Purpose To evaluate the value of preoperative MRI-based radiomic models for assessing tumor-infiltrating CD8+T-cell expression in glioblastoma patients,and to identify the most stable and efficient radiomic feature region for predicting prognosis following immunotherapy.Materials and Methods This retrospective study included 150 patients with histopathologically confirmed glioblastoma from Lanzhou University Second Hospital(January 2018 to April 2022).Tumor-infiltrating CD8+T-cell expression was quantitatively assessed using immunohistochemical staining,with patients stratified into CD8-high and CD8-low expression groups based on overall survival.A total of 1 185 radiomic features were extracted from each patient's contrast-enhanced T1C and T2WI images,covering the original tumor region and sequentially expanded peritumoral regions(2.5 mm,5.0 mm,7.5 mm,10.0 mm,12.5 mm,15.0 mm morphological dilation of tumor core+peritumoral area).Feature selection was performed using variance threshold,minimum redundancy maximum relevance,and least absolute shrinkage and selection operator methods.XGBoost classifier was employed to construct clinical,radiomic,and clinical-radiomic multimodal combined prediction models.Diagnostic performance was evaluated using receiver operating characteristic curve analysis.Results The radiomic model based on tumor expansion of 7.5 mm(tumor+peritumoral region)demonstrated optimal predictive performance.The clinical-radiomic multimodal combined model showed superior predictive capability compared to clinical and radiomic models alone,achieving an area under the curve of 0.991 and accuracy of 99.0%in the training set,and area under the curve of 0.840 with accuracy of 80.0%in the validation set.Conclusion MRI radiomics provides a feasible approach for evaluating tumor-infiltrating CD8+T-cell expression in glioblastoma patients,offering potential for preoperative prognosis prediction.

Metastasis serves as an indicator of malignancy and is a biological characteristic of carcinomas. Epithelial-mesenchymal transition (EMT) plays a key role in the promotion of tumor invasion and metastasis and in the enhancement of tumor cell aggressiveness. Prostaglandin E synthase 3 (p23) is a cochaperone for heat shock protein 90 (HSP90). Our previous study showed that p23 is an HSP90-independent transcription factor in cancer-associated inflammation. The effect and mechanism of action of p23 on lung cancer metastasis are tested in this study. By utilizing cell models in vitro and mouse tail vein metastasis models in vivo, the results provide solid evidence that p23 is critical for promoting lung cancer metastases by regulating downstream CXCL1 expression. Rather than acting independently, p23 forms a complex with RNA-binding motif protein 14 (RBM14) to facilitate EMT progression in lung cancer. Therefore, our study provides evidence for the potential role of the RBM14-p23-CXCL1-EMT axis in the metastasis of lung cancer.

Colorectal cancer is one of the most common malignant tumors of the digestive system in clinical practice.The early detection of colorectal cancer based on artificial intelligence and its further assistance in clinical diagnosis and treatment hold significant clinical importance for achieving long-term benefits for patients.The development and validation of artificial intelligence software rely on high-quality,large-volume,and annotated colorectal cancer imaging datasets.This paper aims to provide a reference for constructing a high-quality colorectal cancer CT database,taking the construction of the database as an example.It discusses the complete process of database establishment,including database description,lesion annotation and storage,database quality evaluation and maintenance.The purpose is to ensure the high quality and exploitability of the source materials in the database,promote the sustainable and healthy development of the medical imaging artificial intelligence industry ecosystem,and accelerate the research,development,and application of industries related to artificial intelligence in colorectal cancer CT imaging.

Objective:To compare the clinical efficacy of fixed and non-fixed coronoid process fractures in the treatment of terrible triad of the elbow.Methods:Databases including CNKI, Wanfang Data Knowledge Service Platform, VIP, China Medical Journal Full-text Database, PubMed, Cochrane Library, Scopus and Web of Science were searched for relevant literatures on the treatment of elbow terrible triad. Postoperative elbow function score, range of motion, postoperative complications and other information were extracted, and meta-analysis was performed using Stata 18.0 statistical software.Results:A total of 139 patients from 5 literatures were included in the meta-analysis. All included literatures were in English, and the Newcastle-Ottawa Scale scores were 7-8 points. The results of meta-analysis showed that there was no statistically significant difference in the postoperative Mayo Elbow Performance Score (MEPS) between the two groups [ SMD=-0.33, 95% CI(-0.67, 0.01), P=0.061]. In Regan-Morrey type I and O'Driscoll type I coronoid process fractures, the MEPS of the coronoid fixation group was lower than that of the non-fixation group, and the difference was statistically significant [ SMD=-0.46, 95% CI(-0.88, -0.03), P=0.032]; the upper extremity functional disability score of the coronoid fixation group was higher than that of the non-fixation group, and the difference was statistically significant [ SMD=0.45, 95% CI(0.02, 0.89), P=0.041]. There were no statistically significant differences in the postoperative elbow flexion-extension range [ SMD=-0.31, 95% CI(-0.68, 0.07), P=0.109] and pronation-supination range [ SMD=-0.14, 95% CI(-0.51, 0.24), P=0.470] between the two groups. Conclusion:In the treatment of elbow terrible triad, the postoperative joint function score of non-fixation of Regan-Morrey type I and O'Driscoll type I coronoid process fractures is better than that of fixation.

