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Abstract Background Trauma can result in systemic inflammation that leads to organ dysfunction, but the impact on the brain, particularly following extracranial insults, has been largely overlooked. Methods Building upon our prior findings, we aimed to understand the impact of systemic inflammation on neuroinflammatory gene transcripts in eight brain regions in rats exposed to (1) blast overpressure exposure [BOP], (2) cutaneous thermal injury [BU], (3) complex extremity injury, 3 hours (h) of tourniquet-induced ischemia, and hind limb amputation [CEI+tI+HLA], (4) BOP+BU or (5) BOP+CEI and delayed HLA [BOP+CEI+dHLA] at 6, 24, and 168 h post-injury (hpi). Results Globally, the number and magnitude of differentially expressed genes (DEGs) correlated with injury severity, systemic inflammation markers, and end-organ damage, driven by several chemokines/cytokines (Csf3, Cxcr2, Il16, and Tgfb2), neurosteroids/prostaglandins (Cyp19a1, Ptger2, and Ptger3), and markers of neurodegeneration (Gfap, Grin2b, and Homer1). Regional neuroinflammatory activity was least impacted following BOP. Non-blast trauma (in the BU and CEI+tI+HLA groups) contributed to an earlier, robust and diverse neuroinflammatory response across brain regions (up to 2–50-fold greater than that in the BOP group), while combined trauma (in the BOP+CEI+dHLA group) significantly advanced neuroinflammation in all regions except for the cerebellum. In contrast, BOP+BU resulted in differential activity of several critical neuroinflammatory-neurodegenerative markers compared to BU. t-SNE plots of DEGs demonstrated that the onset, extent, and duration of the inflammatory response are brain region dependent. Regardless of injury type, the thalamus and hypothalamus, which are critical for maintaining homeostasis, had the most DEGs. Our results indicate that neuroinflammation in all groups progressively increased or remained at peak levels over the study duration, while markers of end-organ dysfunction decreased or otherwise resolved. Conclusions Collectively, these findings emphasize the brain's sensitivity to mediators of systemic inflammation and provide an example of immune-brain crosstalk. Follow-on molecular and behavioral investigations are warranted to understand the short- to long-term pathophysiological consequences on the brain, particularly the mechanism of blood–brain barrier breakdown, immune cell penetration–activation, and microglial activation.

Abstract A combination of improved body armor, medical transportation, and treatment has led to the increased survival of warfighters from combat extremity injuries predominantly caused by blasts in modern conflicts. Despite advances, a high rate of complications such as wound infections, wound failure, amputations, and a decreased quality of life exist. To study the molecular underpinnings of wound failure, wound tissue biopsies from combat extremity injuries had RNA extracted and sequenced. Wounds were classified by colonization (colonized vs. non-colonized) and outcome (healed vs. failed) status. Differences in gene expression were investigated between timepoints at a gene level, and longitudinally by multi-gene networks, inferred proportions of immune cells, and expression of healing-related functions. Differences between wound outcomes in colonized wounds were more apparent than in non-colonized wounds. Colonized/healed wounds appeared able to mount an adaptive immune response to infection and progress beyond the inflammatory stage of healing, while colonized/failed wounds did not. Although, both colonized and non-colonized failed wounds showed increasing inferred immune and inflammatory programs, non-colonized/failed wounds progressed beyond the inflammatory stage, suggesting different mechanisms of failure dependent on colonization status. Overall, these data reveal gene expression profile differences in healing wounds that may be utilized to improve clinical treatment paradigms.

Abstract Introduction Accurate identification of venous thromboembolism (VTE) is critical to develop replicable epidemiological studies and rigorous predictions models. Traditionally, VTE studies have relied on international classification of diseases (ICD) codes which are inaccurate – leading to misclassification bias. Here, we developed ClotCatcher, a novel deep learning model that uses natural language processing to detect VTE from radiology reports. Methods Radiology reports to detect VTE were obtained from patients admitted to Emory University Hospital (EUH) and Grady Memorial Hospital (GMH). Data augmentation was performed using the Google PEGASUS paraphraser. This data was then used to fine-tune ClotCatcher, a novel deep learning model. ClotCatcher was validated on both the EUH dataset alone and GMH dataset alone. Results The dataset contained 1358 studies from EUH and 915 studies from GMH (n = 2273). The dataset contained 1506 ultrasound studies with 528 (35.1%) studies positive for VTE, and 767 CT studies with 91 (11.9%) positive for VTE. When validated on the EUH dataset, ClotCatcher performed best (AUC = 0.980) when trained on both EUH and GMH dataset without paraphrasing. When validated on the GMH dataset, ClotCatcher performed best (AUC = 0.995) when trained on both EUH and GMH dataset with paraphrasing. Conclusion ClotCatcher, a novel deep learning model with data augmentation rapidly and accurately adjudicated the presence of VTE from radiology reports. Applying ClotCatcher to large databases would allow for rapid and accurate adjudication of incident VTE. This would reduce misclassification bias and form the foundation for future studies to estimate individual risk for patient to develop incident VTE.

Abstract Dynamic Network Analysis (DyNA) and Dynamic Hypergraphs (DyHyp) were used to define protein-level inflammatory networks at the local (wound effluent) and systemic circulation (serum) levels from 140 active-duty, injured service members (59 with TBI and 81 non-TBI). Interleukin (IL)-17A was the only biomarker elevated significantly in both serum and effluent in TBI vs. non-TBI casualties, and the mediator with the most DyNA connections in TBI wounds. DyNA combining serum and effluent data to define cross-compartment correlations suggested that IL-17A bridges local and systemic circulation at late time points. DyHyp suggested that systemic IL-17A upregulation in TBI patients was associated with tumor necrosis factor-α, while IL-17A downregulation in non-TBI patients was associated with interferon-γ. Correlation analysis suggested differential upregulation of pathogenic Th17 cells, non-pathogenic Th17 cells, and memory/effector T cells. This was associated with reduced procalcitonin in both effluent and serum of TBI patients, in support of an antibacterial effect of Th17 cells in TBI patients. Dysregulation of Th17 responses following TBI may drive cross-compartment inflammation following combat injury, counteracting wound infection at the cost of elevated systemic inflammation.

ABSTRACT Current diagnostics and clinical management strategies for combat wounds are based on decisions made by expert clinicians. However, even in the hands of experienced surgeons, wounds from combat injuries can exhibit failed healing and complications related to limitations in the rapid and comprehensive generation of diagnostic information. Previous studies have demonstrated the possible use of genomic sequencing approaches to detect microbial signatures involved in combat casualty care. While effective, whole metagenome sequencing is limited by the depth required to confidently detect all relevant signatures. To address this, we developed a targeted capture sequencing panel to detect microbial signatures relevant to wound healing. These targets include known microbial nosocomial pathogens, wound colonizers, and genes involved in virulence and antimicrobial resistance. A bioinformatics pipeline was built to identify genomic regions of interest and over 8,000 oligonucleotide probes were designed for capture. The panel was synthesized and validated using control reference genomes in human background and on wound-effluent samples from a cohort of combat-injured U.S. service members. Our panel was sensitive against wound-colonizing species, Acinetobacter baumannii and Pseudomonas aeruginosa, and was specific in detecting corresponding virulence and antimicrobial-resistance genes as well as other pathogenic species present in microflora mixtures. Random forest feature permutation confirmed the prevalence of Acinetobacter and Pseudomonas in critically colonized wounds and wounds that failed to heal, respectively. Our results demonstrate the capability of targeted sequencing tools and analysis platforms to profile and deliver information on pathogenic factors influencing wound progression, thereby guiding therapeutic intervention. IMPORTANCE Microbial contamination in combat wounds can lead to opportunistic infections and adverse outcomes. However, current microbiological detection has a limited ability to capture microbial functional genes. This work describes the application of targeted metagenomic sequencing to profile wound bioburden and capture relevant wound-associated signatures for clinical utility. Ultimately, the ability to detect such signatures will help guide clinical decisions regarding wound care and management and aid in the prediction of wound outcomes.

