1.Distribution characteristics of pathogens and influencing factors analysis of infections within 90 days after liver transplantation
Huabin PENG ; Haofeng XIONG ; Fei HOU ; Shuang ZHAO ; Yizhi ZHANG ; Tingting CUI ; Zhiying HE ; Jingyi LIU ; Liying SUN
Organ Transplantation 2026;17(2):212-226
Objective To investigate the distribution characteristics of pathogens causing infections within 90 days after liver transplantation and the influencing factors of infection. Methods Clinical data of 176 recipients who underwent liver transplantation at the Liver Transplant Center of Beijing Friendship Hospital Affiliated to Capital Medical University from September 2021 to August 2024 were retrospectively analyzed. Patients were divided into the infection group (n=124) and the non-infection group (n=52) based on whether they developed infection within 90 days after transplantation. The distribution characteristics of pathogens in infected patients were analyzed. Univariate and multivariate logistic regression analyses were used to explore the influencing factors of infection. Results Among the 176 liver transplant recipients, 124 cases developed 243 episodes of 518 bacterial, fungal, viral or mycoplasma infections within 90 days after transplantation, with an overall infection rate of 70.5% (124/176). The composition of pathogens was mainly Gram-negative bacteria (38.6%, 200/518), followed by Gram-positive bacteria (32.2%, 167/518) and viruses (15.4%, 80/518), and fungi accounted for 13.1% (68/518). Among Gram-negative bacteria, the main pathogen was Klebsiella pneumoniae (6.8%, 35/518), and among Gram-positive bacteria, the main pathogen was Enterococcus faecalis (8.5%, 44/518). Viruses included Epstein-Barr virus (3.7%, 19/518) and cytomegalovirus (3.7%, 19/518), and fungi were mainly Candida albicans (6.8%, 35/518). The most common infection site among the 243 episodes was pulmonary infection (42.0%, 102/243), followed by abdominal infection (22.6%, 55/243) and bloodstream infection (18.1%, 44/243). The infections mainly occurred within 2 weeks after transplantation (60.9%, 148/243). Multivariate logistic regression analysis indicated that preoperative infection within 2 weeks, a high preoperative model for end-stage liver disease (MELD) score, and preoperative sarcopenia were independent risk factors for infection within 90 days after liver transplantation (all odds ratio>1, P<0.05). After multivariate correction, the levels of CD4+T cells and CD8+T cells within 90 days after surgery were independently associated with the occurrence of infection. Low levels of CD4+T cells and CD8+T cells might be related to an increased risk of infection. Conclusions The infection rate after liver transplantation is high, and the pathogens are mainly Gram-negative bacteria. The lungs are the most common infection site. Preoperative MELD score, preoperative sarcopenia and preoperative infection within 2 weeks are independent risk factors for infection within 90 days after liver transplantation. Regular monitoring of immune indicators CD4+T cells and CD8+T cells levels after transplantation is helpful to reduce the occurrence of post-transplantation infection.
2.Effect of different exercise interventions on patients with metabolic dysfunction-associated fatty liver disease: A systematic review and network Meta-analysis
Guodong MA ; Zhuojing SUN ; Song HU ; Zijun YE ; Mingchen MA ; Fei CUI ; Jiaju ZHU
Journal of Clinical Hepatology 2026;42(2):326-344
ObjectiveTo investigate the effect of different exercise interventions on metabolism and liver parameters in patients with metabolic dysfunction-associated fatty liver disease (MAFLD), and to provide evidence-based recommendations for clinical exercise rehabilitation. MethodsThis study was conducted according to the PRISMA guidelines, and the protocol was registered on the PROSPERO platform, with a registration number of CRD42025641717. PubMed, Web of Science, Scopus, Wiley Online Library, CNKI, Wanfang Data, and VIP were searched for related articles published up to September 2024. The Cochrane tool for assessing risk of bias was used to assess the quality of articles, and Stata MP 17.0 was used to perform the network meta-analysis. ResultsA