Sichuan University

About: Sichuan University is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Population & Catalysis. The organization has 107623 authors who have published 102844 publications receiving 1612131 citations. The organization is also known as: Sìchuān Dàxué.

Camrelizumab in patients with previously treated advanced hepatocellular carcinoma: a multicentre, open-label, parallel-group, randomised, phase 2 trial

Abstract: Summary Background Blocking the interaction between PD-1 and its ligands is a promising treatment strategy for advanced hepatocellular carcinoma. This study aimed to assess the antitumour activity and safety of the anti-PD-1 inhibitor camrelizumab in pretreated patients with advanced hepatocellular carcinoma. Methods This is a multicentre, open-label, parallel-group, randomised, phase 2 trial done at 13 study sites in China. Eligible patients were aged 18 years and older with a histological or cytological diagnosis of advanced hepatocellular carcinoma, had progressed on or were intolerant to previous systemic treatment, and had an Eastern Cooperative Oncology Group performance score of 0–1. Patients were randomly assigned (1:1) to receive camrelizumab 3 mg/kg intravenously every 2 or 3 weeks, via a centralised interactive web-response system using block randomisation (block size of four). The primary endpoints were objective response (per blinded independent central review) and 6-month overall survival, in all randomly assigned patients who had at least one dose of study treatment. Safety was analysed in all treated patients. This study is registered with ClinicalTrials.gov , number NCT02989922 , and follow-up is ongoing, but enrolment is closed. Findings Between Nov 15, 2016, and Nov 16, 2017, 303 patients were screened for eligibility, of whom 220 eligible patients were randomly assigned and among whom 217 received camrelizumab (109 patients were given treatment every 2 weeks and 108 every 3 weeks). Median follow-up was 12·5 months (IQR 5·7–15·5). Objective response was reported in 32 (14·7%; 95% CI 10·3–20·2) of 217 patients. The overall survival probability at 6 months was 74·4% (95% CI 68·0–79·7)]. Grade 3 or 4 treatment-related adverse events occurred in 47 (22%) of 217 patients; the most common were increased aspartate aminotransferase (ten [5%]) and decreased neutrophil count (seven [3%]). Two deaths were judged by the investigators to be potentially treatment-related (one due to liver dysfunction and one due to multiple organ failure). Interpretation Camrelizumab showed antitumour activity in pretreated Chinese patients with advanced hepatocellular carcinoma, with manageable toxicities, and might represent a new treatment option for these patients. Funding Jiangsu Hengrui Medicine.

Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators

Abstract: Prognostics and health management is an emerging discipline to scientifically manage the health condition of engineering systems and their critical components. It mainly consists of three main aspects: construction of health indicators, remaining useful life prediction, and health management. Construction of health indicators aims to evaluate the system’s current health condition and its critical components. Given the observations of a health indicator, prediction of the remaining useful life is used to infer the time when an engineering systems or a critical component will no longer perform its intended function. Health management involves planning the optimal maintenance schedule according to the system’s current and future health condition, its critical components and the replacement costs. Construction of health indicators is the key to predicting the remaining useful life. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. Because it is difficult to measure and quantify the health conditions of bearings and gears in many cases, numerous vibration-based methods have been proposed to construct bearing and gear health indicators. This paper presents a thorough review of vibration-based bearing and gear health indicators constructed from mechanical signal processing, modeling, and machine learning. This review paper will be helpful for designing further advanced bearing and gear health indicators and provides a basis for predicting the remaining useful life of bearings and gears. Most of the bearing and gear health indicators reviewed in this paper are highly relevant to simulated and experimental run-to-failure data rather than artificially seeded bearing and gear fault data. Finally, some problems in the literature are highlighted and areas for future study are identified.