Traumatic arrests increasingly affect young adults worldwide with low reported survival rates. Entinostat This study examines factors associated with survival (to hospital discharge) in traumatic arrests transported to US trauma centers. This retrospective cohort study used the US National Trauma Databank 2015 dataset and included patients who presented to trauma centers with "no signs of life". Univariate and bivariate analyses were done. Factors associated with survival were identified using multivariate regression analyses. The study included 5980 patients with traumatic arrests. Only 664 patients (11.1%) survived to hospital discharge. Patients were predominantly in age group 16-64 (84.6%), were mostly males (77.8%) and white (55.1%). Most were admitted to Level I (55.5%) or Level II trauma centers (31.6%). Injuries were mostly blunt (56.7%) or penetrating (39.3%). The median of the injury severity score (ISS) was 19 (interquartile range [IQR] 9-30). Factors associated with decreased survival included Age group ≥ 65 (Ref 16-24), male gender, self-inflicted and other or undetermined types of injuries (Ref assault), injuries to head and neck, injuries to torso and ISS ≥ 16 (Ref < 16)and ED thoracotomy. While factors associated with increased survival included All injury mechanisms (with the exception of motor vehicle transportation) (Ref firearm), injuries to extremities or spine and back and all methods of coverage (Ref self-pay). Patients with traumatic arrests have poor outcomes with only 11.1% surviving to hospital discharge. Factors associated with survival in traumatic arrests were identified. These findings are important for devising injury prevention strategies and help guide trauma management protocols to improve outcomes in traumatic arrests. Level III.Level III. Next-Generation-Sequencing (NGS) enables detection of microorganisms present in biological and other matrices of various origin and nature, allowing not only the identification of known phyla and strains but also the discovery of novel ones. The large amount of metagenomic shotgun data produced by NGS require comprehensive and user-friendly pipelines for data analysis, that speed up the bioinformatics steps, relieving the users from the need to manually perform complex and time-consuming tasks. We describe here HOME-BIO (sHOtgun MEtagenomic analysis of BIOlogical entities), an exhaustive pipeline for metagenomics data analysis, comprising three independent analytical modules designed for an inclusive analysis of large NGS datasets. HOME-BIO is a powerful and easy-to-use tool that can be run also by users with limited computational expertise. It allows in-depth analyses by removing low-complexity/ problematic reads, integrating the analytical steps that lead to a comprehensive taxonomy profile of each sample by querying different source databases, and it is customizable according to specific users' needs.HOME-BIO is a powerful and easy-to-use tool that can be run also by users with limited computational expertise. It allows in-depth analyses by removing low-complexity/ problematic reads, integrating the analytical steps that lead to a comprehensive taxonomy profile of each sample by querying different source databases, and it is customizable according to specific users' needs. Available data on influenza burden across Southeast Asia are largely limited to pediatric populations, with inconsistent findings. We conducted a multicenter, hospital-based active surveillance study of adults in Malaysia with community-acquired pneumonia (CAP), acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and acute exacerbation of asthma (AEBA), who had influenza-like illness ≤10 days before hospitalization. We estimated the rate of laboratory-confirmed influenza and associated complications over 13 months (July 2018-August 2019) and described the distribution of causative influenza strains. We evaluated predictors of laboratory-confirmed influenza and severe clinical outcomes using multivariate analysis. Of 1106 included patients, 114 (10.3%) were influenza-positive; most were influenza A (85.1%), with A/H1N1pdm09 being the predominant circulating strain during the study following a shift from A/H3N2 from January-February 2019 onwards. In multivariate analyses, an absence of comorbidities (none versus any comorbidity [OR (95%CI), 0.565 (0.329-0.970)], p= 0.038) and of dyspnea (0.544 (0.341-0.868)], p= 0.011) were associated with increased risk of influenza positivity. Overall, 184/1106 (16.6%) patients were admitted to intensive care or high-dependency units (ICU/HDU) (13.2% were influenza positive) and 26/1106 (2.4%) died (2.6% were influenza positive). Males were more likely to have a severe outcome (ICU/HDU admission or death). Influenza was a significant contributor to hospitalizations associated with CAP, AECOPD and AEBA. However, it was not associated with ICU/HDU admission in this population. Study registration, NMRR ID NMRR-17-889-35,174.Influenza was a significant contributor to hospitalizations associated with CAP, AECOPD and AEBA. However, it was not associated with ICU/HDU admission in this population. Study registration, NMRR ID NMRR-17-889-35,174. Hypertrophic cardiomyopathy (HCM) represents one of the most common inherited heart diseases. To identify key molecules involved in the development of HCM, gene expression patterns of the heart tissue samples in HCM patients from multiple microarray and RNA-seq platforms were investigated. The significant genes were obtained through the intersection of two gene sets, corresponding to the identified differentially expressed genes (DEGs) within the microarray data and within the RNA-Seq data. Those genes were further ranked using minimum-Redundancy Maximum-Relevance feature selection algorithm. Moreover, the genes were assessed by three different machine learning methods for classification, including support vector machines, random forest and k-Nearest Neighbor. Outstanding results were achieved by taking exclusively the top eight genes of the ranking into consideration. Since the eight genes were identified as candidate HCM hallmark genes, the interactions between them and known HCM disease genes were explored through the protein-protein interaction (PPI) network.