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05), despite blunting weight gain in control and WD mice. Anxiogenesis with restraint or WD was nonadditive, whereas anhedonia (reduced sucrose consumption) only arose with their combination. Neuroinflammation markers (hippocampal TNF-α, Il-1b) were unchanged. Myocardial I/R tolerance was unaltered with stress or WD alone, whereas the combination worsened dysfunction and oncosis [lactate dehydrogenase (LDH) efflux]. Apoptosis (nucleosome accumulation) and death protein expression (BAK, BAX, BCL-2, RIP-1, TNF-α, cleaved caspase-3, and PARP) were unchanged. We conclude that mild, anxiogenic yet cardio-metabolically “benign” stress interacts synergistically with a WD to disrupt homeostasis, promote anhedonia (independently of neuroinflammation), and impair myocardial ischemic tolerance (independently of apoptosis and death protein levels).

Venous thromboembolism (VTE) is a preventable cause of morbidity and mortality. Emergency general surgery (EGS) patients comprise 7% of hospital admissions in America with a reported rate of VTE of 2.5%. Of these, >69% required hospital readmission, making VTE the second most common cause for readmission after infection in EGS patients. We hypothesize a correlation between body mass index (BMI) and VTE in EGS patients.

The American College of Surgeons National Surgery Quality Improvement Database (NSQIP) was queried from January 2015 to December 2016. 83 272 patients met inclusion criteria age ≥18 and underwent an EGS procedure. Patients were stratified by BMI. Descriptive statistics were used for demographic and numerical data. Categorical comparisons between covariates were completed using the chi-square test. Continuous variables were compared using Student’s

-test, Mann Whitney U-test, or Kruskal-Wallis H test.

83 272 patients met the inclusion criteria. 1358 patients developed VTE (903 deep vein thrombosis (DVT) only, 335 pulmonary embolism (PE) only, and 120 with DVT and PE). Morbidly obese patients were 1.7 times more likely to be diagnosed with a PE compared with normal BMI (

= .004). Increased BMI was associated with the co-diagnosis of PE and DVT (

= .027). Patients with BMI <18.5 were 1.4 times more likely to experience a VTE compared with normal BMI (

= .018). Patients with a VTE were 3.2 times more likely to die (

< .001) and less likely to be discharged home (

< .001).

Our study found that obese and underweight EGS patients had an increased incidence of VTE. Risk recognition and chemoprophylaxis may improve outcomes in this population.

Our study found that obese and underweight EGS patients had an increased incidence of VTE. Risk recognition and chemoprophylaxis may improve outcomes in this population.

As data-sharing projects become increasingly frequent, so does the need to map data elements between multiple classification systems. A generic, robust, shareable architecture will result in increased efficiency and transparency of the mapping process, while upholding the integrity of the data.

The American Association for Cancer Research’s Genomics Evidence Neoplasia Information Exchange (GENIE) collects clinical and genomic data for precision cancer medicine. As part of its commitment to open science, GENIE has partnered with the National Cancer Institute’s Genomic Data Commons (GDC) as a secondary repository. After initial efforts to submit data from GENIE to GDC failed, we realized the need for a solution to allow for the iterative mapping of data elements between dynamic classification systems. We developed the Linked Entity Attribute Pair (LEAP) database framework to store and manage the term mappings used to submit data from GENIE to GDC.

After creating and populating the LEAP framework, we identments across various dynamic classification systems.

Keratinocyte cancers are exceedingly common in high-risk populations, but accurate measures of incidence are seldom derived because the burden of manually reviewing pathology reports to extract relevant diagnostic information is excessive. Thus, we sought to develop supervised learning algorithms for classifying basal and squamous cell carcinomas and other diagnoses, as well as disease site, and incorporate these into a Web application capable of processing large numbers of pathology reports.

Participants in the QSkin study were recruited in 2011 and comprised men and women age 40-69 years at baseline (N = 43,794) who were randomly selected from a population register in Queensland, Australia. Histologic data were manually extracted from free-text pathology reports for participants with histologically confirmed keratinocyte cancers for whom a pathology report was available (n = 25,786 reports). This provided a training data set for the development of algorithms capable of deriving diagnosis and site from feb application capable of accurately and rapidly classifying large numbers of pathology reports for keratinocyte cancers and related diagnoses. Smad activation Such tools may provide the means to accurately measure subtype-specific skin cancer incidence.

Cancer research using electronic health records and genomic data sets requires clinical outcomes data, which may be recorded only in unstructured text by treating oncologists. Natural language processing (NLP) could substantially accelerate extraction of this information.

Patients with lung cancer who had tumor sequencing as part of a single-institution precision oncology study from 2013 to 2018 were identified. Medical oncologists’ progress notes for these patients were reviewed. For each note, curators recorded whether the assessment/plan indicated any cancer, progression/worsening of disease, and/or response to therapy or improving disease. Next, a recurrent neural network was trained using unlabeled notes to extract the assessment/plan from each note. Finally, convolutional neural networks were trained on labeled assessments/plans to predict the probability that each curated outcome was present. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) ames from oncologist notes at scale. Such models may facilitate identification of clinical and genomic features associated with response to cancer treatment.