Colorectal cancer (CRC) is the third highest incidence and the second highest mortality malignant tumor in the world. The etiology and pathogenesis of CRC are complex. Due to the long course of the disease and no obvious early symptoms, most patients are diagnosed as middle and late stages. CRC is prone to metastasis, most commonly liver metastasis, which is one of the leading causes of death in CRC patients. Ferroptosis is a newly discovered cell death form with iron dependence, which is driven by excessive lipid peroxides on the cell membrane. It is different from other form of programmed cell death in morphology and mechanism, such as apoptosis, pyroptosis and necroptosis. Numerous studies have shown that ferroptosis may play an important role in the development of CRC. For advanced or metastatic CRC, ferroptosis promises to open a new door in the setting of poor response to chemotherapy and targeted therapy. This mini review focuses on the pathogenesis of CRC, the mechanism of ferroptosis and the research status of ferroptosis in CRC treatment. The potential association between ferroptosis and CRC and some challenges are discussed.
Rheumatoid arthritis (RA) is a type of chronic autoimmune and inflammatory disease. In the pathological process of RA, the alteration of fibroblast-like synoviocyte (FLS) and its related factors is the main influence in the clinic and fundamental research. In RA, FLS exhibits a uniquely aggressive phenotype, leading to synovial hyperplasia, destruction of the cartilage and bone, and a pro-inflammatory environment in the synovial tissue for perpetuation and progression. Evidently, it is a highly promising way to target the pathological function of FLS for new anti-RA drugs. Based on this, we summed up the pathological mechanism of RA-FLS and reviewed the recent progress of small molecule drugs, including the synthetic small molecule compounds and natural products targeting RA-FLS. In the end, there were some views for further action. Compared with MAPK and NF-κB signaling pathways, the JAK/STAT signaling pathway has great potential for research as targets. A small number of synthetic small molecule compounds have entered the clinic to treat RA and are often used in combination with other drugs. Meanwhile, most natural products are currently in the experimental stage, not the clinical trial stage, such as triptolide. There is an urgent need to unremittingly develop new agents for RA.
BackgroundShort-term unplanned readmission is always neglected, especially for elderly patients with coronary heart disease (CHD). However, tools to predict unplanned readmission are lacking. This study aimed to establish the most effective predictive model for the unplanned 7-day readmission in elderly CHD patients using machine learning (ML) algorithms.MethodsThe detailed clinical data of elderly CHD patients were collected retrospectively. Five ML algorithms, including extreme gradient boosting (XGB), random forest, multilayer perceptron, categorical boosting, and logistic regression, were used to establish predictive models. We used the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, the F1 value, the Brier score, the area under the precision-recall curve (AUPRC), and the calibration curve to evaluate the performance of ML models. The SHapley Additive exPlanations (SHAP) value was used to interpret the best model.ResultsThe final study included 834 elderly CHD patients, whose average age was 73.5 ± 8.4 years, among whom 426 (51.08%) were men and 139 had 7-day unplanned readmissions. The XGB model had the best performance, exhibiting the highest AUC (0.9729), accuracy (0.9173), F1 value (0.9134), and AUPRC (0.9766). The Brier score of the XGB model was 0.08. The calibration curve of the XGB model showed good performance. The SHAP method showed that fracture, hypertension, length of stay, aspirin, and D-dimer were the most important indicators for the risk of 7-day unplanned readmissions. The top 10 variables were used to build a compact XGB, which also showed good predictive performance.ConclusionsIn this study, five ML algorithms were used to predict 7-day unplanned readmissions in elderly patients with CHD. The XGB model had the best predictive performance and potential clinical application perspective.
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