Objective: To understand the awareness of chikungunya fever knowledge and its related factors among medical college students in Baise City, so as to provide a scientific basis to offer relevant courses and special education. Methods: From July to August 2025, 7 286 enrolled medical students were selected by a sampling method from a medical college in Baise City to participate in the questionnaire survey. The questionnaire covered epidemiological characteristics, clinical symptoms, and prevention/control knowledge of chikungunya fever. Statistical analyses including the Chi quare test and multivariate Logistic regression models were performed. Results: The overall awareness rate of chikungunya fever knowledge among the medical students was 18.89%. Among the knowledge items, the awareness rate of "the high incidence season" was the highest (84.05%), while that of "the infectious period" was the lowest (17.80%). Multivariate Logistic regression analysis showed that medical students with female (a OR= 1.37 , 95%CI =1.20- 1.57 ), the age for over 25 years old (a OR=1.76, 95%CI =1.05-2.93), whose father had a middle school educational level (a OR=1.18, 95%CI =1.05-1.31), and majored in preventive medicine (a OR=1.54, 95%CI =1.10-1.67) had relatively higher awareness rates of chikungunya fever knowledge (all P <0.05). In contrast, students of Zhuang ethnicity (a OR= 0.87 , 95%CI =0.76-0.98) and majoring in nursing (a OR=0.74, 95%CI =0.61-0.91) or pharmacy (a OR=0.70, 95%CI =0.52-0.95) had relatively lower awareness rates (all P <0.05). Conclusions The awareness rate of chikungunya fever related knowledge among medical college students in Baise City is relatively low. Schools should take targeted publicity measures to improve medical students awareness.

Background Meteorological factors are among the key extrinsic triggers for the onset and exacerbation of cardiovascular and cerebrovascular diseases (CVD). Against the backdrop of sustained global warming, elucidating the impact of ambient temperature and atmospheric pressure on CVD, especially on pre-hospital CVD emergent events, has become imperative for evidence-based prevention and emergency preparedness. Objective To quantify the temporal trends of daily mean temperature and atmospheric pressure and their associations with pre-hospital CVD emergent events in Yantai, and to explore effect modification by demographic subgroups and geographic areas, thereby providing an empirical basis for the rational allocation of emergency medical resources. Methods Pre-hospital CVD emergency data from January 1, 2016 to December 31, 2022 were selected from the Yantai 120 Emergency Medical Command System. Synchronous meteorological factors and environmental pollutant data were obtained from the websites of the National Oceanic and Atmospheric Administration and the National Centers for Environmental Information of the United States. Time-series analysis combined with distributed lag non-linear model was used to analyze the association between daily temperature, atmospheric pressure, and pre-hospital CVD emergencies. Average annual percentage changes (AAPC) were calculated using Joinpoint (version 5.2.0.0) to reflect temporal trends. Spearman correlation analysis was employed to screen variables with low collinearity for inclusion in the multi-pollutant adjusted models. Results From 2016 to 2022, a total of 268275 pre-hospital CVD emergency cases were recorded in Yantai City, showing an increasing annual trend (AAPC=7.16%, P=0.002). Emergency volume, daily temperature, atmospheric pressure, and PM₁₀ displayed marked seasonal patterns, with summer bottoms and winter peaks for emergencies, daily temperature higher in summer and lower in winter, and the inverse for atmospheric pressure. Correlation analysis showed that both daily temperature and atmospheric pressure were positively correlated with O₃, with correlation coefficients of 0.745 and 0.723, respectively; negatively correlated with PM_2.5, with correlation coefficients of −0.246 and −0.235, respectively; and negatively correlated with PM₁₀, with correlation coefficients of −0.241 and −0.229, respectively (P < 0.05). A U-shaped correlation was revealed between daily temperature and pre-hospital CVD emergency risk. Using 24 °C as the reference, risk was elevated at both low and high daily temperatures, with the strongest cold effect at –11 ℃ (RR=1.53, 95%CI: 1.35, 1.73) and strongest heat effect at 30 ℃ (RR=1.06, 95%CI: 1.01, 1.11); cold effects were prolonged (lag0–14d) compared to heat effects. Atmospheric pressure exhibited a positive association with emergencies; using a median of 1012.2 hPa as the reference, the maximum risk occurred at 1037 hPa (RR=1.25, 95%CI: 1.03, 1.52), with a 0–5 d lag. In the subgroup analysis, the impact of low temperature was more pronounced among females (RR_{−9 ℃}=1.66), people over 60 years (RR_{−9 ℃}=1.91), and rural residents (RR_{−9 ℃}=1.76). High atmospheric pressure significantly increased the risk of pre-hospital CVD emergency among females (RR_{1035 hPa}=1.54), people under 18 years (RR_{1035 hPa}=3.62), and rural residents (RR_{1035 hPa}=1.46). The sensitivity analysis results indicated that, after excluding the impact of pandemic and the degrees of freedom for time variations, the effects of daily average temperature and atmospheric pressure on pre-hospital CVD emergency volume were consistent with the primary analysis findings. Conclusion Both non-optimal temperature and atmospheric pressure significantly increase the pre-hospital CVD emergency volume; therefore, emergency medical services should develop targeted response protocols, optimize resource allocation, and intensify public health education to mitigate the pre-hospital burden attributable to temperature and pressure fluctuations.

