Contribution of genetic variants in the development of familial premature coronary artery disease in a cohort of cardiac patients.

Coronary artery disease (CAD), the most prevalent cardiovascular disease, is the leading cause of death worldwide. Heritable factors play a significant role in the pathogenesis of CAD. It has been proposed that approximately one-third of patients with CAD have a positive family history, and individuals with such history are at ~1.5-fold increased risk of CAD in their lifespans. Accordingly, the long-recognized familial clustering of CAD is a strong risk factor for this disease. Our study aimed to identify candidate genetic variants contributing to CAD by studying a cohort of 60 large Iranian families with at least two members in different generations afflicted with premature CAD (PCAD), defined as established disease at ≤45 years in men and ≤55 years in women. Exome sequencing was performed for a subset of the affected individuals, followed by prioritization and Sanger sequencing of candidate variants in all available family members. Subsequently, apparently healthy carriers of potential risk variants underwent coronary computed tomography angiography (CCTA), followed by co-segregation analysis of the combined data. Putative causal variants were identified in seven genes, ABCG8, CD36, CYP27A1, PIK3C2G, RASSF9, RYR2, and ZFYVE21, co-segregating with familial PCAD in seven unrelated families. Among these, PIK3C2G, RASSF9, and ZFYVE21 are novel candidate CAD susceptibility genes. Our findings indicate that rare variants in genes identified in this study are involved in CAD development.

Polypharmacy and pattern of medication use among patients with gastroesophageal reflux disease: results from Pars Cohort study.

BACKGROUND: Gastroesophageal Reflux Disease (GERD) is a common chronic condition. Its chronic nature may affect the pattern of medication use. This study aimed to investigate the prevalence, associated factors, and patterns of polypharmacy and medication use among GERD patients in southwestern Iran. METHODS: We used data from the Pars Cohort Study. We classified drugs using the Anatomical Therapeutic Chemical classification system. The Lexicomp® database was used to assess potential drug-drug interactions. Multivariable Poisson regression was applied. Adjusted prevalence ratio (PR) and its 95% confidence interval (CI) were estimated. RESULTS: A total of 9262 participants were included. Among 2,325 patients with GERD, age-standardized prevalence of polypharmacy was 9.5% (95% CI: 7.5%, 11.6%) in males, and 19.3% (95% CI: 17.2%, 21.4%) in females. The PR of experiencing Polypharmacy by GERD patients compared to non-GERD patients was 1.82 (95% CI: 1.61, 2.05%). Multimorbidity (PR: 3.33; CI: 2.66, 4.15), gender (PR: 1.68; CI: 1.30, 2.18), and metabolic syndrome (PR: 1.77; CI: 1.45, 2.15) were associated with polypharmacy among GERD patients. Drugs for acid-related disorders were the most common used drugs among men, women and elders. We found that 13.9%, 4.2%, and 1.1% of GERD patients had type C, D and X drug interactions, respectively. CONCLUSION: GERD is correlated with a higher prevalence of polypharmacy. Among GERD patients, females, those with multi-morbidities, and those with metabolic syndrome may be affected more by polypharmacy. Considering the fairly high rate of interactions identified, a review of the medication list is essential when approaching GERD patients, and physicians must check for medications that may worsen GERD.

A comparative study of fracture conductivity prediction using ensemble methods in the acid fracturing treatment in oil wells.

