Search

Objective: To develop predictive models for in-hospital mortality and length of stay (LOS) for COVID-19 positive patients. Patients and Methods: We performed a multicenter retrospective cohort study of hospitalized COVID-19 positive patients. A total of 764 patients admitted to 14 different hospitals within the Cleveland Clinic from 03/09/2020 to 05/20/2020 who had reverse transcriptase-polymerase chain reaction proven coronavirus infection were included. We used LightGBM, a machine learning algorithm, to predict in-hospital mortality at different time points (after 7 days, 14 days, and 30 days of hospitalization) and in-hospital LOS. Results: Among 764 patients, 116 (15%) either died (n = 87) or were transitioned to hospice care (n = 29) during their hospitalization. The median LOS was 5 days (range 1 - 44 days) for patients admitted to the regular nursing floor and 10 days (range 1-38 days) for patients admitted to the intensive care unit (ICU). Patients who died during hospitalization were older, initially admitted to the ICU, more likely to be white and to have worse organ dysfunction compared to patients who survived their hospitalization. Using the 10 most important variables only, the final model's area under the Receiver Operating Characteristics curve was 0.86 for 7-day, 0.88 for 14-day, and 0.85 for 30-day mortality in the validation cohort. Conclusions: We developed a decision tool that can provide explainable and patient-specific prediction of in-hospital mortality and LOS for COVID-19 positive patients. The model can aid healthcare systems in bed allocation and distribution of vital resources.

Herpes simplex virus 1 (HSV-1) strain McKrae was isolated in 1965 and has been utilized by many laboratories. Three HSV-1 strain McKrae stocks have been sequenced previously, revealing discrepancies in key genes. We sequenced the genome of HSV-1 strain McKrae from James Hill, to better understand the genetic differences between isolates., Herpes simplex virus 1 (HSV-1) strain McKrae was isolated in 1965 and has been utilized by many laboratories. Three HSV-1 strain McKrae stocks have been sequenced previously, revealing discrepancies in key genes. We sequenced the genome of HSV-1 strain McKrae from the laboratory of James M. Hill to better understand the genetic differences between isolates.

Myelodysplastic syndromes are disorders of clonal myelopoiesis having a range of clinical manifestations, from benign and indolent to aggressive with very poor prognosis. Classifying the likely trajectory of disease within a patient largely guides therapeutic decision making and therefore survival. Traditional methods of risk-stratification systems rely on clinical features: simple blood tests, peripheral smears, bone marrow biopsies, and cytogenetics, but do not adequately predict disease severity for a substantial proportion of patients. This article reviews the state of stratification at use in the clinic, describes emerging systems that leverage large-scale genomic data, and summarizes efforts toward truly personalized prediction models.

The emphasis on fibrinolysis as an important contributor to trauma-induced coagulopathy (TIC) has led to a debate regarding the relative clinical significance of fibrinolysis in the setting of trauma. The debate has centered on two camps. The one camp defines fibrinolysis in trauma by standard coagulation tests as well as fibrin split products, D-dimers, and plasmin/antiplasmin levels. This camp favors a more liberal use of tranexamic acid and attributes more significance to hyperfibrinolysis in TIC. The other camp favors a definition of fibrinolysis based on the viscoelastic tests (VET), rotational thromboelastometry (ROTEM), and thromboelastography (TEG). These whole blood assays define hyperfibrinolysis at a higher threshold than plasma-based tests. Therefore, this VET camp reserves antifibrinolytic treatment for patients who demonstrate severe coagulopathy associated with hyperfibrinolysis. This bimodal attribution of the clinical relevance of fibrinolysis in trauma suggests that there may be an underlying "Myth" of the concept of TIC that was historically defined by plasma-based tests and a future "Reality" of the concept of TIC that is grounded on an understanding of TIC based on a VET-defined "fibrinolytic spectrum" of TIC. This narrative review explores this "Myth" and "Reality" of fibrinolysis in TIC and proposes a direction that will allow a "Future" interpretation of TIC that incorporates both the past "Myth" and present "Reality" of fibrinolysis TIC.

Herpes simplex virus 1 (HSV-1) is a widespread global pathogen, of which the strain KOS is one of the most extensively studied. Previous sequence studies revealed that KOS does not cluster with other strains of North American geographic origin, but instead clustered with Asian strains. We sequenced a historical isolate of the original KOS strain, called KOS63, along with a separately isolated strain attributed to the same source individual, termed KOS79. Genomic analyses revealed that KOS63 closely resembled other recently sequenced isolates of KOS and was of Asian origin, but that KOS79 was a genetically unrelated strain that clustered in genetic distance analyses with HSV-1 strains of North American/European origin. These data suggest that the human source of KOS63 and KOS79 could have been infected with two genetically unrelated strains of disparate geographic origins. A PCR RFLP test was developed for rapid identification of these strains.

