The human kindness curriculum: An innovative preclinical initiative to highlight kindness and empathy in medicine.

BackgroundPrior studies have shown a marked drop in empathy among students during their third (clinical) year of medical school. Curricula developed to address this problem have varied greatly in content and have not always been subjected to validated measures of impact.MethodsIn 2015, we initiated a Human Kindness (HK) curriculum for the initial 2 years of medical school. This mandatory 12-h curriculum (6 h/year) included an innovative series of lectures and patient interactions with regard to compassion and empathy in the clinical setting. Both quantitative (Jefferson Scale of Empathy [JSE]) and qualitative data were collected prospectively to evaluate the impact of the HK curriculum.ResultsIn the initial Pilot Year, neither 1st (Group 1) nor 2nd (Group 2) year medical students showed pre-post changes in JSE scores. Substantial changes were made to the curriculum based on faculty and student evaluations. In the following Implementation Year, both the new 1st (Group 3) and the now 2nd year (Group 4) students, who previously experienced the Pilot Year, showed significant improvements in post-course JSE scores; this improvement remained valid across subanalyses of gender, age, and student career focus (e.g., internal medicine, surgery, etc.). Despite the disappointingly flat initial Pilot Year JSE scores, the 3rd year students (Group 2) who experienced only the Pilot Year of the curriculum (i.e., 2nd year students at the time of the Pilot Year) had subsequent JSE scores that did not show the typical decline associated with the clinical years. Students generally evaluated the HK curriculum positively and rated it as being important to their medical education and development as a physician.DiscussionA required preclinical curriculum focused on HK resulted in significant improvements in medical student empathy; this improvement was maintained during the 1st clinical year of training

Designing signaling environments to steer transcriptional diversity in neural progenitor cell populations

Stem cell populations within developing embryos are diverse, composed of many different subpopulations of cells with varying developmental potential. The structure of stem cell populations in cell culture remains poorly understood and presents a barrier to differentiating stem cells for therapeutic applications. In this paper we develop a framework for controlling the architecture of stem cell populations in cell culture using high-throughput single cell mRNA-seq and computational analysis. We find that the transcriptional diversity of neural stem cell populations collapses in cell culture. Cell populations are depleted of committed neuron progenitor cells and become dominated by a single pre-astrocytic cell population. By analyzing the response of neural stem cell populations to forty distinct signaling conditions, we demonstrate that signaling environments can restructure cell populations by modulating the relative abundance of pre-astrocyte and pre-neuron subpopulations according to a simple linear code. One specific combination of BMP4, EGF, and FGF2 ligands switches the default population balance such that 70% of cells correspond to the committed neurons. Our work demonstrates that single-cell RNA-seq can be applied to modulate the diversity of in vitro stem cell populations providing a new strategy for population-level stem cell control

Impact of mentoring on socio�emotional and mental health outcomes of youth with learning disabilities and attention�deficit hyperactivity disorder

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151880/1/camh12331_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151880/2/camh12331.pd

Designing a Context-Sensitive Context Detection Service for Mobile Devices

This paper describes the design, implementation, and evaluation of Amoeba, a context-sensitive context detection service for mobile devices. Amoeba exports an API that allows a client to express interest in one or more context types (activity, indoor/outdoor, and entry/exit to/from named regions), subscribe to specific modes within each context (e.g., "walking" or "running", but no other activity), and specify a response latency (i.e., how often the client is notified). Each context has a detector that returns its estimate of the mode. The detectors take both the desired subscriptions and the current context detection into account, adjusting both the types of sensors and the sampling rates to achieve high accuracy and low energy consumption. We have implemented Amoeba on Android. Experiments with Amoeba on 45+ hours of data show that our activity detector achieves an accuracy between 92% and 99%, outperforming previous proposals like UCLA* (59%), EEMSS (82%) and SociableSense (72%), while consuming 4 to 6× less energy

Triepitopic Antibody Fusions Inhibit Cetuximab-Resistant BRAF and KRAS Mutant Tumors via EGFR Signal Repression

Dysregulation of epidermal growth factor receptor (EGFR) is a hallmark of many epithelial cancers, rendering this receptor an attractive target for cancer therapy. Much effort has been focused on the development of EGFR-directed antibody-based therapeutics, culminating in the clinical approval of the drugs cetuximab and panitumumab. Unfortunately, the clinical efficacy of these drugs has been disappointingly low, and a particular challenge to targeting EGFR with antibody therapeutics has been resistance, resulting from mutations in the downstream raf and ras effector proteins. Recent work demonstrating antibody cocktail-induced synergistic downregulation of EGFR motivated our design of cetuximab-based antibody–fibronectin domain fusion proteins that exploit downregulation-based EGFR inhibition by simultaneously targeting multiple receptor epitopes. We establish that, among our engineered multiepitopic formats, trans-triepitopic antibody fusions demonstrate optimal efficacy, inducing rapid EGFR clustering and internalization and consequently ablating downstream signaling. The combined effects of EGFR downregulation, ligand competition, and immune effector function conspire to inhibit tumor growth in xenograft models of cetuximab-resistant BRAF and KRAS mutant cancers. Our designed triepitopic constructs have the potential to enhance the efficacy and expand the scope of EGFR-directed therapies, and our multiepitopic may be readily applied to other receptor targets to formulate a new class of antibody-based therapeutics.National Institutes of Health (U.S.) (Grant CA96504)American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture, and tracks gene expression and cell abundance changes across subpopulations by constructing and comparing probabilistic models. We apply PopAlign to analyze the impact of 42 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals or physiological change