Systemic inflammation induced from remote extremity trauma is a critical driver of secondary brain injury.

Blast exposure, commonly experienced by military personnel, can cause devastating life-threatening polysystem trauma. Despite considerable research efforts, the impact of the systemic inflammatory response after major trauma on secondary brain injury-inflammation is largely unknown. The aim of this study was to identify markers underlying the susceptibility and early onset of neuroinflammation in three rat trauma models: (1) blast overpressure exposure (BOP), (2) complex extremity trauma (CET) involving femur fracture, crush injury, tourniquet-induced ischemia, and transfemoral amputation through the fracture site, and (3) BOP+CET. Six hours post-injury, intact brains were harvested and dissected to obtain biopsies from the prefrontal cortex, striatum, neocortex, hippocampus, amygdala, thalamus, hypothalamus, and cerebellum. Custom low-density microarray datasets were used to identify, interpret and visualize genes significant (p < 0.05 for differential expression [DEGs]; 86 neuroinflammation-associated) using a custom python-based computer program, principal component analysis, heatmaps and volcano plots. Gene set and pathway enrichment analyses of the DEGs was performed using R and STRING for protein-protein interaction (PPI) to identify and explore key genes and signaling networks. Transcript profiles were similar across all regions in naïve brains with similar expression levels involving neurotransmission and transcription functions and undetectable to low-levels of inflammation-related mediators. Trauma-induced neuroinflammation across all anatomical brain regions correlated with injury severity (BOP+CET > CET > BOP). The most pronounced differences in neuroinflammatory-neurodegenerative gene regulation were between blast-associated trauma (BOP, BOP+CET) and CET. Following BOP, there were few DEGs detected amongst all 8 brain regions, most were related to cytokines/chemokines and chemokine receptors, where PPI analysis revealed Il1b as a potential central hub gene. In contrast, CET led to a more excessive and diverse pro-neuroinflammatory reaction in which Il6 was identified as the central hub gene. Analysis of the of the BOP+CET dataset, revealed a more global heightened response (Cxcr2, Il1b, and Il6) as well as the expression of additional functional regulatory networks/hub genes (Ccl2, Ccl3, and Ccl4) which are known to play a critical role in the rapid recruitment and activation of immune cells via chemokine/cytokine signaling. These findings provide a foundation for discerning pathophysiological consequences of acute extremity injury and systemic inflammation following various forms of trauma in the brain.

ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports.

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.

Viral Micro-RNAs Are Detected in the Early Systemic Response to Injury and Are Associated With Outcomes in Polytrauma Patients.

OBJECTIVES: To evaluate early activation of latent viruses in polytrauma patients and consider prognostic value of viral micro-RNAs in these patients. DESIGN: This was a subset analysis from a prospectively collected multicenter trauma database. Blood samples were obtained upon admission to the trauma bay (T0), and trauma metrics and recovery data were collected. SETTING: Two civilian Level 1 Trauma Centers and one Military Treatment Facility. PATIENTS: Adult polytrauma patients with Injury Severity Scores greater than or equal to 16 and available T0 plasma samples were included in this study. Patients with ICU admission greater than 14 days, mechanical ventilation greater than 7 days, or mortality within 28 days were considered to have a complicated recovery. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Polytrauma patients (n = 180) were identified, and complicated recovery was noted in 33%. Plasma samples from T0 underwent reverse transcriptase-quantitative polymerase chain reaction analysis for Kaposi's sarcoma-associated herpesvirus micro-RNAs (miR-K12_10b and miRK-12-12) and Epstein-Barr virus-associated micro-RNA (miR-BHRF-1), as well as Luminex multiplex array analysis for established mediators of inflammation. Ninety-eight percent of polytrauma patients were found to have detectable Kaposi's sarcoma-associated herpesvirus and Epstein-Barr virus micro-RNAs at T0, whereas healthy controls demonstrated 0% and 100% detection rate for Kaposi's sarcoma-associated herpesvirus and Epstein-Barr virus, respectively. Univariate analysis revealed associations between viral micro-RNAs and polytrauma patients' age, race, and postinjury complications. Multivariate least absolute shrinkage and selection operator analysis of clinical variables and systemic biomarkers at T0 revealed that interleukin-10 was the strongest predictor of all viral micro-RNAs. Multivariate least absolute shrinkage and selection operator analysis of systemic biomarkers as predictors of complicated recovery at T0 demonstrated that miR-BHRF-1, miR-K12-12, monocyte chemoattractant protein-1, and hepatocyte growth factor were independent predictors of complicated recovery with a model complicated recovery prediction area under the curve of 0.81. CONCLUSIONS: Viral micro-RNAs were detected within hours of injury and correlated with poor outcomes in polytrauma patients. Our findings suggest that transcription of viral micro-RNAs occurs early in the response to trauma and may be associated with the biological processes involved in polytrauma-induced complicated recovery.

