[Machine learning algorithms for identifying autism spectrum disorder through eye-tracking in different intention videos].

Objectives: To investigate the differences in visual perception between children with autism spectrum disorder (ASD) and typically developing (TD) children when watching different intention videos, and to explore the feasibility of machine learning algorithms in objectively distinguishing between ASD children and TD children.

Methods: A total of 58 children with ASD and 50 TD children were enrolled and were asked to watch the videos containing joint intention and non-joint intention, and the gaze duration and frequency in different areas of interest were used as original indicators to construct classifier-based models. The models were evaluated in terms of the indicators such as accuracy, sensitivity, and specificity.

Main Risk Factors Related to Activities of Daily Living in Non-Dialysis Patients with Chronic Kidney Disease Stage 3-5: A Case-Control Study.

Introduction: Elderly people are at increased risk of falls, disability and death due to reduced functional reserve, decline in multiple systems functions, which affects their activities of daily living (ADL) and eventually develop into frailty. The ADL assessment is conducive to early detection to avoid further serious situations. Previous studies on patients' activities of daily living with chronic kidney disease (CKD) are mainly focused on dialysis patients.

[Simultaneous determination of four anthraquinones in Polygonum multiflorum by QAMS].

A simple, specific and selective quantitative analysis of multi-components by single marker(QAMS) method for simultaneous determination of anthraquinones and anthraquinone glycosides in Polygonum multiflorum was developed. Four main anthraquinones and its glycosides, emodin, emodin-8-O-β-D-glucoside, physcion and physcion-8-O-β-D-glucoside were selected as analytes to evaluate the quality of P. multiflorum.