Burn injuries are a major cause of morbidity and mortality, largely due to complications such as infection. Impairment of the immune system following burns increases susceptibility to both internal and external infections, underscoring the need for effective infection control strategies in burn care. In addition, burn patients frequently exhibit profound alterations in drug pharmacokinetics and pharmacodynamics (PK/PD), particularly during the resuscitation and hypermetabolic phases. In the resuscitation phase, increased capillary permeability and reduced cardiac output can prolong drug distribution, delay therapeutic response, lower peak plasma concentrations, and slow elimination. In contrast, the hypermetabolic phase is characterized by elevated catecholamine levels and enhanced tissue perfusion, which accelerate drug distribution and clearance. These physiological changes often necessitate antimicrobial dose adjustments to maintain therapeutic efficacy. This review emphasizes the critical importance of infection prevention and management in burn patients, with a focus on optimizing antimicrobial dosing and therapeutic monitoring in the context of PK/PD alterations.

Management of invasive fungal infections (IFI) and subsequent treatment choices remain challenging for physicians in the ICU. Documented evidence shows increased practice of the inappropriate use of antifungal agents in the ICU. Continuous education of healthcare providers (HCPs) represents the cornerstone requirement for starting an antifungal stewardship program (AFS). This study aimed at evaluating knowledge gaps in systemic antifungal prescribing among physicians and clinical pharmacists in a critical care setting. A cross-sectional, multi-center, survey-based study was conducted in five tertiary hospitals located in Al-Ahsaa, Saudi Arabia between January and May 2021. A self-administered questionnaire was distributed among the targeted clinicians. A total of 63 clinicians were involved (65.5% ICU physicians and 34.5% clinical pharmacists). It was noted that a minority of the participating HCPs (3.2%) had overall good knowledge about antifungal prescribing, but the majority had either moderate (46%) or poor (50.8%) knowledge. The difference in overall knowledge scores between the ICU physicians and the clinical pharmacists (p = 0.925) was not significant. However, pharmacists showed better scores for the pharmacokinetics of antifungal therapy (p = 0.05). This study has revealed a significant gap in the knowledge and practice of clinicians as regards prescribing antifungal therapy in our area. Although the results cannot be generalized, the outcome of this study has exposed the need for a tailored training program essential for carrying out an AFS program.

Background: The human epidermal growth factor receptor 2 (HER2) plays a role in the propagation of different types of cancers. It was identified in many types of cancer tissues like; breast, ovarian, lung, prostate, and stomach cancers. Therefore, inhibition of HER2 can lead to the discovery of novel anticancer agents. Objective: The study aims to discover a lead scaffold with drug-like properties and high affinity toward HER2. Methods: A list of HER2 inhibitors were collected, analyzed, and subjected to fragmentation and molecular docking. The in silico study computed the affinity, clash score, and ligand entropy score. A pharmacophore model for an ideal inhibitor designed, and tested against breast, lung, and prostatic cancer cell lines. Results: The discovered lead compound achieved several hydrogen bonds with the primary residues found in the active site of HER2, such as; Met801, Gln99, Lys753, and Thr862 with a computational affinity – 13.45 kcal/mol. In addition to a hydrophobic interaction with leu800. The in vitro cytotoxic activity against; breast cancer MCF-7, prostatic cancer PC-3 and lung cancer A-549 cell lines showed (IC50 = 86.38 ±1.1 mmol/ml), (IC50 = 157.02 ±1.3 mmol/ml), and (IC50 = 181.1 ±2.4 mmol/ml) respectively. Conclusion: The discovered lead is an excellent drug-like candidate for further development and optimization.

Background: Patients with diabetes increasingly consult artificial intelligence (AI) chatbots for medical advice, including guidance on antidiabetic medication management during Ramadan fasting, because AI can simplify and summarize long, complex guidelines. Also, in hospital settings, these tools are being used in hospitals much faster than it takes to establish formal regulations and guidelines for their use. Evaluations of the accuracy, completeness, and reproducibility of such advice across languages are still lacking. Therefore, the study aims to evaluate and compare the accuracy, completeness, safety, and reproducibility of three widely used AI chatbots—ChatGPT, Google Gemini, and Microsoft Copilot—when providing antidiabetic medication adjustment advice during Ramadan in both English and Arabic. Methods: Twenty-three standardized clinical scenarios covering common antidiabetic regimens were presented to each chatbot in both English and Arabic. Each query was repeated to evaluate reproducibility, resulting in 276 responses scored. Responses were assessed against the International Diabetes Federation–Diabetes and Ramadan (IDF-DAR) Guidelines using a 0–2 accuracy scale, a 0–4 completeness scale, and a 0–3 safety scale. Results: Overall, 77% of responses were fully consistent with the guideline, 12% were partially consistent, and 11% (30/276) contained clinically harmful or contradictory advice; harmful responses were about twice as common in Arabic as in English (14% vs. 8%). Completeness and safety were high, with medians at the observed ceiling. In the generalized linear mixed models, chatbots did not differ significantly in accuracy, completeness, or safety, and there was no significant main effect of language or chatbot × language interaction; the strongest signals were a chatbot effect on completeness ( p = 0.068) and a language effect on safety ( p = 0.064), both non-significant. Two-week reproducibility was fair for accuracy (weighted κ = 0.20, p = 0.009) and completeness (κ = 0.29, p = 0.001) and showed a very low κ in the safety scale (κ = 0.02, p = 0.81). Conclusions: AI chatbots demonstrated comparable performance in delivering guideline-based advice for diabetes management during Ramadan, with no significant differences in accuracy, completeness, or safety. While most responses aligned with the IDF-DAR guideline, some harmful recommendations persisted, and response consistency fluctuated over time. These results suggest that AI chatbots should serve as a supplementary resource rather than a substitute for professional medical advice.