total of 57 articles were included, involving 2 648 patients. The results showed that aerobic exercise combined with resistance exercise had the best effect in improving body mass index (mean difference [WMD]=-0.97, 95% confidence interval [CI]: -1.66 to -0.28], P<0.05, surface under the cumulative ranking curve [SUCRA]=85.4) and triglycerides (WMD=-29.6, 95%CI: -46.66 to 12.54, P<0.05, SUCRA=87.3); resistance exercise was the optimal intervention method for improving total cholesterol (WMD=-15.99, 95%CI: -24.19 to -7.79, P<0.05, SUCRA=79.9) and glutamine transaminase (WMD=-8.08, 95%CI: -12.13 to -4.02, P<0.05, SUCRA=87.3); low-intensity aerobic exercise had the best effect in improving aspartate aminotransferase (WMD=-4.3, 95%CI: -8.45 to -0.15, P<0.05, SUCRA=73.5), gamma-glutamyl transpeptidase (GGT) (WMD=-3.26, 95%CI: -7.79 to 1.27, P>0.05, SUCRA=82.3), and glycated hemoglobin (HbA1c) (WMD=-0.6, 95%CI: -2.02 to 0.82, P>0.05, SUCRA=78.8); moderate-intensity aerobic exercise was the optimal intervention modality to improve Homeostasis Model Assessment of Insulin Resistance (WMD=-0.92, 95%CI: -1.51 to -0.33, P<0.05, SUCRA=69.4). It should be noted that there were no significant differences in HbA1c and GGT across different exercise interventions (all P>0.05), suggesting that there was currently no sufficient statistical evidence to support that exercise could improve these two indicators. ConclusionBased on the comprehensive league table and cumulative probability ranking, aerobic exercise combined with resistance exercise, resistance exercise, and low- and moderate-intensity aerobic exercise may be the best exercise modality for improving key indicators in MAFLD patients, and targeted exercise modalities should be selected for intervention against different indicators; however, due to limitations of the original studies, further studies are needed for validation and exploration.
3.Study on quality control of Jinbei oral liquid based on fingerprint,chemical pattern recognition and multi-index content determination
Jing TIAN ; Weiliang CUI ; Yuanfang ZANG ; Bing WANG ; Huifen LI ; Aijun ZHANG ; Fei XUE ; Yingying XIE ; Yongqiang LIN
China Pharmacy 2026;37(13):1704-1709
OBJECTIVE To establish a quality control method for Jinbei oral liquid based on multi-wavelength switching high performance liquid chromatography (HPLC) fingerprint, chemical pattern recognition and multi-index content determination. METHODS A total of 15 batches of Jinbei oral liquid were used as test samples. The Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine ( Version 2012 ) was adopted to establish multi-wavelength switching HPLC fingerprints, followed by chromatographic peak identification and similarity evaluation. Cluster analysis, principal component analysis and orthogonal partial least squares-discriminant analysis were applied to conduct chemical pattern recognition on the 15 batches of samples. The multi-wavelength switching HPLC method was used to simultaneously determine the contents of neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, forsythoside A, baicalin, salvianoli c acid B and wogonoside in samples. RESULTS A total of 25 common peaks were calibrated in the fingerprints of 15 batches of Jinbei oral liquid, among which 7 common peaks were unambiguously identified. The similarity of all samples was higher than 0.960. Chemical pattern recognition results showed that samples S1-S8 were clustered into group 1, and samples S9-S15 were clustered into group 2. Baicalin, wogonoside, neochlorogenic acid, chlorogenic acid and salvianolic acid B were identified as the differential quality markers. The average contents of neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, forsythoside A, baicalin, salvianolic acid B and wogonoside in 15 batches of Jinbei oral liquid were 0.213 7, 0.085 3, 0.104 9, 0.287 3, 0.420 1, 0.062 1, 0.176 0 mg/mL, respectively. CONCLUSIONS The established quality control method for Jinbei oral liquid combining multi-wavelength switching HPLC fingerprint, chemical pattern recognition and multi-index content determination is stable and reliable, which can provide a reference for the formulation of quality standards of this preparation.
