Monogenic Hypertension Linked to the Renin-Angiotensin-Aldosterone System.

Mendelian forms of renin-angiotensin-aldosterone system (RAAS)-related hypertension, commonly referred to as monogenic hypertension, represent a rare but significant subset of hypertensive disorders characterized by genetic mutations that disrupt the normal physiological mechanisms of blood pressure regulation. This review focuses on elucidating the germline mutations affecting RAAS pathways that lead to distinct forms of heritable hypertension. By understanding the pathophysiological basis of conditions such as Gordon's syndrome, Liddle syndrome, congenital adrenal hyperplasia, and familial hyperaldosteronism types, this review aims to highlight the unique clinical features, diagnostic challenges, and therapeutic implications associated with these disorders. Recognizing specific clinical presentations and family histories indicative of monogenic hypertension is crucial for diagnosis, particularly as it often manifests as early-onset hypertension, abnormalities in potassium and blood pH, and occasionally, abnormal sexual development or related syndromes. Therefore, employing a targeted diagnostic approach through next-generation sequencing is essential to pinpoint the responsible genetic mutations, enabling accurate and individualized treatment plans. The critical importance of certain readily available specific channel blockers, such as thiazides or low-dose corticosteroids, in managing these disorders must be emphasized, as they play a key role in preventing serious complications, including cerebrovascular events. As advancements in genetic and molecular sciences continue to evolve, a deeper comprehension of the mechanisms underlying RAAS-related monogenic hypertension promises to revolutionize the management of this complex disorder, offering hope for more effective and individualized treatment options.

In-depth assessment of steatotic liver disease awareness in high-risk groups.

OBJECTIVES: This study aims to determine the awareness levels and factors affecting it, along with prevalent misconceptions about Steatotic Liver Disease (SLD) among participants with high-risk indicators. METHODS: A questionnaire with open-ended questions was utilized. Participants were recruited from two general internal medicine outpatient clinics, focusing on those with high-risk indicators for SLD. Data collection involved a questionnaire covering demographic information, self-reported clinical conditions, and open-ended questions about SLD awareness. Key focus areas included misconceptions, thematic awareness, and the relationship between awareness and educational attainment. RESULTS: The study involved 228 participants, predominantly female (70.4%), with an average age of 53.8 years. Only 33.7% showed a comprehensive understanding of all aspects of SLD. However, 90.4% provided some accurate information, though often limited or incomplete. Higher education and awareness of SLD risks were key predictors of better understanding. The logistic regression model, with an accuracy of 0.76 and recall of 0.84, found higher education inversely related to low awareness. Common misconceptions highlighted included the belief that polypharmacy or certain medications cause SLD, fatigue as an effect, and increased water intake as a treatment. Notably, seven patients mentioned artichoke consumption as a potential treatment. CONCLUSION: The findings highlight the gap between comprehensive and partial awareness of SLD among high-risk individuals. Educational level and informed understanding of SLD risks are crucial for improving awareness, emphasizing the need for specialized educational efforts and risk communication to high-risk patients.

Weight Misperception is Prevalent Among Turkish Adults and Associated with Higher Age, Body Mass Index and Lower Education Status.

Background: Weight misperception (WM) is common among adults, and it is associated with adverse health outcomes. Research has shown that various factors are associated with weight misperception. Turkish adult population data for weight misperception and related factors do not exist. Methodology: We conducted a face-to-face cross-sectional descriptive survey in the general internal medicine outpatient clinics of two academic centers. Perception was analyzed both verbally and visually. Misperception was defined for both verbal and visual scales as being thinner than reality misperceptions (TTRM), fatter than reality misperceptions (FTRM), or either of them (ETFTRM). Demographics, anthropometrics, and social determinants of health were analyzed in different misperception groups. Results: 250 patients participated in the study. The median (interquartile range) age was 55 (14), and the BMI was 28.2 (6.9) for females and 26.9 (4.4) for males. 81.2% had ETFTRM, 45.2% had TTRM, and 22.4% had FTRM. Age and BMI were higher in the ETFTRM and TTRM groups, while education level was lower in both. Multivariate logistic regression showed that higher age, higher BMI, and lower education levels were associated with higher TTRM. Discussion: WM is common among the Turkish adult population. Similar to the previous studies, aging, high BMI, and low education levels are associated with weight misperception. However, in contrast to previous studies, gender, marital status, and employment were not associated with weight misperception in our cohort.