Background AML is a heterogeneous clonal disorder that is characterized by the accumulation of complex genomic alterations that affect disease biology and outcomes. Despite significant advances in our understanding of the impact of these mutations on overall survival (OS), established AML risk stratification guidelines are based primarily on cytogenetic analyses and a limited number of genes, don't take into account the complexity and the interaction between these mutations, and how particular constellations of genomic and clinical risk factors affect patient (pt) outcome. We developed a novel prognostic model that incorporates clinical, cytogenetic, and mutational data to determine personalized outcomes specific to a particular pt. Method A total of 792,779 genomic and clinical data points from 3,421 pts were analyzed. The cohort was comprised of five independent datasets: 443 pts from the Beat AML Master Trial (Tyner et al, Nature, 2018), 855 pts from Cleveland Clinic, 414 pts from Munich Leukemia Laboratory (MLL), 1,509 pts from the German-Austrian Study Group (Papaemmanuil et al, NEJM, 2016), and 200 pts from The Cancer Genome Atlas (NEJM, 2013). A panel of 44 gene mutations commonly implicated in AML was used in the analysis, along with numerous cytogenetic and clinical variables such age, white blood cell count WBC) at diagnosis, and AML subtype (primary vs. secondary vs. therapy-related. A machine learning algorithm capable of accounting for survival (XGBOOST) was used to build the new model, in which clinical and molecular variables were randomly selected for inclusion in determining OS. Feature extraction algorithms were used to isolate the most important variables that impacted decision making within the model. The algorithm can also plot the important features that are specific for a given pt and show the impact of each feature on the outcome (positive vs. negative). The C-index was used to evaluate the accuracy of the new model compared to 2017 ELN risk classification. Results The median age of the cohort was 56 years (range, 18-100); 1,122 pts (32.8%) had favorable risk cytogenetics per ELN criteria, 956 (27.9%) intermediate (INT), and 1,343 (39.3%) adverse. The most commonly mutated genes were: NPM1 (24%), FLT3 (23%), DNMT3A (20%), NRAS (13%), IDH2 (11%), RUNX1 (10%) and TET2 (10%). Mutations occurred in different frequencies in each cytogenetic risk group. The most commonly mutated genes in the favorable risk group were: NRAS (30%), KIT (23%), FLT3 (17%), and KRAS (8%). The most commonly mutated genes in the INT risk group were: NPM1 (28%), FLT3 (26%), DNMT3A (22%), IDH2 (12%), TET2 (11%), NRAS (11%), and RUNX1 (11%). The most commonly mutated genes in pts with adverse cytogenetics included: TP53 (34%), DNMT3A (13%), NRAS (11%), RUNX1 (10%), PTPN11 (8%), IDH2 (7%), U2AF1 (6%) and FLT3 (6%). All genomic-clinical variables were included in the machine learning algorithm. Variable importance analyses (the most important variables that contributed to the outcome) and multiple backward elimination analyses (identifying the least number of variables that can provide the least error rate) identified the following variables that impacted OS: age, transplant (yes vs. no), WBC, bone marrow blast %, cytogenetics, ASXL1, CEBPA, DNMT3A, FLT3, KDM6A, KIT, KRAS, NPM1, NRAS, PHF6, PTPN11, RUNX1, TET2, and TP53. The clinical and mutational variables that impacted each pt outcome can be visualized in a highly personalized manner, Figure 1. The C-index for the new model was 0.80 which significantly outperformed ELN classification (0.59). When applying the new model to each of the five patient cohorts, the c-indices remained high and were as follows: Beat AML (0.81), Cleveland Clinic (0.85), MLL (0.83), Papaemmanuil E, et al (0.79), and TCGA (0.80). Conclusions Genomic alterations have a differential impact on OS in each cytogenetic risk group, highlighting the complexity of incorporating these mutations into risk stratification. A personalized prediction model based on clinical-genomic data can accurately provide survival unique to each individual pt and can significantly outperform ELN classifications or any currently available models. To ease the translation of this model into the clinic, a web application is currently under development and will be publicly available for use. Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. Mukherjee:Bristol-Myers Squibb: Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Projects in Knowledge: Honoraria; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; McGraw Hill Hematology Oncology Board Review: Other: Editor. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Maciejewski:Novartis: Consultancy; Alexion: Consultancy. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Sekeres:Millenium: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nazha:Jazz Pharmacutical: Research Funding; Novartis: Speakers Bureau; Incyte: Speakers Bureau; Tolero, Karyopharma: Honoraria; Abbvie: Consultancy; Daiichi Sankyo: Consultancy; MEI: Other: Data monitoring Committee.