AIM To sequence the chloroplast genome of Artemisia vestita Wall.ex Bess.METHODS Based on ethnobotanical surveys,sample collection and original plant identification were carried out.The chloroplast genome was sequenced using the Illumina platform,followed by assembly and annotation.A comprehensive comparative analysis was conducted with six Artemisia species.The maximum likelihood(ML)phylogenetic tree was constructed based on the chloroplast genome sequences of A.vestita and 32 other Asteraceae species,with Leptocodon hirsutus D.Y.Hong of Campanulaceae as outgroup.RESULTS The chloroplast genome of A.vestita was 151 204 bp in length,including a small single-copy region of 18 331 bp,a large single-copy region of 82 949 bp,and inverted repeat regions of 24 962 bp,with a total GC content of 37.45％.134 genes were annotated,including 89 protein-coding genes,8 ribosomal RNA genes,and 37 transfer RNA genes.A total of 67 SSRs and 44 LSRs were detected in the chloroplast genome.Comparative analysis with closely related species of Artemisia revealed 3 highly variable genes(clpP,rpl36,ycf1)and 6 highly variable intergenic regions(trnK-UUU-matK,rps18-rpl20,rpl36-infA,rpl14-rpl16,rpl16-rpl3 and trnL-UAG-ccsA),which could serve as candidate DNA barcodes for Artemisia identification.Phylogenetic analysis showed that Artemisia formed a highly supportive monophyletic group,with A.vestita and A.gmelinii Web.ex Stechm.being closely related.CONCLUSION This study may provide fundamental data for phylogenetic analysis of Artemisia,taxonomic identification and DNA barcoding construction of Tibetan herb.

Objective To analyze the clinical trial projects of TCM treatment for psoriasis registered at the China Clinical Trial Registry(ChiCTR);To provide references for clinical research in this field.Methods Clinical trial studies on the TCM treatment for psoriasis were retrieved from the ChiCTR database from the establishment of the database to June 17,2024.Excel 2019 and GraphPad Prism 9.0 were used to organize and analyze the general characteristics included in registered clinical trials.Results Totally 65 TCM-related trials were included,of which 58(89.23%)were prospectively registered,and 55(84.62%)had undergone ethical review.The majority of trials were registered in Guangdong,Beijing and Shanghai,collectively accounting for 78.46%(n=51)of the total.The leading institution,Guangdong Provincial Hospital of Chinese Medicine,contributed 19 trials.Funding was predominantly sourced from governmental bodies,with 72.31%(n=47)supported by national or regional grants.Among the registered studies,interventional trials predominated(n=56),including 46 randomised parallel-controlled designs.Blinding was implemented in 23 trials,and biospecimen collection was reported in 48 studies.40 trials were single-centre studies,and a cumulative sample size was 17 695 participants.Common interventions included oral administration of classical TCM formulations or hospital-prepared remedies,alongside topical applications such as medicated baths and ointments.The primary outcome measure in most trials was the Psoriasis Area and Severity Index.Conclusion While the number of registered clinical trials on TCM for psoriasis in China demonstrates a fluctuating yet upward trend,the overall volume remains modest.Disparities in regional distribution,methodological variability,and incomplete trial registration details highlight areas for improvement.

