Measurement of biological age may help to assess the risk of colorectal adenoma in screening colonoscopy

AIM To investigate the clinical utility of biological age(BA) measurement in screening colonoscopy for the detection of colorectal adenomas in the average-risk population.METHODS A consecutive series of asymptomatic subjects aged ≥ 30 years who underwent colonoscopy in routine check-ups were enrolled. Colorectal adenoma was classified according to size, number, and location. BAs were calculated using the MEDIAGETM Biological Age Measurement System.RESULTS A total of 2696 subjects were investigated(1876 men and 820 women). The mean chronological age(CA) was 46.0 years and the mean BA was 44.7 years. Metabolic syndrome(MS) was diagnosed in 218 subjects(8.1%). The prevalence of overall colorectal adenoma was 23.1%(622/2,696). When the subjects were divided into four groups based on BA(≤ 39 years; 40-49 years; 50-59 years; ≥ 60 years), the prevalence of colorectal adenoma was increased as BA increased(P < 0.001). Colorectal adenoma located in the proximal colon was more prevalent in the BA-dominant group(BA-CA ≥ 5years) than the CA-dominant group(CA-BA ≥ 5 years)(P = 0.034). When the subjects were categorized into four groups according to MS and age gap between BA and CA, the incidence of colorectal adenoma increased with MS and BA-dominance(P < 0.05).CONCLUSION Measurement of BA may help to assess the risk of colorectal adenoma in screening colonoscopy.

Exploring Brain Age Calculation Models Available for Alzheimer's Disease

The advantages of structural magnetic resonance imaging(sMRI)-based multidimensional tensor morphological features in brain disease research are the high sensitivity and resolution of sMRI to comprehensively capture the key structural information and quantify the structural deformation.However,its direct application to regression analysis of high-dimensional small-sample data for brain age prediction may cause“dimensional catastrophe”.Therefore,this paper develops a brain age prediction method for high-dimensional small-sample data based on sMRI multidimensional morphological features and constructs brain age gap estimation(BrainAGE)biomarkers to quantify abnormal aging of key subcortical structures by extracting subcortical structural features for brain age prediction,which can then establish statistical analysis models to help diagnose Alzheimer’s disease and monitor health conditions,intervening at the preclinical stage.

Effects of gaps on regeneration of woody plants: a meta-analysis

Forest gaps, openings in the canopy caused by death of one or more trees, have a profound effect on forest regeneration and drive the forest growth cycle. It is therefore necessary to understand the effects of forest gaps on regeneration for modern forest management. In order to provide a quantitative assessment of the effects of forest gaps on regen-eration of woody plants, we conducted this review of gap effects on woody plant regeneration on the basis of 527 observations from 42 indi-vidual papers, and reported the results of these data in a meta-analysis. Overall, densities of regenerated woody plants were significantly greater （359%） in forest gaps than on the closed-canopy forest floor. The regen-eration density in gaps of plantation forests was significantly greater （P＆lt;0.05） than that of natural forest because the regeneration in gaps of plan-tation forests was improved by both gap effects and experimental meas-ures. Similarly, in comparison to natural gaps, regeneration was better enhanced in artificial gaps. Regeneration density exhibited a significantly positive correlation with gap size, but a negative correlation with gap age because the gap size decreased with increasing gap age. Shade tolerance of woody plants affected regeneration density in gaps and understory. Average regeneration density of shade-tolerant species exhibited a sig-nificantly positive response to gaps but densities remained lower in total than those of intermediate and shade-intolerant species. Gap effects on regeneration decreased in response to increasing temperature and pre-cipitation because of the limiting effects of lower temperature and moisture on woody plant regeneration. In summary, forest gaps enhance woody plant regeneration, and the effects of gaps varied by forest type, gap characteristics, environmental factors and plant traits. The results of this meta-analysis are useful for better understanding the effects and roles of gaps on forest regeneration and forest management.