Laboratory animals are the "living" tools of medical research. Through animal experiments, people can gain continuous insights into the laws of life, reveal the essence of diseases, develop vaccines and drugs for prevention and treatment, and play an important role in the technological development of fields related to human health. The environmental conditions of laboratory animals have a direct impact on their health, quality, and the results of animal experiments. The higher the degree of environmental control, the more reliable the experimental results are in terms of quality. Therefore, environmental control of laboratory animal facilities is important for ensuring that laboratory animals live under required conditions, which is a key factor for conducting effective animal experiments. This article analyzes the current status of environmental testing of laboratory animal facilities in Sichuan Province, briefly summarizing their number, area, and other basic information, and provides detailed statistics on the ability of institutions to conduct environmental testing for laboratory animal facilities in Sichuan Province. It also summarizes the testing requirements for laboratory animal facility environments based on national standards, regulatory requirements, and the quality control needs of facility users. In the analysis of testing indicators for laboratory animal facilities, based on testing data from 40 laboratory animal facilities in Sichuan Province, it was found that static pressure difference is the indicator most prone to non-compliance, followed by illumination and air exchange rate. Using barrier environments as examples, common problems in the process of environmental testing for laboratory animal facilities are summarized in six aspects: testing personnel, instruments, methods, technical materials, testing environment, and reports, and targeted suggestions are proposed. These suggestions help improve environmental control in laboratory animal facilities, and provide practical reference and guidance for relevant testing institutions, as well as laboratory animal producers and users in the industry.

Objective:To investigate the effect of Chinese Herbal Anti-Alopecia and Hair Growth Solution(CHAaHGS)on the hair growth through in vitro experiments on the human dermal papilla cells(HDPCs),in vivo experiments in the C57BL/6 mice,and human efficacy tests,and to clarify its potential mechanism.Methods:The HDPCs were divided into control group,CHAaHGS group,and minoxidil group.MTT method was used to detect the proliferation activities of HDPCs in various groups;enzyme-linked immunosorbent assay(ELISA)method was used to detect the levels of vascular endothelial growth factor(VEGF),hepatocyte growth factor(HGF),insulin-like growth factor-1(IGF-1),and transforming growth factor β1(TGF-β1)in the supernatant of HDPCs in various groups;real-time fluorescence quantitative PCR(RT-qPCR)method was used to detect the expression levels of VEGF,HGF,IGF-1,TGF-β1,and alkaline phosphatase(ALP)mRNA in the HDPCs in various groups;Western blotting method was used to detect the expression levels of β-catenin,dishevelled segment polarity protein 1(DVL1),glycogen synthase kinase 3β(GSK-3β),phosphorylated GSK-3β(p-GSK-3β),and wingless-type MMTV integration site family member 3a(Wnt3a)proteins in the HDPCs in various groups.A total of 18 mice were randomly divided into control group,CHAaHGS group,and minoxidil group,with 6 mice in each group.The mouse hair loss model was established using hair removal cream,and corresponding drug treatments were administered immediately after hair removal.The lengths and weights of newly grown hair on day 21 of the mice in various groups were detected;HE staining was used to observe the morphology of hair follicles in the dorsal depilated skin areas of the mice in various groups on day 7;ELISA method was used to detect the levels of VEGF,HGF,IGF-1,and TGF-β1 in the skin tissue of dorsal depilated areas of the mice in various groups.Sixty subjects were randomly divided into control group and CHAaHGS group,with 30 subjects in each group.The numbers of hair loss and hair densities of the subjects in various groups were detected at weeks 0,4,8,and 12.Results:The MTT assay results showed that compared with control group,the proliferation activity of the cells in 50 mg·L-1CHAaHGS group was significantly increased(P＜0.01).The ELISA assay results showed that compared with control group,the levels of VEGF,HGF,and IGF-1 in the cell supernatant of HDPCs in CHAaHGS group were significantly increased(P＜0.05 or P＜0.01),and the TGF-β1 level was significantly decreased(P＜0.01).The RT-qPCR results showed that compared with control group,the expression levels of VEGF,HGF,IGF-1,and ALP mRNA in the cells in CHAaHGS group were significantly increased(P＜0.05 or P＜0.01),and the TGF-β1 mRNA expression level was significantly decreased(P＜0.01).The Western blotting results showed that compared with control group,the expression levels of β-catenin,DVL1,p-GSK-3βand Wnt3a proteins in the cells in CHAaHGS group were significantly increased(P＜0.05 or P＜0.01),and the GSK-3β protein expression level was significantly decreased(P＜0.05).In animal experiments,on day 21,compared with control group,the length of newly grown hair of the mice in CHAaHGS group was significantly increased(P＜0.05),and the hair weight was significantly increased(P＜0.01).On day 7,the HE staining results showed that compared with control group,the hair follicle spacing of the mice in CHAaHGS group was significantly decreased(P＜0.05),and the number of hair follicles was significantly increased(P＜0.01);the ELISA assay results showed that compared with control group,the levels of VEGF,HGF,and IGF-1 in skin tissue of dorsal depilated area of the mice in CHAaHGS group were significantly increased(P＜0.05 or P＜0.01),and the TGF-β1 level was significantly decreased(P＜0.05).In human efficacy test,compared with control group,the number of hair loss of the subjects in CHAaHGS group was significantly decreased at week 12(P＜0.01),and the local hair density was increased(P＜0.05).Conclusion:CHAaHGS promotes hair growth,and the mechanism may be related to its ability to increase the proliferation activity of HDPCs,induce the secretion of VEGF,HGF,and IGF-1,and activate the Wnt/β-catenin signaling pathway.

