Georgebuck2956

Importantly, Kaplan-Meier survival shown that patients with high infiltration of CD163+ macrophages and non-pCR had poor OS and RFS. Conclusions our data showed that TAMs may predict chemotherapeutic response and can be used as a promising prognostic candidate for poor survival in TNBC patients treated with NAC.Background Ovarian cancer (OC) has the highest mortality among gynecological malignancies, and resistance to chemotherapy drugs is common. We aim to develop a machine learning approach based on gut microbiota to predict the chemotherapy resistance of OC. Methods The study included patients diagnosed with OC by pathology and treated with platinum and paclitaxel in Shengjing Hospital of China Medical University between 2017 and 2018. TrichostatinA Fecal samples were collected from patients, and 16S rRNA sequencing was used to analyze the differences in gut microbiota between OC patients with and without chemotherapy resistance. Nine machine learning classifiers were used to derive the chemotherapy resistance of OC from gut microbiota. Results A total of 77 chemoresistant OC patients and 97 chemosensitive OC patients were enrolled. The gut microbiota diversity was higher in OC patients with chemotherapy resistance. There were statistically significant differences between the two groups in Shannon indexes (P less then 0.05) and Simpson indexes (P less then 0.05). Machine learning techniques can predict the chemoresistance of OC, and the random forest showed the best performance among all models. The area under the ROC curve for RF model was 0.909. Conclusions The diversity of gut microbiota was higher in OC patients with chemotherapy resistance. Further studies are warranted to validate our findings based on machine learning techniques.Background and Aims The tumor microenvironment can be divided into inflamed, immune-excluded and immune-desert phenotypes according to CD8+ T cell categories with differential programmed cell death protein 1 (PD-L1) expression. The study aims to construct a novel immunotype-based risk stratification model to predict postsurgical survival and adjuvant trans-arterial chemoembolization (TACE) response in patients with hepatocellular carcinoma (HCC). Methods A total of 220 eligible HCC patients participated in this study. CD8 + T cell infiltration and PD-L1 expression mode were estimated by immunohistochemical staining. A risk stratification model was developed and virtualized by a nomogram that integrated these independent prognostic factors. The postoperative prognosis and adjuvant TACE benefits were evaluated with a novel immunotype-based risk stratification model. Results A total of 220 patients were finally identified. Immune-desert, immune-excluded, and inflamed immunotypes represented 45%, 24%, and 31% of t postoperative prognosis and adjuvant TACE benefit in HCC patients. These tools can assist in building a more customized method of HCC treatment.Objective Recently, Nonalcoholic Steatohepatitis (NASH) has become a major contributor to cirrhosis and liver cancer. Therefore, the Global Burden of Disease (GBD) 2017 was used to comprehensively analyze the global, regional, and national burden of cirrhosis and liver cancer due to NASH between 1990 and 2017. Methods Data for cirrhosis and liver cancer due to NASH were extracted from the GBD study 2017. Socio-demographic Index (SDI) in 2017 was cited as indicators of socioeconomic status. ARIMA model was established to forecast the future health burden. Kruskal-Wallis test and Pearson linear correlation were adopted to evaluate the gender disparity and association with socioeconomic level. Results From 1990-2017, the global disability-adjusted life years (DALYs) numbers of liver cancer due to NASH increased from 0.71 million to 1.46 million. The age-standardized DALYs rates of liver cancer due to NASH were negatively associated with SDI levels (r=0.-409, p less then 0.001). Geographically, Australasia experienced the largest increase in the burden of liver cancer due to NASH, with the age-standardized DALYs rate increasing by 143.54%. The global prevalence number of liver cancer due to NASH peaked at 60-64 years in males and at 65-69 years in females. Globally, the burden was heavier in males compared with females. Male-female-ratio of age-standardized DALYs rates in liver cancer due to NASH were positively related to SDI (r=0.303, P=0.011). Conclusion The global burden of NASH-associated liver cancer has increased significantly since 1990, with age, gender and geographic disparity. Public awareness of liver diseases due to NASH should be emphasized.Radiation-induced lung injury (RILI) is a common serious complication and dose-limiting factor caused by radiotherapy for lung cancer. This study was to investigate radioprotective effects of grape seed proanthocyanidins (GSP) on normal lung as well as radiosensitizing effects on lung cancer. In vitro, we demonstrated radioprotective effects of GSP on normal alveolar epithelial cells (MLE-12 and BEAS/2B) and radiosensitizing effects on lung cancer cells (LLC and A549). In vivo, we confirmed these two-way effects in tumor-bearing mice. The results showed that GSP inhibited tumor growth, and played a synergistic killing effect with radiotherapy on lung cancer. Meanwhile, GSP reduced radiation damage to normal lung tissues. The two-way effects related to the differential regulation of the MAPK signaling pathway by GSP on normal lung and lung cancer. Moreover, GSP regulated secretion of cytokines IL-6 and IFN-γ and expression of p53 and Ki67 on normal lung and lung cancer. Our findings suggest that GSP is expected to be an ideal radioprotective drug for lung cancer patients who are treated with radiotherapy.Objectives In this study, we established a serum protein biomarker panel (consisting of Pro-SFTPB, CA125, Cyfra21-1, and CEA) and evaluated the feasibility and performance for the auxiliary diagnosis of lung cancer in the Chinese population. Materials and Methods The current study was a single-center study based on the Chinese population and performed in two cohorts (training cohort and validation cohort). Serum concentrations of Pro-SFTPB, CA125, Cyfra21-1, and CEA were measured by a bead-based flow fluorescence immunoassay. The discrimination performance of the model was assessed using sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). Results For the biomarker panel model, the AUC was 0.88 (95% CI, 0.85-0.91) in the training cohort and 0.90 (95% CI, 0.86-0.92) in the validation data cohort, which was significantly greater than the AUC of each biomarker alone. For the nodule risk model, the AUC was improved to 0.96 (95% CI, 0.94-0.98) in the training cohort and 0.