Inflammatory Status Assessment by Machine Learning Techniques to Predict Outcomes in Patients with Symptomatic Aortic Stenosis Treated by Transcatheter Aortic Valve Replacement.

(1) Background: Although transcatheter aortic valve replacement (TAVR) significantly improves long-term outcomes of symptomatic severe aortic stenosis (AS) patients, long-term mortality rates are still high. The aim of our study was to identify potential inflammatory biomarkers with predictive capacity for post-TAVR adverse events from a wide panel of routine biomarkers by employing ML techniques. (2) Methods: All patients diagnosed with symptomatic severe AS and treated by TAVR since January 2016 in a tertiary center were included in the present study. Three separate analyses were performed: (a) using only inflammatory biomarkers, (b) using inflammatory biomarkers, age, creatinine, and left ventricular ejection fraction (LVEF), and (c) using all collected parameters. (3) Results: A total of 338 patients were included in the study, of which 56 (16.5%) patients died during follow-up. Inflammatory biomarkers assessed using ML techniques have predictive value for adverse events post-TAVR with an AUC-ROC of 0.743 and an AUC-PR of 0.329; most important variables were CRP, WBC count and Neu/Lym ratio. When adding age, creatinine and LVEF to inflammatory panel, the ML performance increased to an AUC-ROC of 0.860 and an AUC-PR of 0.574; even though LVEF was the most important predictor, inflammatory parameters retained their value. When using the entire dataset (inflammatory parameters and complete patient characteristics), the ML performance was the highest with an AUC-ROC of 0.916 and an AUC-PR of 0.676; in this setting, the CRP and Neu/Lym ratio were also among the most important predictors of events. (4) Conclusions: ML models identified the CRP, Neu/Lym ratio, WBC count and fibrinogen as important variables for adverse events post-TAVR.

Fat Hounsfield Unit Reference Interval Derived through an Indirect Method.

BACKGROUND: In vivo Hounsfield Unit (HU) values have traditionally been determined using direct CT image measurements. These measurements are dependent on the window/level used to examine the CT image and the individual conducting the fat tissue tracing. METHODS: Using an indirect method, a new reference interval (RI) is proposed. A total of 4000 samples of fat tissues were collected from routine abdominal CT examinations. A linear regression equation was then calculated using the linear part of the cumulative frequency plot of their average values. RESULTS: The regression function for total abdominal fat was determined to be y = 35.376*x - 123.48, and a 95% confidence RI of -123 to -89 was computed. A significant difference of 3.82 was observed between the average fat HU values of visceral and subcutaneous areas. CONCLUSIONS: Using statistical methods and the in vivo measurements of patient data, a series of RIs were determined for fat HU that is consistent with theoretical values.

Occupational Stress and Personality in Medical Doctors from Romania.

Occupational stress amongst doctors has been intensively studied as doctors are exposed to several stress factors daily. The purpose of this study was to investigate if there are associations between personality dimensions and the factors that generate stress at work. We conducted a cross-sectional study of 280 medical doctors from Romania between February 2021 and September 2021 who were evaluated using the DECAS and ASSET Scales. Our results showed that the agreeableness and emotional stability dimensions of personality, according to the Big Five model, were statistically associated with work relationships (A p < 0.0001; ES p = 0.0005), work-life balance (A p = 0.008; ES p = 0.01), overload (A p = 0.01; ES p = 0.001), job security (A p < 0.0001; ES p = 0.002), job control (A p = 0.001; ES p = 0.009), resources and communication (A p = 0.0002; ES p < 0.0001), and job conditions (A p = 0.005; ES p = 0.03). The conscientiousness dimension was statistically associated with job control (p = 0.02). Doctors from different specialties experienced stress differently, with psychiatrists and doctors from preclinical specialties reporting the lowest levels of stress. Internists and surgeons reported higher levels of stress. This study showed that the dimensions of agreeableness and emotional stability were both associated with variables indicative of the level of stress felt at work.

