Performance of an Artificial Intelligence Model for Recognition and Quantitation of Histologic Features of Eosinophilic Esophagitis on Biopsy Samples.

We have developed an artificial intelligence (AI)-based digital pathology model for the evaluation of histologic features related to eosinophilic esophagitis (EoE). In this study, we evaluated the performance of our AI model in a cohort of pediatric and adult patients for histologic features included in the Eosinophilic Esophagitis Histologic Scoring System (EoEHSS). We collected a total of 203 esophageal biopsy samples from patients with mucosal eosinophilia of any degree (91 adult and 112 pediatric patients) and 10 normal controls from a prospectively maintained database. All cases were assessed by a specialized gastrointestinal (GI) pathologist for features in the EoEHSS at the time of original diagnosis and rescored by a central GI pathologist (R.K.M.). We subsequently analyzed whole-slide image digital slides using a supervised AI model operating in a cloud-based, deep learning AI platform (Aiforia Technologies) for peak eosinophil count (PEC) and several histopathologic features in the EoEHSS. The correlation and interobserver agreement between the AI model and pathologists (Pearson correlation coefficient [rs] = 0.89 and intraclass correlation coefficient [ICC] = 0.87 vs original pathologist; rs = 0.91 and ICC = 0.83 vs central pathologist) were similar to the correlation and interobserver agreement between pathologists for PEC (rs = 0.88 and ICC = 0.91) and broadly similar to those for most other histologic features in the EoEHSS. The AI model also accurately identified PEC of >15 eosinophils/high-power field by the original pathologist (area under the curve [AUC] = 0.98) and central pathologist (AUC = 0.98) and had similar AUCs for the presence of EoE-related endoscopic features to pathologists' assessment. Average eosinophils per epithelial unit area had similar performance compared to AI high-power field-based analysis. Our newly developed AI model can accurately identify, quantify, and score several of the main histopathologic features in the EoE spectrum, with agreement regarding EoEHSS scoring which was similar to that seen among GI pathologists.

A Novel Selectable Islet 1 Positive Progenitor Cell Reprogrammed to Expandable and Functional Smooth Muscle Cells.

Disorders affecting smooth muscle structure/function may require technologies that can generate large scale, differentiated and contractile smooth muscle cells (SMC) suitable for cell therapy. To date no clonal precursor population that provides large numbers of differentiated SMC in culture has been identified in a rodent. Identification of such cells may also enhance insight into progenitor cell fate decisions and the relationship between smooth muscle precursors and disease states that implicate differentiated SMC. In this study, we used classic clonal expansion techniques to identify novel self-renewing Islet 1 (Isl-1) positive primitive progenitor cells (PPC) within rat bone marrow that exhibited canonical stem cell markers and preferential differentiation towards a smooth muscle-like fate. We subsequently used molecular tagging to select Isl-1 positive clonal populations from expanded and de novo marrow cell populations. We refer to these previously undescribed cells as the PPC given its stem cell marker profile, and robust self-renewal capacity. PPC could be directly converted into induced smooth muscle cells (iSMC) using single transcription factor (Kruppel-like factor 4) knockdown or transactivator (myocardin) overexpression in contrast to three control cells (HEK 293, endothelial cells and mesenchymal stem cells) where such induction was not possible. iSMC exhibited immuno- and cytoskeletal-phenotype, calcium signaling profile and contractile responses similar to bona fide SMC. Passaged iSMC could be expanded to a scale sufficient for large scale tissue replacement. PPC and reprogramed iSMC so derived may offer future opportunities to investigate molecular, structure/function and cell-based replacement therapy approaches to diverse cardiovascular, respiratory, gastrointestinal, and genitourinary diseases that have as their basis smooth muscle cell functional aberrancy or numerical loss. Stem Cells 2016;34:1354-1368.

Synthetic chemically modified mrna-based delivery of cytoprotective factor promotes early cardiomyocyte survival post-acute myocardial infarction.

To extend the temporal window for cytoprotection in cardiomyocytes undergoing apoptosis after hypoxia and myocardial infarction (MI), a synthetic chemically modified mRNA (modRNA) was used to drive delivery of insulin-like growth factor-1 (IGF1) within the area at risk in an in vivo murine model of MI. Delivery of IGF1 modRNA, with a polyethylenimine-based nanoparticle, augmented secreted and cell-associated IGF1, promoting cardiomyocyte survival and abrogating cell apoptosis under hypoxia-induced apoptosis conditions. Translation of modRNA-IGF1 was sufficient to induce downstream increases in the levels of Akt and Erk phosphorylation. Downregulation of IGF1 specific miRNA-1 and -133 but not miR-145 expression was also confirmed. As a proof of concept, intramyocardial delivery of modRNA-IGF1 but not control modRNA-GFP significantly decreased the level of TUNEL positive cells, augmented Akt phosphorylation, and decreased caspase-9 activity within the infarct border zone 24 h post-MI. These findings demonstrate the potential for an extended cytoprotective effect of transient IGF1 driven by synthetic modRNA delivery.

