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Gastric carcinomas have high morbidity and mortality. It produces no noticeable symptoms in the early stage while causing complex complications in its advanced stage, making treatment difficult. Palliative therapy aims to relieve the symptoms of cancer patients and focuses on improving their quality of life. At present, five palliative therapies for advanced gastric carcinomas are offered: resection, gastrojejunostomy, stenting, chemotherapy, and radiotherapy. In recent years, palliative therapy has been used in the clinical treatment of advanced gastric carcinomas and related complications because of its efficacy in gastric outlet obstruction and gastric bleeding. In the future, multimodal and interdisciplinary palliative therapies can be applied to control general symptoms to improve patients’ condition, prolong their lifespan and improve their quality of life.

Metabolic syndrome (MetS) is a major risk factor for cardiovascular disease and negatively affecting the prognosis of patients with ST elevation myocardial infarction (STEMI). Macrophage migration inhibitory factor (MIF) is a multipotent cytokine involved in various cardiovascular and inflammatory diseases. In this prospective study, we investigate the value of MIF in the long-term prognosis of STEMI combined with MetS after emergency PCI. Circulating MIF levels were measured at admission, and major adverse cardiovascular and cerebrovascular events (MACCE) were monitored during the follow-up period of 4.9 (3.9–5.8) years. MACCE occurred in 92 patients (22.9%), which was significantly higher in MetS (69/255, 27.1%) than in the non-MS subgroup (23/146, 15.8%, P < 0.05). Patients with MetS developed MACCE had the highest admission MIF level. Kaplan-Meier survival analysis using the cutoff value of admission MIF (143 ng/ml) showed that patients with a higher MIF level had a greater incidence of MACCE than those with lower MIF levels in both the MetS (P < 0.0001) and non-MetS groups (P = 0.016). After adjustment for clinical variables, the value of MIF ≥ 143 ng/ml still had the predictive power for the MetS group [HR 9.56, 95% CI (5.397–16.944),P < 0.001]; nevertheless, it was not the case in the non-MetS group. Our findings indicated that MetS is a critical risk factor for adverse clinical outcomes in patients with STEMI, and a high admission MIF level has predictive power for the long-term MACCE, which is superior in STEMI patients with MetS and better than other traditional predictors.

Objective: To automatically detect focal cortical dysplasia (FCD) lesion by combining quantitative multimodal surface-based features with machine learning and to assess its clinical value. Methods: Neuroimaging data and clinical information for 74 participants (40 with histologically proven FCD type II) was retrospectively included. The morphology, intensity and function-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface and fed to an artificial neural network. The classifier performance was quantitatively and qualitatively assessed by performing statistical analysis and conventional visual analysis. Results: The accuracy, sensitivity, specificity of the neural network classifier based on multimodal surface-based features were 70.5%, 70.0%, and 69.9%, respectively, which outperformed the unimodal classifier. There was no significant difference in the detection rate of FCD subtypes (Pearson’s Chi-Square = 0.001, p = 0.970). Cohen’s kappa score between automated detection outcomes and post-surgical resection region was 0.385 (considered as fair). Conclusion: Automated machine learning with multimodal surface features can provide objective and intelligent detection of FCD lesion in pre-surgical evaluation and can assist the surgical strategy. Furthermore, the optimal parameters, appropriate surface features and efficient algorithm are worth exploring.