To Scan or Not to Scan: Development of a Clinical Decision Support Tool to Determine if Imaging Would Aid in the Diagnosis of Appendicitis.

BACKGROUND: Appendicitis is one of the most common surgically treated diseases in the world. CT scans are often over-utilized and ordered before a surgeon has evaluated the patient. Our aim was to develop a tool using machine learning (ML) algorithms that would help determine if there would be benefit in obtaining a CT scan prior to surgeon consultation. METHODS: Retrospective chart review of 100 randomly selected cases who underwent appendectomy and 100 randomly selected controls was completed. Variables included components of the patient's history, laboratory values, CT readings, and pathology. Pathology was used as the gold standard for appendicitis diagnosis. All variables were then used to build the ML algorithms. Random Forest (RF), Support Vector Machine (SVM), and Bayesian Network Classifiers (BNC) models with and without CT scan results were trained and compared to CT scan results alone and the Alvarado score using area under the Receiver Operator Curve (ROC), sensitivity, and specificity measures as well as calibration indices from 500 bootstrapped samples. RESULTS: Among the cases that underwent appendectomy, 88% had pathology-confirmed appendicitis. All the ML algorithms had better sensitivity, specificity, and ROC than the Alvarado score. SVM with and without CT had the best indices and could predict if imaging would aid in appendicitis diagnosis. CONCLUSION: This study demonstrated that SVM with and without CT results can be used for selective imaging in the diagnosis of appendicitis. This study serves as the initial step and proof-of-concept to externally validate these results with larger and more diverse patient population.

Correction to: Advanced Modeling to Predict Pneumonia in Combat Trauma Patients.

In the original article, the units indicated on the y-axes of Fig. 3 are incorrectly labelled. The correct label is pg/mL. Following is the corrected Fig. 3.

Advanced Modeling to Predict Pneumonia in Combat Trauma Patients.

BACKGROUND: Tools to assist clinicians in predicting pneumonia could lead to a significant decline in morbidity. Therefore, we sought to develop a model in combat trauma patients for identifying those at highest risk of pneumonia. METHODS: This was a retrospective study of 73 primarily blast-injured casualties with combat extremity wounds. Binary classification models for pneumonia prediction were developed with measurements of injury severity from the Abbreviated Injury Scale (AIS), transfusion blood products received before arrival at Walter Reed National Military Medical Center (WRNMMC), and serum protein levels. Predictive models were generated with leave-one-out-cross-validation using the variable selection method of backward elimination (BE) and the machine learning algorithms of random forests (RF) and logistic regression (LR). BE was attempted with two predictor sets: (1) all variables and (2) serum proteins alone. RESULTS: Incidence of pneumonia was 12% (n = 9). Different variable sets were produced by BE when considering all variables and just serum proteins alone. BE selected the variables ISS, AIS chest, and cryoprecipitate within the first 24 h following injury for the first predictor set 1 and FGF-basic, IL-2R, and IL-6 for predictor set 2. Using both variable sets, a RF was generated with AUCs of 0.95 and 0.87-both higher than LR algorithms. CONCLUSION: Advanced modeling allowed for the identification of clinical and biomarker data predictive of pneumonia in a cohort of predominantly blast-injured combat trauma patients. The generalizability of the models developed here will require an external validation dataset.

Utilizing Precision Medicine to Estimate Timing for Surgical Closure of Traumatic Extremity Wounds.

BACKGROUND: Both the frequency and high complication rates associated with extremity wounds in recent military conflicts have highlighted the need for clinical decision support tools (CDST) to decrease time to wound closure and wound failure rates. METHODS: Machine learning was used to estimate both successful wound closure (based on penultimate debridement biomarker data) and the necessary number of surgical debridements (based on presentation biomarkers) in 73 service members treated according to military guidelines based on clinical data and the local/systemic level of 32 cytokines. Models were trained to estimate successful closure including an additional 8 of 80 civilian patients with similar injury patterns. Previous analysis has demonstrated the potential to reduce the number of operative debridements by 2, with resulting decreases in ICU and hospital LOS, while decreasing the rate of wound failure. RESULTS: Analysis showed similar cytokine responses when civilians followed a military-like treatment schedule with surgical debridements every 24 to 72 hours. A model estimating successful closure had AUC of 0.89. Model performance in civilians degraded when these had a debridement interval > 72 hours (73 of the 80 civilians). A separate model estimating the number of debridements required to achieve successful closure had a multiclass AUC of 0.81. CONCLUSION: CDSTs can be developed using biologically compatible civilian and military populations as cytokine response is highly influenced by surgical treatment. Our CDSTs may help identify who may require serial debridements versus early closure, and precisely when traumatic wounds should optimally be closed.

