Objective: To understand the current status of neglect among rural preschool children in Xi an under the multi child policy and the association with family characteristics, so as to provide a reference for preventing and reducing the occurrence of child neglect. Methods: A total of 7 052 parents of preschool children were selected using stratified cluster sampling across 9 suburban counties/districts in Xi an from March to April 2025. A questionnaire survey was administered using the Chinese Norm Scale for Neglect Assessment of Rural(Preschool) Children Aged 3-6. The t-test, Chi-quare test, and analysis of variance (ANOVA) were used for inter group comparisons. Results: The overall prevalence rate and mean score of neglect among rural preschool aged children in Xi an were 32.4% and 38.27±6.70, respectively. Statistically significant differences were detected in neglect rates and neglect degrees among preschool children of different genders and grade levels ( χ 2=30.41, 15.15, t/F =4.92,7.03, all P <0.05). Statistically significant differences were also detected in neglect rates and neglect degrees among preschool children from whether only one child, different family structures, numbers of children in a family and families with different annual incomes ( χ 2=29.22, 10.41 , 31.99, 186.47, t/F =-9.96, 5.50, 33.57, 68.63, all P <0.05). In multi child families, there was a statistically significant difference in neglect degree among children with different birth orders ( F =4.25, P <0.05), but there was no statistically significant difference in neglect rate ( χ 2=5.73, P >0.05). Among all subgroups, the highest neglect rates and neglect degrees were observed in children from multi child families(35.0%,39.00±6.71), other family types(50.0%，42.38±12.34) and families with three children(39.9%，39.50±7.43). Lower annual family income was associated with higher neglect rates and neglect degrees among preschool children( χ 2 trend =186.47， F =270.68，both P <0.05). Conclusions Under the multiple child policy, the neglect of preschool children in rural areas of Xi an is quite severe, particularly in families with multiple children and low income households. Targeted interventions should be implemented for high risk groups.

Diabetes mellitus type 2 (T2DM) is one of the most common metabolic diseases in the world and has a significant impact on the health of patients. As a key factor in cellular mechanical transduction, Piezo1 protein plays a crucial role in regulating the basic life activities of the body. By participating in energy metabolism, it not only promotes the improvement of basic metabolic rate, but also helps to maintain the stability of the internal environment of the body. The activation of Piezo1 pathway has a significant effect on the release of insulin by islet beta cells, and also plays an important role in the production of adipose tissue after food intake. This study reviews the effects of exercise intervention on the expression and function of Piezo1 protein, as well as its role in metabolic regulation and insulin level regulation in T2DM patients. The study showed that a modest exercise intervention activated Piezo1 signaling pathway, which improved insulin sensitivity and improved sugar metabolism. In addition, the activation of Piezo1 pathway is closely related to the metabolic regulation of adipose tissue, helping to regulate the differentiation and maturation of adipose cells, thereby affecting the metabolic function of adipose tissue. Based on a comprehensive analysis of existing literature, Piezo1 pathway is found to play a complex role in the pathogenesis of T2DM. Exercise intervention, as a non-drug therapy, provides a new strategy for the treatment of T2DM by activating Piezo1 signaling pathway. However, the exact mechanism of action of Piezo1 pathway in T2DM still needs further investigation. Future studies should focus on the interaction between the Piezo1 pathway and T2DM, and how to regulate the Piezo1 pathway to optimize treatment for T2DM. The effects of exercise intervention on Piezo1 protein and its role in metabolic regulation and insulin level regulation of T2DM patients were comprehensively analyzed in this paper, aiming to provide a new perspective for further research and development of therapeutic strategies for metabolic diseases such as diabetes and obesity.

Panvascular disease， with vascular diseases as the common pathological feature， is mainly manifested as atherosclerosis. Panvascular disease mainly affects the important organs of the heart， brain， kidney， and limbs. It is one of the leading causes of death for Chinese residents at present. Previously， due to the narrow branches of disciplines， too much attention was paid to local lesions， resulting in the neglect of panvascular disease as a systemic one. The fact that panvascular disease has overall pathology and comprehensive and individualized treatment strategies， makes the disease highly compatible with the principles of holism concept and syndrome differentiation and treatment in traditional Chinese medicine （TCM）. It is believed that blood stasis is the core pathogenesis of atherosclerosis and is involved in the whole process of atherosclerosis. The theories of ''blood vessel''， ''meridians''， ''visceral manifestation''， and ''organs-meridians'' in TCM are helpful to comprehensively understand the complexity of panvascular diseases. Moreover， those theories can provide systematic treatment strategies. The TCM syndromes of panvascular diseases evolve from ''phlegm， stasis， stagnation， and deficiency''. Panvascular arteriosclerosis is related to the syndrome of ''stasis and phlegm''， and the treatment mainly promotes blood circulation and removes phlegm. There are different specific drugs and mechanisms of action for coronary atherosclerosis， cerebral atherosclerosis， and renal artery atherosclerotic stenosis. Panvascular venous lesions are related to the syndrome of ''deficiency and stasis'' in TCM， and the TCM treatment mainly invigorates Qi and promotes blood circulation， which can inhibit venous thrombosis， improve venous ulcers， and resist venous endothelial damage. Panvascular microcirculatory lesions are inseparable from the ''stagnation and stasis'' in TCM， and the treatment mainly promotes Qi and dredges collaterals， which has a good effect on coronary microvascular lesions， diabetic microvascular lesions， pulmonary microvascular lesions， and pancreatic microvascular lesions. Panvascular lymphatic lesions are related to the syndrome of ''water and stasis'' in TCM. The treatment method focuses on promoting blood circulation and water excretion， which can promote lymphangiogenesis and enhance lymphatic reflux. In addition， the combination of TCM and modern technology， especially the application of artificial intelligence， can improve the efficiency of early identification and personalized treatment， resulting in early screening and comprehensive management of panvascular diseases. Therefore， TCM will play a vital role in the prevention and treatment of panvascular diseases.

