UCLA

As medical schools move to integrate the Core Entrustable Professional Activities for Entering Residency (EPAs) into curricula and address the transition from student to resident, residency preparatory courses have become more prevalent. The authors developed an experiential learning EPA-based capstone course for assessment to determine impact on learner self-assessed ratings of readiness for residency and acquisition of medical knowledge. All fourth-year students from the classes of 2018-2020 completed a required course in the spring for assessment of multiple EPAs, including managing core complaints, performing basic procedures, obtaining informed consent, and providing patient handoffs. Learners selected between three specialty-based parallel tracks - adult medicine, surgery, or pediatrics. Students completed a retrospective pre-post questionnaire to provide self-assessed ratings of residency preparedness and comfort in performing EPAs. Finally, the authors studied the impact of the course on knowledge acquisition by comparing student performance in the adult medicine track on multiple choice pre- and post-tests. Four hundred and eighty-one students were eligible for the study and 452 (94%) completed the questionnaire. For all three tracks, there was a statistically significant change in learner self-assessed ratings of preparedness for residency from pre- to post-course (moderately or very prepared: adult medicine 61.4% to 88.6% [p-value < 0.001]; surgery 56.8% to 81.1% [p-value < 0.001]; pediatrics 32.6% to 83.7% [p-value 0.02]). A similar change was noted in all tracks in learner self-assessed ratings of comfort from pre- to post-course for all studied EPAs. Of the 203 students who participated in the adult medicine track from 2019-2020, 200 (99%) completed both the pre- and post-test knowledge assessments. The mean performance improved from 65.0% to 77.5% (p-value < 0.001). An experiential capstone course for the assessment of EPAs can be effective to improve learner self-assessed ratings of readiness for residency training and acquisition of medical knowledge.

Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50% White, 41% Black or African American, 7% Hispanic or Latino/a, and 2% Native Hawaiian, Asian American, American Indian, or Alaska Native (total n = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (CADM3, NALCN, NLRC5, ZNF792, and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level (p < 1.2E-7). The downstream pathways affected by DNA methylation included the identification of IFI16 and IRF7 CpG-gene transcript pairs which contributed to the innate immune response gene enrichment pathway along with NLRC5, NOD2, and AIM2. Gene enrichment analysis also identified the nuclear factor-kappaB transcription pathway. Using two-sample Mendelian randomization (MR) we inferred methylation at three CpG sites as causal for CRP levels using both White and Black or African American MR instrument variables. Overall, we identified novel CpG sites and gene transcripts that could be valuable in understanding the specific cellular processes and pathogenic mechanisms involved in inflammation.

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Abstract: The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.