Reliability, Validity, and Responsiveness of the Chinese Learning Accomplishment Profile (C-LAP).

OBJECTIVES: To evaluate the reliability, validity, and responsiveness of the Chinese Learning Accomplishment Profile in China. METHODS: 12,098 participants aged from 0 to 36 months from 30 provinces (mostly from Shanghai) in China were enrolled between 2013 and 2020. The reliability was reflected by Pearson correlation coefficients, Cronbach's alpha coefficients and standard errors; the validity was shown by the coefficients between the dimensions, and we also evaluated the responsiveness as a supplement to the validity. RESULTS: Reliability: in six domains among each subgroup, Pearson correlation coefficients between developmental age and chronological age ranged from 0.89 to 0.98, Cronbach's alpha coefficients from 0.71 to 0.99, and standard errors from 0.15 to 2.76. VALIDITY: after controlling for chronological age, the correlation coefficients between the dimensions were between 0.18 and 0.78, and most of them were below 0.70. Responsiveness: developmental age of all domains obtained via the Chinese Learning Accomplishment Profile system changed significantly (p < 0.001) with time (gap of 1-3 months), and the standardized response mean ranged from 0.66 to 2.45. CONCLUSIONS: The Chinese Learning Accomplishment Profile is suitable for assessing children's development in Shanghai, but still needs confirmation when used in other provinces in China due to the great differences between regions in China.

Predictive Model Construction for Social-Emotional Competence of Toddlers in Shanghai, China: A Population-Based Study.

OBJECTIVE: To construct a simple model containing predictors derived from Chinese Learning Accomplishment Profile (C-LAP) to better the evaluation of the social-emotional development of toddlers aged 24-36 months. METHOD: The test results by C-LAP system and demographic information of toddlers aged 24-36 months were collected between 2013 and 2019 in Shanghai, China, whose guardians were voluntary to accept the investigation. We developed a norm with the dataset based on the study population. With the norm, stepwise regression and best subset analysis were applied to select predictors. RESULTS: Relying on the norm established and stepwise regression and also the best subset analysis, an optimal model containing only 6 indicators was finally determined and the nomogram of the model was constructed. In the training and validation dataset, the AUCs of the optimal model were 0.95 (95% CI: 0.94-0.96) and 0.88 (95% CI: 0.85-0.90), respectively. When the cutoff point of the model was set at 0.04, its sensitivity in training and validation dataset was 0.969 and 0.949, respectively, and the specificity in training and validation dataset is 0.802 and 0.736, respectively. CONCLUSION: A simplified predictive model which includes only 6 items derived from C-LAP is developed to evaluate the probabilities of being at risk of developmental problem in social-emotional development for toddlers aged 24-36 months. Meanwhile, specificity and sensitivity of the model may be high enough for future fast screening.

Screening of Sensitive Items on Gross Motor Development in Toddlers Aged 24~36 Months and Analysis of the Factors Influencing the Passing Rate.

BACKGROUND: To save assessment time and improve the efficiency, it is necessary to find sensitive indicators from the test items of gross motor development in the C-LAP system for children aged 24~36 months and analyze the influencing factors of the passing rate of these indicators. METHODS: This retrospective study was conducted among 1354 toddlers (3058 person-times) aged 24 to 36 months in Beijing, Shanghai, Guangdong between January 2013 and December 2019. A linear regression model and Cox regression model were performed to screen sensitive indicators and explore their influencing factors, respectively. RESULTS: "Walk backwards", "Stand from supine position" and "Hop with one foot at least twice" are the three sensitive indicators for evaluating the development of gross motor function in 24~36 month old children. The child's physiological age at first measurement and region are the two common independent factors influencing the passing rate of the three items, while paternal age and education may influence one or two of them. CONCLUSIONS: "Walk backwards", "Stand from supine position" and "Hop with one foot at least twice" are sensitive indicators for the effective evaluation of the development of gross motor function in 24~36 month old children, and their passing rates are influenced by some demographic variables.

Recent advances in understanding the mechanisms of PM2.5-mediated neurodegenerative diseases.

PM2.5 particles are widely believed to be associated with respiratory and cardiovascular diseases. However, recent studies have reported that PM2.5 may be associated with neurodegenerative diseases. The exact mechanism by which PM2.5 mediates neurotoxicity and cognitive dysfunction is still unclear. In the current work, we collected evidence supporting the association between PM2.5 exposure and development of neurodegenerative disorders. Evidence from epidemiological investigations, animal experiments, and ex vivo cell experiments showed that PM2.5 exposure may lead to neuroinflammation, oxidative stress, mitochondrial dysfunction, neuronal apoptosis, synaptic damage and ultimately neurodegenerative diseases.

A review of the possible associations between ambient PM2.5 exposures and the development of Alzheimer's disease.

PM2.5 particles in air pollution have been widely considered associated with respiratory and cardiovascular diseases. Recent studies have shown that PM2.5 can also cause central nervous system (CNS) diseases, including a variety of neurodegenerative diseases, such as Alzheimer's disease (AD). Activation of microglia in the central nervous system can lead to inflammatory and neurological damage. PM2.5 will reduce the methylation level of DNA and affect epigenetics. PM2.5 enters the human body through a variety of pathways to have pathological effects on CNS. For example, PM2.5 can destroy the integrity of the blood-brain barrier (BBB), so peripheral systemic inflammation easily crosses BBB and reaches CNS. The olfactory nerve is another way for PM2.5 particles to enter the brain. Surprisingly, PM2.5 can also enter the gastrointestinal tract, causing imbalances in the intestinal microecology to affect central nervous system diseases. The current work collected and discuss the mechanisms of PM2.5-induced CNS damage and PM2.5-induced neurodegenerative diseases.