Predictive modeling of mortality in carbapenem-resistant Acinetobacter baumannii bloodstream infections using machine learning.

Acinetobacter baumannii, a notable drug-resistant bacterium, often induces severe infections in healthcare settings, prompting a deeper exploration of treatment alternatives due to escalating carbapenem resistance. This study meticulously examined clinical, microbiological, and molecular aspects related to in-hospital mortality in patients with carbapenem-resistant A. baumannii (CRAB) bloodstream infections (BSIs). From 292 isolates, 153 cases were scrutinized, reidentified through matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), and evaluated for antimicrobial susceptibility and carbapenemase genes via multiplex polymerase chain reaction (PCR). Utilizing supervised machine learning, the study constructed models to predict 14- and 30-day mortality rates, revealing the Naïve Bayes model's superior specificity (0.75) and area under the curve (0.822) for 14-day mortality, and the Random Forest model's impressive recall (0.85) for 30-day mortality. These models delineated eight and nine significant features for 14- and 30-day mortality predictions, respectively, with "septic shock" as a pivotal variable. Additional variables such as neutropenia with neutropenic days prior to sepsis, mechanical ventilator support, chronic kidney disease, and heart failure were also identified as ranking features. However, empirical antibiotic therapy appropriateness and specific microbiological data had minimal predictive efficacy. This research offers foundational data for assessing mortality risks associated with CRAB BSI and underscores the importance of stringent infection control practices in the wake of the scarcity of new effective antibiotics against resistant strains. The advanced models and insights generated in this study serve as significant resources for managing the repercussions of A. baumannii infections, contributing substantially to the clinical understanding and management of such infections in healthcare environments.

Machine Learning Insights Into Uric Acid Elevation With Thiazide Therapy Commencement and Intensification.

Background Elevated serum uric acid, associated with cardiovascular conditions such as atherosclerotic heart disease, hypertension, and heart failure, can be elevated by thiazide or thiazide-like drugs (THZ), essential in hypertension management. Identifying clinical determinants affecting THZ-related uric acid elevation is critical. Methods In this retrospective cross-sectional study, we explored the clinical determinants influencing uric acid elevation related to THZ, focusing on patients where THZ was initiated or the dose escalated. A cohort of 143 patients was analyzed, collecting baseline and control uric acid levels, alongside basic biochemical studies and clinical data. Feature selection was conducted utilizing criteria based on mean squared error increase and enhancement in node purity. Four machine learning algorithms - Random Forest, Neural Network, Support Vector Machine, and Gradient Boosting regressions - were applied to pinpoint clinical influencers. Results Significant features include uncontrolled diabetes, index estimated Glomerular Filtration Rate (eGFR) level, absence of insulin, action of indapamide, and absence of statin treatment, with absence of Sodium-glucose cotransporter 2 inhibitors (SGLT2i), low dose aspirin exposure, and older age also being noteworthy. Among the applied models, the Gradient Boosting regression model outperformed the others, exhibiting the lowest Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) values, and the highest R2 value (0.779). While Random Forest and Neural Network regression models were able to fit the data adequately, the Support Vector Machine demonstrated inferior metrics. Conclusions Machine learning algorithms are adept at accurately identifying the factors linked to uric acid fluctuations caused by THZ. This proficiency aids in customizing treatments more effectively, reducing the need to unnecessarily avoid THZ, and providing guidance on its use to prevent instances where uric acid levels could become problematic.