Low- and middle-income countries (LMICs) suffered the most from nutritional deficiencies (NDs). Although decades of efforts have reduced it, little is known about the changing trajectory of ND burden in LMICs. By extracting data of the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, we calculated indicators of incidence and disability-adjusted life years (DALYs) to measure the burden of NDs and its main subcategories in LMICs, including protein-energy malnutrition, iodine deficiency, vitamin A deficiency, dietary iron deficiency, and other nutritional deficiencies by sex, age and spatial patterns. In LMICs, ND incidence still increased in the age group 15+ born before 2005, especially in males. The effort of reducing the DALYs of NDs has generated a strong decline in per age group. In the main subcategories of NDs, protein-energy malnutrition incidence in males age 45+ born before 1970 still increased. Despite vitamin A deficiency incidence and dietary iron deficiency, DALYs strongly experienced decreases over three decades while still remaining at the heaviest level in 2019, especially in females and children under 5 years. The top largest tendency estimates occurred in Mali’ females and Bhutan’ males. Zimbabwe was the only country with increased DALYs rate tendency in both sexes.

With the growing prevalence of anxiety symptoms among university students, self-control is an important potential influence. This study aims to understand the network structure of self-control and anxiety, and to identify the core symptoms within this network. It will provide a theoretical basis for the prevention and intervention of anxiety symptoms in university students. We used network analysis to study anxiety and self-control in 3,792 university students from six schools in Heilongjiang Province, China. We checked for linear connections in the networks using a restricted cubic spline. We conducted the analyses and made graphs using R software. (i) The total sample network showed that higher levels of self-control in university students were associated with lower anxiety levels and were validated by the restrictive cubic spline. (ii) There was the strongest negative correlation (edge weight = -0.42) between Without thinking (SC7) and Panic (A5), and the edge weight coefficients of this self-control component and anxiety symptoms were greater than those of other self-control components. (iii) Physical exertion (A6) and Scared (A7) were identified as the core symptoms of the overall network, with expected influence of 1.08 and 1.08 (Z-score). (iv) A significant difference was observed between the anxiety positive network structure and the total sample network structure, with the strongest positive correlation between Iron self-control (SC3) and Breathing difficulty (A2) (edge weight = 0.22), with the strongest negative correlation between Certain things (SC2) and Situations (A4) (edge weight = -0.35). (v) The self-control component Iron self-control (SC3) had only one positive edge in the rural network, and only two positive edges in not one child network. The present study offered a new perspective on the relationship between self-control and anxiety using network analysis for the first time. The control component Without thinking (SC7) was an important concept influencing the negative correlation of anxiety, and Physical exertion (A6) and Scared (A7) were core symptoms in the total network. Heterogeneity analyses showed a tendency for the more self-controlled to be more anxious in the anxiety positive sample. These results may be a potential target for preventing and intervening anxiety in university students.

Background: There are few studies examining the physical developmental phenotypes of nutritional deficiency diseases (NDDs) among Chinese children aged 1–7 years by anthropometrics and clarifying the specific NDD categories that caused growth faltering. Methods: A total of 3054 cases of NDDs in children aged 1–7 years were investigated. The age, height, and weight of children with NDDs were adjusted by using the skewness coefficient–median–coefficient of variation method, and the results were compared with the WHO standardized level. Comparisons of specific categories of NDDs were performed with respect to the age-specific height standardized values (HAZ), the age-specific weight standardized values (WAZ), and the age-specific body mass index standard values (BAZ). Result: The subtypes of NDDs among Chinese children were mainly characterized by single and co-morbid deficiencies. Calcium deficiency, vitamin A deficiency, vitamin D deficiency, and vitamin B deficiency were the main types, accounting for 11.33%, 9.26%, 8.70%, and 6.29% of the total confirmed cases, respectively; protein–energy malnutrition combined with vitamin C deficiency was the most common type of combined nutrient deficiency (5.76%). The HAZ (−0.0002), WAZ (−0.0210), and BAZ (−0.0018) of children aged 1–3 years with NDDs was lower than the WHO standard (0), as were the HAZ (−0.0003), WAZ (−0.0219), and BAZ (−0.0019) of children aged 3–7 years. The anthropometrics of children with NDDs aged 1–7 years showed that the HAZ and BAZ were slightly lower than the WHO average level, whereas the WAZ was significantly different from the WHO average. The co-morbidity of vitamin A deficiency and vitamin D deficiency, calcium deficiency and vitamin C deficiency, vitamin D deficiency and vitamin C deficiency, and iodine deficiency and vitamin C deficiency were associated with the WAZ. Interpretation: The specific categories of NDDs consist mainly of calcium deficiencies, vitamin A deficiencies, and vitamin D deficiencies. The main signs of growth retardation were low weight and height, which were driven by the specific single and co-morbid micronutrient deficiencies.

Generative artificial intelligence (GenAI) is reshaping medical education while fostering technological dependence among students. This study employed an explanatory sequential mixed-methods design. In the quantitative phase, an empirical analysis was conducted using survey data collected from a sample of 1295 Chinese medical students. The subsequent qualitative phase involved thematic analysis of interview transcripts from 16 medical educators to elucidate the underlying mechanisms. Findings reveal that GenAI was deeply integrated into medical students' daily learning routines. Tool selection favored general-purpose platforms, whereas specialist medical tools exhibited exceptionally low utilization rates. The clinical application possibilities remained below 20% across all situations. With an overall dependency score of 21.91 ± 6.75, over 60% of students reported dependence on GenAI. Multivariate linear regression analysis indicated performance expectancy, academic pressure, and social influence showed significant positive correlations with GenAI dependency. Conversely, critical thinking exhibited a significant negative correlation. Future medical education should strategically reposition GenAI as a "cognitive scaffold" by reinforcing critical thinking and establishing standardized usage guidelines to facilitate high-quality development.