No abstract

Gangrenous cholecystitis is a severe complication of acute cholecystitis. It is often found incidentally during laparoscopic cholecystectomy or during conversion to open surgery and diagnosed with subsequent pathological analysis. While intraoperative diagnosis is typically through direct visualization of the gallbladder, specific diagnostic modalities may guide physicians toward an earlier diagnosis. Surgical intervention and a more aggressive approach are often needed to prevent the advancement of the disease and its catastrophic complications. This case report illustrates the distinct risk factors predisposing a patient to develop gangrenous cholecystitis. Comorbidities such as hypertension, coronary artery disease, age, the relevance of the SIRS criteria, and elevated liver enzymes are explored as predictive factors in a patient with gangrenous cholecystitis.

The task of ensuring cyber-security has grown increasingly challenging as the alarming expansion of computer connectivity and the large number of computer-related applications has expanded recently. It also requires a sufficient protection system against a variety of cyberattacks. Detecting discrepancies and risks in a computer network, as well as creating intrusion detection systems (IDS) to aid in cyber-security. Artificial intelligence (AI), specifically machine learning (ML) approaches, were used to create a practical data-driven intrusion detection system. Two alternative intrusion detection (ID) classification approaches were compared in this study, each with its own set of use cases. Before using the two classifiers for classification, the Particle Swarm Optimization (PSO) approach was used to reduce dimensionality. The classification approaches used to characterise network anomalies were studied in this study. PSO + ANN (Artificial neural network), PSO + Decision Tree (PSO+DT) and PSO + K-Nearest Neighbor (PSO+KNN) are the three classifiers used. The detection approaches' results were confirmed using the KDD-CUP 99 dataset. On the result of the implementation, success indicators like as specificity, recall, f1-score, accuracy, precision, and consistency were used on cyber-security databases for different types of cyber-attacks. The accuracy, detection rate (DR), and false-positive rate of the two classifiers were also compared to see which one outperforms the other (FPR). Finally, the system was compared to the IDS that was already in place. In terms of detecting network anomalies, the results reveal that PSO+ANN outperforms the PSO+KNN and PSO+DT classifier algorithms.

Morbid obesity increases the average risk of a patient developing a paraesophageal or hiatal hernia. Paraesophageal hernias (PEH) include several types, and their treatment is indubitably one of the most contentious topics in minimally invasive surgery. Though it is rare for PEH to manifest as a strangulated, volatilized intrathoracic stomach with infection, the increased risk of mortality is an indication for many to pursue surgical repair. Moreover, morbidly obese individuals represent a substantial rate of failure of PEH repairs. The modes of confirmation diagnostics are barium swallow or upper endoscopy. This case study focuses on a 64-year-old female who presented with several comorbidities, was appropriately evaluated for laparoscopic sleeve gastrectomy, and was previously identified to have a severe type III PEH with grade IV configuration. Additionally, the pathological finding from the extracted specimen was significant for helicobacter pylori gastritis.