Objective To investigate the global burden of visceral leishmaniasis (VL) from 1990 to 2021 and predict the trends in the burden of VL from 2022 to 2035, so as to provide insights into global VL prevention and control. Methods The global age-standardized incidence, prevalence, mortality and disability-adjusted life years (DALYs) rates of VL and their 95% uncertainty intervals (UI) were captured from the Global Burden of Disease Study 2021 (GBD 2021) data resources. The trends in the global burden of VL were evaluated with average annual percent change (AAPC) and 95% confidence interval (CI) from 1990 to 2021, and gender-, age-, country-, geographical area- and socio-demographic index (SDI)-stratified burdens of VL were analyzed. The trends in the global burden of VL were projected with a Bayesian age-period-cohort (BAPC) model from 2022 to 2035, and the associations of age-standardized incidence, prevalence, mortality, and DALYs rates of VL with SDI levels were examined with a smoothing spline model. Results The global age-standardized incidence [AAPC = -0.25%, 95% CI: (-0.25%, -0.24%)], prevalence [AAPC = -0.06%, 95% CI: (-0.06%, -0.06%)], mortality [AAPC = -0.25%, 95% CI: (-0.25%, -0.24%)] and DALYs rates of VL [AAPC = -2.38%, 95% CI: (-2.44%, -2.33%)] all appeared a tendency towards a decline from 1990 to 2021, and the highest age-standardized incidence [2.55/10⁵, 95% UI: (1.49/10⁵, 4.07/10⁵)], prevalence [0.64/10⁵, 95% UI: (0.37/10⁵, 1.02/10⁵)], mortality [0.51/10⁵, 95% UI: (0, 1.80/10⁵)] and DALYs rates of VL [33.81/10⁵, 95% UI: (0.06/10⁵, 124.09/10⁵)] were seen in tropical Latin America in 2021. The global age-standardized incidence and prevalence of VL were both higher among men [0.57/10⁵, 95% UI: (0.45/10⁵, 0.72/10⁵); 0.14/10⁵, 95% UI: (0.11/10⁵, 0.18/10⁵)] than among women [0.27/10⁵, 95% UI: (0.21/10⁵, 0.33/10⁵); 0.06/10⁵, 95% UI: (0.05/10⁵, 0.08/10⁵)], and the highest mortality of VL was found among children under 5 years of age [0.24/10⁵, 95% UI: (0.08/10⁵, 0.66/10⁵)]. The age-standardized incidence (r = -0.483, P < 0.001), prevalence (r = -0.483, P < 0.001), mortality (r = -0.511, P < 0.001) and DALYs rates of VL (r = -0.514, P < 0.001) correlated negatively with SDI levels from 1990 to 2021. In addition, the global burden of VL was projected with the BAPC model to appear a tendency towards a decline from 2022 to 2035, and the age-standardized incidence, prevalence, mortality and DALYs rates were projected to be reduced to 0.11/10⁵, 0.03/10⁵, 0.02/10⁵ and 1.44/10⁵ in 2035, respectively. Conclusions Although the global burden of VL appeared an overall tendency towards a decline from 1990 to 2021, the burden of VL showed a tendency towards a rise in Central Asia and western sub-Saharan African areas. The age-standardized incidence and prevalence rates of VL were relatively higher among men, and the age-standardized mortality of VL was relatively higher among children under 5 years of age. The global burden of VL was projected to continue to decline from 2022 to 2035.

