Objective: To examine the prevalence of e-cigarette use and associated factors among primary and secondary school students in Pudong New Area, Shanghai, and to explore the pathways linking harm perception of e-cigarettes, interpersonal social influence, and attitudes toward e-cigarette use with experimentation behavior, in order to provide scientific basis for further optimizing the prevention and control strategies of e-cigarette among adolescents. Methods: From September to October 2025, a multi stage cluster random sampling method was used to select 5 144 primary and secondary school students aged 8-19 years from 47 primary and secondary schools in Pudong New Area, Shanghai, to conduct an anonymous questionnaire survey. The questionnaire collected information on e-cigarette use, harm perception of e-cigarette, interpersonal social influence, and attitudes toward e-cigarette use. The Chi-square test was applied to analyze the differences in cigarette and e-cigarette use behavior among primary and secondary school students with different demographic characteristics. Structural equation modeling (SEM) was constructed using Mplus 8.3 software, and the bootstrap method was utilized to test the mediating effects. Results: The reported rates of cigarette experimentation, e-cigarette experimentation, and current e-cigarette use among primary and secondary school students were 1.85%, 2.10%, and 0.70%, respectively. Higher reporting rates of e-cigarette experimentation were observed among boys (2.82%), secondary school students aged 16-19 (3.39%), senior high school students (3.05%), and those with weekly pocket money >100 yuan ( 6.11% ) ( χ 2=11.67, 8.61, 8.00, 54.18, all P <0.05). Structural pathway analysis results demonstrated that e-cigarette harm perception positively predicted interpersonal social influence ( β =0.61) and e-cigarette use attitude ( β = 0.53 ), and interpersonal social influence ( β =0.65) and e-cigarette use attitude ( β =0.25) further promoted e-cigarette experimentation behavior among primary and secondary school students (all P <0.01), with interpersonal social influence playing a major mediating role (indirect effect=0.40). Conclusions E-cigarette experimentation behavior among primary and secondary school students in Pudong New Area is jointly influenced by multiple psychosocial factors. Intervention strategies should strengthen interpersonal social factors such as family and peers on the basis of health education, thereby constructing a comprehensive prevention and control strategy for e-cigarette use among primary and secondary school students.

Objective To analyze the infection status and epidemic trends of soil-borne nematode infections in Yubei, Chongqing City, in 2010,2021, and 2022. Methods The local populations from four survey sites of four towns in 2010 and five sites of five towns in 2021 and 2022 were surveyed regarding their basic information using a unified form. Fecal samples of the participants were collected and tested for soil-borne nematode infections using the modified Kato-Katz thick smear method. Results In 2010, 2 049 participants were surveyed, followed by 1 000 participants in 2021 and 2022. The overall prevalence of parasitic infections declined significantly from 3.86% to 0.20%. In 2010, soil-transmitted nematode included hookworms(3.81%) and roundworms(0.29%). In 2021, the infection rates of roundworms and hookworms were 1.70% and 0.10% respectively. Notably, only Ascaris was identified in 2022(0.20%). The≥60 age group consistently exhibited the highest infection rates across all surveys, followed by the 40-59 age group. The infection rates of males in the three surveys were 3.31%,1.92%, and 0.20% respectively, and those of females were 4.37%, 1.46%, and 0.20% respectively. There was no statistically significant difference in the infection rates between males and females. Educational attainment was inversely associated with infection; in 2010, the highest prevalence was observed among those with primary education or below, whereas in 2021, illiterate or semi-literate individuals showed the highest susceptibility. The occupational distribution of infections in 2010 indicated that retirees (8.33%), farmers(4.86%), and homemakers or unemployed individuals(3.45%)were the most affected. However, in 2021 and 2022, farmers emerged as the predominant occupational group with soil-transmitted nematode infections. Conclusions The infection rate of soil-borne nematodes showed a decreasing trend in Yubei, and the infection species changed from hookworms in 2010 to Ascaris in 2022. Farmers, the elderly, and people with low education levels should continue to be the focus of preventive and control efforts.

