Objective To investigate the microbial contamination and its influencing factors of indoor air in public places in Xi'an City, to assess the health risk of employees, and to provide a scientific basis for improving the indoor environment of public places. Methods Total bacterial count and total fungal count in indoor air were monitored in hotels/inns, shopping malls/supermarkets, gyms, and waiting rooms in Xi'an from 2023 to 2024. The health risk assessment of employees was evaluated according to the Chinese Population Exposure Parameters Manual (Adult Volume). Results Overall, the standard-exceeding rate of total bacterial count in Xi'an was 3.85%, and the median values of total bacterial count and total fungal count were 350 CFU/m3 and 300 CFU/m3, respectively. The results of the generalized linear model showed that high indoor temperature and PM10 levels were associated with increased indoor bacterial concentrations (β>0, P<0.05), while high daily passenger flow, and high indoor relative humidity and PM₁₀ levels were associated with increased indoor fungal concentrations (β>0, P<0.05). The multivariate logistic regression showed that high levels of indoor bacterial and fungal concentrations were risk factors for respiratory discomfort among employees. The hazard quotient (HQ) values for all types of public places were less than 1, indicating that the health risk of microbial aerosol exposures for employees was relatively low. Conclusion The indoor microbial pollution in public places in Xi'an is relatively mild, but countermeasures still need to be taken to reduce indoor air microbial contamination.

Objective To analyze the spatial distribution patterns and influencing factors of potential suitable habitats for Saussurea medusa under current climatic conditions.Methods The maximum entropy(MaxEnt)model with 125 selected distribution spots data and 9 environmental factors data of Saussurea medusa as input variables for modeling.Quantitative analysis was conducted on the influencing factors of spatial differentiation in the suitable habitat area of Saussurea medusa using geographic detector factor detection and interaction detection tools.Based on land use type data and national nature reserve distribution data,the distribution range of each land use type in the suitable habitat areas for Saussurea medusa,as well as the distribution pattern of high,medium and low suitable habitat areas in national nature reserves were refined.Results Key environmental determinants included elevation,precipitation in the warmest quarter,and solar radiation in April and May.The total potential suitable habitat area spanned 1.33×106 km2,accounting for 13.89%of China's mainland.Grasslands(67.70%),forests(19.19%),barren land(5.90%),snow-covered areas(2.59%)and croplands(2.08%)dominated the suitable habitats,with 34.98%overlapping national nature reserves.Conclusion The results of this study can provide basis for understanding the ecological needs and resource conservation of Saussurea medusa.

Objective To investigate the geographical distribution patterns and influencing factors of suitable habitats for the desert medicinal plant Cynomorium songaricum Rupr under current climatic conditions;To provide a basis for its resource conservation and sustainable utilization.Methods The MaxEnt model was used to analyze potential suitable habitats for Cynomorium songaricum Rupr.Geographical Detector model was used to identify key environmental factors affecting habitat suitability.Surface cover data were overlaid to assess the distribution of sandy and Gobi regions within suitable habitats,enabling a quantitative evaluation of actual potential suitable areas.Results Model predictions indicated a total suitable habitat area of approximately 2.98×106 km2,representing 30.99%of China's mainland area.Highly suitable habitats are concentrated in desert and Gobi regions of Gansu,Xinjiang,Inner Mongolia,Qinghai and Ningxia.Among climatic factors,precipitation of the coldest quarter(bio19),solar radiation in August(srad8),and mean temperature of the coldest quarter(bio11)significantly influence Cynomorium songaricum Rupr distribution.The interaction between temperature and solar radiation intensity exhibited the highest explanatory power for habitat distribution patterns(q=0.82).Overlay analysis with surface cover data estimated the actual potential suitable area at approximately 9.70×105 km2,with sandy regions comprising 5.73×105 km2 and Gobi regions 3.98×105 km2.Conclusion By integrating multi-source data and modeling approaches,this study delineates the potential suitable habitats for Cynomorium songaricum Rupr across China and evaluates the spatial distribution characteristics and influencing factors of suitable habitats in Cynomorium songaricum Rupr.These findings offer a foundation for conserving wild Cynomorium songaricum Rupr resources,optimizing ecological planting regions,and promoting sustainable industry development.

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 investigate the role and related mechanisms of IgD on the viability , proliferation , apoptosis , and other functions of Molm_13 cells. Methods: Peripheral blood serum was collected from AML patients and healthy controls. The sIgD levels were quantified by ELISA. For in vitro studies , Molm_13 cells were treated with varying concentrations of IgD. Cell viability and proliferation were assessed via CCK_8 assays , CFSE staining , and colony formation assays. Apoptosis rates were determined using an Annexin V/PI apoptosis detection kit. Preliminary exploration of the mechanisms related to IgD_induced proliferation of Molm_13 were analyzed through differential gene analysis. Results: Compared with healthy controls , the levels of sIgD in AML patients were significantly el_ evated (P < 0. 001 ) . IgD treatment dose_dependently increased Molm_13 cell viability and proliferation ( P < 0. 05) , inhibited apoptosis rates (P < 0. 001) . Conclusion IgD promotes the viability and proliferation of Molm_ 13 cells , and reduces apoptosis.

