Objective To analyze the clinical and therapeutic characteristics of Epstein-Barr virus (EBV)-negative posttransplant lymphoproliferative disease (PTLD) with diffuse large B-cell lymphoma (DLBCL) in the context of specific cases and literature. Methods A case of EBV-negative DLBCL (GCB type) after kidney transplantation is reported. The patient was a 45-year-old male who underwent living-related kidney transplantation in 2016 and has been receiving triple immunosuppressive therapy with tacrolimus, mycophenolate mofetil and methylprednisolone since then. In 2024, the patient presented with intermittent fever, night sweats and gastrointestinal symptoms. The diagnosis was confirmed by endoscopic pathology, immunohistochemical staining and positron emission tomography/computed tomography. The R-CDOP regimen (rituximab + cyclophosphamide + liposomal doxorubicin + vincristine + dexamethasone) was used for treatment. Results The patient was diagnosed with EBV-negative DLBCL (GCB type, Ann Arbor stage Ⅳ B). After 4 cycles of R-CDOP chemotherapy, the efficacy assessment was partial remission, and the transplant kidney function remained stable. Conclusions For EBV-negative PTLD after kidney transplantation, it is necessary to break through the "virus-dependent" diagnostic thinking. In clinical practice, the focus should be on protecting the transplant kidney, and individualized treatment plans should be developed for patients.

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.

Objective To investigate the expression of calumenin(CALU)in gastric cancer and its effect on metastasis and invasion of gastric cancer,and analyze its relationship with the prognosis of gastric cancer patients.Methods The Cancer Genome Atlas(TCGA)database was used to analyze the expression level of CALU in gastric cancer and its impact on patient prognosis.A total of 102 pairs of gastric cancer and paracancerous tissue samples were collected from 189 gastric cancer patients who underwent partial gastrectomy in First Affiliated Hospital of Army Medical University from January 2018 to December 2022.The expression of CALU in gastric cancer and paracancerous tissues was detected by immunohistochemical assay,and the relationship of its expression with clinicopathological parameters was statistically analyzed.After gastric cancer cells with CALU knockdown and overexpression were constructed,and the efficiencies of knockdown and overexpression were evaluated by Western blotting as well as RT-qPCR.Transwell assay was applied to determine the effect of CALU on the migration and invasion abilities of gastric cancer cells.Results Bioinformation analysis found that CALU was significantly highly expressed in gastric cancer tissues(P<0.05),and its expression level was negatively correlated with the prognosis of patients(P<0.05).Immunohistochemical results showed that the expression level of CALU was obviously highly in gastric cancer tissues than the paracancerous tissues(P<0.01),and its level was positively correlated with the depth of infiltration(P<0.01),lymph node metastasis(P<0.01),and TNM stage(P<0.05).Statistical analysis revealed that the clinical data of 102 patients showed that CALU expression was positively correlated with the TNM stage(P=0.021)and T stage(P<0.001)and N stage(P=0.028).CALU knockdown significantly inhibited the migration and invasion abilities of gastric cancer cells(P<0.01),while over-expression obtained the opposite results.Conclusion CALU is highly expressed in gastric cancer tissues and promotes metastasis and invasion of gastric cancer and thus leads to poor prognosis in patients.

Nitrogen oxides(NOx=NO+NO2)are important precursors of ozone(O3),and NOx and its oxides together constitute reactive nitrogen oxides(NOy)in the atmosphere.A comprehensive understanding of the total NOy level in the atmosphere is of great significance for a deeper understanding of the atmospheric nitrogen cycle and oxidation,as well as for formulating strategies for air pollution prevention and control.In this work,a thermal decomposition-broadband cavity enhanced absorption spectroscopy(TD-BBCEAS)technique for online measurement of total NOy in the atmosphere was developed.With this method,the NOy was efficiently converted into NO2,and the total NOy concentration in the atmosphere was indirectly obtained by measuring NO2.Focusing on the key factors affecting the measurement of total NOy,the influence of NO titration efficiency and other NOy component TD efficiency on measurement accuracy was emphasized.By changing the oxygen(O2)flow rate through the mercury lamp to alter the O3 concentration for titrating NO,the conversion efficiency of NO was evaluated.At O2 flow rate of 6 mL/min,the conversion efficiency of NO was greater than 99%.TD efficiency testing and analysis on NO2,peroxyacetyl nitrate(PAN),nitric acid(HNO3),and nitrous acid(HONO),which account for a large proportion of atmospheric NOy components,was carried out using 680℃as the optimal TD temperature for efficient conversion of NOy.With NO and HONO sample gases as typical verification gases,the conversion efficiency of NOy and the accuracy of NOy measurement by TD-BBCEAS system were verified by switching the on and off modes of mercury lamp and TD device.At integration time of 60 s,the detection limit of the system for NOy was 2.83×1010 molecules/cm3(60 s,2σ).A comparative measurement of actual atmospheric NOy was conducted between the TD-BBCEAS system and the NOy analyzer.The observation results showed a correlation coefficient(R2)of 0.98 and a slope of 0.93,further verifying the feasibility and accuracy of applying the TD-BBCEAS system to measurement of total NOy.

