The influence of pre-professional curricula on components of the Physical Therapist Clinical Performance Instrument

Background and Purpose: The purpose of this study is to investigate any association between pre-enrollment curricula and clinical performance in physical therapy professional schools. Specifically, does the type of undergraduate institution (as defined by Carnegie classification type) influence performance on components of the Physical Therapist Clinical Performance Instrument? Methods: The study methods include a retrospective quantitative review of student educational records from the Duke Doctor of Physical Therapy (DPT) classes of 2013 to present. Kruskal-Wallis tests were used to determine significance of the dependent variables. Results: Results indicated that when the Carnegie Classifications were consolidated to five categories, there was only a significant difference in score for one of the 108 possible scales in the CPI (Professional Behavior, Final 3). Students who attended an undergraduate institution with a professional focus (category 5) scored significantly (p=.033) higher on this Professional Behavior scale than did students who attended an undergraduate institution with an arts and sciences focus (category 1). When the Carnegie Classifications were consolidated to four categories, two scales showed significant results (Professional Behavior, Final 3; Accountability, Final 3). Conclusions: The study fails to confirm the hypothesis that the type of undergraduate institution influences performance on components of the Physical Therapist Clinical Performance Instrument. There is virtually no difference on clinical performance based on undergraduate institution type

Compositional data analysis with Red-R

The compositional analyst must use a series of software to transform raw compositional data and run statistical analyses on them. Tools for compositional data analysis are available in R, an open source widely-used statistical computing environment. However, using R requires prior programming knowledge. Red-R is an open-source, user-friendly visual data flow interface based on R. The interface uses principles of pipeline programming where functions are represented as icons, termed widgets, and data flows from one function to another by drawing lines between them on a canvas. Red-R is able to perform common data analysis tasks (hypothesis tests, analysis of variance, regressions, principal component analysis, data cloud plots, bar plots, biplots, etc.). We have developed a novel Red-R package which implements the compositions package in R. Our compositions package can be used to perform compositional data operations over raw data (closure, additive, centered and isometric log ratio transformations, perturbations and powering, etc.), and create compositional plots (ternary diagrams, ilrdendrograms, etc.) without prior programming knowledge, after few basic operations. The objective of this work is to present Red-R and its compositions package using an application example for geochemical data. The network of widgets provides an easy-tofollow step-by-step procedure to run a large number of operations available in R, hence facilitating the tasks of the compositional data analyst. Furthermore, the entire analysis network can be saved and reloaded. Reports can be generated from the widget network to document and share results. Non-programmers can have an easy access to the advanced tools available in compositions analysis

The prevalence and effect of burnout on graduate healthcare students

Burnout is a growing epidemic among professional healthcare students. Unaddressed burnout has been shown to have psychological and performance related detriments. The purpose of this scoping literature review was to investigate the prevalence of burnout and its effects on the psychological, professional, empathetic ability, and academic acuity of graduate healthcare students. Inclusion criteria included English language papers published within the last 10 years and subjects in graduate healthcare professional programs. This search encompassed 8,214 articles. After title and abstract screening, 127 articles remained and were sorted into five domains of interest: etiology, professionalism, mental health, empathy, and academic performance. After duplicates were removed, 27 articles remained for the scoping review. Graduate level healthcare students had higher levels of burnout than age matched peers and the general population. The high prevalence of burnout within graduate healthcare students can have an effect on their mental health, empathy, and professional conduct. Understanding the occurrence and effects of burnout within graduate healthcare programs allows faculty and administration to plan curriculum, and provide information to students to understand, recognize, and create opportunities to decrease burnout in order to create long lasting quality clinicians

A novel tool for evaluating non-cognitive traits of doctor of physical therapy learners in the United States

Purpose The primary aim of this study was to develop a survey addressing an individual’s non-cognitive traits, such as emotional intelligence, interpersonal skills, social intelligence, psychological flexibility, and grit. Such a tool would provide beneficial information for the continued development of admissions standards and would help better capture the full breadth of experience and capabilities of applicants applying to doctor of physical therapy (DPT) programs. Methods This was a cross-sectional survey study involving learners in DPT programs at 3 academic institutions in the United States. A survey was developed based on established non-proprietary, non-cognitive measures affiliated with success and resilience. The survey was assessed for face validity, and exploratory factor analysis (EFA) was used to identify subgroups of factors based on responses to the items. Results A total of 298 participants (90.3%) completed all elements of the survey. EFA yielded 39 items for dimensional assessment with regression coefficients < 0.4. Within the 39 items, 3 latent constructs were identified: adaptability (16 items), intuitiveness (12 items), and engagement (11 items). Conclusion This preliminary non-cognitive assessment survey will be able to play a valuable role in DPT admissions decisions following further examination and refinement

Ampullary cancers harbor ELF3 tumor suppressor gene mutations and exhibit frequent WNT dysregulation

The ampulla of Vater is a complex cellular environment from which adenocarcinomas arise to form a group of histopathologically heterogenous tumors. To evaluate the molecular features of these tumors, 98 ampullary adenocarcinomas were evaluated and compared to 44 distal bile duct and 18 duodenal adenocarcinomas. Genomic analyses revealed mutations in the WNT signaling pathway among half of the patients and in all three adenocarcinomas irrespective of their origin and histological morphology. These tumors were characterized by a high frequency of inactivating mutations of ELF3, a high rate of microsatellite instability, and common focal deletions and amplifications, suggesting common attributes in the molecular pathogenesis are at play in these tumors. The high frequency of WNT pathway activating mutation, coupled with small-molecule inhibitors of β-catenin in clinical trials, suggests future treatment decisions for these patients may be guided by genomic analysis

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment