Dyslipidemia in Sub-Saharan Africa has been on the disproportionate rise among diabetes patients across various contextual settings due to its patterns and associated factors. This study determined the patterns and factors associated with dyslipidemia among diabetes patients attending Hoima Regional Referral Hospital (HRRH). This was a hospital-based cross-sectional study conducted at HRRH between October 2022 and January 2023, enrolled 375 adult diabetes patients consecutively from diabetic outpatient clinic. Data on socio-demographic, behavioral, medical history, physical examination, and laboratory diagnoses were collected and summarized using descriptive statistics. Patterns of dyslipidemia were presented as a proportion of each lipid profile either singly or in combination expressed as a percentage. In the bivariate analysis, variables with p-values <0.2, crude odds ratios of ≥2 or ≤0.5, or those with biological plausibility were included in a multiple logistic regression model. Factors with p-values <0.05 were considered statistically significant. All analyses were conducted using Stata version 17. Of the 375 diabetes patients, 260 (69.3%) had abnormal total cholesterol (TC), and 185 (49.3%) had two lipid profile abnormalities. The majority of the patients were female, 235 (62.7%), and 38 (10.1%) had a diagnosis of coronary heart disease (CHD). Additionally, 134 (35.7%) were overweight, and 39 (10.4%) were obese. Female patients had higher odds of dyslipidemia (Adjusted Odds Ratio [AOR] = 2.2, 95% CI: 1.02-4.86, p = 0.045). Those with coronary heart disease (CHD) had increased odds of dyslipidemia (AOR = 4.1, 95% CI: 1.51-11.07, p = 0.006). All diabetes patients who were overweight or obese had dyslipidemia (p < 0.001). The most common pattern of dyslipidemia in patients with diabetes was elevated total cholesterol, followed by high low-density lipoprotein, associated with overweight, obesity, female gender, and CHD. Routine screening of lipid profiles, BMI, and CHD in diabetic clinics is crucial for early intervention and improved outcomes.

With the incorporation of artificial intelligence (AI), significant advancements have occurred in the field of fetal medicine, holding the potential to transform prenatal care and diagnostics, promising to revolutionize prenatal care and diagnostics. This scoping review aims to explore the recent updates in the prospective application of AI in fetal medicine, evaluating its current uses, potential benefits, and limitations. Compiling literature concerning the utilization of AI in fetal medicinedoes not appear tomodify the subjector provide an exhaustive exploration of electronic databases. Relevant studies, reviews, and articles published in recent years were incorporated to ensure up-to-date data. The selected works were analyzed for common themes, AI methodologies applied, and the scope of AI's integration into fetal medicine practice. The review identified several key areas where AI applications are making strides in fetal medicine, including prenatal screening, diagnosis of congenital anomalies, and predicting pregnancy complications. AI-driven algorithms have been developed to analyze complex fetal ultrasound data, enhancing image quality and interpretative accuracy. The integration of AI in fetal monitoring has also been explored, with systems designed to identify patterns indicative of fetal distress. Despite these advancements, challenges related to the ethical use of AI, data privacy, and the need for extensive validation of AI tools in diverse populations were noted. The potential benefits of AI in fetal medicine are immense, offering a brighter future for our field. AI equips us with tools for enhanced diagnosis, monitoring, and prognostic capabilities, promising to revolutionize the way we approach prenatal care and diagnostics. This optimistic outlook underscores the need for further research and interdisciplinary partnerships to fully leverage AI's potential in driving forward the practice of fetal medicine.

To investigate the correlation between premature ventricular contraction (PVC) frequency and heart rate (HR) in Chinese adults, with an emphasis on sex-specific differences in clinical characteristics. This retrospective study analyzed 24-hour Holter monitoring data from 478 inpatients at the First People's Hospital of Yibin between January 2021 and December 2022. The inclusion criteria were age ≥18 years, ≥20hours of Holter recording, frequent PVCs (≥ 500 PVCs), and complete clinical profiles. Patients were stratified into three groups on the basis of the hourly correlation between PVC counts and HR: fast heart rate-related PVC (F-HR-PVC), slow heart rate-related PVC (S-HR-PVC), and independent heart rate-related PVC (I-HR-PVC). Heart rate variability (HRV) indices were assessed to evaluate autonomic nervous system activity. Among the 478 patients, 267 were males and 211 were females with a mean age of 65.7±13.0 years. The mean PVC burden was 5.7±7.0%, and the mean left ventricular ejection fraction (LVEF) was 59.1±8.7%. In males, the F-HR-PVC group was most common (45.3%), while in females, the I-HR-PVC group was most prevalent (50.2%). Despite these observed differences, a chi-square test did not reveal statistically significant differences in the distribution of VPC profiles between sexes (P=0.167). Analysis of clinical characteristics and Holter indices across sex groups showed significant differences in males, particularly in age, maximum heart rate, and minimum heart rate (P < 0.05). In females, significant intergroup differences were observed in VPC burden (P < 0.05). Although no significant sex differences were observed in the correlation between PVC frequency and HR, the study suggests a potential gender influence on VPC characteristics. These findings may inform future research and have implications for the development of sex-specific diagnostic and therapeutic strategies for PVCs.

Glucose metabolism is associated with several endocrine disorders. Anti-diabetes drugs are crucial in controlling diabetes and its complications; nevertheless, few studies have been carried out involving endocrine function. This study aimed to investigate the association between anti-diabetes drugs and endocrine parameters. We performed a study of 180 consecutive patients with type 2 diabetes who attended a medical center. Laboratory measurements of metabolic values and endocrine parameters were assessed after a stable treatment regimen of more than 12weeks. The differences in various endocrine parameters were compared between subjects with or without certain anti-diabetes drugs, with the administrated anti-diabetes drugs being analyzed to find independent risks associated with elevated endocrine parameters. After maintaining stable treatment, acceptable glycemic control was noted with an average HbA1c of 7.55% in females and 7.43% in males. Participants taking sulfonylurea (55.8 vs 26.34 ng/L, P=0.043), dipeptidyl peptidase-4 inhibitor (DPP4i) (47.14 vs 32.26 ng/L, P=0.096), or sodium-glucose co-transporter 2 inhibitor (SGLT2i) (64.58 vs 28.11 ng/L, P=0.117) had higher plasma renin concentrations compared to those without this drug but the aldosterone levels did not differ, as well as for other adrenal tests and thyroid function. Under linear regression modeling, SGLT2i was found to be independently associated with a risk of high renin level (beta coefficient: 30.186, 95% confidence interval: 1.71─58.662, P=0.038), whereas sulfonylurea only had borderline associations (B: 21.143, 95% CI: -2.729─45.014, P=0.082). Additionally, renin-angiotensin-aldosterone system (RAAS) blockade (B: 36.728, 95% CI: 12.16─61.295, P=0.004) or diuretics (B: 47.847, 95% CI: 2.039─93.655, P=0.041) was also independently associated with increased renin levels. SGLT2i was the only class of anti-diabetes drugs independently associated with elevated renin levels, with results similar to RAAS blockade and diuretics. Although SGLT2i appears to protect reno- and cardio-function, the clinical impact of increased renin warrants further precise study for verification.

The aim of this study was to use five machine learning approaches and logistic regression to design and validate the acute kidney injury (AKI) prediction model for critically ill individuals with cardiogenic shock (CS). All patients who diagnosed with CS from the MIMIC-IV database, the eICU database, and Zhongnan hospital of Wuhan university were included in this study. Clinical information, including demographics, comorbidities, vital signs, critical illness scores and laboratory tests was retrospectively collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and one conventional logistic regression were applied for the prediction of AKI in critically ill individuals with CS. ROC curves were generated via python software to assess the overall performance of machine learning algorithms and the SHAP analysis was adopted to reveal the impact of prediction for each feature. The ensemble model exhibited the best predictive ability (AUC:0.91, 95% CI, 0.88-0.94), followed by random forest (AUC:0.90, 95% CI, 0.86-0.94) and XGBoost (AUC:0.89, 95% CI, 0.84-0.92). While the logistic regression model obtained the worst predictive performance (AUC:0.62, 95% CI, 0.56-0.68). When validated the prediction models with eICU database, the ensemble model exhibited the best predictive ability (AUC:0.92, 95% CI, 0.89-0.96), while the logistic model obtained the worst predictive performance (AUC:0.61, 95% CI, 0.56-0.67). Finally, we verified the prediction models using the data from our hospital and ensemble model still exhibited the best predictive ability (AUC:0.74, 95% CI, 0.62-0.86), while the decision tree model obtained the worst predictive performance (AUC:0.52, 95% CI 0.35-0.70). Machine learning algorithms could be utilized for the AKI prediction among critically ill CS patients, and exhibit superior predictive performance compared to the conventional logistic regression analysis.