Objective To analyze the influencing factors of survival of patients with airway stenosis requiring clinical interventions after lung transplantation. Methods Clinical data of 66 patients with airway stenosis requiring clinical interventions after lung transplantation were retrospectively analyzed. Univariate and multivariate Cox’s regression models were adopted to analyze the influencing factors of survival of all patients with airway stenosis and those with early airway stenosis. Kaplan-Meier method was used to calculate the overall survival and delineate the survival curve. Results For 66 patients with airway stenosis, the median airway stenosis-free time was 72 (52,102) d, 27% (18/66) for central airway stenosis and 73% (48/66) for distal airway stenosis. Postoperative mechanical ventilation time [hazard ratio (HR) 1.037, 95% confidence interval (CI) 1.005-1.070, P=0.024] and type of surgery (HR 0.400, 95%CI 0.177-0.903, P=0.027) were correlated with the survival of patients with airway stenosis after lung transplantation. The longer the postoperative mechanical ventilation time, the higher the risk of mortality of the recipients. The overall survival of airway stenosis recipients undergoing bilateral lung transplantation was better than that of their counterparts after single lung transplantation. Subgroup analysis showed that grade 3 primary graft dysfunction (PGD) (HR 4.577, 95%CI 1.439-14.555, P=0.010) and immunosuppressive drugs (HR 0.079, 95%CI 0.022-0.287, P<0.001) were associated with the survival of patients with early airway stenosis after lung transplantation. The overall survival of patients with early airway stenosis after lung transplantation without grade 3 PGD was better compared with that of those with grade 3 PGD. The overall survival of patients with early airway stenosis after lung transplantation treated with tacrolimus was superior to that of their counterparts treated with cyclosporine. Conclusions Long postoperative mechanical ventilation time, single lung transplantation, grade 3 PGD and use of cyclosporine may affect the survival of patients with airway stenosis after lung transplantation.

ObjectiveTo investigate the value of two-dimensional shear wave elastography （2D-SWE） or serological models used alone or in combination in determining the stage of liver fibrosis in patients with chronic hepatitis B. MethodsA retrospective analysis was performed for the clinical data of 327 patients with chronic hepatitis B who were admitted to Mengchao Hepatobiliary Hospital of Fujian Medical University from August 2020 to August 2022 and underwent 2D-SWE and liver histopathological examination， including sex， age， serological markers， and 2D-SWE results. According to the degree of liver fibrosis， they were divided into S0-S1， S≥2， S≥3， and S=4 groups， and the serological models were calculated based on serological markers. A Spearman correlation analysis was used to investigate the correlation of 2D-SWE and serological models with liver fibrosis stage； the receiver operating characteristic curve was plotted with the results of liver histopathology as the standard to compare the efficiency of each parameter used alone or in combination in determining the stage of liver fibrosis； the Delong test was used to investigate the difference between different methods. ResultsLiver stiffness measurement measured by 2D-SWE was strongly correlated with the stage of liver fibrosis （r=0.741， P<0.001）， and as for the serological model， six markers （APRI， FIB-4， GPR， GP， RPR， and S index）， other than AAR， were positively correlated with the stage of liver fibrosis （all P<0.001）. 2D-SWE had an area under the ROC curve （AUC） of 0.878， 0.932， and 0.942， respectively， in the diagnosis of S≥2， S≥3， and S=4 liver fibrosis （all P<0.001）， with an optimal cut-off value of 6.9 kPa， 7.9 kPa， and 9.4 kPa， respectively. Among the serological models， APRI had the largest AUC of 0.788 and 0.875， respectively， in the diagnosis of S≥2 and S=4 liver fibrosis， and S index had the largest AUC of 0.846 in the diagnosis of S≥3 liver fibrosis. In the diagnosis of S≥2， S≥3， and S=4 liver fibrosis， 2D-SWE combined with APRI increased the AUC values to 0.887， 0.938， and 0.950， respectively， and 2D-SWE combined with S index increased the AUC values to 0.879， 0.935， and 0.941， respectively， while there were no significant differences between 2D-SWE and the above combinations （P>0.05）. Conclusion2D-SWE has a better diagnostic efficacy than the above seven serological models in determining liver fibrosis stage. The serological models have a certain diagnostic value， among which APRI and S index have a relatively high diagnostic value. There is no significant difference between 2D-SWE and 2D-SWE combined with serological models， and such combinations cannot significantly improve diagnostic efficiency. Therefore， further studies are needed to explore new combinations of diagnostic methods.

Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators.Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1 ∶3,928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1243 patients were randomly divided into the modeling group(n=869)and the validation group(n=374)in a ratio of 7∶3 using the R language setting code.Preoperative clinical indicators(basic information,medical history,relevant scale score,surgical information and results of laboratory tests)and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms,k-nearest neighbor(KNN),support vector machine linear(SVM),logic regression(LR)and extreme gradient boosting(XGBoost).In the validation group,receiver operating characteristic(ROC)curve,precision-recall curve(P-R),decision curve analysis(DCA)curve,Kappa value,sensitivity,specificity,Brier score,and F1 score were applied to evaluate the efficacy of model.Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve(AUROC)was 0.916(95%CI:0.883～0.949),area under the precision-recall curve(AUPRC)was 0.841,Brier score was 0.097,and sensitivity and specificity was 78.95%and 87.10%,respectively.Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.

Objective To analyze the epidemic trend of type 2 diabetes in Suzhou from 2013 to 2022 and the early death and life loss of type 2 diabetes due to outdoor PM_2.5. Methods The indicators of death number, mortality rate, years of early life lost (YLL) and years of life lost per thousand (YLL rate) were selected, and the data of the Global Burden of Disease Study 2021 (GBD2021) were used to analyze the death information of type 2 diabetes in Suzhou. Results The number of deaths, mortality, YLL and YLL rate caused by type 2 diabetes in Suzhou from 2013 to 2022, showed an upward trend, and the number of deaths , YLL and YLL rate of type 2 diabetes attributed to PM_2.5 also showed an upward trend (P<0.001). The death toll of type 2 diabetes attributable to PM_2.5 totaled 3887 cases , the YLL was 61 974 person years, and the YLL rate was 0.89‰. Conclusion Type 2 diabetes has seriously threatened the health of residents in Suzhou , and the disease burden due to outdoor PM_2.5 is on the rise. It is important to carry out three-level prevention and improve air quality for type 2 diabetes to improve the health of residents.

Objective:To analyze the current allocation situation and equity of Class B large medical equipment in China,and provide references for reasonably optimizing the allocation of medical equipment.Methods:Based on the allocation data of Class B large medical equipment in China at the end of the 13th Five Year Plan period,the agglomeration degree method was used to evaluate the equity of equipment allocation in various regions from the dimensions of geography,population,and economic development.Results:The current allocation of Class B large medical equipment per million population was 3.19,and the eastern region(3.64)was higher than the central region(3.42),the central region(2.24)was higher than the western region.The agglomeration degree of medical equipment based on geographical area showed that most of the equipment in the eastern region was in a rich state,and the allocation equity was relatively good in the central region,but generally poor in the western region.The agglomeration degree of medical equipment based on population distribution was higher in the eastern region than in the central region and even higher in the western region.The equipment in the eastern region could basically meet the demand of the local population,while the allocation in the central and western regions was slightly insufficient.The distribution of medical equipment agglomeration degree based on economic development varied significantly among different regions,and the allocation equity of central region was generally better than that of eastern and western regions.Conclusion:The total allocation of Class B large medical equipment in China is not high,and the government needs to continue planning and improving it.The distribution of medical equipment allocation based on geography,population and economic development is unbalanced among the eastern,central and western regions.Improving the equity of medical equipment allocation also needs to consider regional differences and characteristics according to local conditions.

