Objective To determine the three-dimensional (3D) texture features extracted from intensity and high-order derivative maps that could reflect textural differences between bladder tumors and wall tissues,in order to achieve bladder cancer and wall tissue identification.Methods A total of 62 cancerous and 62 wall volumes of interest (VOI) were extracted from T2-weighted MRI datasets of 62 patients with pathologically confirmed bladder cancer.To reflect heterogeneous distribution of tumor tissues,3D high-order derivative maps (the gradient and curvature maps) were calculated from each VOI.Then 3D Haralick features based on intensity and high-order derivative maps and Tamura features based on intensity maps were extracted from each VOI.Statistical analysis was proposed to first select the features with significant differences and then obtain a more predictive and compact feature subset to verify its differentiation performance.Results From each VOI,a total of 58 texture features were derived.Among them,37 features showed significant inter-class differences (P≤ 0.01).Conclusion The results suggest that 3D texture features deriving from intensity and high-order derivative maps can reflect heterogeneous distribution of cancerous tissues.

Cognitive frailty is a heterogeneous clinical syndrome with cumulative effects in the negative events. The key link of prevention and management of cognitive frailty is to carry out community-based screening, effectively integrate and identify the predictive factors of cognitive frailty, early control the risk factors of cognitive frailty, and reduce the incidence. Building risk prediction models for disease diagnosis and risk prediction gradually becomes a research hotspot. This paper summarizes the risk factors, overview of risk prediction models, and comparison of different risk prediction models for cognitive frailty among the elderly in the community, in order to provide reference for grassroots medical and nursing staff to intervene in cognitive frailty among the elderly in the community.

Objective:To investigate the correlation of serum high-sensitivity cardiac troponin T (hs-cTnT) level with major adverse cardiovascular events (MACE) in patients with chronic coronary syndrome (CCS) undergoing percutaneous coronary intervention (PCI) and to explore its predictive value.Methods:It was a case-control study. Clinical data of 731 patients with CCS who underwent PCI in the Affiliated Zhongshan Hospital of Fudan University between May 2019 and April 2020 were retrospectively analyzed. Baseline clinical characteristics and pre/postoperative laboratory results were gathered, and patients were followed up and the incidence of MACE was documented. The correlation of serum hs-cTnT levels with MACE was analyzed, and the threshold of hs-cTnT for predicting the occurrence of MACE was determined.Results:Among 731 patients there were 560 males (76.61%) with the age of (64.05±9.48) years. Patients were followed up for 29.9 (18.8, 35.3) months, and MACE occurred in 216 cases (MACE group), and did not occur in 515 cases (control group). The X-tile software analysis showed that the optimal cutoff value of post-PCI hs-cTnT was 4.17×upper reference limit (URL) for predicting MACE ( P=0.033). Multivariate Cox regression analysis revealed that postoperative cTnT>6×URL was an independent risk factor for MACE in CCS patients after PCI ( HR=1.87, 95% CI: 1.19-2.94, P=0.007). The net reclassification index pairwise comparison results indicated that hs-cTnT>6×URL had the better predictive performance for MACE in CCS patients after PCI compared to 7×URL, 8×URL, 9×URL, 10×URL and 15×URL (all P<0.05). Conclusion:Postoperative hs-cTnT>6×URL is an independent risk factor for MACE in CCS patients after PCI, and hs-cTnT>6×URL is the optimal threshold for predicting the risk of MACE.

Objective To explore the influencing factors of coronary artery lesion severity in patients with acute coronary syndrome (ACS). Methods Clinical data of ACS patients admitted to Zhongshan Hospital, Fudan University from December 2017 to December 2019 were consecutively collected. The modified Gensini score was used to assess the severity of coronary artery lesions. Univariate and multivariate linear regression analyses were performed to identify independent factors associated with coronary artery lesion severity. Results A total of 1 689 ACS patients were included, with an average age of (64.04±11.45) years; 1 353 (80.11%) were male, and the mean modified Gensini score was (8.12±4.03). Multivariate linear regression analysis revealed that sex (β＝0.97, P＝0.001), age (β＝0.03, P＝0.021), estimated glomerular filtration rate (eGFR; β＝－0.03, P＜0.001), low-density lipoprotein cholesterol (LDL-C; β＝0.58, P＜0.001), apolipoprotein A1 (Apo A1; β＝－1.28, P＝0.012), lipoprotein(a) [Lp(a); β＝0.001, P＝0.033], and glycated hemoglobin A_1C (HbA_1C; β＝0.45, P＜0.001) were independent influencing factors of the modified Gensini score. Conclusions Metabolic indicators, including Apo A1, LDL-C, HbA_1C, and Lp(a), may serve as risk factors for coronary artery lesion severity in ACS patients, with Apo A1 demonstrating the strongest impact.