SummaryGall-forming arthropods are highly specialized herbivores that, in combination with their hosts, produce extended phenotypes with unique morphologies [1]. Many are economically important, and others have improved our understanding of ecology and adaptive radiation [2]. However, the mechanisms that these arthropods use to induce plant galls are poorly understood. We sequenced the genome of the Hessian fly (Mayetiola destructor; Diptera: Cecidomyiidae), a plant parasitic gall midge and a pest of wheat (Triticum spp.), with the aim of identifying genic modifications that contribute to its plant-parasitic lifestyle. Among several adaptive modifications, we discovered an expansive reservoir of potential effector proteins. Nearly 5% of the 20,163 predicted gene models matched putative effector gene transcripts present in the M. destructor larval salivary gland. Another 466 putative effectors were discovered among the genes that have no sequence similarities in other organisms. The largest known arthropod gene family (family SSGP-71) was also discovered within the effector reservoir. SSGP-71 proteins lack sequence homologies to other proteins, but their structures resemble both ubiquitin E3 ligases in plants and E3-ligase-mimicking effectors in plant pathogenic bacteria. SSGP-71 proteins and wheat Skp proteins interact in vivo. Mutations in different SSGP-71 genes avoid the effector-triggered immunity that is directed by the wheat resistance genes H6 and H9. Results point to effectors as the agents responsible for arthropod-induced plant gall formation.

The utilization of tranexamic acid (TXA) for the management of bleeding trauma patients has been a subject of much debate on both sides of the Atlantic and in Australia. As a result of the large randomized controlled study called the Clinical Randomization of an Antifibrinolytic in Severe Hemorrhage (CRASH-2), there was an initial enthusiasm for the use of TXA to treat bleeding patients. However, the adoption of TXA in the United States was delayed by concerns of “knowledge and evidence gaps” of the CRASH-2 study and because of a lack of mechanistic rationale that would explain the survival benefit noted in the study. Subsequent nonrandomized controlled trials questioned the liberal use of TXA in trauma patients. This narrative review explores the historical as well as clinical and theoretical grounds for the more measured use of TXA in the United States and proposes a clinical and point-of-care guided utilization of TXA, blood components, and adjunctive hemostatic agents in bleeding trauma patients.

Background Second generation lignocellulosic feedstocks are being considered as an alternative to first generation biofuels that are derived from grain starches and sugars. However, the current pre-treatment methods for second generation biofuel production are inefficient and expensive due to the recalcitrant nature of lignocellulose. In this study, we used the lower termite Reticulitermes flavipes (Kollar), as a model to identify potential pretreatment genes/enzymes specifically adapted for use against agricultural feedstocks. Results Metatranscriptomic profiling was performed on worker termite guts after feeding on corn stover (CS), soybean residue (SR), or 98% pure cellulose (paper) to identify (i) microbial community, (ii) pathway level and (iii) gene-level responses. Microbial community profiles after CS and SR feeding were different from the paper feeding profile, and protist symbiont abundance decreased significantly in termites feeding on SR and CS relative to paper. Functional profiles after CS feeding were similar to paper and SR; whereas paper and SR showed different profiles. Amino acid and carbohydrate metabolism pathways were downregulated in termites feeding on SR relative to paper and CS. Gene expression analyses showed more significant down regulation of genes after SR feeding relative to paper and CS. Stereotypical lignocellulase genes/enzymes were not differentially expressed, but rather were among the most abundant/constitutively-expressed genes. Conclusions These results suggest that the effect of CS and SR feeding on termite gut lignocellulase composition is minimal and thus, the most abundantly expressed enzymes appear to encode the best candidate catalysts for use in saccharification of these and related second-generation feedstocks. Further, based on these findings we hypothesize that the most abundantly expressed lignocellulases, rather than those that are differentially expressed have the best potential as pretreatment enzymes for CS and SR feedstocks. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1502-8) contains supplementary material, which is available to authorized users.