Chat generative pre-trained transformer (ChatGPT) is a chatbot developed by OpenAI that answers questions in a human-like manner. ChatGPT is a GPT language model that understands and responds to natural language created using a transformer, which is a new artificial neural network algorithm first introduced by Google in 2017. ChatGPT can be used to identify research topics and proofread English writing and R scripts to improve work efficiency and optimize time. Attempts to actively utilize generative artificial intelligence (AI) are expected to continue in clinical settings. However, ChatGPT still has many limitations for widespread use in clinical research, owing to AI hallucination symptoms and its training data constraints. Researchers recommend avoiding scientific writing using ChatGPT in many traditional journals because of the current lack of originality guidelines and plagiarism of content generated by ChatGPT. Further regulations and discussions on these topics are expected in the future.

Few studies to date have assessed the postoperative pulmonary complications after transcatheter aortic valve implantation (TAVI) according to the anesthesia method. The present study aims to compare the effects of general anesthesia (GA) or monitored anesthesia care (MAC) on postoperative outcomes in patients undergoing TAVI. This retrospective cohort study included 578 patients who underwent TAVI through the trans-femoral approach between August 2011 and May 2019 at a single tertiary academic center. The primary outcome was postoperative pulmonary complications, which were defined as the occurrence of one or more pulmonary complications, such as respiratory failure, respiratory infection, and radiologic findings, within 7 days after TAVI. Secondary outcomes included postoperative delirium, all-cause 30-day mortality rate, 30-day readmission rate, reoperation rate, vascular complications, permanent pacemaker/implantable cardioverter-defibrillator insertion, length of stay in the ICU, hospital stay, and incidence of stroke. Of the 589 patients, 171 underwent TAVI under general anesthesia (GA), and 418 under monitored anesthesia care (MAC). The incidence of postoperative pulmonary complications was significantly higher in the GA than in the MAC group (17.0% vs. 5.3%, p < 0.001). Anesthetic method significantly affected the occurrence of postoperative pulmonary complications, but not of delirium. ICU stay was significantly shorter in the MAC group, as were operation time, the volume of fluid administered during surgery, heparin dose, transfusion, and inotrope requirements. TAVI under MAC can increase the efficiency of medical resources, reducing the lengths of ICU stay and the occurrence of postoperative pulmonary complications, compared with TAVI under GA.

This study developed and validated a machine learning model to accurately predict acute kidney injury (AKI) after non-cardiac surgery, aiming to improve patient outcomes by assessing its clinical feasibility and generalizability. We conducted a retrospective cohort study using data from 76,032 adults who underwent non-cardiac surgery at a single tertiary medical center between March 2019 and February 2021, and used data from 5512 patients from the VitalDB open dataset for external model validation. The predictive variables for model training consisted of demographic, preoperative laboratory, and intraoperative data, including calculated statistical values such as the minimum, maximum, and mean intraoperative blood pressure. When predicting postoperative AKI, our gradient boosting machine model incorporating all the variables achieved the best results, with AUROC values of 0.868 and 0.757 for the internal and external validations using the VitalDB dataset, respectively. The model using intraoperative data performed best in internal validation, while the model with preoperative data excelled in external validation. In this study, we developed a predictive model for postoperative AKI in adult patients undergoing non-cardiac surgery using preoperative and intraoperative data, and external validation demonstrated the efficacy of open datasets for generalization in medical artificial modeling research.

Opiranserin injection (Unafra®, Vivozon Inc.) is a first-in-class, non-opioid analgesic for the management of moderate-to-severe postoperative pain. The active ingredient, opiranserin (code name: VVZ-149), is a synthetic molecule that simultaneously antagonizes glycine transporter 2 (GlyT2) and serotonin 2A receptors (5-HT2A), both of which play key roles in pain processing. Opiranserin exemplifies the application of ex vivo phenotypic screening combined with a bait–target approach to identify effective multi-target agents that overcome the limitations of conventional single-target analgesics. In this review, we aim to describe the discovery and optimization of opiranserin through efficacy-based screening using a bait-target approach, outline its pharmacological mechanisms of action as a drug with both central and peripheral activity, and summarize published clinical studies demonstrating its successful translation from preclinical efficacy to reductions in postoperative pain and opioid consumption. We also discuss the clinical implications and future research directions to enhance the therapeutic utility of opiranserin injection and maximize patient benefit within the framework of precision analgesia.