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: This study was performed to characterize the incidence, costs, and risk factors associated with renal and cardiovascular adverse outcomes following primary percutaneous coronary intervention (pPCI) in patients with ST-elevation myocardial infarction (STEMI). Methods: Patients with STEMI who underwent pPCI using iso-osmolar contrast were enrolled at 39 centers. The incidence of acute kidney injury (AKI) and major adverse renal and cardiovascular events (MARCE) was analyzed, as well as inpatient costs. Logistic regression analysis was performed to identify risk factors. Results: Among 2,293 patients, the incidence of AKI and MARCE within 72 hours post-pPCI was 4.14% (n = 95) and 4.40% (n = 101), respectively. AKI and/or MARCE were associated with systolic blood pressure (AKI: odds ratio [OR], 1.009; 95% confidence interval [CI], 1.000–1.018), hypertension (AKI: OR, 1.815; 95% CI, 1.133–2.906; MARCE: OR, 1.760;95% CI, 1.118–2.769), anterior wall infarction (AKI: OR, 1.895; 95% CI, 1.196–3.004; MARCE:OR, 1.939; 95% CI, 1.240–3.032), Killip class (AKI: OR, 1.465; 95% CI, 1.117–1.922; MARCE:OR, 1.467; 95% CI, 1.131–1.903), and serum creatinine (SCr; MARCE: OR, 1.006; 95% CI, 1.000–1.012). Hospitalization costs for patients with STEMI who developed AKI or MARCE were significantly higher than for those without AKI (9,595 ± 5,795 vs. 8,279 ± 3,872 USD, P = 0.003) or without MARCE (9,890 ± 5,616 vs. 8,255 ± 3,859 USD, P < 0.001). Conclusions In patients with STEMI undergoing pPCI with iso-osmolar contrast, the incidence of AKI and MARCE was associated with higher hospitalization costs. Systolic blood pressure, hypertension, anterior wall infarction, Killip class, and SCr were identified as risk factors for these outcomes.

Effective symptom management can alleviate the physical and psychological distress experienced by patients with cancer, improve quality of life, and contribute to treatment adherence and improve clinical outcomes. However, most existing guidelines are developed for healthcare professionals, and patients and the public have limited access to standardized and comprehensible guidance on symptom management. To address this gap and to facilitate effective communication and shared decision-making, this study proposes the development of a patient version of the cancer symptom management guideline. The development process will adhere to the methodological framework recommended by the Guidelines International Network and the World Health Organization. The GRADE approach will be employed to assess the certainty of evidence and to formulate recommendations. In addition, the process will be informed by the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ) instrument and the Reporting Items for Practice Guidelines in Healthcare-Public or Patient Versions of Guidelines (RIGHT-PVG). This protocol outlines the establishment of the guideline working group, the identification and prioritization of key questions, evidence retrieval and appraisal, and the formulation of recommendations, with the aim of ensuring methodological rigor and transparency in the development of the patient guideline and providing methodological reference for similar guideline initiatives.

