ratio, OR [within a 95% confidence interval, CI]; 1.40 [1.05–1.86]) and did not improve survival of septic shock patients (1.19 [0.85–1.67]). Independent variables with a significant impact on mortality included the use of extracorporeal membrane oxygenation (ECMO, 2.15 [1.68–2.76]) and CPR (1.60 [1.03–2.45]). The timing of the haemoadsorption therapy had no effect on patients´ outcomes. Due to inconclusive evidence for benefit and potential harm, haemoadsorption therapy should be limited to thoroughly designed clinical trials before introduced into clinical routine in the context of COVID-19.
Assessing buprenorphine treatment utilization and SAMHSA DATA waiver provider distribution in 2021: a real-world analysis in California
Abstract Qualified clinicians previously required a practitioner waiver (i.e., “DATA-waiver” or "X-waiver") to offer buprenorphine, a medication for opioid use disorder (OUD). However, many counties in the US experience fewer buprenorphine prescriptions due to factors such as “DATA-waiver” providers underutilizing their buprenorphine prescribing ability. Our study aimed to compare the availability of active buprenorphine-prescribing clinicians in California to the Substance Abuse and Mental Health Services Administration (SAMSHA)-listed DATA waived prescribers under each 5-digit ZIP Code. This study utilized the buprenorphine prescription record data in 2021 from California’s Controlled Substance Utilization Review and Evaluation System (CURES). The list of the locators for all DATA-waiver clinicians was obtained from the SAMHSA webpage. Among 1,600 ZIP Codes where patients resided, 62.1% housed DATA-waived physicians, and 57.8% had active buprenorphine-prescribing clinicians. A disproportional distribution between buprenorphine-prescribing clinicians and DATA-waived physicians was evident. Among physicians listed in the SAMHSA roster, not all were actively prescribing buprenorphine. Significant disparities in access to active prescribers and DATA waiver prescribers persist between rural and urban areas in California. Addressing these issues requires resource allocation and inter-professional collaboration. The 2022 Omnibus bill’s policy changes hold promise, necessitating further effort into OUD-based policy changes.