Objective To evaluate the effect of LifePort combined with polymyxin B in preventing donor-derived infections caused by preservation solution contamination. Methods Clinical data of 110 kidney transplant recipients were retrospectively analyzed. According to the decontamination status of preservation solution, the recipients were divided into the decontamination group (n=62) and the non-decontamination group (n=48). The general data of the two groups were compared, and the preventive effect of polymyxin B on possible donor-derived infections (p-DDI) was analyzed, especially infections associated with multidrug-resistant Gram-negative bacteria (MDR GNB). Results There were no statistically significant differences in baseline data (gender, age, preservation solution contamination status, etc.) between the decontamination group and the non-decontamination group (all P > 0.05). The overall contamination rate of preservation solution was 80.0%, and 68 contaminated samples were with single microorganism and 20 with multiple microorganisms. Coagulase-negative staphylococci, Enterococcus and Klebsiella pneumoniae were the most common microorganisms in the positive samples. Fifteen cases of preservation solution were contaminated by MDR GNB, including 10 cases in the non-decontamination group and 5 cases in the decontamination group, with no statistically significant difference between the two groups (P = 0.053). Postoperative infection-related events occurred in 69 recipients, including 39 cases in the non-decontamination group and 30 cases in the decontamination group, with the incidence rate in the non-decontamination group significantly higher than that in the decontamination group (P < 0.001). Only 10 cases of infections were identified as p-DDI, all of which were positive for preservation solution culture, including 8 cases in the non-decontamination group and 2 cases in the decontamination group (P < 0.05). There were 5 cases of p-DDI related to MDR GNB in the non-decontamination group, while no such cases occurred in the decontamination group (P < 0.05). No adverse reactions related to polymyxin B were observed, and no recipient death or renal allograft dysfunction occurred in either group. Conclusions Adding polymyxin B to the preservation fluid during hypothermic machine perfusion with LifePort before renal transplantation may reduce p-DDI and its potential adverse consequences.

Objective: To analyze the school tuberculosis (TB) outbreaks and preventive treatment in Hefei from 2022 to 2024, so as to provide reference for TB prevention and control in schools. Methods: Data were collected on all school based TB outbreaks occurring during 2022-2024 in Hefei, defined as ≥2 epidemiologically linked TB cases within the same school during a single semester. Statistical analyses were performed using the Chi square test. Results: Close contacts exhibited significantly higher TB incidence (2.88%) and latent mycobacterium tuberculosis infection (LTBI) rates (13.80%) in the school TB outbreaks, compared to non close contacts (0.12% and 2.63%, respectively). Among close contacts, secondary school students showed lower TB incidence (0.48%) and LTBI prevalence (3.42%) than both primary school or younger children (0.68%, 6.95%) and college students ( 0.78% , 6.50%), with statistically significant differences ( χ 2=360.91, 6.37; 791.71, 102.03, all P <0.05). The proportion of LTBI individuals recommended for preventive therapy was higher in primary school or younger groups (98.59%) than in secondary (95.25%) or college students (86.34%) ( χ 2=25.86, P <0.01). However, among those recommended, close contacts had higher uptake (85.82%) and completion rates (87.25%) of preventive therapy than non close contacts (69.63% and 70.57%); similarly, secondary school students demonstrated higher uptake (91.21%) and completion rates (86.45%) compared to primary school or younger (88.57%, 83.87%) and college students (57.28%, 64.08%) ( χ 2=30.52, 26.72; 125.17, 38.84, all P <0.01). Subsequent TB incidence among LTBI close contacts (13.30%) and among those who did not complete preventive therapy (22.73%) were significantly higher than among non close contacts (2.80%, 2.41%), respectively ( χ 2=32.19, 13.87, both P <0.05). Conclusions In school TB outbreaks, close contacts face higher LTBI prevalence and subsequent TB risk than non close contacts. College students show notably low adherence to preventive therapy. It is necessary to take targeted measures to improve the compliance of preventive measures among students.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

Diabetes mellitus, characterized by glucose metabolism disorders and marked by insulin deficiency or insulin resistance, has seen a continuous rise in prevalence. In recent years, islet transplantation has matured as a therapeutic approach for diabetes, becoming an important method for glycemic control and the reduction of diabetes-related complications. Donor selection directly influences transplant outcomes, and various research institutions worldwide have proposed multiple scoring systems to optimize donor assessment, such as the University of Alberta scoring system and the North American Islet Donor Score. This article explores the impact of key factors such as donor age, body mass index and ischemia time on islet transplantation. Combining practical experience in pancreatic donor selection from Shanghai Changzheng Hospital, it proposes screening criteria for pancreatic donors suitable for China, aiming to provide new evidence for improving the success rate of islet transplantation.