Development and technical validation of an artificial intelligence model for quantitative analysis of histopathologic features of eosinophilic esophagitis.

Background: In an attempt to provide quantitative, reproducible, and standardized analyses in cases of eosinophilic esophagitis (EoE), we have developed an artificial intelligence (AI) digital pathology model for the evaluation of histologic features in the EoE/esophageal eosinophilia spectrum. Here, we describe the development and technical validation of this novel AI tool. Methods: A total of 10 726 objects and 56.2 mm2 of semantic segmentation areas were annotated on whole-slide images, utilizing a cloud-based, deep learning artificial intelligence platform (Aiforia Technologies, Helsinki, Finland). Our training set consisted of 40 carefully selected digitized esophageal biopsy slides which contained the full spectrum of changes typically seen in the setting of esophageal eosinophilia, ranging from normal mucosa to severe abnormalities with regard to each specific features included in our model. A subset of cases was reserved as independent "test sets" in order to assess the validity of the AI model outside the training set. Five specialized experienced gastrointestinal pathologists scored each feature blindly and independently of each other and of AI model results. Results: The performance of the AI model for all cell type features was similar/non-inferior to that of our group of GI pathologists (F1-scores: 94.5-94.8 for AI vs human and 92.6-96.0 for human vs human). Segmentation area features were rated for accuracy using the following scale: 1. "perfect or nearly perfect" (95%-100%, no significant errors), 2. "very good" (80%-95%, only minor errors), 3. "good" (70%-80%, significant errors but still captures the feature well), 4. "insufficient" (less than 70%, significant errors compromising feature recognition). Rating scores for tissue (1.01), spongiosis (1.15), basal layer (1.05), surface layer (1.04), lamina propria (1.15), and collagen (1.11) were in the "very good" to "perfect or nearly perfect" range, while degranulation (2.23) was rated between "good" and "very good". Conclusion: Our newly developed AI-based tool showed an excellent performance (non-inferior to a group of experienced GI pathologists) for the recognition of various histologic features in the EoE/esophageal mucosal eosinophilia spectrum. This tool represents an important step in creating an accurate and reproducible method for semi-automated quantitative analysis to be used in the evaluation of esophageal biopsies in this clinical context.

Medical school selection criteria as predictors of medical student empathy: a cross-sectional study of medical students, Ireland.

OBJECTIVES: To determine whether performance in any of the Health Professions Admissions Test (HPAT) sections, most specifically the interpersonal understanding section, correlates with self-reported empathy levels in medical students. SETTING: The study was conducted in University College Cork, Ireland. PARTICIPANTS: 290 students participated in the study. Matching HPAT scores were available for 263 students. All male and female undergraduate students were invited to participate. Postgraduate and international students were excluded. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary measures: HPAT-Ireland and Jefferson Scale of Physician Empathy (JSE) scores were compared including subsection analysis. Secondary measures: comparisons were made between groups such as gender and year of programme. RESULTS: A total of 290 students participated. Males scored significantly higher than females for total HPAT-Ireland (U=7329, z=-2.04, p<0.05), HPAT-Ireland section 1 (U=5382, z=-5.21, p<0.001) and section 3 scores (U=6833, z=-2.85, p<0.01). In contrast, females scored significantly higher than males on HPAT-Ireland section 2 (U=5844, z=-4.46, p<0.001). Females demonstrated significantly higher total JSE scores relative to males (mean score ± SEM: 113.33±1.05vs 109.21±0.95; U=8450, z=-2.83, p<0.01). No significant association was observed between JSE scores and any of the HPAT-Ireland measures (all p>0.05). There was no effect of programme year on JSE scores (all p>0.05). CONCLUSION: The introduction of the HPAT-Ireland test was partly designed to identify students with strong interpersonal skills. A significant finding of this study is that JSE values did not correlate with HPAT-Ireland scores. This study suggests no clear link between scores on a selection test, the HPAT-Ireland, which is designed to assess several skill domains including interpersonal skills, and scores on a psychometric measure of empathy, at any point during medical education.