Serine protease inhibitor Kazal-type (SPINK) is a skin keratinizing protease inhibitor, which was initially found in animal serum and is widely present in plants, animals, bacteria, and viruses, and they act as key regulators of skin keratinizing proteases and are involved in the regulation of keratinocyte proliferation and inflammation, primarily through the inhibition of deregulated tissue kinin-releasing enzymes (KLKs) in skin response. This process plays a crucial role in alleviating various skin problems caused by hyperkeratinization and inflammation, and can greatly improve the overall condition of the skin. Specifically, the different members of the SPINK family, such as SPINK5, SPINK6, SPINK7, and SPINK9, each have unique biological functions and mechanisms of action. The existence of these members demonstrates the diversity and complexity of skin health and disease. First, SPINK5 mutations are closely associated with the development of various skin diseases, such as Netherton’s syndrome and atopic dermatitis, and SPINK5 is able to inhibit the activation of the STAT3 signaling pathway, thereby effectively preventing the metastasis of melanoma cells, which is important in preventing the invasion and migration of malignant tumors. Secondly, SPINK6 is mainly distributed in the epidermis and contains lysine and glutamate residues, which can act as a substrate for epidermal transglutaminase to maintain the normal structure and function of the skin. In addition, SPINK6 can activate the intracellular ERK1/2 and AKT signaling pathways through the activation of epidermal growth factor receptor and protease receptor-2 (EphA2), which can promote the migration of melanoma cells, and SPINK6 further deepens its role in stimulating the migration of malignant tumor cells by inhibiting the activation of STAT3 signaling pathway. This process further deepens its potential impact in stimulating tumor invasive migration. Furthermore, SPINK7 plays a role in the pathology of some inflammatory skin diseases, and is likely to be an important factor contributing to the exacerbation of skin diseases by promoting aberrant proliferation of keratinocytes and local inflammatory responses. Finally, SPINK9 can induce cell migration and promote skin wound healing by activating purinergic receptor 2 (P2R) to induce phosphorylation of epidermal growth factor and further activating the downstream ERK1/2 signaling pathway. In addition, SPINK9 also plays an antimicrobial role, preventing the interference of some pathogenic microorganisms. Taken as a whole, some members of the SPINK family may be potential targets for the treatment of dermatological disorders by regulating multiple biological processes such as keratinization metabolism and immuno-inflammatory processes in the skin. The development of drugs such as small molecule inhibitors and monoclonal antibodies has great potential for the treatment of dermatologic diseases, and future research on SPINK will help to gain a deeper understanding of the physiopathologic processes of the skin. Through its functions and regulatory mechanisms, the formation and maintenance of the skin barrier and the occurrence and development of inflammatory responses can be better understood, which will provide novel ideas and methods for the prevention and treatment of skin diseases.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To explore the value of deep learning imageomics based on MRI sagittal T2WI images in predicting placenta accreta spectrum in high-risk pregnant women.Methods:The complete data of 265 pregnant women who underwent MRI due to suspected placenta implantation in The Second Hospital&Clinical Medical School,Lanzhou University and Zhangye People's Hospital Affiliated to Hexi University from January 2019 to De-cember 2023 were analyzed retrospectively.The patients were randomly divided into training group(n=172)and validation group(n=93)at 7∶3.Multivariate Logistic regression analysis was used to screen the independent risk factors among clinical and imaging characteristics.Radiomics features were extracted based on sagittal T2WI images.Using the DenseNet-121 model as the basic model for deep learning feature extraction,traditional clinical model,radiomic model and deep learning model were constructed to predict PAS.The diagnostic efficiency of each model was evaluated by the area under the receiver operating characteristic(ROC)curve(AUC).Finally,the model with the highest performance was determined as the optimal model.Results:In both the training and validation groups,the PAS group and normal group exhibited statistically significant differences(P<0.05)in terms of the number of cesarean section≥2,history of placenta previa,and placental thickness>40 mm.Multivariate Logistic regression analysis revealed that cesarean section history,placental thickness and placenta previa were independent risk factors for predicting PAS.Among all the models constructed,the diagnostic performance of the combination model of deep learning combined with clinic was higher than the other three models.The AUC in training group and verification group were 0.96(95%CI 0.93-0.98)and 0.91(95%CI 0.87-0.95)respectively.Conclusions:The combined clinical model of deep learning based on MRI may have better performance in the di-agnosis of PAS than clinical or traditional radiomic models.

