RESUMEN
The Golgi compartment performs a number of crucial roles in the cell. However, the exact molecular mechanisms underlying these actions are not fully defined. Pathogenic mutations in genes encoding Golgi proteins may serve as an important source for expanding our knowledge. For instance, mutations in the gene encoding Transmembrane protein 165 (TMEM165) were discovered as a cause of a new type of congenital disorder of glycosylation (CDG). Comprehensive studies of TMEM165 in different model systems, including mammals, yeast, and fish uncovered the new realm of Mn2+ homeostasis regulation. TMEM165 was shown to act as a Ca2+/Mn2+:H+ antiporter in medial- and trans-Golgi network, pumping the metal ions into the Golgi lumen and protons outside. Disruption of TMEM165 antiporter activity results in defects in N- and O-glycosylation of proteins and glycosylation of lipids. An impaired glycosylation of TMEM165-CDG arises from lack of Mn2+ within the Golgi. Nevertheless, Mn2+ insufficiency in the Golgi is compensated by the activity of the ATPase SERCA2. TMEM165 turnover has also been found to be regulated by Mn2+ cytosolic concentration. Besides causing CDG, recent investigations have demonstrated the functional involvement of TMEM165 in several other pathologies including cancer and mental health disorders. This systematic review summarizes the available information on TMEM165 molecular structure, cellular function, and its roles in health and disease.
RESUMEN
Background With ChatGPT demonstrating impressive abilities in solving clinical vignettes and medical questions, there is still a lack of studies assessing ChatGPT using real patient data. With real-world cases offering added complexity, ChatGPT's utility in treatment using such data must be tested to better assess its accuracy and dependability. In this study, we compared a rural cardiologist's medication recommendations to that of GPT-4 for patients with lab review appointments. Methodology We reviewed the lab review appointments of 40 hypertension patients, noting their age, sex, medical conditions, medications and dosage, and current and past lab values. The cardiologist's medication recommendations (decreasing dose, increasing dose, stopping, or adding medications) from the most recent lab visit, if any, were recorded for each patient. Data collected from each patient was inputted into GPT-4 using a set prompt and the resulting medication recommendations from the model were recorded. Results Out of the 40 patients, 95% had conflicting overall recommendations between the physician and GPT-4, with only 10.2% of the specific medication recommendations matching between the two. Cohen's kappa coefficient was -0.0127, indicating no agreement between the cardiologist and GPT-4 for providing medication changes overall for a patient. Possible reasons for this discrepancy can be differing optimal lab value ranges, lack of holistic analysis by GPT-4, and a need for providing further supplementary information to the model. Conclusions The study findings showed a significant difference between the cardiologist's medication recommendations and that of ChatGPT-4. Future research should continue to test GPT-4 in clinical settings to validate its abilities in the real world where more intricacies and challenges exist.