Fibroblast growth factor 21 improves insulin sensitivity by modulating the bile acid-gut microbiota axis in type Ⅱ diabetic mice.

Background: Fibroblast growth factor 21 (FGF21) is an important regulator of glycolipid metabolism. However, whether the gut microbiota is related to the anti-diabetic and obesity effects of FGF21 remains unclear.

Methods: Our research used KO/KO db/db male mice and streptozotocin (STZ)-induced to simulate the construction of two type II diabetic mellitus (T2DM) models, and detected impaired glucose tolerance in the model by using the ipGTT and ITT assays, and collected feces from the model mice for sequencing of the intestinal flora and the content of short-chain fatty acids.

polysaccharides reshape the gut microbiota to ameliorate dextran sodium sulfate-induced ulcerative colitis in mice.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized inflammatory imbalance, intestinal epithelial mucosal damage, and dysbiosis of the gut microbiota. polysaccharides (PCPs) can regulate gut microbiota and inflammation. Here, the different doses of PCPs were administered to dextran sodium sulfate-induced UC mice, and the effects of the whole PCPs were compared with those of the fractionated fractions PCP-1 (19.

Score for the Risk and Overall Survival of Lung Metastasis in Patients First Diagnosed With Soft Tissue Sarcoma: A Novel Nomogram-Based Risk Assessment System.

Metastatic soft tissue sarcoma (STS) patients have a poor prognosis with a 3-year survival rate of 25%. About 30% of them present lung metastases (LM). This study aimed to construct 2 nomograms to predict the risk of LM and overall survival of STS patients with LM.

Score for the Overall Survival Probability Scores of Fibrosarcoma Patients after Surgery: A Novel Nomogram-Based Risk Assessment System.

Background: The primary purpose of this study was to determine the risk factors affecting overall survival (OS) in patients with fibrosarcoma after surgery and to develop a prognostic nomogram in these patients.

Methods: Data were collected from the Surveillance, Epidemiology, and End Results database on 439 postoperative patients with fibrosarcoma who underwent surgical resection from 2004 to 2015. Independent risk factors were identified by performing Cox regression analysis on the training set, and based on this, a prognostic nomogram was created.

The Clinical Characteristics and Prognostic Nomogram for Head and Neck Cancer Patients with Bone Metastasis.

Background: Head and neck cancer (HNC) is the sixth most common malignancy globally, and many demographics and clinicopathological factors influence its prognosis. This study aimed to construct and validate a prognostic nomogram to predict the prognosis of HNC patients with bone metastasis (BM).

Methods: A total of 326 patients with BM from HNC were collected from the SEER database as the subjects of this study.

Construction and Validation of a Convenient Clinical Nomogram to Predict the Risk of Brain Metastasis in Renal Cell Carcinoma Patients.

Brain metastasis (BM) is a typical type of metastasis in renal cell carcinoma (RCC) patients. The early detection of BM is likely a crucial step for RCC patients to receive appropriate treatment and prolong their overall survival. The aim of this study was to identify the independent predictors of BM and construct a nomogram to predict the risk of BM.

Independent risk factors evaluation for overall survival and cancer-specific survival in thyroid cancer patients with bone metastasis: A study for construction and validation of the predictive nomogram.

Bone is a frequent site for the occurrence of metastasis of thyroid cancer (TC). TC with bone metastasis (TCBM) is associated with skeletal-related events (SREs), with poor prognosis and low overall survival (OS). Therefore, it is necessary to develop a predictive nomogram for prognostic evaluation.

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm.

Non-small-cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall survival for these patients is usually perishing. However, a model with high accuracy for predicting the survival of NSCLC with BM is still lacking. Here, we aimed to establish a model based on artificial intelligence for predicting the 1-year survival rate of NSCLC with BM by using extreme gradient boosting (XGBoost), a large-scale machine learning algorithm.