Introduction: The preferred PK/PD goal for vancomycin is now AUC_0–24/MIC-guided dosing, which requires two blood samples, additional time, increased cost, and possibly specialized staff, potentially limiting its use. Our study compared vancomycin target attainment using trough versus single-sample Bayesian AUC_0–24/MIC in critically ill patients, using a validated online calculator. Methods: This retrospective cohort study included adults aged ≥18 years with stable renal function who were receiving vancomycin. Exclusions were age under 18, unstable renal function, hemodialysis, missing data, or non-steady-state vancomycin. Steady-state troughs were obtained, and AUC_0–24/MIC was calculated using ClinCalc. The primary endpoint was discordance in target attainment between trough (15–20 mg/L) and AUC_0–24/MIC (400–600 mg·h/L). The secondary endpoint was AKI within 48 h of initiating vancomycin. Results: The mean trough was 14.8 ± 8.0 mg/L, and the mean AUC_0–24/MIC was 499.7 ± 201.3 mg·h/L. Trough levels were within target in 20%, and AUC_0–24/MIC in 67%. The 3 × 3 cross-tabulation showed a significant association (χ² = 27.33, p < 0.001), but only fair agreement (Cohen’s κ = 0.215) and a biased disagreement pattern (p < 0.001). Thirty-eight patients (38%) had “false alarming” results: trough < 15 mg/L despite AUC_0–24/MIC ≥ 400 mg·h/L. Three patients (3.0%) showed “falsely reassuring” results: trough 15–20 mg/L with AUC_0–24/MIC > 600 mg·h/L. The odds of supratherapeutic AUC_0–24/MIC > 600 mg·h/L were tenfold higher in patients with trough 15–20 mg/L than in those below 15 mg/L (OR 10.24, p = 0.048). Conclusions: In critically ill ICU patients, calculator-derived AUC_0–24/MIC showed significant discordance with single-trough vancomycin monitoring, primarily indicating apparent under-exposure by trough criteria in patients with adequate AUC_0–24/MIC levels, which may lead to unnecessary dose escalation.

Introduction: Total parenteral nutrition (TPN) is essential for critically ill adults unable to meet their nutritional needs via the gastrointestinal tract. Recent advancements in large language models (LLMs) have sparked interest in their potential to support healthcare providers in complex clinical decisions. Nonetheless, no prior research has systematically compared publicly accessible chatbots to pharmacist-developed reference TPN plans across a diverse patient cohort, using a predefined, guideline-based rubric assessing accuracy, completeness, safety, error types, and numerical precision. This study presents a simulation-based benchmarking to evaluate (i) the accuracy and comprehensiveness of chatbot-generated TPN plans, (ii) their safety, acceptability, and error profiles, and (iii) the numerical agreement and concordance with expert pharmacist references. Methods: This benchmarking study, based on simulations and cross-sectional analysis, evaluated the performance of four publicly accessible AI chatbots—ChatGPT, Gemini, Copilot, and Perplexity—in formulating TPN plans for critically ill adults. Copilot declined to generate patient-specific TPN plans; this refusal was retained as a performance outcome, while Copilot was excluded from analyses requiring an analyzable TPN prescription. Each chatbot produced one plan for each of 10 standardized clinical cases, resulting in a total of 30 TPN plans. Friedman tests, Kendall’s W , and Holm-adjusted Wilcoxon signed-rank tests were used for comparative analyses. Results: ChatGPT produced comprehensive plans for all 10 cases, compared to 6 plans from Gemini and 4 from Perplexity. The median accuracy was 57.7% for ChatGPT, 43.9% for Gemini, and 38.5% for Perplexity, with no statistically significant overall difference ( p = 0.124). However, ChatGPT performed better than Perplexity in pairwise comparison (adjusted p = 0.035). There was a significant difference in completeness among the chatbots ( p = 0.003; Kendall’s W = 0.57), with median scores of 64.6, 41.9, and 32.3%, respectively. All 30 plans had at least one major or critical safety issue, 7 (23.3%) contained a critical concern, and 19 (63.3%) were rejected. The most common errors were related to electrolytes (90.0%), micronutrients (70.0%), and monitoring (66.7%). Recommendations from chatbots often exceeded pharmacist-derived values, with median relative differences of +47% for energy, +120% for lipids, and +100% for potassium. Bland–Altman analysis revealed considerable variability in the agreement. Conclusion: ChatGPT produced the most comprehensive TPN plans among chatbots. However, none showed enough accuracy, safety, or concordance for independent use. All plans had critical issues, especially with electrolytes, micronutrients, monitoring, and refeeding precautions. AI chatbots can support education or planning but require review by a well-trained clinician before clinical use.