Objective To establish a reliable prediction model for radiation pneumonitis(RP)based on multi-regional radiomics analysis of localizable CT images.Methods A retrospective analysis was conducted on 185 patients who received radiotherapy from January 2021 to June 2023 in the Department of Radiotherapy,Xuzhou Cancer Hospital.Patients were classified as having RP or not based on imaging combined with clinical diagnosis.Three regions of interest(ROI)were defined in the localizable CT images:Lung,Lung-PTV and PTV,and their radiomics features were extracted.After feature screening using methods such as Mann-Whitney Utest,recursive feature elimination,and Lasso,a prediction model was established using support vector machine classification algorithm.The model performance was validated using 6 evaluation metrics:the area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,positive predictive value,and negative predictive value.Results The prediction model consisted of 7 radiomics features.The clinical model of target-to-lung ratio,PTV model,Lung model,and Lung-PTV model achieved AUC values of 0.535,0.801,0.672,and 0.706 in the test set,respectively.The AUC value and accuracy of PTV model reached 0.843 and 0.775 in the training set,while 0.801 and 0.750 in the test set.PTV model was superior to Lung model,Lung-PTV model,and clinical model in predictive performance.The AUC values of the combined PTV+(Lung-PTV)model in the training and test sets were 0.867 and 0.806,respectively,higher than those of PTV model and Lung-PTV model.Conclusion The predictive ability of the prediction models constructed from radiomics features in different ROI for symptomatic RP varies.The radiomics prediction model using PTV as ROI exhibits superior predictive performance,and the combined multi-regional radiomics model can further improve the predictive ability for RP.

OBJECTIVE To establish a method for determining the content of multiple components in Astragalus membranaceus,compare the content differences of Astragalus membranaceus in different regions and with different extraction methods in Gansu,and e-valuate the quality of Astragalus membranaceus from different origins of Gansu.METHODS Astragalus membranaceus samples from 40 origins in Gansu were collected on site.The content of Astragaloside Ⅳ,Calycosin-7-O-β-D-glucopyranoside,Ononin,Form-ononetin and Calycosin was determined by HPLC external standard method,and the quality differences of Astragalus membranaceus from 40 origins of Gansu were analyzed by combining bar graphs and line graphs.RESULTS Weiyuan County and Min County of Dingxi City showed high Astragaloside Ⅳ content in both extraction methods,indicating that the Astragalus membranaceus in these two regions has advantages in Astragaloside Ⅳ as an active component;for Calycosin-7-O-β-D-glucopyranoside and Ononin,although the performance of different regions in different extraction methods was different,in general,Weiyuan County and Min County of Dingxi City and Longnan City showed high content in both extraction methods.Water extraction method was more conducive to the extraction of Calycosin-7-O-β-D-glucopyranoside and Ononin,while alcohol extraction method was more conducive to the extraction of Astragalo-side Ⅳ,Calycosin and Ononin.CONCLUSION The content of chemical components of Astragalus membranaceus from different ori-gins is quite different,and the choice of extraction method has a significant effect on the content of active components,while the change trend of different compounds in the two extraction methods is basically the same.The established HPLC multi-component content de-termination method of Astragalus membranaceus is stable and reliable,which can provide a scientific basis for the quality control and comprehensive evaluation of Astragalus membranaceus medicinal materials.

Objective To identify high-risk factors for healthcare-associated infection(HAI)in patients in inten-sive care units(ICUs),and develop a quick response(QR)code-based APP prediction tool.Methods Information of inpatients in general ICUs of three hospitals in Guizhou Province from January to December 2024 were collected.Risk factors were analyzed with a logistic regression model.QR code-based APP was constructed and validated.Results A total of 1 782 patients in general ICUs of three hospitals in Guizhou Province in 2024 were included in the analysis,out of which 410 were HAI cases,and the incidence of HAI was 23.01％.Multivariate logistic regre-ssion analysis results of HAI in ICU inpatients showed that regional gross domestic product(GDP)≥58 685 Yuan,performing pathogen culture during this hospitalization,history of diabetes mellitus,history of cancer,length of hospital stay ≥7 days before infection,and duration of persistent fever＞5 days before infection were independent risk factors for HAI in ICU patients(all P＜0.05).The discrimination of the model(area under the receiver operating characteristic curve[AUC]of 0.841),calibration(Brier score of 0.129),and clinical effectiveness(net benefit of 11.4％when the risk threshold was 5％-74％)all performed well.Conclusion The QR code-based APP prediction tool is of great significance for scientific research transformation and precise HAI control.