ObjectiveTo examine the influences of Shenfu injection on the endogenous metabolic byproducts in the myocardium of the rat model exhibiting chronic heart failure， thus deciphering the therapeutic mechanism of the Qi-reinforcing and Yang-warming method. MethodsSD rats were randomly allocated into a control group and a modeling group. Chronic heart failure with heart-Yang deficiency syndrome in rats was modeled by multi-point subcutaneous injection of isoproterenol， and the rats were fed for 14 days after modeling. The successfully modeled rats were randomized into model， Shenfu injection （6.0 mL·kg^-1）， and trimetazidine （10 mg·kg^-1） groups and treated with corresponding agents for 15 days. The control group and the model group were injected with equal doses of normal saline， and the samples were collected after the intervention was completed. Cardiac color ultrasound was performed. Hematoxylin-eosin （HE） staining was used to observe histopathological morphology， and the serum level of N-terminal pro-brain natriuretic peptide （NT-proBNP） was assessed by enzyme-linked immunosorbent assay （ELISA）. The mitochondrial morphological and structural changes of cardiomyocytes were observed by transmission electron microscopy， and the metabolic profiling was carried out by ultra high performance liquid chromatography-quantitative exactive-mass spectrometry （UHPLC-QE-MS）. Differential metabolites were screened and identified by orthogonal partial least squares-discriminant analysis （OPLS-DA） and other methods， and then the MetaboAnalyst database was used for further screening. The relevant biological pathways were obtained through pathway enrichment analysis. The receiver operating characteristic （ROC） curve was established to evaluate the diagnostic value of each potential biomarker for myocardial injury and the evaluation value for drug efficacy. ResultsThe results of color ultrasound showed that Shenfu Injection improved the cardiac function indexes of model rats （P<0.05）. The results of HE staining showed that Shenfu injection effectively alleviated the pathological phenomena such as myocardial tissue structure disorder and inflammatory cell infiltration in model rats. The results of ELISA showed that Shenfu injection effectively regulated the serum NT-proBNP level in the model rats. Transmission electron microscopy （TEM） showed that Shenfu injection effectively restored the mitochondrial morphological structure. The results of metabolomics showed that the metabolic phenotypes of myocardial samples presented markedly differences between groups. Nine differential metabolites could be significantly reversed in the Shenfu injection group， involving three metabolic pathways： pyruvate metabolism， histidine metabolism， and citric acid cycle （TCA cycle）. The results of ROC analysis showed that the area under the curve （AUC） values of all metabolites were between 0.75 and 1.0， indicating that the differential metabolites had high diagnostic accuracy for myocardial injury， and the changes in their expression levels could be used as potential markers for efficacy evaluation. ConclusionShenfu injection significantly alleviated the damage of cardiac function， myocardium， and mitochondrial structure in the rat model of chronic heart failure with heart-Yang deficiency syndrome by ameliorating energy metabolism remodeling. Reinforcing Qi and warming Yang is a key method for treating chronic heart failure with heart-Yang deficiency syndrome.