Objective To study the diffusion mode and characteristics of large medical equipment allocation and use management policies among different institutions or regions,so as to provide reference for the improvement of policies.Methods The policies on the allocation and use management of large medical equipment released from Janu-ary 1994 to December 2022 were searched on the official websites of the central government,local governments,and health commissions,and databases such as"Peking University Magic".Based on the theory of policy diffu-sion,It analyzed the diffusion characteristics,diffusion mode and influencing factors of large medical equipment allo-cation and use management policy in combination with factors such as policy diffusion intensity,diffusion width,dif-fusion speed,and diffusion direction,and through the policy network analysis identified interrelationships between policies across agencies or across regions.Results It included 161 policy documents for analysis,including 25 central policies and 136 local policies.The development of large medical equipment allocation and use management policies in China can be divided into four periods.The policies with high diffusion intensity,breadth and fast diffusion speed were mainly policy documents such as large medical equipment allocation plan and management measures issued by the National Health Commission,and at the same time,the"top-down"policy vertical diffusion modelwas the main one.Conclusion There were differences in the diffusion effects of large medical equipment allocation and use management policies at different stages,and the policy diffusion effect was obvious in the innovation and develop-ment period.It found that policy diffusion was affected by factors such as the division of administrative powers and responsibilities,and policy types.

Objective To isolate endophytic fungi from Angelica sinensis and evaluate the bioactivity of their secondary metabolites.Methods Angelica sinensis and rhizosphere soil were utilized as materials.The tissue homogenization method was employed with six diverse culture media to isolate endophytic fungi.The antibacterial activity of secondary metabolites was gauged using a 96-well plate assay,while UV spectrophotometry was used to evaluate the inhibitory activity of four enzymes.Results A total of 153 fungal strains were isolated and purified from Angelica sinensis roots,stems,leaves,and soil.The samples exhibited specific inhibitory activities against adenosine deaminase(ADA),β-lactamase,xanthine oxidase(XO),and tyrosinase(TYR),with rates of 45.83%,52.78%,51.39%and 55.56%,respectively.Furthermore,1.39%of the samples displayed wide-ranging inhibitory effects against four indicator bacteria.Strain 6B also showcased the lowest inhibitory concentration values of 62.5 and 7.81 μg/mL against Escherichia coli ATCC25922 and ATCC35218,respectively,signifying its potential research significance.Conclusion Angelica sinensis has abundant endophytic fungal resources and is a good source for discovering active compounds,demonstrating certain research value.

Objective:To explore the effects of estrogen receptor α (ERα) encoded by protein encoding gene ESR1 on the radiation resistance of breast cancer cells and their molecular mechanisms.Methods:The ESR1 overexpression plasmid was transfected into estrogen receptor (ER)-negative breast cancer cells. Then, the shRNA-ESR1 vector was introduced into ER-positive cell to establish models with different phenotype. The ATG5 mRNA level and protein expression levels of LC3B-I, LC3B-II, P62, FIP200, ATG5, ATG7, ATG12, Beclin1, ULK1 were detected using qPCR and Western blot techniques. Cell death was measured using flow cytometry. The radiation sensitivity was determined through the colony formation assay. The mortality of breast cancer cells under the autophagy gene knockdown and overexpression or treated with estrogen receptor inhibitor (TAM) combined with ionizing radiation were detected by trypan blue staining.Results:Under the condition of 8 Gy X-ray irradiation, the knockdown of ESR1 in ER-positive ZR75 breast cancer cells promoted cell death ( t = 3.49, 3.13, P < 0.05), while the overexpression of ESR1 in ER-negative MDA-MB-231 breast cancer cells inhibited cell death ( t = 4.16, 7.48, P < 0.05). Compared to the control group, the treatment with chloroquine increased the number of formed colonies of ESR1 knockdown ZR75 cells ( t = 8.49, P < 0.05), and inhibiting autophagy could reduce the death of ZR75 cells caused by ESR1 silencing. Under the treatment with ionizing radiation, the overexpression of ESR1 in MDA-MB-231 cells promoted protective autophagy, which, however, was reduced after ESR1 knockdown in ZR75 cells. Furthermore, it was observed that the knockdown of ATG5 in ZR75 cells was associated with reduced autophagy and an increase in cell death ( t = 4.19, 6.39, P < 0.05). In contrast, the overexpression of ATG5 in ZR75 cells reversed the increase in cell death caused by ESR1 knockdown ( t = 1.70, 4.65, P < 0.05). After the treatment of ER-positive ZR75 breast cancer cells with TAM, the expressions of ATG5 and ATG12 decreased, suggesting inhibited autophagy and an increase in cell death ( t = 18.70, P < 0.05). Furthermore, these processes were promoted by ionizing radiation ( t = 16.82, P < 0.05). Conclusions:The estrogen receptor encoded by ESR1 promotes protective autophagy of ER-positive breast cancer cells by increasing ATG5, further leading to radiation resistance in ER-positive breast cancer cells. Treatment with tamoxifen combined with ionizing radiation can increase the radiation sensitivity of ER-positive breast cancer cells.