Objective:To explore the value of deep learning imageomics based on MRI sagittal T2WI images in predicting placenta accreta spectrum in high-risk pregnant women.Methods:The complete data of 265 pregnant women who underwent MRI due to suspected placenta implantation in The Second Hospital&Clinical Medical School,Lanzhou University and Zhangye People's Hospital Affiliated to Hexi University from January 2019 to De-cember 2023 were analyzed retrospectively.The patients were randomly divided into training group(n=172)and validation group(n=93)at 7∶3.Multivariate Logistic regression analysis was used to screen the independent risk factors among clinical and imaging characteristics.Radiomics features were extracted based on sagittal T2WI images.Using the DenseNet-121 model as the basic model for deep learning feature extraction,traditional clinical model,radiomic model and deep learning model were constructed to predict PAS.The diagnostic efficiency of each model was evaluated by the area under the receiver operating characteristic(ROC)curve(AUC).Finally,the model with the highest performance was determined as the optimal model.Results:In both the training and validation groups,the PAS group and normal group exhibited statistically significant differences(P<0.05)in terms of the number of cesarean section≥2,history of placenta previa,and placental thickness>40 mm.Multivariate Logistic regression analysis revealed that cesarean section history,placental thickness and placenta previa were independent risk factors for predicting PAS.Among all the models constructed,the diagnostic performance of the combination model of deep learning combined with clinic was higher than the other three models.The AUC in training group and verification group were 0.96(95%CI 0.93-0.98)and 0.91(95%CI 0.87-0.95)respectively.Conclusions:The combined clinical model of deep learning based on MRI may have better performance in the di-agnosis of PAS than clinical or traditional radiomic models.

Objective To establish an inductively coupled plasma-mass spectrometry(ICP-MS)method for the analysis of 29 mineral elements in Artemisiae Argyi Folium(AAF),and to develop a model for judging the origin of AAF based on the elemental fingerprint combined with chemometrics.Methods The variance method was used to compare the contents of 29 mineral elements in AAF samples from Qichun of Hubei province,Hebei province and Henan province,respectively.The discriminant models of AAF from different habitats were established by discriminant analysis,PLS-DA and PCA-Logistic regression algorithms.Results The differences of the 22 elements(V,Cr,Ni,As,Se,Rb,Mo,Cd,Sb,Ba,Hg,Pb,K,Li,B,Mg,Al,P,Ca,Fe,Zn,and Ga)in samples from Qichun of Hubei province,Hebei province and Henan province are extremely significant(P＜0.01).The discriminant analysis model showed that eight kinds of element variables(P,Cr,K,Li,Hg,Ba,Mg,and Mo),which have a significant effect on the origin discrimination are gradually introduced into the discriminant model,and the correct rate of back test is 100％.The correct rate of"leave one method"cross-validation is 96.7％.The PLS-DA model showed good prediction ability,and the variable importance projection values of K,P and Mg elements were greater than one,which could be used as the difference marker of AAF from different habitats.The accuracy of the origin discrimination model constructed by PC A-Logistic regression analysis was 100％.The elements with high first principal component loadings were Li,B,Mg,Al,P,K,Ca,V,Cr,Fe,Ni,Zn,As,Rb,Cd,Sb,and Ba.Conclusion This study shows that elemental fingerprint technology combined with chemometric analysis can identify AAF from Qichun of Hubei province,Hebei province,or Henan,providing technical support for the traceability of the origin of Artemisia argyi.

Sarin(GB)is a typical representative of nerve agents with high toxicity,and very low amount can cause death.GB can cause water and atmospheric environment poisoning,so the detection of GB in water and air is of great significance.In this work,a colorimetric sensor array(CSA)based on GB inhibition of cholinesterase activity was constructed to detect GB with high selectivity.A 4×4 colorimetric array was constructed using acetylcholinesterase(AChE),butyryl cholinesterase(BuChE)and the corresponding substrate acetylthiocholine iodide(S-ACh),butyryl thiocholine iodide(S-BCh),acetylcholine chloride(ACh),butyryl choline chloride(BCh)and 2,6-dichloroindophenol ethyl ester(DCIE).The linear curve of the sensor was Y=131.3×lgC+271.6(R2=0.997),where Y was the array response Euclidean distance,C was the concentration of GB(mg/L),the linear range was 0.03?0.32 mg/L,and the detection limit was 27.6 μg/L.The method could effectively distinguish chemical warfare agents(CWA)such as VX,Soman(GD),mustard gas(HD),Louie reagent(L),and had high anti-interference ability,sensitivity and good repeatability.It was successfully applied to the detection of GB in simulated water and simulated air samples,and the sample recovery rate was 97.2% ?100.9%.This method would be potentially applied to the field rapid detection of nerve agents.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.