Pharmacotherapeutic Potential of Bitter Gourd (Momordica charantia) in Age-related Neurological Diseases.

Due to the growth of the elderly population, age-related neurological disorders are an increasing problem. Aging begins very gradually and later leads to several neurological issues such as lower neurotransmitter levels, oxidative stress, neuronal inflammation, and continual neuronal loss. These changes might contribute to brain disorders such as Alzheimer's disease (AD), dementia or mild cognitive impairment, and epilepsy and glioma, and can also aggravate these disorders if they were previously present. Momordica charantia (bitter gourd), a member of the Cucurbitaceae family, is a good source of carbohydrates, proteins, vitamins, and minerals. It is used for diabetes and known for its hypoglycemic and antioxidant effects. In this review, we discuss the pharmaceutical effects of M. charantia on age-related neurological disorders. We searched several databases, including PubMed and Google Scholar, using MeSH terms. We searched articles published up until 2022 regardless of publication language. M. charantia is rich in luteolin, which increases acetylcholine in neurons by binding to enzymes in acetylcholine metabolism pathways, including butyrylcholinesterase and acetylcholinesterase. This binding inhibits the hyperphosphorylation of tau protein by restraining its kinase enzyme. Furthermore, this substance can lower serum cholesterol and has multi-target activity in AD and memory loss. M. charantia can also improve memory by decreasing tau protein and it also has potent antioxidant activity and anti-inflammatory effects. This review highlights that M. charantia has effects on many age-related neurological disorders, and can be a cost-effective supplement with minimal side effects.

Medical, dental, and nursing students' attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis.

BACKGROUND: Nowadays, Artificial intelligence (AI) is one of the most popular topics that can be integrated into healthcare activities. Currently, AI is used in specialized fields such as radiology, pathology, and ophthalmology. Despite the advantages of AI, the fear of human labor being replaced by this technology makes some students reluctant to choose specific fields. This meta-analysis aims to investigate the knowledge and attitude of medical, dental, and nursing students and experts in this field about AI and its application. METHOD: This study was designed based on PRISMA guidelines. PubMed, Scopus, and Google Scholar databases were searched with relevant keywords. After study selection according to inclusion criteria, data of knowledge and attitude were extracted for meta-analysis. RESULT: Twenty-two studies included 8491 participants were included in this meta-analysis. The pooled analysis revealed a proportion of 0.44 (95%CI = [0.34, 0.54], P < 0.01, I2 = 98.95%) for knowledge. Moreover, the proportion of attitude was 0.65 (95%CI = [0.55, 0.75], P < 0.01, I2 = 99.47%). The studies did not show any publication bias with a symmetrical funnel plot. CONCLUSION: Average levels of knowledge indicate the necessity of including relevant educational programs in the student's academic curriculum. The positive attitude of students promises the acceptance of AI technology. However, dealing with ethics education in AI and the aspects of human-AI cooperation are discussed. Future longitudinal studies could follow students to provide more data to guide how AI can be incorporated into education.

Comparing In-Hospital Mortality Prediction by Senior Emergency Resident's Judgment and Prognostic Models in the Emergency Department.

Background: A comparison of emergency residents' judgments and two derivatives of the Sequential Organ Failure Assessment (SOFA), namely, the mSOFA and the qSOFA, was conducted to determine the accuracy of predicting in-hospital mortality among critically ill patients in the emergency department (ED). Methods: A prospective cohort research was performed on patients over 18 years of age presented to the ED. We used logistic regression to develop a model for predicting in-hospital mortality by using qSOFA, mSOFA, and residents' judgment scores. We compared the accuracy of prognostic models and residents' judgment in terms of the overall accuracy of the predicted probabilities (Brier score), discrimination (area under the ROC curve), and calibration (calibration graph). Analyses were carried out using R software version R-4.2.0. Results: In the study, 2,205 patients with median age of 64 (IQR: 50-77) years were included. There were no significant differences between the qSOFA (AUC 0.70; 95% CI: 0.67-0.73) and physician's judgment (AUC 0.68; 0.65-0.71). Despite this, the discrimination of mSOFA (AUC 0.74; 0.71-0.77) was significantly higher than that of the qSOFA and residents' judgments. Additionally, the AUC-PR of mSOFA, qSOFA, and emergency resident's judgments was 0.45 (0.43-0.47), 0.38 (0.36-0.40), and 0.35 (0.33-0.37), respectively. The mSOFA appears stronger in terms of overall performance: 0.13 vs. 0.14 and 0.15. All three models showed good calibration. Conclusion: The performance of emergency residents' judgment and the qSOFA was the same in predicting in-hospital mortality. However, the mSOFA predicted better-calibrated mortality risk. Large-scale studies should be conducted to determine the utility of these models.

Selective serotonin reuptake inhibitors: New hope in the fight against COVID-19.

The emerging COVID-19 pandemic led to a dramatic increase in global mortality and morbidity rates. As in most infections, fatal complications of coronavirus affliction are triggered by an untrammeled host inflammatory response. Cytokine storms created by high levels of interleukin and other cytokines elucidate the pathology of severe COVID-19. In this respect, repurposing drugs that are already available and might exhibit anti-inflammatory effects have received significant attention. With the in vitro and clinical investigation of several studies on the effect of antidepressants on COVID-19 prognosis, previous data suggest that selective serotonin reuptake inhibitors (SSRIs) might be the new hope for the early treatment of severely afflicted patients. SSRIs' low cost and availability make them potentially eligible for COVID-19 repurposing. This review summarizes current achievements and literature about the connection between SSRIs administration and COVID-19 prognosis.