4.Epidemiological characteristics and prevention strategies of gouty arthritis in Northwest China in 2023-2025
Ling FU ; Ning CUI ; Peijie XIA ; Nan ZHAO ; Aiqing PAN ; Xiaomin RAN ; Fei CHEN ; Kaiyue CAO
Journal of Public Health and Preventive Medicine 2026;37(4):56-60
Objective To explore the epidemiological characteristics of gouty arthritis in Northwest China from 2023 to 2025, and to formulate preventive intervention measures. Methods By retrospectively analyzing the case data of medical institutions in Northwest China from January 2023 to June 2025 (a total of 8 139 cases), the incidence trend and population distribution characteristics of gouty arthritis in this area were analyzed. Multivariate logistic regression analysis was performed to identify risk factors. Results From 2023 to 2025, the prevalence rate of gouty arthritis in adult residents in Northwest China was 3.74% (304/8 139). Among the sites affected by gouty arthritis, the metatarsophalangeal joint accounted for the highest proportion (P<0.05). From the perspective of age distribution, the prevalence rate was the highest in 40-59 years old group (5.90%), followed by 66-80 years old group (4.21%) and >80 years old group (2.75%). The incidence rate in 40-65 years old group was higher than that in the other age groups (P<0.05). High blood uric acid and creatinine, alcohol drinking, obesity, high purine diet, concurrent chronic kidney disease and metabolic syndrome were risk factors affecting gouty arthritis in Northwest China (P<0.05). Conclusion High blood uric acid level, high creatinine level, alcohol drinking, obesity, high purine diet, concurrent chronic kidney disease and metabolic syndrome are risk factors influencing gouty arthritis in Northwest China. Based on the above factors, this study proposes comprehensive prevention strategies from the individual, community and public health levels to provide a scientific basis for reducing the incidence rate of gouty arthritis.
5.Study on quality control of Jinbei oral liquid based on fingerprint,chemical pattern recognition and multi-index content determination
Jing TIAN ; Weiliang CUI ; Yuanfang ZANG ; Bing WANG ; Huifen LI ; Aijun ZHANG ; Fei XUE ; Yingying XIE ; Yongqiang LIN
China Pharmacy 2026;37(13):1704-1709
OBJECTIVE To establish a quality control method for Jinbei oral liquid based on multi-wavelength switching high performance liquid chromatography (HPLC) fingerprint, chemical pattern recognition and multi-index content determination. METHODS A total of 15 batches of Jinbei oral liquid were used as test samples. The Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine ( Version 2012 ) was adopted to establish multi-wavelength switching HPLC fingerprints, followed by chromatographic peak identification and similarity evaluation. Cluster analysis, principal component analysis and orthogonal partial least squares-discriminant analysis were applied to conduct chemical pattern recognition on the 15 batches of samples. The multi-wavelength switching HPLC method was used to simultaneously determine the contents of neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, forsythoside A, baicalin, salvianoli c acid B and wogonoside in samples. RESULTS A total of 25 common peaks were calibrated in the fingerprints of 15 batches of Jinbei oral liquid, among which 7 common peaks were unambiguously identified. The similarity of all samples was higher than 0.960. Chemical pattern recognition results showed that samples S1-S8 were clustered into group 1, and samples S9-S15 were clustered into group 2. Baicalin, wogonoside, neochlorogenic acid, chlorogenic acid and salvianolic acid B were identified as the differential quality markers. The average contents of neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, forsythoside A, baicalin, salvianolic acid B and wogonoside in 15 batches of Jinbei oral liquid were 0.213 7, 0.085 3, 0.104 9, 0.287 3, 0.420 1, 0.062 1, 0.176 0 mg/mL, respectively. CONCLUSIONS The established quality control method for Jinbei oral liquid combining multi-wavelength switching HPLC fingerprint, chemical pattern recognition and multi-index content determination is stable and reliable, which can provide a reference for the formulation of quality standards of this preparation.
6.Preliminary application of human-computer interaction CT imaging AI recognition and positioning technology in the treatment of type C1 distal radius fractures.
Yong-Zhong CHENG ; Xiao-Dong YIN ; Fei LIU ; Xin-Heng DENG ; Chao-Lu WANG ; Shu-Ke CUI ; Yong-Yao LI ; Wei YAN
China Journal of Orthopaedics and Traumatology 2025;38(1):31-40
OBJECTIVE:
To explore the accuracy of human-computer interaction software in identifying and locating type C1 distal radius fractures.