Metal-organic frameworks(MOFs)are a class of organic-inorganic hybrid materials formed by the self-assembly of metal ions or metal clusters with organic ligands through coordination,and possess high specific surface area,tunable pore size and diverse structures.In recent years,MOFs and their composites have shown great application potential in the field of drug detection,especially in selective recognition,enhancing detection sensitivity and on-site rapid detection.This paper summarized the structural characteristics,synthesis methods and detection principles of MOFs and their composites,and reviewed the latest research progresses in detection of various drugs such as opioids,amphetamines,cannabinoids,cathinones,cocaine,ketamine,fentanyls and psychotropic drugs.The advantages and challenges of MOFs materials in the pretreatment of complex biological samples,sensor construction and on-site rapid detection were discussed,and the prospects for future development were analyzed,with the aim of providing theoretical support and technical references for promoting the applications of MOFs in anti-drug practice.

Objective To analyze the risk factors of critically ill patients infected with Acinetobacter bau-mannii(AB)and carbapenem resistant Acinetobacter baumannii(CRAB).Methods From January 2022 to June 2023,the data of Intensive Care Unit(ICU)patients admitted to Second Hospital of Shanxi Medical Uni-versity in Shanxi Province were collected.According to whether they were infected with AB,the patients were divided into an observation group and a control group(98 cases each).The observation group was further di-vided into a carbapenem sensitive Acinetobacter baumannii(CSAB)group(72 cases)and a CRAB group(26 cases).Mann-Whitney U test,chi-square test and other univariate and multivariate binary Logistic regression were used to analyze the risk factors of AB and CRAB infection for critically ill patients.The prognosis was analyzed by Kaplan Meier survival analysis.Results Long stay in ICU,previous use of carbapenem drugs and high Acute Physiology and Health Evaluation(APACHE Ⅱ)score were independent risk factors for AB sus-ceptibility(P＜0.05),while the independent risk factors for CRAB susceptibility were invasive ventilation and delayed surgery(P＜0.01).In addition,CRAB infection,COVID-19 and shock was risk factors for death in critically ill patients,and invasive ventilation,indwelling drainage tube and operation could reduce the risk of death in critically ill patients(P＜0.05).Conclusion ICU stay time,APACHE Ⅱ score,previous use of car-bapenem drugs and invasive ventilation increase the risk of AB and CRAB infection in critically ill patients.In-vasive ventilation,indwelling drainage and early surgery could reduce the risk of death from AB and CRAB in-fection in critically ill patients.

BACKGROUND:Primary osteoporosis has a high incidence,but the pathogenesis is not fully understood.Currently,there is a lack of effective early screening indicators and treatment programs. OBJECTIVE:To further explore the mechanism of primary osteoporosis through comprehensive bioinformatics analysis. METHODS:The primary osteoporosis data were obtained from the gene expression omnibus(GEO)database,and the differentially expressed genes were screened for Gene Ontology(GO)function and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis.In addition,the differentially expressed genes were subjected to protein-protein interaction network to determine the core genes related to primary osteoporosis,and the least absolute shrinkage and selection operator algorithm was used to identify and verify the primary osteoporosis-related biomarkers.Immune cell correlation analysis,gene enrichment analysis and drug target network analysis were performed.Finally,the biomarkers were validated using qPCR assay. RESULTS AND CONCLUSION:A total of 126 differentially expressed genes and 5 biomarkers including prostaglandins,epidermal growth factor receptor,mitogen-activated protein kinase 3,transforming growth factor B1,and retinoblastoma gene 1 were obtained in this study.GO analysis showed that differentially expressed genes were mainly concentrated in the cellular response to oxidative stress and the regulation of autophagy.KEGG analysis showed that autophagy and senescence pathways were mainly involved.Immunoassay of biomarkers showed that prostaglandins,retinoblastoma gene 1,and mitogen-activated protein kinase 3 were closely related to immune cells.Gene enrichment analysis showed that biomarkers were associated with immune-related pathways.Drug target network analysis showed that the five biomarkers were associated with primary osteoporosis drugs.The results of qPCR showed that the expression of prostaglandins,epidermal growth factor receptor,mitogen-activated protein kinase 3,and transforming growth factor B1 in the primary osteoporosis sample was significantly increased compared with the control sample(P<0.001),while the expression of retinoblastoma gene 1 in the primary osteoporosis sample was significantly decreased compared with the control sample(P<0.001).Overall,the study screened and validated five potential biomarkers of primary osteoporosis,providing a reference basis for further in-depth investigation of the pathogenesis,early screening and diagnosis,and targeted treatment of primary osteoporosis.

