Prominent Injury Types in Vehicle Underbody Blast.

BACKGROUND: To fully understand the injury mechanisms during an underbody blast (UBB) event with military vehicles and develop new testing standards specific to military vehicles, one must understand the injuries sustained by the occupants. METHODS: Injury data from Service Members (SM) involved in UBB theater events that occurred from 2010 to 2014 were analyzed. Analysis included the investigation of prominent skeletal and visceral torso injuries. Results were categorized by killed-in-action (n = 132 SM) and wounded-in-action (n = 1,887 SM). RESULTS: Over 90% (553/606 SM) of casualties in UBB events with Abbreviated Injury Scale (AIS) 2+ injury sustained at least one skeletal fracture, when excluding concussion. The most frequent skeletal injuries from UBB were foot fractures (13% of injuries) for wounded-in-action and tibia/fibula fractures (10% of injuries) for killed-in-action. Only 1% (11/1037 SM) of all casualties with AIS 2+ injuries had visceral torso injuries without also sustaining skeletal fractures. In these few casualties, the coded injuries were likely due to trauma from a loading path other than direct UBB loading. CONCLUSION: Skeletal fractures are the most frequent AIS 2+ injury resulting from UBB events. Visceral torso injuries are infrequent in individuals that survive and they generally occur in conjunction with skeletal injuries

Development of an expert derived ICD-AIS map for serious AIS3+ injury identification

Objective: The objective of the mapping project was to develop an expert derived map between the International Statistical Classification of Diseases and Related Health Problems (ICD) clinical modifications (CM) and the Abbreviated Injury Scale (AIS) to be able to relate AIS severity to ICD coded data road traffic collision data in EU datasets. The maps were developed to enable the identification of serious AIS3+ injury and provide details of the mapping process for assumptions to be made about injury severity from mass datasets. This article describes in detail the mapping process of the International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) and the International Classification of Diseases Tenth Revision, Clinical Modification (ICD-10-CM) codes to the Abbreviated Injury Scale 2005, Update 2008 (AIS08) codes to identify injury with an AIS severity of 3 or more (AIS3+ severity) to determine ‘serious’ (MAIS3+) road traffic injuries.Methods: Over 19,000 ICD codes were mapped from the following injury categories; injury ICD-9-CM (Chapter 17) codes between ‘800 and 999.9’ and injury ICD-10-CM (Chapter 19) ‘S’ and ‘T’ prefixed codes were reviewed and mapped to an AIS08 category and then relate the severity to three groups; AIS3+, AIS Results: In total 2,504 ICD-9-CM codes were mapped to the AIS, of which 780 (31%) were assigned an AIS3+ severity. For the16,508 ICD-10-CM mapped codes a total of 2,323 (14%) were assigned an AIS3+ severity. Some 17% (n=426) and 27% (n=4,485) of ICD-9-CM and ICD-10-CM codes respectively were assigned to AIS9 (no-map) following the mapping process. It was evident there were ‘problem’ codes that could not be easily mapped to an AIS code to reflect severity. Problem maps affect the specificity of the map and severity when used to translate historical data in large datasets.Conclusions: The Association for the Advancement in Automotive Medicine, AAAM-endorsed expert-derived map offers a unique tool to road safety researchers to establish the number of MAIS3+ serious injuries occurring on the roads. The detailed process offered in this paper will enable researchers to understand the decision making and identify limitations when using the AIS08/ICD map on country-specific data. The results could inform protocols for dealing with problem codes to enable country comparisons of MAIS3+ serious injury rates.</div

Development of an expert based ICD-9-CM and ICD-10-CM map to AIS 2005 update 2008

Objective: This paper describes how maps were developed from the Clinical Modifications of the 9th and 10th Revisions of the International Classification of Diseases (ICD) to the Abbreviated Injury Scale 2005 Update 2008 (AIS08). The development of the mapping methodology is described, with discussion of the major assumptions used in the process to map ICD codes to AIS severities. There were many intricacies to developing the maps, since the two coding systems, ICD and AIS, were developed for different purposes and contain unique classification structures to meet these purposes. Methods: Experts in ICD and AIS analyzed the rules and coding guidelines of both injury coding schemes to develop rules for mapping ICD injury codes to the AIS08. This involved subject matter expertise, detailed knowledge of anatomy, and an in-depth understanding of injury terms and definitions as applied in both taxonomies. The official ICD-9-CM and ICD-10-CM versions (injury sections) were mapped to the AIS08 codes and severities, following the rules outlined in each coding manual. The panel of experts was comprised of coders certified in ICD and/or AIS from around the world. In the process of developing the map from ICD to AIS, the experts created rules to address issues with the differences in coding guidelines between the two schemas and assure a consistent approach to all codes. Results: Over 19,000 ICD codes were analyzed and maps were generated for each code to AIS08 chapters, AIS08 severities, and ISS body regions. After completion of the maps, 14,101 (74%) of the eligible 19,012 injury related ICD-9-CM and ICD-10-CM codes were assigned valid AIS08 severity scores between 1 and 6. The remaining 4,911 codes were assigned an AIS08 of 9 (unknown) or were determined to be non-mappable because the ICD description lacked sufficient qualifying information for determining severity according to AIS rules. There were also 15,214 (80%) ICD codes mapped to AIS08 chapter and ISS body region, which allow for ISS calculations for patient datasets. Conclusion: This mapping between ICD and AIS provides a comprehensive, expert-designed solution for analysts to bridge the data gap between the injury descriptions provided in hospital codes (ICD-9-CM, ICD-10-CM) and injury severity codes (AIS08). By applying consistent rules from both the ICD and AIS taxonomies, the expert panel created these definitive maps, which are the only ones endorsed by AAAM. Initial validation upheld the quality of these maps for the estimation of AIS severity, but future work should include verification of these maps for MAIS and ISS estimations with large datasets. These ICD-AIS maps will support data analysis from databases with injury information classified in these two different systems and open new doors for the investigation of injury from traumatic events using large injury datasets

Expert-developed ICD-AIS map for measuring serious road traffic injuries

Expert-developed ICD-AIS map for measuring serious road traffic injurie

Modularized CRISPR/dCas9 Effector Toolkit for Target-Specific Gene Regulation

The ability to control mammalian genes in a synergistic mode using synthetic transcription factors is highly desirable in fields of tissue engineering, stem cell reprogramming and fundamental research. In this study, we developed a standardized toolkit utilizing an engineered CRISPR/Cas9 system that enables customizable gene regulation in mammalian cells. The RNA-guided dCas9 protein was implemented as a programmable transcriptional activator or repressor device, including targeting of endogenous loci. For facile assembly of single or multiple CRISPR RNAs, our toolkit comprises a modular RNAimer plasmid, which encodes the required noncoding RNA components

The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans

Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, identification of the tissue-specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type-specific RNA splicing was discovered and analyzed across tissues within an individual