Aspirin Desensitization: Implications for Acetylsalicylic Acid-Sensitive Pregnant Women

Hypertension and associated complications in pregnant women with chronic kidney disease

The growing incidence of obesity and the rising trend of increased age during pregnancy have led to a high number of pregnant women with chronic kidney disease (CKD). Chronic hypertension is commonly associated with CKD and is not only the result of renal damage but is also the cause of declining renal function. Pregnancy and its unique physiological adaptations are affected by a decrease in the filtration capacity of the kidneys. Preeclampsia is a disorder of the vascular endothelium and is exacerbated by endothelial dysfunction resulting from CKD. Blood pressure targets must be strictly maintained owing to overlapping disease pathogenesis and to minimize cardiovascular damage. Moreover, preexisting renal dysfunction poses a challenge in identifying superimposed preeclampsia, which alters the management strategies in pregnancy. Fetal outcomes in patients with CKD are considerably affected by the presence of hypertension. This review is expected to aid in developing a focused and individualized treatment plan for hypertension in pregnant women with CKD to improve pregnancy outcomes and preserve postpartum renal function.

Predicting laboratory aspirin resistance in Chinese stroke patients using machine learning models by GP1BA polymorphism

This study aims to use machine learning model to predict laboratory aspirin resistance (AR) in Chinese stroke patients by incorporating patient characteristics and single nucleotide polymorphisms of GP1BA and LTC4S. 2405 patients were analyzed to measure the Mutation frequency of GP1BA rs6065 and LTC4S rs730012. 112 patients with first-stroke arteriostenosis were prospectively enrolled to establish machine learning model. GP1BA rs6065 mutation frequency is 5.26% and LTC4S rs730012 is 14.78%. GP1BA rs6065 CT patients have more sensitivity to aspirin than CC genotype. Simple linear regression identified significant associations with age, smoking, HDL and GP1BA rs6065. Random forest (RF) and extreme gradient boosting (XGBoost) demonstrated predictive capabilities for AR. Findings suggest pre-identifying GP1BA rs6065 could optimize aspirin treatment, enabling personalized care and future research avenues.