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Background Age- and intent-related differences in the burden and costs of firearm injury treated in emergency departments are not well-documented. Methods We performed a serial cross-sectional study of the Healthcare Cost and Utilization Program Nationwide Emergency Department Survey from 2006 to 2016. We used International Classification of Diseases diagnoses codes revisions 9 and 10 to identify firearm injuries. We calculated survey-weighted counts, proportions, means, and rates and confidence intervals of national, age-specific (0–4, 5–9, 10–14, 15–17, 18–44, 45–64, 65–84, >84) and intent-specific (assault, unintentional, suicide, undetermined) emergency department discharges for firearm injuries. We used survey-weighted regression to assess temporal trends. Results There was a total of 868,483 (25.5 per 100,000) emergency department visits for firearm injuries from 2006 to 2016, and 7.8% died in the emergency department. Overall, firearm injury rates remained steady (P = .78). The largest burden was among those 25 to 44 years of age, but their rates remained stable (10.8 per 100,000). Overall assault injuries declined from 39.7% to 36.4%, and overall unintentional injuries increased from 46.4% to 54.7%. Legal-intervention injuries declined from 0.6 to 0.3 per 100,000. The charges (total $4,059,070,364, $369,006,396/year) increased across time in age and intent groups. The mean predicted charges increased from $1,922 to $3,348 in those alive versus $3,741 to $6,515 among those who died. Conclusion Interventions and programs to manage the consequences of firearm injury in persons who live with ongoing morbidity and economic burden are warranted.

Emerging data science techniques of predictive analytics expand the quality and quantity of complex data relevant to human health and provide opportunities for understanding and control of conditions such as heart, lung, blood, and sleep disorders. To realize these opportunities, the information sources, the data science tools that use the information, and the application of resulting analytics to health and health care issues will require implementation research methods to define benefits, harms, reach, and sustainability; and to understand related resource utilization implications to inform policymakers. This JACC State-of-the-Art Review is based on a workshop convened by the National Heart, Lung, and Blood Institute to explore predictive analytics in the context of implementation science. It highlights precision medicine and precision public health as complementary and compelling applications of predictive analytics, and addresses future research and training endeavors that might further foster the application of predictive analytics in clinical medicine and public health.

We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains.We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review.The initial search from PubMed resulted in 5,326 articles using the two sets of keywords. We then removed 44 duplicates and 5,282 articles were retained for abstract screening. After abstract screening, we included 246 articles for full-text screening, among which 87 articles were deemed eligible for full-text extraction. We summarized the 87 articles from four aspects: (1) methods for the global schema; (2) data integration strategies (i.e., federated system vs. data warehousing); (3) the sources of the data; and (4) downstream applications.SDI approach can effectively resolve the semantic heterogeneities across different data sources. We identified two key gaps and challenges in existing SDI studies that (1) many of the existing SDI studies used data from only single-level data sources (e.g., integrating individual-level patient records from different hospital systems), and (2) documentation of the data integration processes is sparse, threatening the reproducibility of SDI studies.

Pseudomonas aeruginosa causes a wide variety of nosocomial infections. In the study, phylogenetic, selective pressure analysis and homology modelling were applied to oprD efflux pump gene with the aim to characterize multi-drug resistant strains circulating in the nosocomial setting, their transmission dynamics and ongoing evolution. One hundred ninety-three consecutive inpatients with Pseudomonas aeruginosa infection were enrolled at the University Campus Bio-Medico of Rome, between January 2015 and December 2016. oprD gene was sequenced in 20 nosocomial multi-drug resistant P. aeruginosa strains. Phylogeographic, selective pressure, residue conservation analysis and homology modelling were performed. Clinical epidemiological data were extracted from patient medical records. Multi-drug resistant strains accounted for the 36% of total strains and were responsible of 20 cases of nosocomial infections. P. aeruginosa infections occurred prevalently in the West area, especially at the location IIIW and in the Geriatric ward. The time of the most recent common ancestor indicated that strains could have been introduced in the hospital since the end of the year 2009 with the most probable location in general surgery ward. By selective pressure analysis, 29 positions under diversifying selection have been identified and mapped onto the OprD model. Most of the observed residue substitutions are predicted to be destabilizing and some of them occurred in the Loops 2 and 3 that are involved in solute selection and carbapenem susceptibility. The molecular and evolutionary analysis of Multi-drug resistant strains circulating in the nosocomial setting may provide useful insights into the epidemiology and the mechanisms leading to resistance, contributing to infection control improvement.