The study of acid fracture conductivity stands as a pivotal aspect of petroleum engineering, offering a well-established technique to amplify production rates in carbonate reservoirs. This research delves into the intricate dynamics influencing the conductivity of acid fractures, particularly under varying closure stresses and in diverse rock formations. The conductivity of acid fractures is intricately interconnected with the dissolution of rock, etching patterns on fracture surfaces, rock strength, and closure stress. To accurately predict fracture conductivity under different closure stresses, a robust model is necessary. This model involves assessing both the baseline fracture conductivity under zero closure stress and the rate of conductivity variation as closure stress fluctuates. Key among the influential factors affecting fracture conductivity is the type of rock within the reservoir. Understanding and predicting the behavior of different formations under disparate closure stresses poses a significant challenge, as does deciphering the diverse effects of treatment parameters such as acid injection rate and strength on fracture conductivity. In this study, the predictive power of XGBoost, a machine learning algorithm, was explored in assessing acid fracture conductivity in dolomite and limestone formations. The findings revealed XGBoost's ability to outperform previous studies in predicting fracture conductivity in both types of formations. Notably, it exhibited superior accuracy in forecasting fracture conductivity under varying treatment conditions, underscoring its robustness and versatility. The research underscores the pivotal role of closure stress, dissolution rate of rock (DREC), and rock strength in influencing fracture conductivity. By integrating these parameters into the design of acid fracturing operations, accurate predictions can be achieved, allowing for the optimization of treatment designs. This study illuminates the potential of XGBoost in optimizing acid fracturing treatments, ultimately bolstering well productivity in carbonate reservoirs. Furthermore, it advocates for the essential nature of separate modeling and analysis based on rock types to comprehend and optimize fracturing processes. The comparison between dolomite and limestone formations unveiled distinct conductivity behaviors, underlining the significance of tailored analyses based on rock type for precise operational optimization.

The medication usage pattern and prevalence of polypharmacy among patients with sickle cell disease: a population-based study in southern Iran.

BACKGROUND: Due to the numerous complications associated with sickle cell disease (SCD), patients often receive a variety of medications alongside their SCD treatment. However, a notable gap exists in the current literature regarding medication use patterns among them. This study aimed to investigate medication usage patterns in patients with SCD. RESEARCH DESIGN AND METHODS: This cross-sectional study, conducted in Bushehr Province, employed a stratified random sampling method to select eligible participants with SCD. A thorough interview gathered various information, including details about the medications. The Anatomical Therapeutic Chemical classification system was utilized for drug classification. Polypharmacy was defined as the concurrent use of at least five medications. RESULTS: A total of 300 individuals with SCD were included in this study. Polypharmacy was observed in 26.3% (95% CI: 20.8%-32.8%) of the study population. The analyses revealed positive associations between the use of more concurrent medication use and higher age groups and having multimorbidity. Antianemic preparations (86.7%), antineoplastic agents (58.3%), and vitamins (41.0%) were the most frequent medication classes used by the study participants. CONCLUSIONS: Our study revealed notable underutilization of hydroxyurea and a high rate of polypharmacy, associated with age and multimorbidity, among patients with SCD in southern Iran.

Polypharmacy and medication usage patterns in hypertensive patients: Findings from the Pars Cohort Study.

Polypharmacy, defined here as the concomitant use of five or more medications, is a significant health issue, particularly affecting individuals with chronic diseases like hypertension (HTN). To compare individuals with and without HTN in term of polypharmacy, and to investigate correlates of polypharmacy and medication use patterns in individuals with HTN in southwest Iran. This cross-sectional study used the baseline data of 9270 participants of the Pars Cohort Study (PCS) with a mean age of 52.6 ± 9.7 years. Poisson multivariable modeling was applied to identify correlates of polypharmacy, and Lexicomp® was used to assess drug-drug interactions. Anatomical Therapeutic Chemical classification was used to describe the pattern of medication use. The prevalence of polypharmacy in individuals without hypertension was 4.7 % (4.2%-5.2 %) vs. 23.7 % (22.1%-25.3 %) in individuals with hypertension (P < 0.001). Individuals with hypertension from middle-high socioeconomic status (SES) had a 1.51-fold higher prevalence of polypharmacy than vs. low SES. Those with more than three comorbidities had a 5.18 times higher prevalence of polypharmacy than those with isolated hypertension. Calcium channel blockers were the most common antihypertensives (20.9 %). In terms of drug-drug interactions, type C interactions were most prevalent among participants with hypertension and polypharmacy (76.0 %). Our findings imply a fairly high prevalence of polypharmacy and drug-drug interactions among individuals with hypertension; to tackle this issue, we recommend a national pharmacovigilance system, training programs for primary care physicians, public education and awareness campaigns, drug-checking campaigns, targeted screenings to alter modifiable risk factors, and the use of safe combination pills.