Objective Scutellaria baicalensis stems and leaves glucuronic hydrolase(sbsl GUS)was used to enzymatically hydrolyze scutellarin in Erigeron breviscapus(Vant.)Hand.Mazz.to prepare scutellarein,and the high-purity scutellarein was obtained through separation and purification.Methods Orthogonal experiments were used to optimize the process parameters for the extraction of Erigeron breviscapus(Vant.)Hand.Mazz..Using the rate of enzymatic hydrolysis conversion of scutellarin as the index,the amount of enzyme,pH,temperature,time and antioxidant were investigated,and the preparation process parameters of scutellarein were optimized.Ethanol extraction,activated carbon decolorization,and fractional crystallization were used to purify the crude extract.Results The extraction process was determined to be:segments of Erigeron breviscapus were decocted twice with 10 times water for 1 hour each time.The preparation process of scutellarein was as follows:the amount of sbsl GUS extract and Erigeron breviscapus decoction was 1∶10 based on crude drugs,0.5%sodium metabisulfite was added,pH value was about 6.0,the temperature was about 45℃,and the time was 20 hours.The crude extract of scutellarein with the content more than 60%was obtained.The crude extract was purified by fractional crystallization,refluxed with 80%ethanol,decolorized with activated carbon,concentrated and crystallized,and the scutellarein extract with content more than 85%was obtained.Conclusion sbsl GUS enzymatic hydrolysis technology,which was used to prepare scutellarein,is simple and feasible.This study provides a new way for the manufacture of scutellarein.

Objective To comprehensively analyze the reported preparation methods for animal models of perimenopausal syndrome (PS), to compare the advantages and disadvantages of various preparation elements and detection indexes, so as to provide useful references for the optimization of the relevant animal models as well as the standardization of their application in the efficacy evaluation of new drugs.MethodsIn this paper, literature research methods were applied using "perimenopausal syndrome" as the subject term. The publication period of the literature was limited to January 2016 to February 2023. Relevant literature on the preparation of PS animal models was retrieved from databases such as China National Knowledge Infrastructure, Wanfang database, and PubMed. After screening the experimental literature that met the inclusion and exclusion criteria, detailed information on experimental animal strains, modeling methods, duration of drug administration, positive drugs, detection indexes and other relevant information were collected. After the above information was standardized, the PS animal model database was established using Excel 2010 software. The model preparation elements and evaluation indexes were summarized systematically, and the statistical results were processed and analyzed using Excel 2010 software.Results A total of 247 articles were screened. SD rats (164 times, 65.86%) and Wistar rats (35 times, 14.06%) were often used to prepare PS animal models. Bilateral ovariectomy (139 times, 53.87%) and natural aging (43 times, 16.80%) were chosen as modeling methods. The ages of rats used for modeling ranged from 7 weeks to 18 months, with 3-month-old rats (22 times, 21.78%) being the most common. The detection indexes were comprehensively evaluated from multiple perspectives, including serum biochemistry, vaginal exfoliated cell smear, histomorphology, general observation, behavioral observation, and organ tissue protein immunoblotting. Western medical evaluation indexes were commonly used to test the successful preparation of models, with vaginal exfoliated cell smears being the most frequently used method (125 times, 85.04%). A model was considered successfully prepared when estrous cycle disorder or irregularity was observed. Some literature also determined modeling success by detecting a significant decrease in serum estradiol levels (5 times, 3.04%). Traditional Chinese medicine (TCM) syndrome evaluation often used a combination of Chinese and Western medical evaluation indexes for comprehensive evaluation, with researchers determining the TCM syndrome through vaginal exfoliated cell smears supplemented by general observation (3 times, 2.04%).Conclusion There are many methods for preparing PS animal models, but there are still significant differences in the selection of animal species, age, criteria for successful modeling, and TCM syndrome evaluation in the related literature.