Objective To investigate the preoperative prediction of the expression level of programmed cell death ligand 1(PD-L1)in non-small cell lung cancer(NSCLC)by a nomogram model constructed with clinical data,conventional CT signs and spectral CT parameters.Methods A retrospective analysis was conducted on 52 patients with pathologically confirmed NSCLC and undergoing preoperative spectral CT examination.The patients were categorized into positive and negative groups according to PD-L1 expression level,and their clinical data,conventional CT signs and spectral CT parameters were collected.Specifically,clinical data included gender,age,Ki-67 and tumor markers;conventional CT signs included tumor density,margins,calcification,spiculation,lobulation,pleural indentation and cavitation;and spectral CT parameters measured in the arterial and venous phases included effective atomic number(Eff-Z),iodine concentration(IC),water concentration(WC)and normalized iodine concentration(NIC).Intergroup differences were analyzed,and multivariate Logistic regression was used to identify independent predictors and establish the prediction model which was evaluated for prediction performance and accuracy using receiver operating characteristic(ROC)curves,calibration curve and decision curve analyses.Results For clinical data,only the difference in gender between two groups had statistical significance(P<0.05).The spectral CT parameters(IC,NIC and Eff-Z)in the arterial and venous phases of PD-L1 positive group were all greater than those of PD-L1 negative group,with statistically significant differences(P<0.05).Multivariate Logistic regression analysis identified gender(P=0.024),venous-phase Eff-Z(P=0.002),and venous-phase IC(P=0.003)as independent predictive factors for PD-L1 expression.The nomogram prediction model constructed with these independent predictors had an area under curve of 0.80,a sensitivity of 88.00%,and a specificity of 59.00%.The calibration curve showed that the predicted values had a high consistency with the actual values.The decision curve revealed that when the high-risk threshold was between 0.10 and 0.83,the model could achieve the maximum net benefit.Conclusion The nomogram model constructed with spectral CT parameters and clinical data has certain value in predicting the expression level of PD-L1 in NSCLC.

Objective:To compare the clinical efficacy of fixed and non-fixed coronoid process fractures in the treatment of terrible triad of the elbow.Methods:Databases including CNKI, Wanfang Data Knowledge Service Platform, VIP, China Medical Journal Full-text Database, PubMed, Cochrane Library, Scopus and Web of Science were searched for relevant literatures on the treatment of elbow terrible triad. Postoperative elbow function score, range of motion, postoperative complications and other information were extracted, and meta-analysis was performed using Stata 18.0 statistical software.Results:A total of 139 patients from 5 literatures were included in the meta-analysis. All included literatures were in English, and the Newcastle-Ottawa Scale scores were 7-8 points. The results of meta-analysis showed that there was no statistically significant difference in the postoperative Mayo Elbow Performance Score (MEPS) between the two groups [ SMD=-0.33, 95% CI(-0.67, 0.01), P=0.061]. In Regan-Morrey type I and O'Driscoll type I coronoid process fractures, the MEPS of the coronoid fixation group was lower than that of the non-fixation group, and the difference was statistically significant [ SMD=-0.46, 95% CI(-0.88, -0.03), P=0.032]; the upper extremity functional disability score of the coronoid fixation group was higher than that of the non-fixation group, and the difference was statistically significant [ SMD=0.45, 95% CI(0.02, 0.89), P=0.041]. There were no statistically significant differences in the postoperative elbow flexion-extension range [ SMD=-0.31, 95% CI(-0.68, 0.07), P=0.109] and pronation-supination range [ SMD=-0.14, 95% CI(-0.51, 0.24), P=0.470] between the two groups. Conclusion:In the treatment of elbow terrible triad, the postoperative joint function score of non-fixation of Regan-Morrey type I and O'Driscoll type I coronoid process fractures is better than that of fixation.

Colorectal cancer is one of the most common malignant tumors of the digestive system in clinical practice.The early detection of colorectal cancer based on artificial intelligence and its further assistance in clinical diagnosis and treatment hold significant clinical importance for achieving long-term benefits for patients.The development and validation of artificial intelligence software rely on high-quality,large-volume,and annotated colorectal cancer imaging datasets.This paper aims to provide a reference for constructing a high-quality colorectal cancer CT database,taking the construction of the database as an example.It discusses the complete process of database establishment,including database description,lesion annotation and storage,database quality evaluation and maintenance.The purpose is to ensure the high quality and exploitability of the source materials in the database,promote the sustainable and healthy development of the medical imaging artificial intelligence industry ecosystem,and accelerate the research,development,and application of industries related to artificial intelligence in colorectal cancer CT imaging.