ObjectiveTo construct a large language model （LLM）-based intelligent pre-consultation system for traditional Chinese medicine （TCM） to improve efficacy of clinical practice. MethodsA TCM large language model was fine-tuned using DeepSpeed ZeRO-3 distributed training strategy based on YAYI 2-30B. A weighted undirected graph network was designed and an agent-based syndrome differentiation model was established based on relationship data extracted from TCM literature and clinical records. An agent collaboration framework was developed to integrate the TCM LLM with the syndrome differentiation model. Model performance was comprehensively evaluated by Loss function， BLEU-4， and ROUGE-L metrics， through which training convergence， text generation quality， and language understanding capability were assessed. Professional knowledge test sets were developed to evaluate system proficiency in TCM physician licensure content， TCM pharmacist licensure content， TCM symptom terminology recognition， and meridian identification. Clinical tests were conducted to compare the system with attending physicians in terms of diagnostic accuracy， consultation rounds， and consultation duration. ResultsAfter 100 000 iterations， the training loss value was gradually stabilized at about 0.7±0.08， indicating that the TCM-LLM has been trained and has good generalization ability. The TCM-LLM scored 0.38 in BLEU-4 and 0.62 in ROUGE-L， suggesting that its natural language processing ability meets the standard. We obtained 2715 symptom terms， 505 relationships between diseases and syndromes， 1011 relationships between diseases and main symptoms， and 1 303 600 relationships among different symptoms， and constructed the Agent of syndrome differentiation model. The accuracy rates in the simulated tests for TCM practitioners， licensed pharmacists of Chinese materia medica， recognition of TCM symptom terminology， and meridian recognition were 94.09%， 78.00%， 87.50%， and 68.80%， respectively. In clinical tests， the syndrome differentiation accuracy of the system reached 88.33%， with fewer consultation rounds and shorter consultation time compared to the attending physicians （P＜0.01）， suggesting that the system has a certain pre- consultation ability. ConclusionThe LLM-based intelligent TCM pre-diagnosis system could simulate diagnostic thinking of TCM physicians to a certain extent. After understanding the patients' natural language， it collects all the patient's symptom through guided questioning， thereby enhancing the diagnostic and treatment efficiency of physicians as well as the consultation experience of the patients.

Objective: To investigate the status quo and associated factors of drinking water knowledge, attitude and behavior among primary school students in rural areas, so as to provide evidence for health behavioral intervention of drinking water in primary school. Methods: Twentythree primary schools in rural area from Hebei, Henan, Shandong and Shanxi provinces were selected by using purposive sampling method from March 1 to April 27 in 2023. Selfdesigned questionnaires regarding knowledge, attitude and behavior of drinking water were distributed to all students in grade 3-6, and 2 173 valid questionnaires were obtained. Multivariate Logistic regression was used to analyze the influencing factors of drinking water knowledge, attitude and behavior of primary school students. Results: The attainment rates of drinking water knowledge, attitude and behavior level were 20.02%, 26.65%, and 31.20%, respectively, among primary school students. The median of daily water intake was 1 000 mL, and the average daily water intake was (1 172.99±771.89)mL. In addition, 66.31% of students water intake reached the minimum standard of 800 mL recommended. The results of multiple Logistic regression indicated that drinking water accessibility in school, health education of drinking water, and individual selfcontrol ability were positively correlated with the knowledge (OR=1.31, 1.57, 1.58), attitude (OR=2.07, 1.65, 1.73), behavior (OR=1.40, 1.49, 1.91) of drinking water and daily water intake (OR=1.41, 1.38, 1.20) (P<0.05). Conclusions Primary school students in rural areas are generally lack of appropriate health awareness on drinking water including knowledge, attitude and behavior. Schools should take targeted measures to focus on the cultivation of students selfcontrol ability, so as to improve students knowledge and attitudes of drinking water, and furthermore help students shape their healthy behaviors of drinking water.

Objective: This study aimed to explore the correlation between balance function and core muscle activation in patients with adolescent idiopathic scoliosis (AIS), compared to healthy individuals. Methods: A total of 24 AIS patients and 25 healthy controls were recruited. The limits of stability (LOS) test were conducted to assess balance function, while surface electromyography was used to measure the activity of core muscles, including the internal oblique, external oblique, and multifidus. Diaphragm thickness was measured using ultrasound during different postural tasks. Center of pressure (COP) displacement and trunk inclination distance were also recorded during the LOS test. Results: AIS patients showed significantly greater activation of superficial core muscles, such as the internal and external oblique muscles, compared to the control group (p < 0.05). Diaphragm activation was lower in AIS patients during balance tasks (p < 0.01). Although no significant difference was observed in COP displacement between the groups, trunk inclination was significantly greater in the AIS group during certain tasks (p < 0.05). Conclusion These findings suggest distinct postural control patterns in AIS patients, highlighting the importance of targeted interventions to improve balance and core muscle function in this population.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