As new evidence emerges, treatment strategies toward the functional cure of chronic hepatitis B are evolving. In 2019, a panel of national hepatologists published a Consensus Statement on the functional cure of chronic hepatitis B. Currently, an international group of hepatologists has been assembled to evaluate research since the publication of the original consensus, and to collaboratively develop the updated statements. The 2.0 Consensus was aimed to update the original consensus with the latest available studies, and provide a comprehensive overview of the current relevant scientific literatures regarding functional cure of hepatitis B, with a particular focus on issues that are not yet fully clarified. These cover the definition of functional cure of hepatitis B, its mechanisms and barriers, the effective strategies and treatment roadmap to achieve this endpoint, in particular new surrogate biomarkers used to measure efficacy or to predict response, and the appropriate approach to pursuing a functional cure in special populations, the development of emerging antivirals and immunomodulators with potential for curing hepatitis B. The statements are primarily intended to offer international guidance for clinicians in their practice to enhance the functional cure rate of chronic hepatitis B.

Objective: To understand 24 h physical activity levels of children aged 8-9 years in Pudong New Area and to explore its association with metabolic syndrome, so as to provide scientific basis for children s participation in physical activities and reducing the risk of metabolic syndrome. Methods: A stratified cluster random sampling method was adopted to select 13 schools in Pudong New Area, Shanghai. A total of 2 013 primary school students aged 8-9 years old were included as the research subjects. During September 2021 to December 2022, Actigraph GT3X accelerometer, height measuring gauge, electronic sphygmomanometer and waist circumference tape was used to measure physical activity, height, blood pressure and waist circumference, respectively. A total of 5 mL of venous blood was collected from students, and the levels of triglycerides (TG), highdensity lipoprotein cholesterol (HDL-C) and fasting plasma glucose (FPG) were detected, and online questionnaires were conducted. The ttest and oneway ANOVA were employed to compare the differences in 24 h physical activity levels among children with different characteristics. Multivariate Logistic regression was used to analyze the association between the 24 h physical activity levels and metabolic syndrome as well as its components. Results: Among primary school students, the average daily time of moderate to vigorous physical activity (MVPA) was (34.25±13.49)min, the attainment rate was 1.59%. The average daily sleep (SLP) time was (538.27±28.53) min, attainment rate was 1.89%. The detection rates of metabolic syndrome, abdominal obesity (AO), elevated blood pressure (BP), elevated TG, low HDL-C, and elevated FPG were 2.48%, 34.53%, 10.38%, 10.73%, 1.24% and 0.70%, respectively. Multivariate Logistic regression analysis showed that, for every 10minute increase in sedentary behavior (SB) time, the risks of AO, elevated BP, and elevated TG increased by 2% ( OR=1.02, 95%CI =1.01-1.04), 5% ( OR=1.05, 95%CI =1.01-1.08), and 6% ( OR= 1.06, 95%CI =1.02-1.11), respectively ( P <0.05). For every 10minute increase in MVPA time, the risk of metabolic syndrome decreased by 27% ( OR=0.73, 95%CI=0.57-0.93, P <0.05). For every 10 minute increase in SLP time, the risks of AO, elevated BP, and metabolic syndrome decreased by 16% ( OR=0.84, 95%CI =0.80-0.88), 9% ( OR=0.91, 95%CI =0.82- 0.99 ), and 15% ( OR=0.85, 95%CI =0.77-0.94), respectively （P <0.05). Conclusions The time of MVPA and SLP are seriously insufficient among children aged 8-9 years in Pudong New Area. There is an association between physical activity levels and metabolic syndrome as well as its components. Increasing the time of MVPA and SLP is of great significance for maintaining a relatively low risk of metabolic syndrome in children.