Panvascular disease， with vascular diseases as the common pathological feature， is mainly manifested as atherosclerosis. Panvascular disease mainly affects the important organs of the heart， brain， kidney， and limbs. It is one of the leading causes of death for Chinese residents at present. Previously， due to the narrow branches of disciplines， too much attention was paid to local lesions， resulting in the neglect of panvascular disease as a systemic one. The fact that panvascular disease has overall pathology and comprehensive and individualized treatment strategies， makes the disease highly compatible with the principles of holism concept and syndrome differentiation and treatment in traditional Chinese medicine （TCM）. It is believed that blood stasis is the core pathogenesis of atherosclerosis and is involved in the whole process of atherosclerosis. The theories of ''blood vessel''， ''meridians''， ''visceral manifestation''， and ''organs-meridians'' in TCM are helpful to comprehensively understand the complexity of panvascular diseases. Moreover， those theories can provide systematic treatment strategies. The TCM syndromes of panvascular diseases evolve from ''phlegm， stasis， stagnation， and deficiency''. Panvascular arteriosclerosis is related to the syndrome of ''stasis and phlegm''， and the treatment mainly promotes blood circulation and removes phlegm. There are different specific drugs and mechanisms of action for coronary atherosclerosis， cerebral atherosclerosis， and renal artery atherosclerotic stenosis. Panvascular venous lesions are related to the syndrome of ''deficiency and stasis'' in TCM， and the TCM treatment mainly invigorates Qi and promotes blood circulation， which can inhibit venous thrombosis， improve venous ulcers， and resist venous endothelial damage. Panvascular microcirculatory lesions are inseparable from the ''stagnation and stasis'' in TCM， and the treatment mainly promotes Qi and dredges collaterals， which has a good effect on coronary microvascular lesions， diabetic microvascular lesions， pulmonary microvascular lesions， and pancreatic microvascular lesions. Panvascular lymphatic lesions are related to the syndrome of ''water and stasis'' in TCM. The treatment method focuses on promoting blood circulation and water excretion， which can promote lymphangiogenesis and enhance lymphatic reflux. In addition， the combination of TCM and modern technology， especially the application of artificial intelligence， can improve the efficiency of early identification and personalized treatment， resulting in early screening and comprehensive management of panvascular diseases. Therefore， TCM will play a vital role in the prevention and treatment of panvascular diseases.

Amblyopia is a common visual development disorder and is the main cause of monocular vision impairment in children and adults. Photobiomodulation(PBM), a non-invasive treatment method, has gradually gained attention in the field of ophthalmology. This paper begins with the macroscopic manifestation of light on the animal model of amblyopia. Additionally, it discusses the pathological changes of the amblyopic retina and the human eye's central nervous system, as well as the influence and mechanism of PBM on the visual perception and processing system and its chemical effect on the visual system through dopamine and melatonin. It examines its mechanism of action, current clinical application status, and future development direction in order to provide new ideas and theoretical foundation for amblyopia treatment.

ObjectiveTo observe the clinical effectiveness and safety of Xiaozhong Zhitong Mixture （消肿止痛合剂） combined with antibiotic bone cement in the treatment of diabetic foot ulcer （DFU） with damp-heat obstructing syndrome. MethodsA total of 72 DFU patients with damp-heat obstructing syndrome were randomly assigned to treatment group （36 cases） and the control group （36 cases）. Both groups received standard treatment and topical antibiotic bone cement for ulcer wounds， while the treatment group received oral Xiaozhong Zhitong Mixture （50 ml per time， three times daily） in additionally. Both groups underwent daily wound dressing changes for 21 consecutive days. Ulcer healing rate， serum levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1 beta （IL-1β）， malondialdehyde （MDA）， superoxide dismutase （SOD）， C-reactive protein （CRP）， and white blood cell （WBC） count were observed before and after treatment， and visual analog scale （VAS） scores for wound pain， traditional Chinese medicine （TCM） syndrome scores， and the DFU Healing Scale （DMIST scale） were also compared. Liver and kidney function were evaluated before and after treatment， and adverse events such as allergic reactions， worsening ulcer pain were recorded. ResultsTotally 35 patients in the treatment group and 33 in the control group were included in the final analysis. The ulcer healing rate in the treatment group was （87.93±9.34）%， significantly higher than （81.82±12.02）% in the control group （P = 0.035）. Compared to pre-treatment levels， both groups showed significant reductions in serum CRP， WBC， MDA， IL-1β， and TNF-α levels， with an increase in SOD level （P＜0.05）. TCM syndrome scores， VAS， and DMIST scores also significantly decreased in both groups （P＜0.05）， with greater improvements in the treatment group （P＜0.05）. No significant adverse reactions were observed in either group during treatment. ConclusionXiaozhong Zhitong Mixture combined with antibiotic bone cement has significant advantages in promoting DFU healing， reducing inflammatory response， and alleviating oxidative stress in DFU patients with damp-heat obstructing syndrome， with good safety for DFU patients with damp-heat obstructing syndrome.

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: 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: 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: 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.