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.

Compared with traditional therapies for chronic liver disease （CLD）， Bifidobacterium has the characteristics of multi-target intervention， high biosafety， and good host compatibility and provides new strategies for intervention of CLD progression in terms of microecological regulation. Various studies have shown that Bifidobacterium regulates liver homeostasis and exerts a therapeutic effect on CLD by regulating intestinal flora， maintaining antioxidation， promoting energy consumption， alleviating inflammation， improving glycolipid metabolism， and exerting an antitumor effect. This article systematically reviews the studies on Bifidobacterium in the treatment of CLD in China and globally， explores their different mechanisms， and elaborates on the interaction between related signaling pathways （such as the nuclear factor erythroid 2-related factor 2 signaling pathway and the adenosine monophosphate-activated protein kinase signaling pathway） and the liver， in order to provide a basis for probiotic intervention in liver pathology， as well as new ideas for the comprehensive treatment of CLD.

ObjectiveTo compare the differences in color， odor， coumarin content and microbial community composition of Angelicae Dahuricae Radix（ADR） during different drying processes， and to explore the correlation between changes in microbial community composition and changes in quality indexes of ADR. MethodsThe fresh ADR was processed at three drying temperatures（50， 70， 100 ℃） by drying and steaming cutting， semi-fresh cutting and drying， fresh cutting and drying， and sulfur fumigation methods. The color values of samples were extracted by Adobe Photoshop 2022 software and subjected to principal component analysis（PCA）， electronic nose was used to identify the odor information of medicinal powders and subjected to loadings analysis， PCA， and linear discriminant analysis（LDA）， and high performance liquid chromatography（HPLC） was used to determine the contents of five coumarins（bergapten， oxypeucedanin， imperatorin， phellopterin， isoimperatorin）. The samples for microbial detection were taken from fresh dried samples， 50 ℃（dried and steamed cut， sulfur fumigated） samples， and 100 ℃（dried and steamed cut） samples when the water content was 50% and 14%， respectively. And the changes of microbial community composition during processing were determined by high-throughput sequencing method. The relationship between the changes of microbial community composition and the changes of odor， color and active component content of ADR during drying process was analyzed by Pearson correlation analysis. ResultsThe color quantification results showed that an increase in drying temperature led to the decrease of brightness value（L）， and the increases of red-green value（a） and yellow-blue value（b）， and the change of processing method had no obvious effect on the color of medicinal materials. The results of odor quantification showed that W1S， W2S， W5S， W2W and W1W sensor were sensitive to the odor changes of ADR and could be used to distinguish ADR decoction pieces from different processing methods. The results of HPLC showed that the coumarin content of ADR decreased with the increase of drying temperature and the delay of processing time， the optimal processing method was drying and steaming cutting method， and the optimal temperature was 50 ℃. High-throughput sequencing results showed that the dominant bacteria in ADR during processing were Achromobacter， Agrobacterium， Nocardioides， Mycobacterium and Enterobacter， the dominant fungi were Coprinopsis， Meyerozyma and Apiotrichum. The results of correlation analysis showed that the quality indexes of ADR were positively correlated with Agrobacterium， Mycobacterium in bacteria， Candida in fungi， and negatively correlated with Bacillus in bacteria. ConclusionThere are significant differences in the color， odor， coumarin content and microbial community composition of ADR in different drying processes， and the best drying method is drying and steaming cutting at 50 ℃. The relative abundance changes of 9 bacterial genera and 4 fungal genera are closely related to the quality formation of ADR during the drying process.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background: Intraoperative hypercapnia reduces the time to emergence from volatile anesthetics, but few clinical studies have explored the effect of hypercapnia on the emergence time from intravenous (IV) anesthesia. We investigated the effect of inducing mild hypercapnia during the recovery period on the emergence time after total IV anesthesia (TIVA). Methods: Adult patients undergoing transurethral lithotripsy under TIVA were randomly allocated to normocapnia group (end-tidal carbon dioxide [ETCO2] 35–40 mmHg) or mild hypercapnia group (ETCO2 50-55 mmHg) during the recovery period. The primary outcome was the extubation time. The spontaneous breathing-onset time, voluntary eye-opening time, and hemodynamic data were collected. Changes in the cerebral blood flow velocity in the middle cerebral artery were assessed using transcranial Doppler ultrasound. Results: In total, 164 patients completed the study. The extubation time was significantly shorter in the mild hypercapnia (13.9 ± 5.9 min, P = 0.024) than in the normocapnia group (16.3 ± 7.6 min). A similar reduction was observed in spontaneous breathing-onset time (P = 0.021) and voluntary eye-opening time (P = 0.008). Multiple linear regression analysis revealed that the adjusted ETCO2 level was a negative predictor of extubation time. Middle cerebral artery blood flow velocity was significantly increased after ETCO2 adjustment for mild hypercapnia, which rapidly returned to baseline, without any adverse reactions, within 20 min after extubation. Conclusions Mild hypercapnia during the recovery period significantly reduces the extubation time after TIVA. Increased ETCO2 levels can potentially enhance rapid recovery from IV anesthesia.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Background: Intraoperative hypercapnia reduces the time to emergence from volatile anesthetics, but few clinical studies have explored the effect of hypercapnia on the emergence time from intravenous (IV) anesthesia. We investigated the effect of inducing mild hypercapnia during the recovery period on the emergence time after total IV anesthesia (TIVA). Methods: Adult patients undergoing transurethral lithotripsy under TIVA were randomly allocated to normocapnia group (end-tidal carbon dioxide [ETCO2] 35–40 mmHg) or mild hypercapnia group (ETCO2 50-55 mmHg) during the recovery period. The primary outcome was the extubation time. The spontaneous breathing-onset time, voluntary eye-opening time, and hemodynamic data were collected. Changes in the cerebral blood flow velocity in the middle cerebral artery were assessed using transcranial Doppler ultrasound. Results: In total, 164 patients completed the study. The extubation time was significantly shorter in the mild hypercapnia (13.9 ± 5.9 min, P = 0.024) than in the normocapnia group (16.3 ± 7.6 min). A similar reduction was observed in spontaneous breathing-onset time (P = 0.021) and voluntary eye-opening time (P = 0.008). Multiple linear regression analysis revealed that the adjusted ETCO2 level was a negative predictor of extubation time. Middle cerebral artery blood flow velocity was significantly increased after ETCO2 adjustment for mild hypercapnia, which rapidly returned to baseline, without any adverse reactions, within 20 min after extubation. Conclusions Mild hypercapnia during the recovery period significantly reduces the extubation time after TIVA. Increased ETCO2 levels can potentially enhance rapid recovery from IV anesthesia.