In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Q: Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.

A: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol
****
Q: Autophagy and autophagy-related processes are fundamentally important in human health and disease. These processes are viewed primarily as cellular degradative pathways that recycle macromolecules and dysfunctional or redundant organelles into amino acids, sugars and lipids, especially during starvation. However, the ubiquitin-like autophagy proteins and other components of the autophagic machinery additionally participate in cellular reprogramming. We highlight these non-autophagic roles of autophagy proteins with the aim of drawing attention to this growing, but unexplored, research topic. We focus on the non-autophagic functions of autophagy proteins in cell survival and apoptosis, modulation of cellular traffic, protein secretion, cell signalling, transcription, translation and membrane reorganization.

A: review review Non-autophagic roles of autophagy-related proteins
****
Q: The genus Coronavirus contains about 25 species of coronaviruses (CoVs), which are important pathogens causing highly prevalent diseases and often severe or fatal in humans and animals. No licensed specific drugs are available to prevent their infection. Different host receptors for cellular entry, poorly conserved structural proteins (antigens), and the high mutation and recombination rates of CoVs pose a significant problem in the development of wide-spectrum anti-CoV drugs and vaccines. CoV main proteases (M pro s), which are key enzymes in viral gene expression and replication, were revealed to share a highly conservative substrate-recognition pocket by comparison of four crystal structures and a homology model representing all three genetic clusters of the genus Coronavirus. This conclusion was further supported by enzyme activity assays. Mechanism-based irreversible inhibitors were designed, based on this conserved structural region, and a uniform inhibition mechanism was elucidated from the structures of M pro -inhibitor complexes from severe acute respiratory syndrome-CoV and porcine transmissible gastroenteritis virus. A structureassisted optimization program has yielded compounds with fast in vitro inactivation of multiple CoV M pro s, potent antiviral activity, and extremely low cellular toxicity in cell-based assays. Further modification could rapidly lead to the discovery of a single agent with clinical potential against existing and possible future emerging CoV-related diseases. Citation: Yang H, Xie W, Xue X, Yang K, Ma J, et al. (2005) Design of wide-spectrum inhibitors targeting coronavirus main proteases. PLoS Biol 3(10): e324.

A:
Design of Wide-Spectrum Inhibitors Targeting Coronavirus Main Proteases
****