Predictors of Academic Success in Students of Tabriz University of Medical Sciences: A Cross-Sectional Study.

Introduction: Considering that academic success is one of the most important topics for medical sciences schools and faculty members, this study was conducted to determine the predictors of academic success in students of Tabriz University of Medical Sciences. Methods: This cross-sectional study was performed on 542 students of the Tabriz University of Medical Sciences in Iran. The sampling method was stratified at random. The socio-demographic characteristics questionnaire, Multiple Intelligences Profiling Questionnaire (MIPQ), College Academic Self-Efficacy Scale (CASES), Personal Resource Questionnaire (PRQ-85-PART2), and the General Health Questionnaire (GHQ-28) were used to collect data. Data analysis was performed using the SPSS 16 software. The General Linear Model (GLM) was used to determine the predictors of academic success. Results: According to the Pearson correlation test, there was a significant positive correlation between academic grade point average (GPA) and social support (r=0.10, P=0.048), academic self-efficacy (r=0.36, P<0.001) and there was a significant negative relationship between GPA and total mental health score (r=-0.14; P=0.003) and its subdomains including anxiety (r=-0.10, P= 0.027), depression (r = -0.15, P = 0.002), and social dysfunction (r=-0.12; P=0.010). According to GLM, the variables of academic self-efficacy, and level of education were among the predictors of academic success, so the GPA increased significantly with academic self-efficacy (ß:0.02, P<0.001). The GPA was greater in bachelor's students than in professional doctorate students (ß:0.76, P<0.001). The significance level was considered at P<0.05. Conclusion: Due to the significant relationship between academic self-efficacy, and educational level with academic success, the promotion of self-efficacy is necessary for all students of all educational levels.

Solid-state characterization of ibuprofen-isonicotinamide cocrystals prepared by electrospraying and solvent evaporation.

Aim: Electrospraying (ELS) was used to prepare micronized ibuprofen-isonicotinamide cocrystal (IBU-INA-ELS) and its properties were compared with the solvent evaporated cocrystal (IBU-INA-SE). Methods: Solid-state characterization of crystalline phase, production yield, particle size, powder flow, wettability, solution mediated phase transformation (SMPT), and dissolution rate were measured. Results: The ELS produced phase pure particles of IBU-INA with a size of 1.46 µm and yield of 72.3%. This cocrystal improved the intrinsic dissolution rate and powder dissolution rate of IBU by 3.6- and 1.7-fold, respectively. Our experiments showed that the dissolution of IBU-INA was affected by particle size, solubility, SMPT and wettability. Conclusion: ELS produced micronized cocrystals for improving dissolution of ibuprofen with a high yield in a single step and mild conditions.

The intestinal absorption of ibuprofen is limited by low dissolution rate in the gastrointestinal fluids. This drug needs applied in an appropriate method to enhance its dissolution. One way to improve dissolution of ibuprofen is preparing micronized cocrystal of this drug and a water soluble compound, isonicotinamide. In this study, we prepared and characterized micronized particles of ibuprofen-isonicotinamide cocrystal by electrospraying, a single step and continuous method without need for any added material. Our experiments showed that the prepared micronized cocrystal could improve the dissolution of ibuprofen but the cocrystal is rapidly precipitated as ibuprofen crystals in contact with dissolution medium. This precipitation hampered the expected increase in dissolution. Therefore, solution mediated phase transformation should be considered in formulating micronized cocrystals.

Prediction of the Fatal Acute Complications of Myocardial Infarction via Machine Learning Algorithms.

