Analysis of Physical Education Classroom Teaching after Implementation of the Chinese Health Physical Education Curriculum Model: A Video-Based Assessment.

This study assessed the Chinese health physical education curriculum model recently suggested to meet the recommended physical education curriculum reforms addressing the declining physical and mental health of students in China. We used video analyses of 41 physical education classroom teaching cases with a physical education classroom teaching behavior analysis system to provide quantitative and qualitative behavioral data. We established reference ranges for classroom teaching behavior indicators, summarized classroom teaching patterns, and assessed classroom discourse and the emotional climate. Notable findings included teachers in elementary schools using closed-ended questions, predictable responses, and general feedback significantly more often than teachers in senior high school, and ball sports instructors using demonstration and competition significantly more frequently than instructors in athletics. Overall, three teaching patterns were most commonly used-lecture, practice, and dialogue-with practice being dominant. Analysis of the top 50 most commonly spoken words by teachers identified five types of discourse-motivational, directive, specialized, transitional, and regulatory-with motivational words being most frequent. The classroom atmosphere was mainly positive. These findings provide evidence that the use of this curriculum model may bring positive changes to physical education classroom teaching methods in China and will inform subsequent innovative physical education classroom teaching practices.

Rural Industrial Integration's Impact on Agriculture GTFP Growth: Influence Mechanism and Empirical Test Using China as an Example.

Agricultural carbon emission is an significant cause of global climate change and many environmental and health problems. Achieving low-carbon and green development in agriculture is not only an inevitable choice for countries around the world to cope with climate change and the accompanying environmental and health problems, but also a necessary path for the sustainable development of global agriculture. The promotion of rural industrial integration is a practical way to realize sustainable agricultural growth and urban-rural integration development. The analysis framework of agriculture GTFP is creatively extended in this study to include the integration and growth of rural industries, rural human capital investment and rural land transfer. According to the sample data of 30 provinces in China from 2011 to 2020 and the systematic GMM estimation method, and through the combination of theoretical analysis and empirical testing, this paper discusses the influence mechanism of rural industrial integration development on agriculture GTFP growth, as well as the regulating role of rural human capital investment and rural land transfer. The results show that rural industrial integration has significantly promoted the growth of agriculture GTFP. Additionally, after decomposing agriculture GTFP into the agricultural green technology progress index and agricultural green technology efficiency index, it is found that rural industrial integration has a more obvious role in promoting agricultural green technology progress. Furthermore, quantile regression found that with the increase in agricultural GTFP, the promoting effect of rural industrial integration showed an "inverted U-shaped" feature. Through heterogeneity testing, it is found that the agriculture GTFP growth effect of rural industrial integration is more obvious in areas with high level of rural industrial integration. Additionally, as the nation places more and more focus on rural industrial integration, the promotion role of rural industrial integration has become more and more obvious. The moderating effect test showed that health, education and training, migration of rural human capital investment and rural land transfer all strengthened the promoting effect of rural industrial integration on agricultural GTFP growth to varying degrees. This study provides rich policy insights for China and other developing countries around the world to address global climate change and many related environmental and monitoring issues by developing rural industrial integration, strengthening rural human capital investment and promoting agricultural land transfer to achieve sustainable agricultural growth and reduce undesirable output outputs such as agricultural carbon emissions.

A preliminary study on the feasibility of industrialization for n-caproic acid recovery from food wastewater: From lab to pilot.

Food wastewater is associated with greenhouse gas emission and has a significant water footprint. Here, the platform chemical n-caproate was recovered from liquor brewing wastewater at maximum and mean concentrations of 26.4 g/L and 17.0 ± 4.3 g/L, respectively, after 377 d operation. Laboratory-scale lactate-driven chain elongation (CE) process was implemented first. Taxonomic composition and metagenomic data analyses revealed that Caproiciproducens (e.g., Ruminococcaceae bacterium CPB6) and bacteria affiliated with Lachnospiraceae transformed lactate to n-caproate by reverse ß-oxidation and/or fatty acid biosynthesis. The lactate-driven CE process was then scaled up from 2.5 L to 500 L and achieved a n-caproate production of 14.5 ± 0.6 g/L within 96 h. n-Caproic acid was extracted at a concentration and purity of 815.9 ± 8.3 g/L and 88.6 ± 8.9 %, respectively. The present study demonstrated a commercially viable strategy for resource recovery and carbon fixation from food waste streams.

A Wearable Diagnostic Assessment System vs. SNAP-IV for the auxiliary diagnosis of ADHD: a diagnostic test.