OBJECTIVE To study the characteristics,micro-traits and microscopic characteristics of Peucedani radix and seven kinds of its common varieties and summarize the key identification characteristics so as to provide a reference for the effective identifica-tion of Peucedani radix and its common varieties.METHODS The key identification features and high-definition images of Peucedani radix and its common varieties were obtained by using the identification methods of traits,microtraits and microscopy,combined with the techniques of depth-of-field extended imaging and image stitching,and some of the features were digitally extracted and statistical-ly analyzed by SPSS26.0 software.RESULTS The high-definition color image data of Peucedani radix and its common varieties were obtained.Its specific identification features were:root head length and annular sparseness,skin pore shape and area,root texture,and fracture surface oil spot density,etc.under the property identification;the diameter and number of oil chambers,the number of cathe-ters,the presence or absence of bast fibers and wood fibers,etc.under the microscopic identification.The results of statistical analysis showed that there were significant differences in skin pore area,oil chamber diameter and density,and conduit density among different varieties of Peucedani radix(P<0.01).CONCLUSION Micro-traits and microidentification methods can be comprehensively ap-plied to distinguish Peucedani radix and its common varieties.In particular,the microscopic features of polarized light holographic col-or images in cross section have significant distinguishing significance,and some of the features are digitally extracted and statistically analyzed,which makes up for the shortcomings of subjective factors in the traditional empirical identification research,and provides a reference for the circulation,testing,clinical medication,and standard drafting of Peucedani radix.

Objective To identify high-risk factors for healthcare-associated infection(HAI)in patients in inten-sive care units(ICUs),and develop a quick response(QR)code-based APP prediction tool.Methods Information of inpatients in general ICUs of three hospitals in Guizhou Province from January to December 2024 were collected.Risk factors were analyzed with a logistic regression model.QR code-based APP was constructed and validated.Results A total of 1 782 patients in general ICUs of three hospitals in Guizhou Province in 2024 were included in the analysis,out of which 410 were HAI cases,and the incidence of HAI was 23.01％.Multivariate logistic regre-ssion analysis results of HAI in ICU inpatients showed that regional gross domestic product(GDP)≥58 685 Yuan,performing pathogen culture during this hospitalization,history of diabetes mellitus,history of cancer,length of hospital stay ≥7 days before infection,and duration of persistent fever＞5 days before infection were independent risk factors for HAI in ICU patients(all P＜0.05).The discrimination of the model(area under the receiver operating characteristic curve[AUC]of 0.841),calibration(Brier score of 0.129),and clinical effectiveness(net benefit of 11.4％when the risk threshold was 5％-74％)all performed well.Conclusion The QR code-based APP prediction tool is of great significance for scientific research transformation and precise HAI control.

Objective To investigate the relationship between serum cystatin C(CysC)level and the risk of Parkinson's disease(PD)in middle-aged and elderly people in China.Methods Based on the baseline survey data from the China Health and Retirement Longitudinal Study(CHARLS)in 2011,participants who were not diagnosed with PD at the time of the baseline survey were recruited.The onset of PD was tracked and followed up until 2020,and the participants were divided into PD group and non-PD group according to whether they were newly diagnosed with PD in 2020.Multivariable Logistic regression analysis was performed to assess the association between serum CysC level and the risk of PD.Subgroup and interaction analyses were performed to assess effect modifications by age,gender and depression.Additionally,restricted cubic spline(RCS)was used to explore the linear or non-linear relationship between serum CysC level and the risk of PD in different subgroups.Results We included a total of 3 339 subjects in this study,who consisted of 1 495 males(44.77％)and 1 844 females(55.23％).While baseline participants were followed until 2020,32 subjects had a new PD,and the incidence of PD was 0.96％.The median age of PD group was 63.00 years.Multivariable Logistic regression analysis found that CysC was an independent risk factor for the risk of PD,and CysC was positive significantly associated with the risk of PD(OR=2.34,95％ CI:1.14-4.82,P=0.021).Subgroup analysis showed that CysC was positively associated with PD in females(OR=2.70,95％ CI:1.30-5.58,P=0.007)and subjects aged 60 years or older(OR=5.29,95％ CI:1.69-16.53,P=0.004).RCS model indicated a linear relationship between serum CysC level and the risk of PD in females(Ptotal=0.018,Pnon-linear=0.062)and subjects aged 60 years or older(Ptotal=0.024,Pnon-linear=0.379).Conclusion High level of CysC may increase the risk of PD in middle-aged and elderly people,especially in females and those aged 60 years or older.