The Relationship between Personality, Motivation and Academic Performance at Medical Students from Romania.

The academic and health system requirements are constantly growing due to the continuous development of this sector. Therefore, it is important to investigate the structural factors that improve performance in the medical system. The aim of our pilot study is to analyze if there are associations or correlations between personality and motivation and the results obtained for the National Residency Exam of Romanian medical graduates. We conducted a prospective pilot study on 179 medical students from George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, Romania between February 2021 and December 2021, who were evaluated by the DECAS, IM, and SPM scale. Our results showed that all the dimensions of personality according to the Big Five Model, which include openness (OR = 0.392, p = 0.01), extraversion (OR = 0.512, p = 0.03), conscientiousness (OR = 3.671, p = 0.004), agreeableness (OR = 2.791, p = 0.07), and emotional stability (OR = 4.863, p = 0.0003), are statistically associated with the result obtained. Motivation also plays an important role in academic achievements, through motivational persistence and motivational involvement which correlates with the conscientiousness dimension and the result obtained. This study confirms that both personality structure and motivation are associated or correlated with the academic results of medical students and represent a starting point for future research.

Prediction of 3-year all-cause and cardiovascular cause mortality in a prospective percutaneous coronary intervention registry: Machine learning model outperforms conventional clinical risk scores.

BACKGROUND AND AIMS: Machine learning (ML) models have been proposed as a prognostic clinical tool and superiority over clinical risk scores is yet to be established. Our aim was to analyse the performance of predicting 3-year all-cause- and cardiovascular cause mortality using ML techniques and compare it with clinical scores in a percutaneous coronary intervention (PCI) population. METHODS: An all-comers patient population treated by PCI in a tertiary cardiovascular centre that have been included prospectively in the local registry between January 2016-December 2017 was analysed. The ML model was trained to predict 3-year mortality and prediction performance was compared with that of GRACE, ACEF, SYNTAX II 2020 and TIMI scores. RESULTS: A total number of 2242 patients were included with 12.1% and 14.9% 3-year cardiovascular and -all-cause mortality, respectively. The area under receiver operator characteristic curve for the ML model was higher than that of GRACE, ACEF, SYNTAX II and TIMI scores: 0.886 vs. 0.797, 0.792, 0.757 and 0.696 for 3-year cardiovascular- and 0.854 vs. 0.762, 0.764, 0.730 and 0.691 for 3-year all-cause mortality prediction, respectively (all p ≤ 0.001). Similarly, the area under precision-recall curve for the ML model was higher than that of GRACE, ACEF, SYNTAX II and TIMI scores: 0.729 vs. 0.474, 0.469, 0.365 and 0.389 for 3-year cardiovascular- and 0.718 vs. 0.483, 0.466, 0.388 and 0.395 for 3-year all-cause mortality prediction, respectively (all p ≤ 0.001). CONCLUSION: The ML model was superior in predicting 3-year cardiovascular- and all-cause mortality when compared to clinical scores in a prospective PCI registry.

Tribulus terrestris Efficacy and Safety Concerns in Diabetes and Erectile Dysfunction, Assessed in an Experimental Model.

The present project aims to evaluate Tribulus terrestris (TT) extracts by addressing various possible mechanisms of action in order to see whether the use of TT supplements in diabetes and diabetes complications is justified. Diabetic rats were divided into three groups: diabetic control group, TT extract with low protodioscin content group (TT-LPC) and TT extract with high protodioscin content group (TT-HPC). After twelve weeks of treatment, fasting blood glucose, insulin, LH, FSH and testosterone levels were measured. Both TT preparations reduced elevated blood glucose level. Insulin and luteinizing hormone levels were not significantly different compared with the control group; however, the FSH and testosterone levels were significantly higher in the TT-HPC group compared with the diabetic control group. The testosterone level is correlated in part with the protodioscin concentration in extracts and is probably mediated through an FSH-linked pathway.