Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as habits,the family setting,socioeconomic status,and the work environment [1].In contrast to chronological age (CA),brain(or biological) age (BA) is conceptualized as an important index for characterizing the aging process and neuropsychological state,as well as individual cognitiveperformance.Growing evidence indicates that BA can be assessed by neuroimaging techniques,including MRI [2].

Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

The skeletal bone age assessment(BAA)was extremely implemented in development prediction and auxiliary analysis of medicinal issues.X-ray images of hands were detected from the estimation of bone age,whereas the ossification centers of epiphysis and carpal bones are important regions.The typical skeletal BAA approaches remove these regions for predicting the bone age,however,few of them attain suitable efficacy or accuracy.Automatic BAA techniques with deep learning(DL)methods are reached the leading efficiency on manual and typical approaches.Therefore,this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning(ISBAAC-MDL)model.The presented ISBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification process.To attain this,the presented ISBAAC-MDL model derives a mask Region-related Convolutional Neural Network(Mask-RCNN)with MobileNet as baseline model to extract features.Followed by,the whale optimization algorithm(WOA)is implemented for hyperparameter tuning of the MobileNet method.At last,Deep Feed-Forward Module(DFFM)based age prediction and Radial Basis Function Neural Network(RBFNN)based stage classification approach is utilized.The experimental evaluation of the ISBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct factors.The experimental outcomes reported the better performances of the ISBAACMDL model over recent approaches with maximum accuracy of 0.9920.

DNA methylation clocks for estimating biological age in Chinese cohorts

Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation(DNAm)at specific CpG sites.However,a systematic comparison between DNA methylation data and other omics datasets has not yet been performed.Moreover,available DNAm age predictors are based on datasets with limited ethnic representation.To address these knowledge gaps,we generated and analyzed DNA methylation datasets from two independent Chinese cohorts,revealing age-related DNAm changes.Additionally,a DNA methylation aging clock(iCAS-DNAmAge)and a group of DNAm-based multi-modal clocks for Chinese individuals were developed,with most of them demonstrating strong predictive capabilities for chronological age.The clocks were further employed to predict factors influencing aging rates.The DNAm aging clock,derived from multi-modal aging features(compositeAge-DNAmAge),exhibited a close association with multi-omics changes,lifestyles,and disease status,underscoring its robust potential for precise biological age assessment.Our findings offer novel insights into the regulatory mechanism of age-related DNAm changes and extend the application of the DNAm clock for measuring biological age and aging pace,providing the basis for evaluating aging intervention strategies.