ObjectiveTo establish a high-performance liquid chromatography（HPLC） for the quantitative analysis of multiple components in Gegen Qinlian tablets， and to comprehensively evaluate the quality of samples from different manufacturers by integrating chemical pattern recognition and technique for order preference by similarity to ideal solution（TOPSIS）， in order to provide a reference basis for quality evaluation and control of Gegen Qinlian tablets. MethodsHPLC was employed to determine the contents of 10 components in 28 batches of Gegen Qinlian tablets collected from 6 manufacturers， and taking the detection results as variables， SIMCA 14.1 and SPSS 26.0 were employed for cluster analysis（CA）， principal component analysis（PCA）， and orthogonal partial least squares-discriminant analysis（OPLS-DA） to identify key components affecting the quality. Then， TOPSIS analysis was employed to rank the quality of Gegen Qinlian tablets from the 6 manufacturers and establish a comprehensive quality evaluation method. ResultsA quantitative method for Gegen Qinlian tablets was established. After methodological validation， the method was found to be stable and reliable， and could be used for the quantitative analysis of this preparation. The contents of 3′-hydroxy puerarin， puerarin， 3′-methoxy puerarin， daidzein， coptisine hydrochloride， epiberberine， jatrorrhizine hydrochloride， berberine hydrochloride， palmatine hydrochloride and baicalin in 28 batches of samples were 3.58-7.35， 24.88-42.32， 4.20-9.36， 4.33-7.60， 2.52-6.44， 0.93-4.10， 0.58-3.05， 10.68-22.92， 0.82-4.82， 11.73-60.16 mg·g^-1， respectively. Among them， puerarin， berberine hydrochloride and baicalin all met the limit requirements for this preparation specified in the 2025 edition of the Pharmacopoeia of the People's Republic of China. CA and PCA clustered the 28 batches of samples into 5 categories， PCA extracted 2 principal components with a cumulative variance contribution rate of 90.588%， and OPLS-DA screened out 4 differential markers with variable importance in the projection（VIP） values>1.0， namely baicalin， 3′-hydroxy puerarin， coptisine hydrochloride and palmatine hydrochloride， which might be the main components affecting the quality of Gegen Qinlian tablets. TOPSIS analysis showed that the comprehensive score of each evaluation index（C_i） values of different manufacturers were different. Among them， the C_i of manufacturer B was ranked higher， indicating potentially superior quality， while the C_i of manufacturer A was ranked lower， suggesting potentially inferior quality. ConclusionThis study establishes a quantitative method for Gegen Qinlian tablets， and the content uniformity of the same manufacturer is good， while there are differences in the contents of active components among different manufacturers. Through the chemical pattern recognition analysis， it is found that the content differences of Gegen Qinlian tablets may be related to baicalin， 3′-hydroxy puerarin， coptisine hydrochloride and palmatine hydrochloride.

Objective To analyze the characteristics of postoperative hospital-acquired infections and drug sensitivity in lung transplant recipients over the past 5 years in a single center. Methods A total of 724 lung transplant recipients at Wuxi People's Hospital from January 2019 to December 2023 were selected. Based on the principles of hospital-acquired infection diagnosis, a retrospective analysis was conducted on the hospital infection situation and infection sites of lung transplant recipients, and an analysis of the distribution of hospital-acquired infection pathogens and their antimicrobial susceptibility test status was performed. Results Among the 724 lung transplant recipients, 275 cases of hospital-acquired infection occurred, with an infection rate of 38.0%. The case-time infection rate decreased from 54.2% in 2019 to 22.8% in 2023, showing a downward trend year by year (Z=30.98, P<0.001). The main infection site was the lower respiratory tract, accounting for 73.6%. The pathogens were mainly Gram-negative bacteria, with the top four being Acinetobacter baumannii (37.1%), Pseudomonas aeruginosa (17.3%), Klebsiella pneumoniae (13.7%), and Stenotrophomonas maltophilia (13.4%), with imipenem resistance rates of 89%, 53%, 58% and 100%, respectively. Gram-positive bacteria were mainly Staphylococcus aureus (3.6%), with a methicillin resistance rate of 67%. Conclusions Over the past 5 years, the hospital-acquired infections in lung transplant recipients have shown a downward trend, mainly involving lower respiratory tract infections, with the main pathogens being Acinetobacter baumannii, Pseudomonas aeruginosa and Klebsiella pneumoniae, all of which have high resistance rates to imipenem.

