It's "not black and white": Students' perceptions of "altruism" content in preclinical medical education.

OBJECTIVE: This study investigated how students as stakeholders viewed behavioral and social science (BSS) content in a preclinical longitudinal course entitled "Medicine, Body, and Society" (MBS) at UT Health San Antonio Long School of Medicine (LSOM). We present students' perceptions of successes and challenges tied to "altruism" and other non-biomedical objectives outlined by this institution. METHODS: We conducted a qualitative thematic analysis of MBS course evaluation data. Two researchers independently performed initial coding followed by interrater reliability checks to revise codes and a final MAXQDA lexical search to refine three themes. RESULTS: Three major themes emerged: (1) Students shared pedagogical preferences strongly favoring stories. (2) Students detected deficits in the module content tied to identities. (3) Students labelled BSS content as "soft," "subjective," and "siloed" which confounded its role in the course. CONCLUSIONS: Advancing altruism aligned with BSS content in preclinical medical education remains a challenge. A closer review of student evaluations framed as learner-centeredness is key to a greater understanding and resolution of competency issues in preclinical curriculum and its impact on mastery in subsequent clinical education and practice.

Predicting nodal metastases in squamous cell carcinoma of the oral tongue using artificial intelligence.

OBJECTIVE: The presence of occult nodal metastases in patients with squamous cell carcinoma (SCC) of the oral tongue has implications for treatment. Upwards of 30% of patients will have occult nodal metastases, yet a significant number of patients undergo unnecessary neck dissection to confirm nodal status. This study sought to predict the presence of nodal metastases in patients with SCC of the oral tongue using a convolutional neural network (CNN) that analyzed visual histopathology from the primary tumor alone. METHODS: Cases of SCC of the oral tongue were identified from the records of a single institution. Only patients with complete pathology data were included in the study. The primary tumors were randomized into 2 groups for training and testing, which was performed at 2 different levels of supervision. Board-certified pathologists annotated each slide. HALO-AI convolutional neural network and image software was used to perform training and testing. Receiver operator characteristic (ROC) curves and the Youden J statistic were used for primary analysis. RESULTS: Eighty-nine cases of SCC of the oral tongue were included in the study. The best performing algorithm had a high level of supervision and a sensitivity of 65% and specificity of 86% when identifying nodal metastases. The area under the curve (AUC) of the ROC curve for this algorithm was 0.729. CONCLUSION: A CNN can produce an algorithm that is able to predict nodal metastases in patients with squamous cell carcinoma of the oral tongue by analyzing the visual histopathology of the primary tumor alone.

The inflammatory response of human pancreatic cancer samples compared to normal controls.

Pancreatic ductal adenocarcinoma (PDAC) is a poor prognosis cancer with an aggressive growth profile that is often diagnosed at late stage and that has few curative or therapeutic options. PDAC growth has been linked to alterations in the pancreas microbiome, which could include the presence of the fungus Malassezia. We used RNA-sequencing to compare 14 matched tumor and normal (tumor adjacent) pancreatic cancer samples and found Malassezia RNA in both the PDAC and normal tissues. Although the presence of Malassezia was not correlated with tumor growth, a set of immune- and inflammatory-related genes were up-regulated in the PDAC compared to the normal samples, suggesting that they are involved in tumor progression. Gene set enrichment analysis suggests that activation of the complement cascade pathway and inflammation could be involved in pro PDAC growth.

Lymph Node Metastases in Papillary Thyroid Carcinoma can be Predicted by a Convolutional Neural Network: a Multi-Institution Study.

OBJECTIVES: The presence of nodal metastases in patients with papillary thyroid carcinoma (PTC) has both staging and treatment implications. However, lymph nodes are often not removed during thyroidectomy. Prior work has demonstrated the capability of artificial intelligence (AI) to predict the presence of nodal metastases in PTC based on the primary tumor histopathology alone. This study aimed to replicate these results with multi-institutional data. METHODS: Cases of conventional PTC were identified from the records of 2 large academic institutions. Only patients with complete pathology data, including at least 3 sampled lymph nodes, were included in the study. Tumors were designated "positive" if they had at least 5 positive lymph node metastases. First, algorithms were trained separately on each institution's data and tested independently on the other institution's data. Then, the data sets were combined and new algorithms were developed and tested. The primary tumors were randomized into 2 groups, one to train the algorithm and another to test it. A low level of supervision was used to train the algorithm. Board-certified pathologists annotated the slides. HALO-AI convolutional neural network and image software was used to perform training and testing. Receiver operator characteristic curves and the Youden J statistic were used for primary analysis. RESULTS: There were 420 cases used in analyses, 45% of which were negative. The best performing single institution algorithm had an area under the curve (AUC) of 0.64 with a sensitivity and specificity of 65% and 61% respectively, when tested on the other institution's data. The best performing combined institution algorithm had an AUC of 0.84 with a sensitivity and specificity of 68% and 91% respectively. CONCLUSION: A convolutional neural network can produce an accurate and robust algorithm that is capable of predicting nodal metastases from primary PTC histopathology alone even in the setting of multi-institutional data.

Surgery for rheumatic heart disease in the Northern Territory, Australia, 1997-2016: what have we gained?

