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]: Bacille Calmette-Guérin (BCG) is an attenuated strain of Mycobacterium bovis currently used as a vaccine against tuberculosis. Global distribution and propagation of BCG has contributed to the in vitro evolution of the vaccine strain and is thought to partially account for the different outcomes of BCG vaccine trials. Previous efforts by several molecular techniques effectively identified large sequence polymorphisms among BCG daughter strains, but lacked the resolution to identify smaller changes. In this study, we have used a NimbleGen tiling array for whole genome comparison of 13 BCG strains. Using this approach, in tandem with DNA resequencing, we have identified six novel large sequence polymorphisms including four deletions and two duplications in specific BCG strains. Moreover, we have uncovered various polymorphisms in the phoP-phoR locus. Importantly, these polymorphisms affect genes encoding established virulence factors including cell wall complex lipids, ESX secretion systems, and the PhoP-PhoR two-component system. Our study demonstrates that major virulence factors are different among BCG strains, which provide molecular mechanisms for important vaccine phenotypes including adverse effect profile, tuberculin reactivity and protective efficacy. These findings have important implications for the development of a new generation of vaccines.
[A]: BMC Genomics Novel genome polymorphisms in BCG vaccine strains and impact on efficacy


[Q]: Community-ARI and inpatient-ARI was also combined with mild and severe cases of influenza from a historical prospective study as mild-ARI and severe-ARI respectively to evaluate the performance of clinical case definitions. We analysed 130 community-ARI and 140 inpatient-ARI episodes (5 inpatient-ARI excluded because multiple pathogens were detected), involving 138 and 207 samples respectively. Detection by PCR declined with days post-onset for influenza virus; decrease was faster for community-ARI than for inpatient-ARI. No such patterns were observed for noninfluenza respiratory virus infections. PathChip added substantially to viruses detected for community-ARI only. Clinical case definitions discriminated influenza from other mild-ARI but performed poorly for severe-ARI and for older participants. Rational strategies for diagnosis and surveillance of influenza and other respiratory virus must acknowledge the differences between ARIs presenting in community and hospital settings.
[A]: Detection of viral respiratory pathogens in mild and severe acute respiratory infections in Singapore To investigate the performance of laboratory methods and clinical case definitions in detecting the viral pathogens for acute respiratory infections (ARIs) from a prospective community cohort and hospital inpatients, nasopharyngeal swabs from cohort members reporting ARIs (community-ARI) and inpatients admitted with ARIs (inpatient-ARI) were tested by Singleplex Real Time-Polymerase Chain Reaction (SRT-PCR), multiplex RT-PCR (MRT-PCR) and pathogen-chip system (PathChip) between OPEN


[Q]: In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used documented data of 117,000 patients world-wide with laboratory-confirmed COVID-19. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 93% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model.
[A]:
Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making