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 develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective:The aim of this study was to investigate the mechanism of resveratrol activation of Sirt1/PGC-1α/Nrf2 pathway and its protective effect on apoptosis in rats with sepsis cardiomyopathy.Methods:In this study, 32 SD male rats were randomly (random number) divided into sham operation group (sham group), sepsis group (C group), Sirt1 agonist group (S group), Sirt1 agonist +PGC-1α inhibitor group (SP group), and cecal exploration was performed in rats of Sham group. The other three groups underwent Cecal ligation and puncture (CLP) to establish a rat model of septic cardiomyopathy, and the corresponding groups were given Sirt1 agonist resveratrol and PGC-1α inhibitor SR-18292. The heart of the rats were examined by B ultrasound 20 hours after CLP to compare left ventricular end-diastolic volume (LVEDV), left ventricular ejection fraction (LVEF), left ventricular end-systolic volume (LVESV) and left ventricular fraction shortening rate (LVFS). Myocardial tissue was stained with HE to observe the pathological changes, and serum myocardial troponin I (cTnI), interleukin-1β (IL-1β), interleukin-18 (IL-18) and oxidative stress indexes such as ROS and MDA were detected by ELISA. The expression levels of Sirt1, PGC-1α, Nrf2 and pyro related proteins (GSDMD, Caspase-1, NLRP3) in myocardial tissue were detected by Immunohistochemistry and Western Blotting.Results:Immunohistochemical and Western Blotting experiments showed that compared with sham group, the protein expressions of Sirt1, PGC-1α and Nrf2 in C group were decreased ( P < 0.05), and the expressions of GSDMD, Caspase-1 and NLRP3 were increased. The LVEF of group S rats was higher than that of group C rats ( P<0.05); Although the LVEDV and LVFS of group S were higher and the LVESV was lower, there was no statistically significant difference ( P > 0.05). Compared with group C, the edema and necrosis of cardiomyocytes in group S were alleviated, the arrangement of cardiomyocytes was slightly regular, and the inflammatory infiltration was reduced by HE staining. The cTnI, IL-1 β, IL-18, MDA, and ROS levels in group S were lower than those in group C ( P<0.05). Immunohistochemical and Western Blotting experiments showed that compared with group C, the protein expressions of Sirt1, PGC-1α and Nrf2 in group S were increased ( P < 0.05), and the expressions of GSDMD, Caspase-1 and NLRP3 were decreased ( P < 0.05). The LVESV of the SP group was higher than that of the S group rats ( P<0.05), while LVEDV, LVFS, and LVEF were all lower ( P>0.05). HE staining showed that the edema and necrosis of cardiomyocytes in SP group were more severe than those in S group, the arrangement of cardiomyocytes was more disordered than that in the former group, the vacuol-like deformation of cardiomyocytes was increased, and the infiltration of interstitial inflammatory cells was increased. The cTnI, IL-1 β, IL-18, MDA, and ROS levels in the SP group were higher than those in the S group rats ( P<0.05). There was no significant difference in Sirt1 expression between the SP group and the S group in myocardial tissue detected by immunohistochemistry and Western Blot. However, the expression levels of PGC-1 α and Nrf2 were lower in the SP group, while the expression levels of NLRP3, GSDMD, and Caspase-1 were higher in the SP group (all P<0.05). Conclusion:Resveratrol enhances myocardial function and decreases inflammatory cytokine levels in rats suffering from septic cardiomyopathy, potentially through a mechanism involving the Sirt1/PGC-1α/Nrf2 pathway.

Objective:To compare the degranulation levels of basophils in peripheral blood mononuclear cell (PBMC) and granulocyte populations between healthy subjects and patients with sepsis, and to explore the underlying mechanisms. Additionally, plasma cytokine levels were measured in these volunteers.Methods:Peripheral blood samples were collected from both healthy individuals and sepsis patients. The degranulation level of basophils in sepsis patients and its potential mechanisms were examined. Plasma levels of IL-1β, IL-9, and IL-10 were detected, and Pearson correlation analysis was performed to assess the relationship between degranulated basophils in the granulocyte population and IL-9 levels.Results:Compared with healthy subjects, sepsis patients showed a reduction in basophil percentages within PBMC and granulocyte populations by 94.8% and 37.9%, respectively ( Z = -6.441, P < 0.05; Z = -2.681, P < 0.05). In contrast, both the proportion and number of degranulated basophils in the granulocyte population were increased (both P < 0.05). Plasma levels of IL-1β, IL-9, and IL-10 were significantly elevated in sepsis patients--by 80.6%, 36.7%, and 11.9-fold, respectively ( Z = -4.258, P < 0.05; Z = -3.606, P < 0.05; Z = -4.814, P < 0.05). Moreover, plasma IL-9 levels were highly correlated with both the percentage and count of degranulated basophils in the granulocyte population (both P < 0.05). GO and KEGG enrichment analyses revealed cytological changes and potential mechanisms involving basophils in the PBMC of sepsis patients. Conclusions:The decreased total count of basophils in sepsis patients may elevate the risk of secondary infection. Degranulated basophils in the granulocyte population may contribute to excessive inflammatory responses through IL-9 secretion.