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Objective To evaluate the application of shear wave elastography(SWE)in the diagnosis of peripheral neuropathy in patients with diabetes.Methods Totally 85 patients with type 2 diabetes(T2DM)were selected from the Chengdu Office Hospital of People's Government of Tibetan Autonomous Region,including 46 patients with peripheral neuropathy(DPN)and 39 patients without peripheral neuropathy(NDPN).Compared for clinical data(gender,age,disease duration),cross-sectional area of the median nerve measured by high-frequency ultra-sound(CSA)and shear wave elastography(SWE)parameters(mean Young's modulus value,Emean)and shear wave velocity(SWV)between two groups of patients.Multifactor Logistic regression analysis was carried out on the indicators between the above groups to screen independent predictors in the diagnosis of peripheral neuropathy in diabetes patients,and a combined model was constructed.The area under the operating characteristic curve(AUC),sensitivity and specificity of the subjects were used to evaluate the diagnostic efficacy of the single model and com?bined model of the quantitative parameters(CSA,Emean,SWV)measured by clinical data,high?frequency ultra?sound and SWE in the diagnosis of peripheral neuropathy in diabetes patients.Results Age,course of disease,Emean,SWV and CSA were statistically significant in the diagnosis of peripheral neuropathy in diabetes patients(all P<0.05).AUC was 0.658,0.754,0.839,0.822 and 0.736,respectively.The combination model based on disease course,CSA and SWV showed the highest diagnostic efficiency,with AUC,sensitivity,and specificity of 0.887(0.800-0.946),80.43％,and 84.62％,respectively.Conclusions The combined model based on the course of disease,CSA and SWV have a high diagnostic efficiency in peripheral neuropathy of diabetes patients,and has good clinical application value.

Inhalation therapy,as a drug delivery method directly targeting the respiratory tract and lungs,has been widely used in the treatment of respiratory diseases due to its characteristics of efficient drug delivery,rapid onset,and low systemic side effects.However,traditional inhalation particles still have some limitations in drug stability,release control,and pulmonary delivery efficiency.In recent years,with the continuous development of biochemical materials,the performance of new inhaled particles has been significantly improved,which can provide better drug-loading capacity,more precise release control,and more efficient pulmonary delivery,showing great potential in improving drug efficacy and reducing side effects.This review comprehensively summarizes the classification,preparation techniques,and applications of inhalable particles.It further explores their prospects in precise therapy,personalized medication,and the next-generation drug delivery systems,aiming to promote research and technological innovation in inhalation therapy,ultimately advancing therapeutic solutions for respiratory diseases.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

Objective:To analyze the etiological composition and clinical characteristics of infant hypertension，and provide reference for its diagnosis and treatment.Methods:This is a retrospective case-control study.Retrospective investigation and analysis were conducted on the clinical data of infants discharged from Beijing Children's Hospital Affiliated to Capital Medical University with a diagnosis of "hypertension" from June 1，2016 to September 30，2021，including clinical manifestations，auxiliary examinations，treatment plans，and prognosis.Results:A total of 74 eligible children were collected，including 42 male infants（56.8%）and 32 female infants（43.2%）.A total of 67 cases（90.5%）had clear secondary factors，including 35 cases of kidney disease（47.3%），12 cases of connective tissue disease（16.2%），and 9 cases of hematological tumor disease（12.2%）.At the beginning of the disease，cardiac ultrasound showed that 54 cases（73.0%）had ventricular wall thickening，including mild thickening in 31 cases（57.4%），moderate thickening in 11 cases（20.3%），and severe thickening in 12 cases（22.2%）.After grouping by etiology，the incidence of proteinuria and severe hypertension in the renal hypertension group，as well as those receiving multiple antihypertensive drugs，was significantly higher than that in the non-renal hypertension group（ χ 2=28.493， P<0.001； χ 2=17.283， P<0.001； χ 2=17.358， P<0.001）；Renal disease was risk factor for severe hypertension in infants according to univariate and multivariate logistic regression analysis respectively（ OR=11.176，95% CI：2.882~43.339， P<0.001； OR=11.669，95% CI：2.921~46.624， P<0.001）.Thirty-one children had follow-up records for 6 months or more，and 13（41.9%）still required antihypertensive treatment，of whom 26（83.9%）were no longer recorded as having elevated blood pressure. Conclusion:Infant hypertension is mainly secondary，with a high proportion of renal factors and predisposition to severe hypertension，which requires multiple antihypertensive drugs for control.Active antihypertensive treatment and removal of secondary factors during the acute phase are helpful for controlling hypertension in infants，but further research is needed on treatment options and long-term prognosis.