Background: Myocardial infarction (MI) is a major cause of death, particularly during the first year. The avoidance of potentially fatal outcomes requires expeditious preventative steps. Machine learning (ML) is a subfield of artificial intelligence science that detects the underlying patterns of available big data for modeling them. This study aimed to establish an ML model with numerous features to predict the fatal complications of MI during the first 72 hours of hospital admission. Methods: We applied an MI complications database that contains the demographic and clinical records of patients during the 3 days of admission based on 2 output classes: dead due to the known complications of MI and alive. We utilized the recursive feature elimination (RFE) method to apply feature selection. Thus, after applying this method, we reduced the number of features to 50. The performance of 4 common ML classifier algorithms, namely logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost), was evaluated using 8 classification metrics (sensitivity, specificity, precision, false-positive rate, false-negative rate, accuracy, F1-score, and AUC). Results: In this study of 1699 patients with confirmed MI, 15.94% experienced fatal complications, and the rest remained alive. The XGBoost model achieved more desirable results based on the accuracy and F1-score metrics and distinguished patients with fatal complications from surviving ones (AUC=78.65%, sensitivity=94.35%, accuracy=91.47%, and F1-score=95.14%). Cardiogenic shock was the most significant feature influencing the prediction of the XGBoost algorithm. Conclusion: XGBoost algorithms can be a promising model for predicting fatal complications following MI.

Mental health and its relationship with social support in Iranian students during the COVID-19 pandemic.

BACKGROUND: In addition to physical problems, the COVID-19 crisis continues to impose serious psychological adverse effects on people's mental health, which plays a major role in the efficiency of every community. Students, especially medical sciences students, suffer from more stress as a result of exposure to COVID-induced stressors. It is, therefore, essential to measure mental health and its relationship with social support in medical sciences students during the COVID pandemic. The present study was conducted to determine the mental health status of students and its correlation with social support. METHODS: This cross-sectional study was conducted using random sampling on 280 students of Tabriz University of Medical Sciences in Iran in 2020. Socio-demographic profile scale, Mental Health Test (GHQ-28), and the scale of Perceived Social Support (PRQ-85) were used to collect data. Participants completed the questionnaires online. RESULTS: Considering the potential confounding variables, a general linear model (GLM) was utilized to determine the relationship between mental health and perceived social support. Mean (± standard deviation) of total mental health score 26.5 (12.5) was in the acceptable range of 0-63., and 56% of students suffered from a mental disorder. Mean (± standard deviation) of social support score 128.2 (21.0) ranged from 25 to175. According to Pearson's correlation coefficient, there was a significant inverse correlation between social support score and total mental health score and all its subscales [p < 0.001; r = - 0.294 to - 0.536]. According to the GLM, mental health score decreased significantly with social support score [p = 0.0001; - 0.32 to - 0.20; CI 95%; B = 0.26]. CONCLUSIONS: Given the inverse relationship between social support and mental health, it is suggested to increase the level of social support for students at all times, especially during the stressful COVID-19 pandemic to improve their mental health.

Prevalence of Cryptosporidium species isolated from HIV/AIDS patients in southwest of Iran.

This study aimed to determine the prevalence and species of Cryptosporidium among HIV/AIDS patients in southwest of Iran. Two hundred fifty faecal samples from HIV patients were examined for the presence of Cryptosporidium oocysts using a conventional coproscopic approach. Such oocysts were detected in 18 (7.2%) out of 250 faecal samples. Genomic DNAs from 250 samples were then subjected to a nested-PCR-RFLP technique targeting different loci of 18S rRNA gene for species identification. Out of 250 samples, 27 (10.8%) were positive for different Cryptosporidium spp; Restriction patterns resulting from the digestion of the nested amplicon with restriction endonucleases VspI and SspI showed that C. parvum (70.38%) was the most prevalent species, followed by C. hominis (25.92%) and C. meleagridis (3.7%), respectively. The mean CD4+ T-cell count was 215 cells/µL. There was a strong association between cryptosporidiosis and CD4+ T-cell count (P = 0.000) with the highest prevalence recorded among patients with CD4+ T-cell count < 200 cells/µL. This confirms that there is a low opportunity for this parasite to get established as the patients CD4+ T-cell count increases. Also HIV infection increased the risk of having Cryptosporidium. Our epidemiological findings are useful for any preventive intervention to control disease diffusion.