The transmembrane protein gene family(TMEM)refers to a group of genes that encode trans-membrane proteins.The TMEM family influences tumor angiogenesis by affecting key signaling pathways involved in angiogen-esis,modulating the interactions between tumor cells and endothelial cells and regulating the expression or function of VEGF and its receptor VEGFR.Thereby it may have impact on tumor blood vessel formation.Additionally,the TMEM family regulates mechanisms such as post-translational modifications of histone,which also play a role in tumor initiation and progression.Post-translational modifications of TMEM proteins including phosphorylation,gly-cosylation,acetylation and palmitoylation.TMEM family contributes to tumorigenesis and may identify some novo targets used in precise medical treatment.

Objective To evaluate the relationship between nutritional parameters and the risk of venous thromboembolicism(VTE)in patients with tuberculosis so as to identify the risk factors and predictors of thrombosis and assist in the early identification of high-risk factors for VTE in patients with the pulmonary tuberculosis.Methods A total of 323 patients diagnosed with the pulmonary tuberculosis and hospitalized in Kunming Third People's Hospital from August 2021 to August 2023 were collected.According to the VTE risk assessment of non-operative patients,they were divided into the high-risk group and the low-risk group respectively with 116 and 207 in each group.The nutritional indicators with statistically significant differences between the two groups were screened by Lasso regression.Multivariate Logistic regression was used to screen the independent risk factors for high VTE risk in pulmonary tuberculosis patients,and a nomogram prediction model was constructed.The prediction model was evaluated by receiver operating characteristic curve(ROC),calibration curve,decision curve,and influence curve.Results Patients in the high-risk group were significantly older than those in the low-risk group(59 vs.41,P<0.001),hypertension,gender,and Type 2 diabetes did not differ significantly(P values were 0.084,0.724 and 0.488,respectively).9 variables were selected from the inter-group comparison and Lasso regression,including ALB,HCT,NRS2002 scores,HBDH,RDW-SD,RDW-CV,TG,CONUT scores,and NEFA.Multivariate Logistic regression analysis showed that ALB,NRS2002 scores,RDW-SD,and CONUT scores were independent influencing factors for the high risk of VTE scores in patients with tuberculosis(P<0.005).Area under the ROC curve showed that the AUC(0.892)for high-risk VTE scores in patients with the pulmonary tuberculosis was greater than that of ALB(0.803),NRS2002 score(0.735),RDW-SD(0.685),and CONUT score(0.774).Fitting prediction model:Logit(P):Y=0.433×NRS-0.136×ALB+0.411×CONUT score+0.072×RDW-SD-1.770,P=1/(1+e-Y)(Y:prediction index,P:prediction probability).Calibration curve showed that the model prediction tended to be consistent with the actual results(U:>0.05),and the decision curve and influence curve showed that the model can bring clinical benefits.Conclusion ALB,NRS2002 scores,RDW-SD,and CONUT scores are independent influencing factors for the high risk of VTE scores in patients with tuberculosis.They can guide the clinical practice,improve these indicators as soon as possible,reduce VTE scores,and reduce the thrombosis risk.At the same time,the prediction model performs well in the verification cohort,with its discrimination ability,calibration accuracy and clinical utility(decision curve analysis)all reaching a satisfactory level.