OBJECTIVE: We design a diagnostic test to evaluate the effectiveness and accuracy of A non-intrusive Wearable Diagnostic Assessment System versus SNAP-IV for auxiliary diagnosis of children with ADHD. METHODS: This study included 55 children aged 6-16 years who were clinically diagnosed with ADHD by DSM-5, and 55 healthy children (typically developing). Each subject completes 10 tasks on the WeDA system (Wearable Diagnostic Assessment System) and Parents of each subject complete the SNAP-IV scale. We will calculate the validity indexes, including sensitivity, specificity, Youden's index, likelihood ratio, and other indexes including predictive value, diagnostic odds ratio, diagnostic accuracy and area under the curve [AUC] to assess the effectiveness of the WeDA system as well as the SNAP-IV. RESULTS: The sensitivity (94.55% vs. 76.36%) and the specificity (98.18% vs. 80.36%) of the WeDA system were significantly higher than the SNAP-IV. The AUC of the WeDA system (0.964) was higher than the SNAP-IV (0.907). There is non-statistically significant difference between groups (p = 0.068), and both of them have high diagnostic accuracy. In addition, the diagnostic efficacy of the WeDA system was higher than that of SNAP-IV in terms of the Youden index, diagnostic accuracy, likelihood ratio, diagnostic odds ratio and predictive value. CONCLUSION: The advantages of the WeDA system in terms of diagnostic objectivity, scientific design and ease of operation make it a promising system for widespread use in clinical practice.

Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study.

Background: Comprehensive eye examinations for diabetic retinopathy is poorly implemented in medically underserved areas. There is a critical need for a widely available and economical tool to aid patient selection for priority retinal screening. We investigated the possibility of a predictive model for retinopathy identification using simple parameters. Methods: Clinical data were retrospectively collected from 4, 159 patients with diabetes admitted to five tertiary hospitals. Independent predictors were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, and a nomogram was developed based on a multivariate logistic regression model. The validity and clinical practicality of this nomogram were assessed using concordance index (C-index), area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC). Results: The predictive factors in the multivariate model included the duration of diabetes, history of hypertension, and cardiovascular disease. The three-variable model displayed medium prediction ability with an AUROC of 0.722 (95%CI 0.696-0.748) in the training set, 0.715 (95%CI 0.670-0.754) in the internal set, and 0.703 (95%CI 0.552-0.853) in the external dataset. DCA showed that the threshold probability of DR in diabetic patients was 17-55% according to the nomogram, and CIC also showed that the nomogram could be applied clinically if the risk threshold exceeded 30%. An operation interface on a webpage (https://cqmuxss.shinyapps.io/dr_tjj/) was built to improve the clinical utility of the nomogram. Conclusions: The predictive model developed based on a minimal amount of clinical data available to diabetic patients with restricted medical resources could help primary healthcare practitioners promptly identify potential retinopathy.

Analysis of hospitalization costs related to fall injuries in elderly patients.

BACKGROUND: With the aging world population, the incidence of falls has intensified and fall-related hospitalization costs are increasing. Falls are one type of event studied in the health economics of patient safety, and many developed countries have conducted such research on fall-related hospitalization costs. However, China, a developing country, still lacks large-scale studies in this area. AIM: To investigate the factors related to the hospitalization costs of fall-related injuries in elderly inpatients and establish factor-based, cost-related groupings. METHODS: A retrospective study was conducted. Patient information and cost data for elderly inpatients (age ≥ 60 years, n = 3362) who were hospitalized between 2016 and 2019 due to falls was collected from the medical record systems of two grade-A tertiary hospitals in China. Quantile regression (QR) analysis was used to identify the factors related to fall-related hospitalization costs. A decision tree model based on the chi-squared automatic interaction detector algorithm for hospitalization cost grouping was built by setting the factors in the regression results as separation nodes. RESULTS: The total hospitalization cost of fall-related injuries in the included elderly patients was 180479203.03 RMB, and the reimbursement rate of medical benefit funds was 51.0% (92039709.52 RMB/180479203.03 RMB). The medical material costs were the highest component of the total hospitalization cost, followed (in order) by drug costs, test costs, treatment costs, integrated medical service costs and blood transfusion costs The QR results showed that patient age, gender, length of hospital stay, payment method, wound position, wound type, operation times and operation type significantly influenced the inpatient cost (P < 0.05). The cost grouping model was established based on the QR results, and age, length of stay, operation type, wound position and wound type were the most important influencing factors in the model. Furthermore, the cost of each combination varied significantly. CONCLUSION: Our grouping model of hospitalization costs clearly reflected the key factors affecting hospitalization costs and can be used to strengthen the reasonable control of these costs.