Acute hyperglycemia is a transient increase in plasma glucose level (PGL) frequently observed in patients with ST-elevation myocardial infarction (STEMI). The aim of this review is to clarify the molecular mechanisms whereby acute hyperglycemia impacts coronary flow and myocardial perfusion in patients with acute myocardial infarction (AMI) and to discuss the consequent clinical and prognostic implications. We conducted a comprehensive literature review on the molecular causes of myocardial damage driven by acute hyperglycemia in the context of AMI. The negative impact of high PGL on admission recognizes a multifactorial etiology involving endothelial function, oxidative stress, production of leukocyte adhesion molecules, platelet aggregation, and activation of the coagulation cascade. The current evidence suggests that all these pathophysiological mechanisms compromise myocardial perfusion as a whole and not only in the culprit coronary artery. Acute hyperglycemia on admission, regardless of whether or not in the context of a diabetes mellitus history, could be, thus, identified as a predictor of worse myocardial reperfusion and poorer prognosis in patients with AMI. In order to reduce hyperglycemia-related complications, it seems rational to pursue in these patients an adequate and quick control of PGL, despite the best pharmacological treatment for acute hyperglycemia still remaining a matter of debate.

Objective: To compare statistical outputs from ChatGPT 4.0 and human experts in both comparative and correlation analyses in the evaluation of multiparametric MRI/ultrasound fusion-targeted biopsy plus random biopsy versus standard random biopsy alone, in terms of upstaging. Methods: Authors performed a retrospective evaluation on 101 patients undergoing robot-assisted radical prostatectomy (RaRP) between 2021 and 2023. Patients were divided in two groups, according to the type of prostatic biopsy received: combined fusion (MRI/US) targeted and random biopsy versus standard random biopsy. Clinical and histological data were anonymized and analyzed using logistic regression models, ANOVA, and Chi-square tests. Analysis generated by ChatGPT and by an experienced human statistician were compared. The Q-EVAL and Q-EVA tools were used to assess the quality of user-formulated questions and AI-generated answers, respectively.Results: Results revealed high concordance between statistical outputs generated by AI and expert human statistician with perfect concordance using Cohen’s kappa coefficient (κ = 1.0). Logistic regression analysis demonstrated that fusion biopsy was associated with a reduced likelihood of upstaging, a consistent finding across statistical evaluations. Additionally, user interaction assessments indicated high-quality in question formulation. Conclusions: ChatGPT (version 4.0) proved reliable for statistical analysis, showing strong concordance with human statisticians (κ = 1.0) in performing logistic regression, chi-square, and ANOVA tests. The Q-EVAL tool could reduce query errors, though ChatGPT's lack of automatic citations remains a limitation. Fusion biopsy significantly lowered upstaging risk after RaRP. In conclusion, ChatGPT is a valuable assistive tool but further research is required to optimize human-AI collaboration in clinical research.