Efficacy of LigaSure Versus Harmonic Devices in Laparoscopic Sleeve Gastrectomy: A Systematic Review and Meta-Analysis.

Our goal is to conduct a thorough systematic review and meta-analysis of comparative studies to evaluate the efficacy of LigaSure (Valleylab, Boulder, CO) compared with Harmonic (Ethicon Endo-Surgery, Inc., Cincinnati, OH) devices in patients undergoing laparoscopic sleeve gastrectomy (LSG). Our search strategy, from inception until March 1, 2024, involved multiple databases, including the Cochrane Controlled Register of Trials (CENTRAL), Web of Science (WOS), PubMed, Scopus, and Google Scholar. We evaluated randomized clinical trials using the Cochrane Risk of Bias tool for randomized trials (RoB-2) tool and non-randomized studies using the Risk of Bias In Non-randomized Studies for Interventions (ROBINS-I) tool. The primary outcomes assessed were operative time, mean length of hospital stay, and the rates of intraoperative complications such as bleeding, organ injury, leakage, and hematoma formation. Additionally, we collected data on postoperative complications, including bleeding, abscess formation, leakage, fever (>38°C), and reoperation rates. Data were analyzed using random-effects models and reported as mean difference (MD) or risk ratio (RR) with a 95% confidence interval (CI) using Review Manager software (RevMan, version 5.4 for Windows, The Cochrane Collaboration, 2020). Four studies, comprising two randomized clinical trials (RCTs) and two retrospective cohort studies, involving a total of 692 patients, were included in the analysis. Both the operative time and length of hospital stay did not significantly differ between the LigaSure and Harmonic groups (p>0.05). The pooled analysis also revealed no significant difference between the LigaSure and Harmonic groups in terms of intraoperative and postoperative complications (p>0.05). In conclusion, our systematic review and meta-analysis found no significant statistical or clinical differences between LigaSure and Harmonic devices in terms of operative outcomes and complication rates in patients undergoing LSG.

Tailoring magnetic Sn-MOFs for efficient amoxicillin antibiotic removal through process optimization.

This study investigated the efficacy of magnetic Sn metal-organic frameworks (MSn-MOFs) in removing the insecticide amoxicillin (AMX) from aqueous solutions. Our thorough experimental investigation showed that MSn-MOFs were an incredibly effective adsorbent for removing AMX. Several methods were used to characterize the material. BET investigation of the data displayed a significant surface area of 880 m2 g-1 and a strong magnetic force of 89.26 emu g-1. To identify the point of zero charge, surface characterization was carried out and the value was 7.5. This shows that the adsorbent carries a positive and negative charge below and above this position, respectively. Moreover, the impact of pH on adsorption equilibrium was explored. The results of kinetic models to explore the adsorption of AMX on MSn-MOFs supported the pseudo-second-order, and the adsorption complied well with the Langmuir isotherm. The results revealed that the overall adsorption mechanism may entail chemisorption via an endothermic spontaneous process with MSn-MOFs. The precise modes by which MSn-MOFs and AMX interacted may involve pore filling, H-bonding, π-π interaction, or electrostatic interaction. Determining the nature of this interaction is essential in understanding the adsorption behavior of the MOFs and optimize the adsorbent design for real-world applications. The use of the MSn-MOF adsorbent provides a straightforward yet efficient method for the filtration of water and treatment of industrial effluents. The results showed 2.75 mmol g-1 as the maximum capacity for adsorption at pH = 6. Additional tests were conducted to assess the adsorbent regeneration, and even after more than six cycles, the results demonstrated a high level of efficiency. The adsorption results were enhanced by the application of the Box-Behnken design.

Explainable Artificial Intelligence-Based IoT Device Malware Detection Mechanism Using Image Visualization and Fine-Tuned CNN-Based Transfer Learning Model.

Automated malware detection is a prominent issue in the world of network security because of the rising number and complexity of malware threats. It is time-consuming and resource intensive to manually analyze all malware files in an application using traditional malware detection methods. Polymorphism and code obfuscation were created by malware authors to bypass the standard signature-based detection methods used by antivirus vendors. Malware detection using deep learning (DL) approaches has recently been implemented in an effort to address this problem. This study compares the detection of IoT device malware using three current state-of-the-art CNN models that have been pretrained. Large-scale learning performance using GNB, SVM, DT, LR, K-NN, and ensemble classifiers with CNN models is also included in the results. In light of the findings, a pretrained Inception-v3 CNN-based transfer learned model with fine-tuned strategy is proposed to identify IoT device malware by utilizing color image malware display of android Dalvik Executable File (DEX). Inception-v3 retrieves the malware's most important features. After that, a global max-pooling layer is applied, and a SoftMax classifier is used to classify the features. Finally, gradient-weighted class activation mapping (Grad-CAM) along the t-distributed stochastic neighbor embedding (t-SNE) is used to understand the overall performance of the proposed method. The proposed method achieved an accuracy of 98.5% and 91%, respectively, in the binary and multiclass prediction of malware images from IoT devices, exceeding the comparison methods in different evaluation parameters.

Some Socioeconomic Factors and Lifestyle Habits Influencing the Prevalence of Obesity among Adolescent Male Students in the Hail Region of Saudi Arabia.

A cross-sectional study was conducted to investigate the effect of some socioeconomic factors and lifestyle habits on the prevalence of obesity among adolescent male students in the Hail region, Saudi Arabia. A questionnaire was filled by 1495 male adolescents distributed among 12 schools in the Hail region. Body weight and height were taken, and the Z-score of students was measured using Anthroplus software with a cutoff 1-2 and +2 standard deviations to determine overweight and obesity, respectively. The study revealed that 21.3% of students were overweight and 27% were obese, respectively. There was a negative association between family size of >8 and obesity (OR: 0.68, CI: 0.48-0.92, p = 0.05). Family income of <5000 SR was negatively associated with obesity (OR: 0.59, CI: 0.36-0.97, p = 0.03). Whether a subject's mother worked (odds ratio (OR): 1.43, confidence interval CI: 1.03-1.99, p = 0.03) as well as the subject's mother's education-whether she can read and write, has a middle school degree, or has done postsecondary studies-were positively associated with obesity. Exercise, regardless of the duration, was negatively associated with obesity. In addition, sleeping <6 h/day had a positive association with obesity. CONCLUSION: a >8 family size and a low family income were negatively associated with obesity, while having an educated and working mother was positively associated with obesity.