Correlation between classical transient receptor potential channel 1 gene polymorphism and microalbuminuria in patients with primary hypertension

Calcium signaling in neurodevelopment and pathophysiology of autism spectrum disorders

BACKGROUND

Autism spectrum disorder (ASD) covers a group of neurodevelopmental disorders with complex genetic background. Several genetic mutations, epigenetic alterations, copy number variations and single nucleotide polymorphisms have been reported that cause ASD or modify its phenotype. Among signaling pathways that influence pathogenesis of ASD, calcium signaling has a prominent effect.

METHODS

We searched PubMed and Google Scholar databases with key words "Calcium signaling" and "Autism spectrum disorder".

CONCLUSION

This type of signaling has essential roles in the cell physiology. Endoplasmic reticulum and mitochondria are the key organelles involved in this signaling. It is vastly accepted that organellar disorders intensely influence the central nervous system (CNS). Several lines of evidence indicate alterations in the function of calcium channels in polygenic disorders affecting CNS. In the current review, we describe the role of calcium signaling in normal function of CNS and pathophysiology of ASD.

Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms

Objective

Microalbuminuria (MAU) occurs due to universal endothelial damage, which is strongly associated with kidney disease, stroke, myocardial infarction, and coronary artery disease. Screening patients at high risk for MAU may aid in the early identification of individuals with an increased risk of cardiovascular events and mortality. Hence, the present study aimed to establish a risk model for MAU by applying machine learning algorithms.

Methods

This cross-sectional study included 3,294 participants ranging in age from 16 to 93 years. R software was used to analyze missing values and to perform multiple imputation. The observed population was divided into a training set and a validation set according to a ratio of 7:3. The first risk model was constructed using the prepared data, following which variables with P <0.1 were extracted to build the second risk model. The second-stage model was then analyzed using a chi-square test, in which a P ≥ 0.05 was considered to indicate no difference in the fit of the models. Variables with P <0.05 in the second-stage model were considered important features related to the prevalence of MAU. A confusion matrix and calibration curve were used to evaluate the validity and reliability of the model. A series of risk prediction scores were established based on machine learning algorithms.

Results

Systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride (TG) levels, sex, age, and smoking were identified as predictors of MAU prevalence. Verification using a chi-square test, confusion matrix, and calibration curve indicated that the risk of MAU could be predicted based on the risk score.

Conclusion

Based on the ability of our machine learning algorithm to establish an effective risk score, we propose that comprehensive assessments of SBP, DBP, FBG, TG, gender, age, and smoking should be included in the screening process for MAU.