BACKGROUND: Lung adenocarcinoma is an important pathohistologic subtype of non-small cell lung cancer (NSCLC). Invasive non-mucinous pulmonary adenocarcinomas (INMA) tend to have a poor prognosis due to their significant heterogeneity and diverse histologic components. Establishing a histologic grading system for INMA is crucial for evaluating its malignancy. In 2021, the International Association for the Study of Lung Cancer (IASLC) proposed that a new histological grading system could better stratify the prognosis of INMA patients. The aim of this study was to establish a visualized nomogram model to predict INMA IASLC grading preoperatively by means of dual-energy computed tomography (DECT), fractal dimension (FD), clinical features and conventional CT parameters. METHODS: A total of 112 patients with INMA who underwent preoperative DECT were retrospectively enrolled from March 2021 to January 2025. Patients were categorized into low-intermediate grade and high grade groups based on IASLC grading. The clinical characteristics and conventional CT parameters, including baseline features, biochemical markers, and serum tumor markers, were collected. DECT-derived parameters, including iodine concentration (IC), effective atomic number (eff-Z), and normalized IC (NIC), were collected and determined as NIC ratio (NICr) and fractal dimension (FD). Univariate analysis was employed to compare differences in conventional characteristics and DECT parameters between the two groups. Variables demonstrating statistical significance were subsequently incorporated into a multivariate Logistic regression analysis. A nomogram model integrating clinical data, conventional CT parameters, and DECT parameters was developed to identify independent predictors for IASLC grading of INMA. The discriminatory performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Multivariate analysis identified smoking history [odds ratio (OR)=2.848, P=0.041], lobulation sign (OR=2.163, P=0.004), air bronchogram (OR=7.833, P=0.005), eff-Z in arterial phase (OR=4.266, P<0.001), and IC in arterial phase (OR=1.290, P=0.012) as independent and significant predictors for IASLC grading of INMA. The nomogram model constructed based on these indicators demonstrated optimal predictive performance, achieving an area under the curve (AUC) of 0.804 (95%CI: 0.725-0.883), with specificity and sensitivity of 85.3% and 65.7%, respectively. CONCLUSIONS The nomogram model based on clinical features, imaging features and spectral CT parameters have a large potential for application in the preoperative noninvasive assessment of INMA IASLC grading.
Humans ; Nomograms ; Female ; Male ; Middle Aged ; Tomography, X-Ray Computed/methods* ; Lung Neoplasms/pathology* ; Aged ; Retrospective Studies ; Adenocarcinoma of Lung/pathology* ; Neoplasm Grading ; Adult

Background The association between occupational physical activity (OPA) and cardiometabolic risk factors remains controversial, potentially due to differences in the associations between OPA and various cardiometabolic indicators, as well as the lack of a clearly defined optimal OPA range for multiple-indicator synergistic benefits. Objective To investigate the relationship between OPA and cardiometabolic risk factors in individuals at high risk of cardiovascular disease (CVD) in Hubei Province, and to explore an optimal OPA range for multi-indicator improvements. Methods Data were derived from the Hubei Province dataset of the China Health Evaluation And Risk Reduction Through Nationwide Teamwork from 2015 to 2023, including 19028 high-risk CVD individuals. OPA (metabolic equivalent·h·d⁻¹, MET·h·d⁻¹) was assessed using the China Kadoorie Biobank physical activity questionnaire, anthropometric measurements (height, weight, waist circumference) and cardiometabolic indicators (blood pressure, fasting blood glucose, lipid profiles) were measured. Multivariate logistic regression was used to analyze the association between logOPA quartiles (Q1-Q4) and abnormal cardiometabolic indicators, while restricted cubic spline (RCS) models were employed to explore their dose-response relationships. Subgroup and interaction analyses were also conducted by gender. Results The median OPA of all participants was 12.86 MET·h·d⁻¹ (male: 14.40 MET·h·d⁻¹, female: 11.14 MET·h·d⁻¹). After adjusting for confounders, compared with the Q1 group, logOPA in the Q4 group was associated with 20% (OR=0.80, 95%CI: 0.72, 0.89), 14% (OR=0.86, 95%CI: 0.78, 0.96), and 13% (OR=0.87, 95%CI: 0.79, 0.95) reduction in the risk of abdominal obesity, low high-density lipoprotein cholesterol (HDL-C), and metabolic syndrome (MS), respectively, and 33% (OR=1.33, 95%CI: 1.18, 1.49), 33% (OR=1.33, 95%CI: 1.18, 1.50), and 38% (OR=1.38, 95%CI: 1.21, 1.57) increase in the risk of high total cholesterol (TC), high low-density lipoprotein cholesterol (LDL-C), and high non-HDL-C, respectively. Similar trends were observed in males and females, with significant positive associations of logOPA with high TC and high non-HDL-C in males (P_interaction<0.05). The risk of low HDL-C decreased rapidly when logOPA was more than 2.213 (OPA>9.14 MET·h·d⁻¹), showing an inverted L-shaped trend. The risk of high TC, high LDL-C, and high non-HDL-C increased beyond logOPA thresholds of 3.244, 2.476, and 3.377 (OPA >25.64, 11.89, and 29.28 MET·h·d⁻¹), respectively, showing a J-shaped trend. However, logOPA exhibited a U-shaped nonlinear dose-response relationship with blood pressure and fasting blood glucose abnormalities (P_non-linear<0.05) , with minimal risks at logOPA 2.969 and 2.372 (OPA of 19.47 and 10.72 MET·h·d⁻¹). Conclusion OPA exhibits heterogeneous dose-response patterns across cardiometabolic indicators. Integrating the inverted L-, J-, and U-shaped association patterns, we suggest an optimal OPA range of 9.14−25.64 MET·h·d⁻¹ for multiple cardiometabolic indicators in total individuals at high-risk of CVD, with ranges of 9.61−31.34 MET·h·d⁻¹ for males and 8.97−22.87 MET·h·d⁻¹ for females. These findings provide critical evidence for developing OPA interventions strategies targeting high-risk population for CVD.