METHODS:
Based on relevant inclusion and exclusion criteria, 14 cases of type C1 distal radius fractures between September 2023 and March 2024 were retrospectively analyzed, comprising 3 males and 11 females(aged from 27 to 82 years). The data were assigned randomized identifiers. A senior orthopedic physician reviewed the films and measured the ulnar deviation angle, radial height, palmar inclination angle, intra-articular step, and intra-articular gap for each case on the hospital's imaging system. Based on the reduction standard for distal radius fractures, cases were divided into reduction group and non-reduction group. Then, the data were sequentially imported into a human-computer interaction intelligent software, where a junior orthopedic physician analyzed the same radiological parameters, categorized cases, and measured fracture details. The categorization results from the software were consistent with manual classifications (6 reduction cases and 8 non-reduction cases). For non-reduction cases, the software performed further analyses, including bone segmentation and fracture recognition, generating 8 diagnostic reports containing fracture recognition information. For the 6 reduction cases, the senior and junior orthopedic physicians independently analyzed the data on the hospital's imaging system and the AI software, respectively. Bone segments requiring reduction were identified, verified by two senior physicians, and measured for displacement and rotation along the X (inward and outward), Z (front and back), and Y (up and down) axes. The AI software generated comprehensive diagnostic reports for these cases, which included all measurements and fracture recognition details.
RESULTS:
Both the manual and AI software methods consistently categorized the 14 cases into 6 reduction and 8 non-reduction groups, with identical data distributions. A paired sample t-test revealed no statistically significant differences (P>0.05) between the manual and software-based measurements for ulnar deviation angle, radial ulnar bone height, palmar inclination angle, intra-articular step, and joint space. In fracture recognition, the AI software correctly identified 10 C-type fractures and 4 B-type fractures. For the 6 reduction cases, a total of 24 bone fragments were analyzed across both methods. After verification, it was found that the bone fragments identified by the two methods were consistent. A paired sample t-tests revealed that the identified bone fragments and measured displacement and rotation angles along the X, Y, and Z axes were consistent between the two methods. No statistically significant differences(P>0.05) were found between manual and software measurements for these parameters.
CONCLUSION
Human-computer interaction software employing AI technology demonstrated comparable accuracy to manual measurement in identifying and locating type C1 distal radius fractures on CT imaging.
Humans
;
Male
;
Female
;
Radius Fractures/surgery*
;
Middle Aged
;
Adult
;
Aged
;
Aged, 80 and over
;
Tomography, X-Ray Computed/methods*
;
Retrospective Studies
;
Software
;
Wrist Fractures
7.Association between atherogenic index of plasma trajectory and new-onset coronary heart disease in Chinese elderly people: a prospective cohort study.
Wan-Li HU ; Yv-Lin CHENG ; Dong-Hai SU ; Yv-Fang CUI ; Zi-Hao LI ; Ge-Fei LI ; Hai-Yun GAO ; Da-Tian GAO ; Xiao-Ke ZHANG ; Song-He SHI
Journal of Geriatric Cardiology 2025;22(10):835-843
BACKGROUND:
The atherogenic index of plasma (AIP) has been shown to be positively correlated with cardiovascular disease in previous studies. However, it is unclear whether elderly people with long-term high AIP levels are more likely to develop coronary heart disease (CHD). Therefore, the aim of this study was to investigate the relationship between AIP trajectory and CHD incidence in elderly people.
METHODS:
19,194 participants aged ≥ 60 years who had three AIP measurements between 2018 and 2020 were included in this study. AIP was defined as log10 (triglyceride/high-density lipoprotein cholesterol). The group-based trajectory model was used to identify different trajectory patterns of AIP from 2018 to 2020. Cox proportional hazards models were used to estimate the hazard ratio (HR) with 95% CI of CHD events between different trajectory groups from 2020 to 2023.
RESULTS:
Three different trajectory patterns were identified through group-based trajectory model: the low-level group (n = 7410, mean AIP: -0.25 to -0.17), the medium-level group (n = 9981, mean AIP: 0.02-0.08), and the high-level group (n = 1803, mean AIP: 0.38-0.42). During a mean follow-up of 2.65 years, a total of 1391 participants developed CHD. After adjusting for potential confounders, compared with the participants in the low-level group, the HR with 95% CI of the medium-level group and the high-level group were estimated to be 1.24 (1.10-1.40) and 1.43 (1.19-1.73), respectively. These findings remained consistent in subgroup analyses and sensitivity analyses.
CONCLUSIONS
There was a significant correlation between persistent high AIP level and increased CHD risk in the elderly. This suggests that monitoring the long-term changes in AIP is helpful to identify individuals at high CHD risk in elderly people.