Parkinson's disease （PD） is a common neurodegenerative disorder characterized by motor impairments， with its pathological mechanisms involving multiple processes such as the degeneration of dopaminergic neurons and the abnormal aggregation of α-synuclein. Current Western medical treatments face challenges including diminished long-term efficacy and motor complications. In recent years， Traditional Chinese Medicine （TCM） has demonstrated advantages in the prevention and treatment of PD through its systematic regulatory capabilities， featuring multi-component， multi-target， and multi-pathway approaches.This article systematically reviews the roles of seven key signaling pathways-NF-κB， AMPK/mTOR， PI3K/Akt， MAPKs， Nrf2/ARE， Wnt/β-catenin， and BDNF/TrkB-in the pathological process of PD and the regulatory mechanisms of TCM. Research indicates that active ingredients of Chinese herbs and compound formulations can synergistically modulate these pathways， exerting comprehensive effects in inhibiting neuroinflammation， alleviating oxidative stress， promoting autophagy to clear abnormal proteins， and enhancing neurotrophic support. These signaling pathways form a complex regulatory network through crosstalk among key nodal molecules， constituting an intricate regulatory system in PD pathology. The multi-target intervention characteristics of TCM align well with this network-based regulatory requirement， achieving integrated anti-inflammatory， antioxidant， autophagy-regulating， and neurorestorative effects through synergistic multi-pathway modulation. This article systematically outlines the mechanisms of TCM in the coordinated regulation of multiple pathways， providing a theoretical basis for elucidating the pathological process of PD and the intervention mechanisms of TCM， while also offering new perspectives and directions for modern research on TCM in the prevention and treatment of PD.

ObjectiveThe malaria parasites remodel the host erythrocyte structure by exporting parasite proteins that interact with the membrane skeleton proteins of red blood cells (RBCs), facilitating their intracellular survival and pathogenicity. Skeleton-binding protein 1 (SBP1) is a conserved exported protein across Plasmodium species. In Plasmodium falciparum, SBP1 has been reported to interact with erythrocyte membrane skeleton proteins 4.1R and spectrin, while its contribution to erythrocyte remodeling and parasite virulence in Plasmodium berghei (Pb) remains unclear. This study aims to determine whether PbSBP1 associates with the host cytoskeletal protein 4.1R and to investigate its role in the remodeling of host RBCs and the pathogenicity of Plasmodium berghei. MethodsIn Plasmodium berghei, the relationship between PbSBP1 and the erythrocyte cytoskeletal protein 4.1R was examined using co-immunoprecipitation. A Pbsbp1 gene knockout mutant of Plasmodium berghei (Pbsbp1∆) was generated based on the principle of double crossover homologous recombination. The deformability of erythrocytes infected with Pbsbp1∆ parasites was assessed using microfluidic methods. Microchannels with an array of cylindrical pillars were used to detect modifications in infected RBC deformability. The infected RBCs were squashed between the rows and recovered between the columns and the transit velocity (μm/s) of infected RBCs travelling through the microchannel was recorded. The component of the erythrocyte membrane skeleton junctional complex, tropomodulin (TMOD), was fluorescently labeled, and the cytoskeletal network of infected erythrocytes was imaged using super-resolution stochastic optical reconstruction microscopy (STORM) to analyze ultrastructural changes in the cytoskeleton of wild-type (WT) and Pbsbp1∆-infected erythrocytes. Actin-based junctional complexes were displayed as individual clusters by the labeled TMOD in the STORM images, and the cluster densities and distances between adjacent clusters of infected RBCs were calculated. Additionally, rodent malaria models (BALB/c mice) and experimental cerebral malaria models (C57BL/6 mice) were employed to monitor the growth of Pbsbp1∆ and WT parasites during the intraerythrocytic stage and their capacity to induce cerebral malaria in mice. ResultsPbSBP1 may participate in the remodeling of infected erythrocytes through direct or indirect interaction with the erythrocyte cytoskeletal protein 4.1R. Microfluidic assays revealed that the deformability of erythrocytes infected with Pbsbp1∆ parasites was significantly enhanced compared to those infected with WT parasites. STORM imaging further demonstrated that the ultrastructure of the erythrocyte cytoskeleton in Pbsbp1∆-infected cells was altered relative to that in WT-infected erythrocytes. The distances between nearest neighbors of clusters had a tendency to increase while the cluster densities were decreased in Pbsbp1∆-infected RBCs compared to WT-infected RBCs. Subsequent phenotypic analysis indicated that the growth rate of Pbsbp1∆ parasites during the intraerythrocytic stage was significantly slower than that of WT parasites, and their ability to induce cerebral malaria in mice was also attenuated. These findings suggest that PbSBP1 is involved in the remodeling of the erythrocyte membrane skeleton, likely through its direct or indirect interaction with protein 4.1R, thereby regulating the deformability of infected erythrocytes and influencing the pathogenicity of the blood-stage parasites. ConclusionThis study establishes a role for PbSBP1 in host erythrocyte remodeling and parasite virulence, providing new research strategies for the prevention and treatment of malaria.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.