Nasopharyngeal Lactobacillus is associated with a reduced risk of childhood wheezing illnesses following acute respiratory syncytial virus infection in infancy.

BACKGROUND: Early life acute respiratory infection (ARI) with respiratory syncytial virus (RSV) has been strongly associated with the development of childhood wheezing illnesses, but the pathways underlying this association are poorly understood. OBJECTIVE: To examine the role of the nasopharyngeal microbiome in the development of childhood wheezing illnesses following RSV ARI in infancy. METHODS: We conducted a nested cohort study of 118 previously healthy, term infants with confirmed RSV ARI by RT-PCR. We used next-generation sequencing of the V4 region of the 16S ribosomal RNA gene to characterize the nasopharyngeal microbiome during RSV ARI. Our main outcome of interest was 2-year subsequent wheeze. RESULTS: Of the 118 infants, 113 (95.8%) had 2-year outcome data. Of these, 46 (40.7%) had parental report of subsequent wheeze. There was no association between the overall taxonomic composition, diversity, and richness of the nasopharyngeal microbiome during RSV ARI with the development of subsequent wheeze. However, the nasopharyngeal detection and abundance of Lactobacillus was consistently higher in infants who did not develop this outcome. Lactobacillus also ranked first among the different genera in a model distinguishing infants with and without subsequent wheeze. CONCLUSIONS: The nasopharyngeal detection and increased abundance of Lactobacillus during RSV ARI in infancy are associated with a reduced risk of childhood wheezing illnesses at age 2 years.

Araport11: a complete reannotation of the Arabidopsis thaliana reference genome.

The flowering plant Arabidopsis thaliana is a dicot model organism for research in many aspects of plant biology. A comprehensive annotation of its genome paves the way for understanding the functions and activities of all types of transcripts, including mRNA, the various classes of non-coding RNA, and small RNA. The TAIR10 annotation update had a profound impact on Arabidopsis research but was released more than 5 years ago. Maintaining the accuracy of the annotation continues to be a prerequisite for future progress. Using an integrative annotation pipeline, we assembled tissue-specific RNA-Seq libraries from 113 datasets and constructed 48 359 transcript models of protein-coding genes in eleven tissues. In addition, we annotated various classes of non-coding RNA including microRNA, long intergenic RNA, small nucleolar RNA, natural antisense transcript, small nuclear RNA, and small RNA using published datasets and in-house analytic results. Altogether, we identified 635 novel protein-coding genes, 508 novel transcribed regions, 5178 non-coding RNAs, and 35 846 small RNA loci that were formerly unannotated. Analysis of the splicing events and RNA-Seq based expression profiles revealed the landscapes of gene structures, untranslated regions, and splicing activities to be more intricate than previously appreciated. Furthermore, we present 692 uniformly expressed housekeeping genes, 43% of whose human orthologs are also housekeeping genes. This updated Arabidopsis genome annotation with a substantially increased resolution of gene models will not only further our understanding of the biological processes of this plant model but also of other species.

Introduction, Evolution, and Dissemination of Influenza A Viruses in Exhibition Swine in the United States during 2009 to 2013.

