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.

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.

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

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 evaluate the clinical efficacy of digitally guided precision crown lengthening in secondary aesthetic rehabilitation cases, and to provide a clinical reference for digitally guided crown lengthening procedures and secondary aesthetic restorations. Methods: We present a case of a patient with tetracycline-stained teeth, partial detachment of anterior resin veneers, and gingival margin discrepancies. The patient underwent digitally guided precision crown lengthening followed by secondary aesthetic rehabilitation. Multimodal data, including intraoral, facial, and CBCT scans, were integrated to construct a four-dimensional virtual patient model (incorporating teeth, face, bone, and occlusion) for surgical planning and 3D-printed guide fabrication. Secondary aesthetic restoration was performed after achieving stable post-surgical outcomes. Based on this case, we conducted a detailed analysis and reviewed relevant literature on crown lengthening in secondary aesthetic rehabilitation. Results: The gingival contour of the anterior teeth exhibited significant improvement, with enhanced symmetry and stable gingival margin positioning that closely matched the preoperative design. The crown lengthening procedure demonstrated high precision, and the final outcome was aesthetic and functional. Literature review indicated that secondary restorations frequently present challenges such as gingival contour discrepancies and inflammation. Aesthetic crown lengthening in the anterior region should optimize both soft and hard tissue morphology to meet aesthetic standards, with digital technology improving procedural accuracy. Conclusion Precision crown lengthening effectively addresses gingival margin discrepancies in secondary aesthetic rehabilitation, ensuring stable gingival positioning and superior aesthetic outcomes. This approach is particularly suitable for cases with high aesthetic demands.

ObjectiveTo investigate the current status of latent tuberculosis infection (LTBI) among the elderly population in rural areas of Changxing County, Zhejiang Province, and to provide an evidence for the development of LTBI prevention and control measures. MethodsBetween January and May 2024, elderly individuals participating in urban and rural residents’ health checkups were screened for Mycobacterium tuberculosis infection using a domestically produced interferon-γ release assay (IGRA) kit. Individuals tested positive by IGRA but without active tuberculosis were classified as LTBI cases. The prevalence of LTBI among the participants was subsequently analyzed. ResultsAmong the 6 765 subjects, 637 tested positive by IGRA, including one identified active tuberculosis patient, resulting in a LTBI prevalence rate of 9.40%. There was a statistically significant difference in positivity rates across different IGRA methodologies (χ²=35.530, P<0.001). Higher LTBI rate was observed in males, individuals with a history of diabetes mellitus, and those with a history of pulmonary tuberculosis, exhibiting statistically significant differences (χ²=32.401, P<0.001; χ²=5.789, P=0.020; χ²=39.248, P<0.001, respectively.) No statistically significant difference in LTBI rate was found across different age groups (χ²=0.238, P=0.971). ConclusionThe prevalence of LTBI among the elderly rural residents in Changxing County is relatively low. Male, individuals with a history of diabetes mellitus, and those with a history of pulmonary tuberculosis have an increased risk of LTBI, warranting targeted risk monitoring and timely interventions.

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.

AIM To investigate the ameliorative effects of Compound Fufangteng Mixture(CFM)on cyclophosphamide(CTX)-induced immunosuppression in mice.METHODS Forty-eight male C57BL/6J mice were randomly divided into the blank control group,the model group,the levamisole hydrochloride group(40 mg/kg)and the low-dose,medium-dose and high-dose CFM groups(3.75,7.5,10 g/kg),with 8 mice in each group,and given respective intervention orally once daily for 14 days.On the 5th to 7th day of administration,with the blank control group given normal saline intraperitoneally,the other groups underwent intraperitoneal CTX injections(80 mg/kg).24 hours after the last administration,organ indices of thymus and spleen were calculated;splenic histopathological alterations were assessed by HE staining;serum levels of IL-2,IL-6 and IgG were quantified using ELISA;splenic CD4+,CD8+T lymphocytes,alongside CD86+and CD206+macrophages populations were analyzed by flow cytometry;and splenic expression of CD4,CD8 and F4/80 was evaluated by immunohistochemical staining.RESULTS In CTX-treated mice,CFM administration mitigated body weight loss;enhanced thymus weight and thymic index;ameliorated splenic immune cell populations,elevated serum levels of cytokines IL-2,IL-6 and IgG in serum;and upregulated splenic levels of CD45+CD3+T lymphocytes and F4/80+CD11b+macrophages,alongside increasing the expression of CD4,CD8 and F4/80 surface markers.CONCLUSION CFM alleviates CTX-induced immunosuppression state in mice by modulating immune cells,restoring immune function and enhancing anti-inflammatory and tissue repair capabilities.