Wound healing is a complex system including such key players as host, microbe, and treatments. However, little is known about their dynamic interactions. Here we explored the interplay between: (1) bacterial bioburden and host immune responses, (2) bacterial bioburden and wound size, and (3) treatments and wound size, using murine models and various treatment modalities: Phosphate buffer saline (PBS or vehicle, negative control), doxycycline, and two doses of A. baumannii phage mixtures. We uncovered that the interplay between bacterial bioburden and host immune system may be bidirectional, and that there is an interaction between host CD3+ T-cells and phage dosage, which significantly impacts bacterial bioburden. Furthermore, the bacterial bioburden and wound size association is significantly modulated by the host CD3+ T-cells. When the host CD3+ T-cells (x on log10 scale) are in the appropriate range (1.35

Abstract Battlefield injury management requires specialized care, and wound infection is a frequent complication. Challenges related to characterizing relevant pathogens further complicates treatment. Applying metagenomics to wounds offers a comprehensive path toward assessing microbial genomic fingerprints and could indicate prognostic variables for future decision support tools. Wound specimens from combat-injured U.S. service members, obtained during surgical debridements before delayed wound closure, were subjected to whole metagenome analysis and targeted enrichment of antimicrobial resistance genes. Results did not indicate a singular, common microbial metagenomic profile for wound failure, instead reflecting a complex microenvironment with varying bioburden diversity across outcomes. Genus-level Pseudomonas detection was associated with wound failure at all surgeries. A logistic regression model was fit to the presence and absence of antimicrobial resistance classes to assess associations with nosocomial pathogens. A. baumannii detection was associated with detection of genomic signatures for resistance to trimethoprim, aminoglycosides, bacitracin, and polymyxin. Machine learning classifiers were applied to identify wound and microbial variables associated with outcome. Feature importance rankings averaged across models indicated the variables with the largest effects on predicting wound outcome, including an increase in P. putida sequence reads. These results describe the microbial genomic determinants in combat wound bioburden and demonstrate metagenomic investigation as a comprehensive tool for providing information toward aiding treatment of combat-related injuries.

Introduction: Trauma is the leading cause of death in persons under the age of 45. Recovery in patients who survive initial trauma are frequently complicated by sequelae of injury that increases susceptibility to infection and inflammation. Uncontrolled inflammation can advance into life-threatening organ failure, including acute respiratory distress syndrome (ARDS). Similarities exist between biomarkers established in the etiology of acute respiratory distress syndrome and those identified in the acute inflammatory and healing phase of bone fractures. This study investigates the impact of long bone fractures on the development of acute respiratory distress syndrome where it is hypothesized that patients with long bone fractures would have different biomarker profiles and increased development of lung injury compared to patients without long bone fractures.Methods: This is a retrospective data analysis of patients from an observational data repository from three trauma centers. Trauma patients with and without long bone fractures were matched and analyzed for the presence of known biomarkers of acute respiratory distress syndrome and for the development of acute respiratory distress syndrome.Results: There were no differences in overall acute respiratory distress syndrome development or hospital outcomes, however long bone fracture patients had a 2.35-fold higher hazard ratio of acute respiratory distress syndrome in the first 10 hospital days. There was a statistically significant increase in the levels of IL-6 in patients with long bone fractures (p = .0007). Structural equations modeling demonstrated that IL-6 was positively influenced by long bone fractures and IL-8.Conclusion: The presence of long bone fractures did not result in differences in the overall development of acute respiratory distress syndrome or hospital outcomes, though was found to have an increased hazard ratio for acute respiratory distress syndrome development in the first 10 days. Further research is needed to better characterize the relationship between varying cytokine profiles and the development of acute respiratory distress syndrome in a trauma population.