The swine-human interface created at agricultural fairs, along with the generation of and maintenance of influenza A virus diversity in exhibition swine, presents an ongoing threat to public health. Nucleotide sequences of influenza A virus isolates collected from exhibition swine in Ohio (n = 262) and Indiana (n = 103) during 2009 to 2013 were used to investigate viral evolution and movement within this niche sector of the swine industry. Phylogenetic and Bayesian analyses were employed to identify introductions of influenza A virus to exhibition swine and study viral population dynamics. In 2013 alone, we identified 10 independent introductions of influenza A virus into Ohio and/or Indiana exhibition swine. Frequently, viruses from the same introduction were identified at multiple fairs within the region, providing evidence of rapid and widespread viral movement within the exhibition swine populations of the two states. While pigs moving from fair to fair to fair is possible in some locations, the concurrent detection of nearly identical strains at several fairs indicates that a common viral source was more likely. Importantly, we detected an association between the high number of human variant H3N2 (H3N2v) virus infections in 2012 and the widespread circulation of influenza A viruses of the same genotype in exhibition swine in Ohio fairs sampled that year. The extent of viral diversity observed in exhibition swine and the rapidity with which it disseminated across long distances indicate that novel strains of influenza A virus will continue to emerge and spread within exhibition swine populations, presenting an ongoing threat to humans. IMPORTANCE: Understanding the underlying population dynamics of influenza A viruses in commercial and exhibition swine is central to assessing the risk for human infections with variant viruses, including H3N2v. We used viral genomic sequences from isolates collected from exhibition swine during 2009 to 2013 to understand how the peak of H3N2v cases in 2012 relates to long-term trends in the population dynamics of pandemic viruses recently introduced into commercial and exhibition swine in the United States. The results of our spatial analysis underscore the key role of rapid viral dispersal in spreading multiple genetic lineages throughout a multistate network of agricultural fairs, providing opportunities for divergent lineages to coinfect, reassort, and generate new viral genotypes. The higher genetic diversity of genotypes cocirculating in exhibition swine since 2013 could facilitate the evolution of new reassortants, potentially with even greater ability to cause severe infections in humans or cause human-to-human transmission, highlighting the need for continued vigilance.

Differences in the Nasopharyngeal Microbiome During Acute Respiratory Tract Infection With Human Rhinovirus and Respiratory Syncytial Virus in Infancy.

Respiratory viruses alter the nasopharyngeal microbiome and may be associated with a distinct microbial signature. To test this hypothesis, we compared the nasopharyngeal microbiome of 135 previously healthy infants with acute respiratory infection due to human rhinovirus (HRV; n = 52) or respiratory syncytial virus (RSV; n = 83). The nasopharyngeal microbiome was assessed by sequencing the V4 region of the 16S ribosomal RNA. Respiratory viruses were identified by quantitative reverse-transcription polymerase chain reaction. We found significant differences in the overall taxonomic composition and abundance of certain bacterial genera between infants infected with HRV and those infected with RSV. Our results suggest that respiratory tract viral infections are associated with different nasopharyngeal microbial profiles.

Derivation and Validation of Generalized Sepsis-induced Acute Respiratory Failure Phenotypes Among Critically Ill Patients: A Retrospective Study.

Background: Septic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of these patients may reveal insights about the broader heterogeneity in the clinical course of sepsis. We aimed to derive novel phenotypes of sepsis-induced ARF using observational clinical data and investigate their generalizability across multi-ICU specialties, considering multi-organ dynamics. Methods: We performed a multi-center retrospective study of ICU patients with sepsis who required mechanical ventilation for ≥24 hours. Data from two different high-volume academic hospital systems were used as a derivation set with N=3,225 medical ICU (MICU) patients and a validation set with N=848 MICU patients. For the multi-ICU validation, we utilized retrospective data from two surgical ICUs at the same hospitals (N=1,577). Clinical data from 24 hours preceding intubation was used to derive distinct phenotypes using an explainable machine learning-based clustering model interpreted by clinical experts. Results: Four distinct ARF phenotypes were identified: A (severe multi-organ dysfunction (MOD) with a high likelihood of kidney injury and heart failure), B (severe hypoxemic respiratory failure [median P/F=123]), C (mild hypoxia [median P/F=240]), and D (severe MOD with a high likelihood of hepatic injury, coagulopathy, and lactic acidosis). Patients in each phenotype showed differences in clinical course and mortality rates despite similarities in demographics and admission co-morbidities. The phenotypes were reproduced in external validation utilizing an external MICU from second hospital and SICUs from both centers. Kaplan-Meier analysis showed significant difference in 28-day mortality across the phenotypes (p<0.01) and consistent across both centers. The phenotypes demonstrated differences in treatment effects associated with high positive end-expiratory pressure (PEEP) strategy. Conclusion: The phenotypes demonstrated unique patterns of organ injury and differences in clinical outcomes, which may help inform future research and clinical trial design for tailored management strategies.

The influence of microbial colonization on inflammatory versus pro-healing trajectories in combat extremity wounds.

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.

A random forest model using flow cytometry data identifies pulmonary infection after thoracic injury.