Objective To evaluate the effect of donor gender on short-term survival rate of lung transplant recipients. Methods A retrospective analysis was conducted on the data of 1 066 lung transplant recipients. The log-rank test was used to evaluate the differences in short-term fatality among different donor gender groups and donor-recipient gender combination groups. Multivariate Cox regression, propensity score (PS) regression, and propensity score matching (PSM) were employed to control for confounding factors and further assess the differences in fatality. Subgroup analyses were also performed based on donor gender. Results Multivariate Cox regression analysis showed no statistically significant differences in fatality at 30 days, 1 year, 2 years and 3 years postoperatively between male and female donor groups (all P>0.05). After PS regression and PSM, univariate Cox regression analysis indicated that recipients from female donors had a higher fatality at 2 years postoperatively compared to those from male donors, with hazard ratios (95% confidence intervals) of 1.29 (1.01-1.65) and 1.36 (1.03-1.80) respectively. Multivariate Cox regression analysis also revealed no statistically significant differences in fatality at various follow-up time points among different donor-recipient gender combination groups (all P>0.05). Subgroup analyses based on donor sex showed no statistically significant differences in fatality among recipients of different gender within either male or female donor groups (all P>0.05). Conclusions Female donors may reduce the short-term postoperative survival rate of lung transplant recipients, but this negative impact is not sustainable in the long term. At present, there is no evidence to support the inclusion of sex as a factor in lung allocation rules.

Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

Objective To analyze the relationship between changes of CD80, CD86 and Th1/Th2 cytokines and the severity of coxsackievirus A6 (CVA6) hand-foot-mouth disease (HFMD). Methods Among the 380 children with CVA6 HFMD in the 971th Hospital of the People's Liberation Army Navy and the 94th Hospital of the Joint Logistic Support Force were selected from January 2021 to January 2024. According to the disease severity, the children were divided into common group and severe group. The levels of CD80, CD86 and Th1/Th2 cytokines (interferon-γ (IFN-γ), interleukin-4 (IL-4)) were compared between the two groups. The ROC curve was used to analyze the evaluation efficiency of CD80, CD86, IFN gamma, and IL-4 levels on the severity of CVA6 HFMD. Multivariate logistic regression analysis was used to analyze the influencing factors of the severity of CVA6 HFMD. Results The levels of CD80, CD86, IFN-γ and IL-4 in the severe group were higher than those in the common group (P<0.05). The AUCs of CD80, CD86, IFN-γ, IL-4 and their combined detection in evaluating severity of CVA6 HFMD were 0.769, 0.717, 0.756, 0.864, and 0.917, respectively. The AUC value and specificity of combined detection were higher than those of single detection of each index (P<0.05). Multivariate logistic regression analysis indicated that age ≤ 3 years old was a risk factors (P<0.05), while time from onset to diagnosis ≤ 3d, duration of fever ≤ 3d, WB ≤ 12×109/L, low expression of CD80, low expression of IFN-γ, and low expression of IL-4 were protective factors (P<0.05). Conclusion CD80, CD86 and Th1/Th2 cytokines (IFN-γ, IL-4) are related to the severity of CVA6 HFMD. Early monitoring of CD80, CD86, IFN-γ and IL-4 levels in children is conducive to understanding their disease progression and guiding physician treatment.

Multidrug resistant organism refers to bacteria that are insensitive to three or more antibiotics commonly used in clinic, including Pseudomonas aeruginosa, Acinetobacter Baumannii and Klebsiella pneumoniae, etc. MDRO infection is a major factor affecting the survival rate after lung transplantation (LTx), accounting for 30% of the causes of death in the first year after transplantation. Antibiotic treatment has low specificity and is prone to drug resistance. The development of new drugs has a long cycle and high cost, with significant limitations. Phage has high specificity for bacteria, which can proliferate in large quantities in the infected lesion and co-evolve with bacteria during the action process. Phage also have a good killing effect on MDRO, which is expected to make up for the deficiencies of existing antibiotic therapy. This article reviews the development background and mechanism of action of phage therapy, and summarizes its application status and early clinical trial results in the field of LTx, in order to providing new thinking paths for clinical work.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.