Mobile (third-generation) sequencing technologies, including Oxford Nanopore’s MinION and SmidgION, have the benefit of outputting long sequence reads (up to hundred thousands of bases) in a portable manner. These sequencing devices fit in the palm of a hand and only require a USB outlet. Unfortunately, the development of data analysis tools for these technologies is in a nascent stage, impeding on the portability of these devices. The objective of this work is to introduce an out-of-core approach to port Nanopore analytics on mobile devices such as tablets or smartphones, often used in extreme experimental settings with special ergonomics needs and ease of sterilization. In this paper, we present a serial k-mer parser/counter for FAST5 files, and a de Bruijn graph construction method which can run on a hand-held device. In order to accomplish this portability we develop novel cache oblivious data structures and out-of-core chunked processing methods. Our toolset, which we refer to as Nanopore Portable Analytics Library (NanoPAL), wase implemented in ISO C++ v.14 and compiled for Android devices. Using MinION data (Zaire Ebolavirus species and others), we evaluate the time required to parse and build the de Bruijn graph with respect to the file sizes and RAM allocation. These metrics were compared to those of minimap/miniasm. On an LG Nexus 5 with 2GB or RAM, 2MB L2 cache and 16GB storage, the out-of-core NanoPAL is able to process FAST5 files at about 30 minutes per 0.5 GB, creating sorted k-mer and de Bruijn graph files. The recompiled minimap/miniasm tool cannot complete FAST5 files larger than 170MB. In conjunction with base calling/error correction, and with addition of assembly procedures downstream, NanoPAL can be effectively used to perform analyses of MinION/SmidgION data locally on a mobile device.

Mobile/portable (third-generation) sequencing technologies, including Oxford Nanopore’s MinION and SmidgION, are revolutionizing once again –after the advent of high-throughput sequencing– biomedical sciences. They combine an increase in sequence length (up to hundred thousands of bases) with extreme portability. While a sequencer now fits the palm of a hand and needs only a USB outlet or a mobile phone/tablet to work, the data analysis phases are bound to an available Internet connection and cloud computing. This somehow hampers the portability paradigm, especially if the technology is used in resource-limited settings or remote areas with limited connectivity. In this work, we introduce efficient data structures to effectively enable portable data analytics by means of third-generation sequencing. Specifically, we show how sequence overlap graphs (fixed length k-mers, with an extension on variable lengths) can be built and stored on a mobile phone, thereby allowing the execution of de novo genome assembly algorithms (along with ad-hoc strategies for error correction) without the need of transfer data over the Internet nor execution on a desktop.

We evaluated factors associated with normalization of the absolute CD4+ T-cell counts, per cent CD4+ T cells and CD4+/CD8+ T-cell ratio. A multicentre observational study was carried out in patients with sustained HIV-RNA 500/mm3 and multiple T-cell marker recovery (MTMR), defined as CD4+ T cells >500/mm3 plus %CD4 T cells >29% plus CD4+/CD8+ T-cell ratio >1. Kaplan-Meier survival analysis and Cox regression analyses to predict odds for achieving outcomes were performed. Three hundred and fifty-two patients were included and followed-up for a median of 4.1 (IQR 2.1–5.9) years, 270 (76.7%) achieving a CD4+ T-cell count >500 cells/mm3 and 197 (56%) achieving MTMR. Using three separate Cox models for both outcomes we demonstrated that independent predictors were: both absolute CD4+ and CD8+ T-cell counts, %CD4+ T cells, a higher CD4+/CD8+ T-cell ratio, and age. A likelihood-ratio test showed significant improvements in fitness for the prediction of either CD4+ >500/mm3 or MTMR by multivariable analysis when the other immune markers at baseline, besides the absolute CD4+ count alone, were considered. In addition to baseline absolute CD4+ T-cell counts, pretreatment %CD4+ T cells and the CD4+/CD8+ T-cell ratio influence recovery of T-cell markers, and their consideration should influence the decision to start antiretroviral therapy. However, owing to the small sample size, further studies are needed to confirm these results in relation to clinical endpoints.