BACKGROUND: Thoracic injury can cause impairment of lung function leading to respiratory complications such as pneumonia (PNA). There is increasing evidence that central memory T cells of the adaptive immune system play a key role in pulmonary immunity. We sought to explore whether assessment of cell phenotypes using flow cytometry (FCM) could be used to identify pulmonary infection after thoracic trauma. METHODS: We prospectively studied trauma patients with thoracic injuries who survived >48 hours at a Level 1 trauma center from 2014 to 2020. Clinical and FCM data from serum samples collected within 24 hours of admission were considered as potential variables. Random forest and logistic regression models were developed to estimate the risk of hospital-acquired and ventilator-associated PNA. Variables were selected using backwards elimination, and models were internally validated with leave-one-out. RESULTS: Seventy patients with thoracic injuries were included (median age, 35 years [interquartile range (IQR), 25.25-51 years]; 62.9% [44 of 70] male, 61.4% [42 of 70] blunt trauma). The most common injuries included rib fractures (52 of 70 [74.3%]) and pulmonary contusions (26 of 70 [37%]). The incidence of PNA was 14 of 70 (20%). Median Injury Severity Score was similar for patients with and without PNA (30.5 [IQR, 22.6-39.3] vs. 26.5 [IQR, 21.6-33.3]). The final random forest model selected three variables (Acute Physiology and Chronic Health Evaluation score, highest pulse rate in first 24 hours, and frequency of CD4 + central memory cells) that identified PNA with an area under the curve of 0.93, sensitivity of 0.91, and specificity of 0.88. A logistic regression with the same features had an area under the curve of 0.86, sensitivity of 0.76, and specificity of 0.85. CONCLUSION: Clinical and FCM data have diagnostic utility in the early identification of patients at risk of nosocomial PNA following thoracic injury. Signs of physiologic stress and lower frequency of central memory cells appear to be associated with higher rates of PNA after thoracic trauma. LEVEL OF EVIDENCE: Diagnostic Test/Criteria; Level IV.

Host CD3+ T-cells can significantly modulate phage treatment effects on bacterial bioburden in mouse models.

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 < x < = 1.5), we observed a strong association between colony forming units (CFU) and wound size, indicating a hallmark of wound healing. On the basis of the findings and our previous work, we proposed an integrated parallel systems biology model.

Central role for neurally dysregulated IL-17A in dynamic networks of systemic and local inflammation in combat casualties.

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.

Targeted metagenomic assessment reflects critical colonization in battlefield injuries.

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.

The Protein Naming Utility: a rules database for protein nomenclature.

Generation of syntactically correct and unambiguous names for proteins is a challenging, yet vital task for functional annotation processes. Proteins are often named based on homology to known proteins, many of which have problematic names. To address the need to generate high-quality protein names, and capture our significant experience correcting protein names manually, we have developed the Protein Naming Utility (PNU, http://www.jcvi.org/pn-utility). The PNU is a web-based database for storing and applying naming rules to identify and correct syntactically incorrect protein names, or to replace synonyms with their preferred name. The PNU allows users to generate and manage collections of naming rules, optionally building upon the growing body of rules generated at the J. Craig Venter Institute (JCVI). Since communities often enforce disparate conventions for naming proteins, the PNU supports grouping rules into user-managed collections. Users can check their protein names against a selected PNU rule collection, generating both statistics and corrected names. The PNU can also be used to correct GenBank table files prior to submission to GenBank. Currently, the database features 3080 manual rules that have been entered by JCVI Bioinformatics Analysts as well as 7458 automatically imported names.

Pathema: a clade-specific bioinformatics resource center for pathogen research.

Pathema (http://pathema.jcvi.org) is one of the eight Bioinformatics Resource Centers (BRCs) funded by the National Institute of Allergy and Infectious Disease (NIAID) designed to serve as a core resource for the bio-defense and infectious disease research community. Pathema strives to support basic research and accelerate scientific progress for understanding, detecting, diagnosing and treating an established set of six target NIAID Category A-C pathogens: Category A priority pathogens; Bacillus anthracis and Clostridium botulinum, and Category B priority pathogens; Burkholderia mallei, Burkholderia pseudomallei, Clostridium perfringens and Entamoeba histolytica. Each target pathogen is represented in one of four distinct clade-specific Pathema web resources and underlying databases developed to target the specific data and analysis needs of each scientific community. All publicly available complete genome projects of phylogenetically related organisms are also represented, providing a comprehensive collection of organisms for comparative analyses. Pathema facilitates the scientific exploration of genomic and related data through its integration with web-based analysis tools, customized to obtain, display, and compute results relevant to ongoing pathogen research. Pathema serves the bio-defense and infectious disease research community by disseminating data resulting from pathogen genome sequencing projects and providing access to the results of inter-genomic comparisons for these organisms.

Multidimensional machine learning models predicting outcomes after trauma.

BACKGROUND: An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients. METHODS: This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation. RESULTS: A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data. CONCLUSION: Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs.