Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective:To analyze the effect of acidic or alkaline diluents of the direct dilution method on the results of multiple elements measurement in whole blood by inductively coupled plasma mass spectrometry (ICP-MS) and to explore the possibility whether the two diluents can be substituted for each other in elemental measurement results.Methods:A total of 162 human whole blood samples collected from the National Human Biomonitoring Programme in August 2018 were used for dilution with different diluents followed by centrifugation, then the supernatants of the samples were measured by ICP-MS. The methodological characteristics of the two pre-treatment methods including acidic diluent (0.1% nitric acid+0.01% tralatone solution) and alkaline diluent (0.05% n-butanol+0.01% tralatone+1% ammonium hydroxide solution) were evaluated separately. Spearman correlation coefficient analysis was used for the correlation of 19 elements results in whole blood measured by 2 diluents, then, Passing-Bablok linear regression and Bland-Altman plots were used to evaluate the consistency of the 19 elements results in 162 whole blood samples between the two diluents.Results:The methodological data of 19 elements using the two diluents were good, with the limits of quantification (LOQ) of the 19 elements were 0.1-15.8 μg/L for acidic diluents and 0.3-19.2 μg/L for alkaline diluents, and the linear correlation coefficients of the standard curves of the 19 elements using the acidic and alkaline diluents were all≥0.995. Except for strontium, cadmium, tin, and thallium, the recovery percents of the 19 elements were all in the range of 80%-120%, and for all elements the total coefficients of variation of within-and between-run in the acidic and alkaline diluents were 0.5%-12.4%. The correlation coefficients of the two diluents for the measured values of chromium, manganese, cobalt, zinc, copper, arsenic, selenium, strontium, molybdenum, silver, cadmium, antimony, barium, mercury, and lead were relatively strong ( R2>0.8), while the correlation coefficients of vanadium, nickel, tin, and thallium were relatively weak ( R2<0.8). For the vanadium, cadmium, tin, barium, and mercury, 95% confidence intervals of slopes were<1. The 95% confidence intervals of intercepts of chromium, nickel, arsenic, silver, barium, and mercury contain point 0. The Bland-Altman plot showed that vanadium, chromium, arsenic, strontium, silver, cadmium, tin, and mercury have good consistency in using acidic and alkaline diluents. Conclusion:The results of the mean values measured with the 2 diluents differed among different elements and could not be completely substituted.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Background and objective Both of lung cancer incidence and mortality rank first among all cancers in China.Previous lung cancer screening trials were mostly selective screening for high-risk groups such as smokers.Non-smoking women accounted for a considerable proportion of lung cancer cases in Asia.This study aimed to evaluate the outcome of community-based mass screening in Guangzhou and identify the high-risk factors for lung cancer.Methods Residents aged 40-74 years in Guangzhou were screened with low-dose computed tomography(LDCT)for lung cancer and the pulmonary nodules were classified and managed according to China National Lung Cancer Screening Guideline with Low-dose Computed Tomography(2018 version).The detection rate of positive nodules was calculated.Before the LDCT examination,residents were required to complete a"lung cancer risk factors questionnaire".The risk factors of the questionnaire were analyzed by least absolute shrinkage and selection operator(LASSO)penalized Logistic regression analysis.Results A total of 6256 residents were included in this study.1228 positive nodules(19.63％)and 117 lung cancers were confirmed,including 6 cases of Tis,103 cases of stage Ⅰ(accounting for 88.03％of lung cancer).The results of LASSO penalized Logistic regression analysis indicated that age ≥50 yr(OR=1.07,95％CI:1.06-1.07),history of cancer(OR=3.29,95％CI:3.22-3.37),textile industry(OR=1.10,95％CI:1.08-1.13),use coal for cooking in childhood(OR=1.14,95％CI:1.13-1.16)and food al-lergy(OR=1.10,95％CI:1.07-1.13)were risk factors of lung cancer for female in this district.Conclusion This study highlighted that numerous early stages of lung cancer cases were detected by LDCT,which could be applied to screen-ing of lung cancer in women.Besides,age ≥50 yr,personal history of cancer,textile industry and use coal for cooking in childhood are risk factors for women in this district,which suggested that it's high time to raise the awareness of early lung cancer screening in this group.

Renal fibrosis is a devastating consequence of progressive chronic kidney disease,representing a major public health challenge worldwide.The underlying mechanisms in the pathogenesis of renal fibrosis remain unclear,and effective treatments are still lacking.Renal tubular epithelial cells(RTECs)maintain kidney function,and their dysfunction has emerged as a critical contributor to renal fibrosis.Cellular quality control comprises several components,including telomere homeostasis,ubiquitin-proteasome system(UPS),autophagy,mitochondrial homeostasis(mitophagy and mitochondrial metabolism),endoplasmic reticulum(ER,unfolded protein response),and lysosomes.Failures in the cellular quality control of RTECs,including DNA,protein,and organelle damage,exert profibrotic functions by leading to senescence,defective autophagy,ER stress,mitochondrial and lysosomal dysfunction,apoptosis,fibro-blast activation,and immune cell recruitment.In this review,we summarize recent advances in un-derstanding the role of quality control components and intercellular crosstalk networks in RTECs,within the context of renal fibrosis.

Objective To investigate the safety and efficacy of novel bioabsorbable vascular scaffold(BVS)in the treatment of patients with complex coronary artery disease.Methods This was a retrospective,matched,single-center observational study.45 patients with coronary atherosclerotic cardiopathy received BVS treatment in the cardiovascular medicine department Department of the Affiliated Hospital of Guangdong Medical University from June 2020 to June 2021(BVS),and 45 patients treated with drug-eluting stents(DES)group were selected according to matching study requirements during the same period.Baseline,surgical,and follow-up data were compared between the two groups to evaluate safety and efficacy.The main measures of safety were:surgical time,intraoperative adverse events,etc.,and the end point of efficacy was target lesion failure(TLF),including cardiac death,target vessel myocardial infarction,and ischa-driven target lesion revascularization.Results A total of 90 patients were enrolled in this study,all of whom were followed up for at least 3 years.There were 20 cases of bifurcation lesions and 25 cases of diffuse long lesions in the two groups,and 50 cases of imaging were reviewed among the 90 patients.The proportion of stable coronary heart disease,history of diabetes,history of hypertension,history of smoking,pre-dilated balloon pressure and postoperative diastolic blood pressure in BVS group was higher than that in DES group,and the proportion of family history was lower than that in DES group(all P＜0.05).There were no statistically significant differences in the rates of cardiac death,target vessel myocardial infarction,and ischemia-driven revascularization of target lesions between the two groups(all P＞0.05).Binary Logistic regression model analysis showed that the diameter stenosis ratio of target lesions was an independent risk factor for intrastent restenosis(OR 2.786,95％CI 1.096-7.081,P=0.031).Conclusions Compared with traditional DES,BVS implantation has consistent safety and efficacy in the treatment of complex coronary artery disease within 3 years.The diameter stenosis ratio of target lesions was an independent risk factor for intrastent restenosis.

Objective To investigate the value of deep learning(DL)model based on integrated 18F-FDG PET/MRI for evaluating cerebral metabolic status around cerebral infarction.Methods A total of 46 patients with cerebral infarction caused by unilateral internal carotid artery(ICA)or middle cerebral artery(MCA)steno-occlusion were retrospectively collected.Based on integrated 18F-FDG PET/MRI,DL model was used to automatically segment cerebral infarction area.Asymmetry index(AI)was used to evaluate the volume of reduced metabolic areas in the segmented affected frontal lobe,temporal lobe,parietal lobe,occipital lobe and cerebral hemisphere of cerebral infarction area as well as their proportions,while their correlations with National Institutes of Health stroke scale(NIHSS)score of neurological function were analyzed.Results Among 46 patients,the volume of decreased metabolism in the affected temporal lobe,parietal lobe and cerebral hemisphere was(41.35±10.52)ml,(65.58±14.82)ml and(178.89±34.23)ml,respectively,all positively correlated with NIHSS scores(rs=0.359,0.343,0.362,all P＜0.05).The proportion of the reduced metabolic volume in the affected frontal lobe,temporal lobe,parietal lobe and cerebral hemisphere was(45.68±10.35)%,(42.32±10.19)%,(45.05±9.44)%and(44.11±8.63)%,respectively,all positively correlated with NIHSS scores(rs=0.344,0.340,0.439,0.393,all P＜0.05).Conclusion DL model based on integrated 18F-FDG PET/MRI was of important clinical value for evaluating cerebral metabolic state around cerebral infarction.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.