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.
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Question: Influenza viruses are a major public health problem worldwide. Although influenza has been extensively researched, there are still many aspects that are not fully understood such as the effects of within and between-hosts dynamics and their impact on behavior change. Here, we develop mathematical models with multiple infection stages and estimate parameters based on within-host data to investigate the impact of behavior change on influenza dynamics. We divide the infected population into three and four groups based on the age of the infection, which corresponds to viral load shedding. We consider within-host data on viral shedding to estimate the length and force of infection of the different infectivity stages. Our results show that behavior changes, due to exogenous events (e.g., media coverage) and disease symptoms, are effective in delaying and lowering an epidemic peak. We show that the dynamics of viral shedding and symptoms, during the infection, are key features when considering epidemic prevention strategies. This study improves our understanding of the spread of influenza virus infection in the population and provides information about the impact of emergent behavior and its connection to the within and between-hosts dynamics.

Answer: Connecting within and between-hosts dynamics in the influenza infection-staged epidemiological models with behavior change


Question: 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.

Answer: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol


Question: Bidens pilosa L. is an edible herb and has been traditionally used for a wide range of ailments in many countries. The aim of this review is to present comprehensive information of the chemical constituents, nutraceutical and ethnomedical uses as well as the biological and pharmacological effects and toxicity of this plant based on 218 literary sources reported over 40 years. Major chemical constituents (including 301 compounds) belonging to polyacetylenes, polyacetylene glycosides, flavonoids, flavone glycosides, aurones, chalcones, okanin glycosides, phenolic acids, terpenes, pheophytins, fatty acids and phytosterols have been identified or isolated from the different parts of this plant. Many of them have been considered as the bioactive compounds which are potentially responsible for the pharmacological actions. Various types of preparations, extracts and individual compounds derived from this plant have been found to possess biological and pharmacological activities such as anti-malarial, anti-allergy, antihypertensive and smooth muscle relaxant, anti-cancerogenic, anti-diabetic, anti-inflammatory, anti-microbial, antioxidant. The results of data analysis on the chemicals, pharmacological and toxicological characteristics of B. pilosa validate the view of its folk worldwide-medicinal uses. This herb has a great beneficial therapeutic property and is possibly used for complement or alternative to pharmaceutical drugs in some specific cases. However, this herb is known as hyperaccumulator and as-excluder; therefore, harvesting the herb for medicinal uses should be judiciously cautioned.

Answer:
Chemistry and pharmacology of Bidens pilosa: an overview