Background: Parenteral nutrition (PN) is a life‑saving intervention for neonates who are unable to meet their nutritional requirements through enteral feeding. Despite its essential role, PN is frequently associated with metabolic, electrolyte, and hepatobiliary complications. Objective: To evaluate the frequency and pattern of metabolic, electrolyte, and hepatobiliary complications associated with PN administration in neonates. Methods: A prospective case-series study was conducted in two tertiary neonatal intensive care units (NICUs) in Baghdad, Iraq. Term and preterm neonates who received PN for at least five days were included. PN‑related metabolic, electrolyte, hepatobiliary, and hematological complications were recorded an

Antimicrobial resistance (AMR) is a global concern, especially in low- and middle-income countries, threatening food production, healthcare, and life expectancy. Antimicrobial stewardship (AMS) programs can optimize antibiotic use, improve patient outcomes, lower AMR, and save healthcare costs. This observational-retrospective study in Dhi Qar Governorate aimed to assess antimicrobial prescribing patterns in Al-Nasiriya public hospitals. Dhi Qar Health Directorate comprises ten hospitals, and only one hospital was excluded from the study. The study used data from antibiotic stewardship committees, including antibiogram, antibiotic, and meropenem surveys, hospital pharmacies’ medical files, and the directorate statistics from 1/1/2

Large language models (LLMs) are a rapidly evolving class of artificial intelligence with significant potential in clinical healthcare. Despite accelerating adoption, rigorous systematic evidence on clinical utility, patient safety, and implementation feasibility remains fragmented. To systematically review LLM applications across clinical domains, evaluate performance with appropriate contextual caveats, characterize implementation barriers, and identify ethical and regulatory considerations. Scientific databases were searched from January 2020 to January 2025. Studies evaluating transformer-based LLMs (≥10M parameters) in clinical settings were eligible. Data were independently double-extracted; quality was assessed using QUADAS-2, RE-A

Abstract Background Deep learning (DL) has attracted increasing attention in orthodontics, with many studies reporting high diagnostic and predictive accuracy. However, despite these promising results, routine clinical adoption remains limited. Objectives This scoping review aimed to explore the main barriers that restrict the translation of DL systems from research environments into everyday orthodontic practice. Methodology The review was conducted in accordance with PRISMA-ScR guidelines. Electronic searches were performed in PubMed, Scopus, Web of Science, and IEEE Xplore for English-language studies published between 2015 and June 2025. Studies reporting quantitative performance of DL-based orthodontic applications were included. Dat

One of the unique properties of laser heating applications is its powerful ability for precise pouring of energy on the needed regions in heat treatment applications. The rapid rise in temperature at the irradiated region produces a high temperature gradient, which contributes in phase metallurgical changes, inside the volume of the irradiated material. This article presents a comprehensive numerical work for a model based on experimentally laser heated AISI 1110 steel samples. The numerical investigation is based on the finite element method (FEM) taking in consideration the temperature dependent material properties to predict the temperature distribution within the irradiated material volume. The finite element analysis (FEA) was carried