9.Highly Sensitive Detection of Water in Organic Solvents Using Pyrene-Phenol-based Fluorescent Probe
Jing LIANG ; Yan-Li WANG ; Cui-Wen JIANG ; Xiao-Chuan HUANG ; Li TANG ; Tao LI ; Yu YA ; De-Jiao NING ; Li-Ping XIE ; Fei-Yan YAN
Chinese Journal of Analytical Chemistry 2025;53(6):894-902,中插1-中插5
A pyrene-phenol-based fluorescent probe PyP which showed typical intramolecular charge transfer(ICT)and monomer-excimer activities was synthesized by using pyrene carboxaldehyde hydrazone and 4-tert-butyl-2,6-diformylphenol as the raw materials.The effects of solvents on PyP were studied,and the results showed that the color of protic polar solvents(Ethanol,N,N-dimethylformamide,methanol and H2O)were successfully identified.Based on the solvent polarity-regulated PyP monomer-excimer switching,the rapid and highly sensitive ratiometric probe,"Turn-off"and"Turn-on"multimodal probes were established for detection of trace water content in organic solvents(Dimethyl sulfoxide,N,N-dimethylformamide,ethanol and methanol),with detection limits(3σ/k)of 0.0021%,0.046%,0.062%and 0.024%.The method was successfully used to detect water content in dimethyl sulfoxide,N,N-dimethy lformamide,ethanol and methanol commercial organic solvents,with recoveries ranging from 97.2%to 108.0%.The developed method showed good accuracy and stability,and had good application prospect.
10.Predictive value of preoperative combined detection of NLR and PTAR for early abdominal infection after liver transplantation
Huabin PENG ; Ying LIU ; Fei HOU ; Shuang ZHAO ; Yizhi ZHANG ; Tingting CUI ; Zhiying HE ; Jingyi LIU ; Haofeng XIONG ; Liying SUN
Organ Transplantation 2025;16(6):931-943
Objective To investigate the predictive value of preoperative combined detection of neutrophil-to-lymphocyte ratio (NLR) and prothrombin time-international normalized ratio to albumin ratio (PTAR) for early abdominal infection after liver transplantation. Methods Clinical data of 287 recipients who underwent liver transplantation at the Liver Transplant Center of Beijing Friendship Hospital, Affiliated to Capital Medical University, from January 2020 to April 2024 were retrospectively analyzed. The patients were divided into infection group (n=60) and non-infection group (n=227) based on whether abdominal infection occurred within 30 days after surgery. The distribution characteristics of pathogens and infection time in infected patients were analyzed. Spearman correlation analysis was used to assess the correlation between NLR, PTAR, Child-Pugh score and preoperative model for end-stage liver disease (MELD) score. Univariate and multivariate logistic regression analyses were performed to identify risk factors for abdominal infection. Receiver operating characteristic (ROC) curves were plotted for NLR, PTAR, and the combined prediction model to evaluate their predictive efficacy for abdominal infection after liver transplantation. Based on the cutoff value of the combined model, recipients were divided into low-risk and high-risk groups, and Kaplan-Meier analysis was used to compare the cumulative incidence of abdominal infection within 30 days after surgery between the two groups. Results Among the 287 recipients who underwent liver transplantation, 60 developed bacterial or fungal abdominal infections postoperatively. A total of 86 strains were isolated from infected patients, with Gram-negative bacteria accounting for 58%, Gram-positive bacteria for 36%, and fungi for 5%. Preoperative NLR and PTAR were positively correlated with Child-Pugh and MELD scores (all 1 > r > 0, P < 0.05). Logistic regression analysis showed that preoperative NLR, preoperative PTAR, postoperative ICU stay duration and postoperative biliary leakage were risk factors for abdominal infection within 30 days after surgery. The area under the curve (AUC) for NLR, PTAR, Child-Pugh score and MELD score were 0.771, 0.735, 0.650 and 0.741, respectively. The AUC for the combined NLR and PTAR prediction model was 0.824 (95% confidence interval: 0.763-0.885, P < 0.001), with a cutoff value of 0.168. Kaplan-Meier analysis showed that the cumulative incidence of abdominal infection within 30 days after surgery was lower in the low-risk group than in the high-risk group, with statistically significant difference (P < 0.001). Conclusions Preoperative NLR and PTAR are independent risk factors for abdominal infection within 30 days after liver transplantation. The combined prediction model of NLR and PTAR may effectively identify high-risk recipients for early abdominal infection after liver transplantation, providing basis for early intervention.


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