BACKGROUND:Human periodontal ligament stem cells are potential functional cells for periodontal tissue engineering.However,long-term in vitro culture may lead to reduced stemness and replicative senescence of periodontal ligament stem cells,which may impair the therapeutic effect of human periodontal ligament stem cells. OBJECTIVE:To investigate the effect of oxymatrine on the stemness maintenance and osteogenic differentiation of periodontal ligament stem cells in vitro,and to explore the potential mechanism. METHODS:Periodontal ligament stem cells were isolated from human periodontal ligament tissues by tissue explant enzyme digestion and cultured.The surface markers of mesenchymal cells were identified by flow cytometry.Periodontal ligament stem cells were incubated with 0,2.5,5,and 10 μg/mL oxymatrine.The effect of oxymatrine on the proliferation activity of periodontal ligament stem cells was detected by CCK8 assay.The appropriate drug concentration for subsequent experiments was screened.Western blot assay was used to detect the expression of stem cell non-specific proteins SOX2 and OCT4 in periodontal ligament stem cells.qRT-PCR and western blot assay were used to detect the expression levels of related osteogenic genes and proteins in periodontal ligament stem cells. RESULTS AND CONCLUSION:(1)The results of CCK8 assay showed that 2.5 μg/mL oxymatrine significantly enhanced the proliferative activity of periodontal stem cells,and the subsequent experiment selected 2.5 μg/mL oxymatrine to intervene.(2)Compared with the blank control group,the protein expression level of SOX2,a stem marker of periodontal ligament stem cells in the oxymatrine group did not change significantly(P>0.05),and the expression of OCT4 was significantly up-regulated(P<0.05).(3)Compared with the osteogenic induction group,the osteogenic genes ALP,RUNX2 mRNA expression and their osteogenic associated protein ALP protein expression of periodontal ligament stem cells were significantly down-regulated in the oxymatrine+osteogenic induction group(P<0.05).(4)The oxymatrine up-regulated the expression of stemness markers of periodontal ligament stem cells and inhibited the bone differentiation of periodontal ligament stem cells,and the results of high-throughput sequencing showed that it may be associated with WNT2,WNT16,COMP,and BMP6.

Objective: This study aimed to explore the correlation between balance function and core muscle activation in patients with adolescent idiopathic scoliosis (AIS), compared to healthy individuals. Methods: A total of 24 AIS patients and 25 healthy controls were recruited. The limits of stability (LOS) test were conducted to assess balance function, while surface electromyography was used to measure the activity of core muscles, including the internal oblique, external oblique, and multifidus. Diaphragm thickness was measured using ultrasound during different postural tasks. Center of pressure (COP) displacement and trunk inclination distance were also recorded during the LOS test. Results: AIS patients showed significantly greater activation of superficial core muscles, such as the internal and external oblique muscles, compared to the control group (p < 0.05). Diaphragm activation was lower in AIS patients during balance tasks (p < 0.01). Although no significant difference was observed in COP displacement between the groups, trunk inclination was significantly greater in the AIS group during certain tasks (p < 0.05). Conclusion These findings suggest distinct postural control patterns in AIS patients, highlighting the importance of targeted interventions to improve balance and core muscle function in this population.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: This study aimed to explore the correlation between balance function and core muscle activation in patients with adolescent idiopathic scoliosis (AIS), compared to healthy individuals. Methods: A total of 24 AIS patients and 25 healthy controls were recruited. The limits of stability (LOS) test were conducted to assess balance function, while surface electromyography was used to measure the activity of core muscles, including the internal oblique, external oblique, and multifidus. Diaphragm thickness was measured using ultrasound during different postural tasks. Center of pressure (COP) displacement and trunk inclination distance were also recorded during the LOS test. Results: AIS patients showed significantly greater activation of superficial core muscles, such as the internal and external oblique muscles, compared to the control group (p < 0.05). Diaphragm activation was lower in AIS patients during balance tasks (p < 0.01). Although no significant difference was observed in COP displacement between the groups, trunk inclination was significantly greater in the AIS group during certain tasks (p < 0.05). Conclusion These findings suggest distinct postural control patterns in AIS patients, highlighting the importance of targeted interventions to improve balance and core muscle function in this population.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.