BACKGROUND: Approximately 40% of individuals with diabetes worldwide are at risk of developing diabetic kidney disease (DKD), which is not only the leading cause of kidney failure, but also significantly increases the risk of cardiovascular disease, causing significant societal health and financial burdens. This study aimed to describe the burden of DKD and explore its cross-country epidemiological status, predict development trends, and assess its risk factors and sociodemographic transitions. METHODS: Based on the Global Burden of Diseases (GBD) Study 2021, data on DKD due to type 1 diabetes (DKD-T1DM) and type 2 diabetes (DKD-T2DM) were analyzed by sex, age, year, and location. Numbers and age-standardized rates were used to compare the disease burden between DKD-T1DM and DKD-T2DM among locations. Decomposition analysis was used to assess the potential drivers. Locally weighted scatter plot smoothing and Frontier analysis were used to estimate sociodemographic transitions of DKD disability-adjusted life years (DALYs). RESULTS: The DALYs due to DKD increased markedly from 1990 to 2021, with a 74.0% (from 2,227,518 to 3,875,628) and 173.6% (from 4,122,919 to 11,278,935) increase for DKD-T1DM and DKD-T2DM, respectively. In 2030, the estimated DALYs for DKD-T1DM surpassed 4.4 million, with that of DKD-T2DM exceeding 14.6 million. Notably, middle-sociodemographic index (SDI) quintile was responsible for the most significant DALYs. Decomposition analysis revealed that population growth and aging were major drivers for the increased DKD DALYs in most regions. Interestingly, the most pronounced effect of positive DALYs change from 1990 to 2021 was presented in high-SDI quintile, while in low-SDI quintile, DALYs for DKD-T1DM and DKD-T2DM presented a decreasing trend over the past years. Frontiers analysis revealed that there was a negative association between SDI quintiles and age-standardized DALY rates (ASDRs) in DKD-T1DM and DKD-T2DM. Countries with middle-SDI shouldered disproportionately high DKD burden. Kidney dysfunction (nearly 100.0% for DKD-T1DM and DKD-T2DM), high fasting plasma glucose (70.8% for DKD-T1DM and 87.4% for DKD-T2DM), and non-optimal temperatures (low and high, 5.0% for DKD-T1DM and 5.1% for DKD-T2DM) were common risk factors for age-standardized DALYs in T1DM-DKD and T2DM-DKD. There were other specific risk factors for DKD-T2DM such as high body mass index (38.2%), high systolic blood pressure (10.2%), dietary risks (17.8%), low physical activity (6.2%), lead exposure (1.2%), and other environmental risks. CONCLUSIONS DKD markedly increased and varied significantly across regions, contributing to a substantial disease burden, especially in middle-SDI countries. The rise in DKD is primarily driven by population growth, aging, and key risk factors such as high fasting plasma glucose and kidney dysfunction, with projections suggesting continued escalation of the burden by 2030.
Humans ; Global Burden of Disease ; Risk Factors ; Male ; Female ; Disability-Adjusted Life Years ; Diabetic Nephropathies/epidemiology* ; Middle Aged ; Diabetes Mellitus, Type 2/epidemiology* ; Adult ; Diabetes Mellitus, Type 1/complications* ; Aged ; Adolescent ; Young Adult ; Quality-Adjusted Life Years