Objective A survival outcome prediction model for postoperative pancreatic cancer patients was constructed by applying large-scale data based on the new version of American Joint Committee on Cancer(AJCC)staging using different machine learning algorithms.Methods Based on the Surveillance,Epidemiology,and End Results(SEER)database,synthetic minority over-sampling technique(SMOTE)and synthetic minority over-sampling technique and edited nearest neighbors(SMOTE-ENN)algorithms were used to process unbalanced data,random forest,support vector machine,decision tree,and extreme gradient boosting(XGBoost)algorithms were used to build and compare prognostic models,and Shapley additive explanation(SHAP)was introduced to interpret the models.Results The SMOTE-ENN combined with XGBoost model had the best performance(accuracy rate was 86.2％,precision rate was 95.2％,recall rate was 71.2％,F1 value was 0.762,area under the curve was 0.884,Brier score was 0.108).The calibration curve and decision curve respectively showed that this model had good calibration effect and high clinical application value.In addition,SHAP analysis showed that the most important impact on prognostic outcomes was N stage.Conclusion The XGBoost model has the best performance and can be used as a new high-performance postoperative prognosis prediction model under AJCC staging that conforms to the current clinical staging system,providing theoretical support for predicting postoperative patient survival outcomes and formulating personalized treatment plans.

This study aims to prepare nanobodies for the epitopes of spike protein(S)of porcine ep-idemic diarrhea virus(PEDV),and verify its reactivity.The expression vector pCZN1-SN was con-structed by the prokaryotic expression system,and SN fragment was expressed in prokaryotic ex-pression and purified by Ni-NTA chromatography column.The SN nanobodies were displayed u-sing phage display technology and screened from the natural nanobody library.The results showed that SN fragments with a size of 26 kDa were obtained by prokaryotic induction,and the specific nanobodies were obtained through three rounds screening by phage display technology.One strain with the best reactivity was selected for prokaryotic expression and purified.The nanobodies were obtained with a size of 14 kDa by Western blot and demonstrated to have a good binding ability to PEDV SN protein.In summary,nanobodies with epitope fragments of PEDV S protein were suc-cessfully screened and prepared based on the phage display technology,which provided a new way for the in-depth application of nanobodies.

Objective To systematically retrieve,evaluate and summarise the best available evidence on prevention and management of diagnostic blood loss in critically ill adult patients and to provide guidance for standardising diagnostic blood sampling in clinical practice.Methods A structured evidence question was created using the PIPOST framework(Population,Intervention,Professional,Outcome,Setting and Type of evidence).Guided by the"5S"levels-of-evidence pyramid,a top-down systematic search was conducted on databases of BMJ Best Practice,UpToDate,the Joanna Briggs Institute(JBI)EBP Database,GIN,SIGN,RNAO,NICE,Medlive Guideline,WHO,National Blood Authority(Australia),National Health Commission of China,Cochrane Library,PubMed,EMbase,CINAHL,Web of Science,Wanfang Data,CNKI,VIP,and SinoMed.Searched literature included clinical decision aids,guidelines,evidence summaries,systematic reviews Meta analysis,RCTs,quasi-experimental,cross-sectional,cohort studies and expert consensus/opinions.Search period covered between 1st January 2015 and 10th February 2025.Quality appraisal,evidence extraction,synthesis and grading were performed according to JBI and GRADE approaches.Results Nineteen articles were retrieved including five guidelines,five systematic reviews,one RCT,two quasi-experimental studies,two cross-sectional studies,one professional standard and three expert consensuses.A total of 26 recommendations were extracted and they were organised into five domains:education and training,blood-sampling assessment,frequency of sampling,strategies to minimise blood volume drawn,and quality control.Conclusion This evidence summary provides the best current guidance for preventing and managing iatrogenic blood loss caused by diagnostic testing in critically ill adults,providing evidence-based basis for standardizing blood tests by medical staff.

