Single-Stage Laparoscopic Common Bile Duct Exploration and Cholecystectomy Versus Two-Stage Endoscopic Stone Extraction Followed by Laparoscopic Cholecystectomy for Patients With Cholelithiasis and Choledocholithiasis: A Systematic Review.

Gallbladder stones with common bile duct (CBD) stones can be managed by a single-stage laparoscopic approach with transcystic or transcholedochal CBD exploration and cholecystectomy or a two-stage approach with endoscopic retrograde cholangiopancreatography (ERCP) for stone extraction followed by laparoscopic cholecystectomy. Comparative outcomes between these approaches remain controversial. The objective was to compare single-stage laparoscopic CBD exploration and cholecystectomy versus two-stage ERCP stone removal followed by laparoscopic cholecystectomy for clearance of CBD stones, complications, length of stay, and costs. We systematically searched PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials for randomized trials and observational studies comparing outcomes of interest between single and two-stage approaches. Meta-analyses using random effects models were conducted. Seven studies with 382 patients were included. The single-stage approach achieved higher stone clearance rates (OR: 1.53, 95% CI: 1.12-2.08) with a shorter length of stay (mean duration: 3.5 days, 95% CI: -5.1 to -1.9 days) compared to the two-stage method. No significant difference was seen in complication rates (45% vs 40%, p=0.43) or costs ($19,000 vs $18,000, p=0.34). For patients with gallbladder and CBD stones, single-stage laparoscopic CBD exploration with cholecystectomy appears superior for stone clearance while comparable in safety and cost to a two-stage approach. Further randomized trials are warranted.

Unusual Triggers of Acute Intestinal Obstruction in Surgical Emergencies: A Series of Five Cases.

Intestinal obstruction is a common surgical emergency that can be caused by mechanical causes or by different pathological processes. The most common cause of small bowel intestinal obstruction is post-operative adhesion, and the most common cause of large bowel obstruction is malignancy. These are classified into dynamic and adynamic types. The patient was selected based on the presentation management plan. Some cases require immediate operative intervention; however, some cases, as mentioned in this case series, require further investigation and a different approach. In this study, we report the rare causes of intestinal obstruction presented to Safdarjung Hospital, Department of General Surgery, New Delhi: one case of foreign body impaction, one case of spontaneous migration of feeding jejunostomy, one case of extrauterine IUCD causing intestinal obstruction, one case of mesentery band causing obstruction, and one case of abdominopelvic mass causing small bowel obstruction. These cases presented to the surgical emergency department with challenges in their diagnosis, intraoperative findings, and their outcomes.

Revolutionizing Breast Healthcare: Harnessing the Role of Artificial Intelligence.

Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.