Objective:To explore the impact of sleep disturbances on the symptoms and functional impairment outcomes in children with attention deficit hyperactivity disorder (ADHD) and their path analysis.Methods:A cross-sectional study was made.Two hundred and seventy-eight ADHD children aged 6-12 years were selected from the Center for Cognition and Sleep, the People′s Hospital of Guangxi Zhuang Autonomous Region from January 2022 to March 2023.The Swanson, Nolan and Pelham Rating Scale Ⅳ was used to evaluate the core symptoms of ADHD (namely, attention deficit and hyperactivity).The sleep disturbance scale for children was used to assess sleep disturbances [including disorders in initiating and maintaining sleep(DIMS), sleep breathing disorders, sleep-wake transition disorders (SWTD), disorders of arousal, disorders of excessive somnolence (DOES) and nocturnal hyperhidrosis].The Weiss Functional Impairment Rating Scale-Parent Report (WFIRS-P) was used to assess functional impairment (in family, learning and school, life skills, self-concept, social activities, and risky activities).The descriptive statistical analysis, t test, multivariate linear regression and path analysis were used for statistical analysis. Results:Compared with those without sleep disturbances, ADHD children with sleep disturbances had significantly more serious functional impairment in family [(0.87±0.42) scores vs.(0.63±0.45) scores, P<0.001], learning and school [(0.75±0.37) scores vs.(0.62±0.35) scores, P=0.011], life skills [(1.30±0.37) scores vs.(1.08±0.30) scores, P<0.001], self-concept [(1.20±0.57) scores vs.(0.92±0.49) scores, P<0.001], social activities [(0.88±0.44) scores vs.(0.67±0.42) scores, P<0.001] and risky activities [(0.28±0.24) scores vs.(0.21±0.21) scores, P=0.043].The results of multivariate linear regression analysis showed that after adjusting for gender, age, total intelligence quotient, and core symptoms of ADHD, the regression coefficients of DOES in domains of family ( B=0.02, P=0.042) and life skills ( B=0.03, P<0.001) and the regression coefficient of DIMS in self-concept were still statistically significant ( B=0.02, P=0.013).The path analysis results showed that DOES ( β=0.170, P=0.004), attention deficit ( β=0.223, P<0.001) and hyperactivity ( β=0.385, P<0.001) directly affected WFIRS-P.DOES indirectly affected WFIRS-P through attention deficit ( β=0.270, P<0.001), and SWTD indirectly affected WFIRS-P through hyperactivity ( β=0.199, P=0.004). Conclusions:Severer sleep disturbances are associated with severer functional impairment in children with ADHD, and sleep phenotypes can directly or indirectly affect functional impairment outcomes in different domains in children with ADHD through core symptoms of ADHD.

Objective A survival outcome prediction model for postoperative pancreatic cancer patients was constructed by applying large-scale data based on the new version of American Joint Committee on Cancer(AJCC)staging using different machine learning algorithms.Methods Based on the Surveillance,Epidemiology,and End Results(SEER)database,synthetic minority over-sampling technique(SMOTE)and synthetic minority over-sampling technique and edited nearest neighbors(SMOTE-ENN)algorithms were used to process unbalanced data,random forest,support vector machine,decision tree,and extreme gradient boosting(XGBoost)algorithms were used to build and compare prognostic models,and Shapley additive explanation(SHAP)was introduced to interpret the models.Results The SMOTE-ENN combined with XGBoost model had the best performance(accuracy rate was 86.2％,precision rate was 95.2％,recall rate was 71.2％,F1 value was 0.762,area under the curve was 0.884,Brier score was 0.108).The calibration curve and decision curve respectively showed that this model had good calibration effect and high clinical application value.In addition,SHAP analysis showed that the most important impact on prognostic outcomes was N stage.Conclusion The XGBoost model has the best performance and can be used as a new high-performance postoperative prognosis prediction model under AJCC staging that conforms to the current clinical staging system,providing theoretical support for predicting postoperative patient survival outcomes and formulating personalized treatment plans.