BACKGROUND: Between 1964 and 1996, the 10-year survival of patients having valve replacement surgery for rheumatic heart disease (RHD) in the Northern Territory, Australia, was 68%. As medical care has evolved since then, this study aimed to determine whether there has been a corresponding improvement in survival. METHODS: A retrospective study of Aboriginal patients with RHD in the Northern Territory, Australia, having their first valve surgery between 1997 and 2016. Survival was examined using Kaplan-Meier and Cox regression analysis. FINDINGS: The cohort included 281 adults and 61 children. The median (IQR) age at first surgery was 31 (18-42) years; 173/342 (51%) had a valve replacement, 113/342 (33%) had a valve repair and 56/342 (16%) had a commissurotomy. There were 93/342 (27%) deaths during a median (IQR) follow-up of 8 (4-12) years. The overall 10-year survival was 70% (95% CI: 64% to 76%). It was 62% (95% CI: 53% to 70%) in those having valve replacement. There were 204/281 (73%) adults with at least 1 preoperative comorbidity. Preoperative comorbidity was associated with earlier death, the risk of death increasing with each comorbidity (HR: 1.3 (95% CI: 1.2 to 1.5), p<0.001). Preoperative chronic kidney disease (HR 6.5 (95% CI: 3.0 to 14.0) p≤0.001)), coronary artery disease (HR 3.3 (95% CI: 1.3 to 8.4) p=0.012) and pulmonary artery systolic pressure>50 mm Hg before surgery (HR 1.9 (95% CI: 1.2 to 3.1) p=0.007) were independently associated with death. INTERPRETATION: Survival after valve replacement for RHD in this region of Australia has not improved. Although the patients were young, many had multiple comorbidities, which influenced long-term outcomes. The increasing prevalence of complex comorbidity in the region is a barrier to achieving optimal health outcomes.

G-CSF Is a Novel Mediator of T-Cell Suppression and an Immunotherapeutic Target for Women with Colon Cancer.

PURPOSE: G-CSF enhances colon cancer development. This study defines the prevalence and effects of increased G-CSF signaling in human colon cancers and investigates G-CSF inhibition as an immunotherapeutic strategy against metastatic colon cancer. EXPERIMENTAL DESIGN: Patient samples were used to evaluate G-CSF and G-CSF receptor (G-CSFR) levels by IHC with sera used to measure G-CSF levels. Peripheral blood mononuclear cells were used to assess the rate of G-CSFR+ T cells and IFNγ responses to chronic ex vivo G-CSF. An immunocompetent mouse model of peritoneal metastasis (MC38 cells in C57Bl/6J) was used to determine the effects of G-CSF inhibition (αG-CSF) on survival and the tumor microenvironment (TME) with flow and mass cytometry. RESULTS: In human colon cancer samples, the levels of G-CSF and G-CSFR are higher compared to normal colon tissues from the same patient. High patient serum G-CSF is associated with increases in markers of poor prognosis, (e.g., VEGF, IL6). Circulating T cells from patients express G-CSFR at double the rate of T cells from controls. Prolonged G-CSF exposure decreases T cell IFNγ production. Treatment with αG-CSF shifts both the adaptive and innate compartments of the TME and increases survival (HR, 0.46; P = 0.0237) and tumor T-cell infiltration, activity, and IFNγ response with greater effects in female mice. There is a negative correlation between serum G-CSF levels and tumor-infiltrating T cells in patient samples from women. CONCLUSIONS: These findings support G-CSF as an immunotherapeutic target against colon cancer with greater potential benefit in women.

Revisiting Feed Forward: Promoting a Student-Centered Approach to Education Handoffs, Remediation, and Clerkship Success.

Issue: Throughout medical school, and especially during clerkships, students experience changing work and learning environments and are exposed to new academic, interpersonal, and professional challenges unique to clinical learning. Given the siloed nature of clinical rotations, students often "fall through the cracks" and may repeatedly struggle through clerkships without support and coaching from which they would otherwise benefit. Many institutions have grappled with creating feed forward processes, that is, educational handoffs in which information is shared among faculty about struggling students with the intention of providing longitudinal support to ensure their success, while protecting students from negative bias that may follow them throughout the remainder of their medical school tenure. Evidence: Here, the authors describe the feed forward processes of four medical schools. Each school's process relies on close collaboration between course directors and deans to identify students and develop intervention plans. Course leadership and administration are typically the primary drivers for long-term follow-up with students. The number of participants in the process varies, with only one school directly involving students. Two schools hold larger, regularly scheduled meetings with up to 12 faculty present in their institution's feed forward process. Across these institutions, students can "graduate" from the feed forward process once they achieve competency in the areas of concern. Implications: The authors believe the most important outcome achieved is the formalization and adherence to a feed forward process. Thus, risk to students in the form of negative bias is mitigated by the flow of information, the extent to which information is available, and permitting students to be part of the process. These exemplars give insight into variable approaches to feed forward systems adopted by medical schools and demonstrate highly visible methodologies by which educational leadership empower students and educators toward a shared goal of student progress and achievement.

The Validity of MCAT Scores in Predicting Students' Performance and Progress in Medical School: Results From a Multisite Study.

PURPOSE: This is the first multisite investigation of the validity of scores from the current version of the Medical College Admission Test (MCAT) in clerkship and licensure contexts. It examined the predictive validity of MCAT scores and undergraduate grade point averages (UGPAs) for performance in preclerkship and clerkship courses and on the United States Medical Licensing Examination Step 1 and Step 2 Clinical Knowledge examinations. It also studied students' progress in medical school. METHOD: Researchers examined data from 17 U.S. and Canadian MD-granting medical schools for 2016 and 2017 entrants who volunteered for the research and applied with scores from the current MCAT exam. They also examined data for all U.S. medical schools for 2016 and 2017 entrants to regular-MD programs who applied with scores from the current exam. Researchers conducted linear and logistic regression analyses to determine whether MCAT total scores added value beyond UGPAs in predicting medical students' performance and progress. Importantly, they examined the comparability of prediction by sex, race and ethnicity, and socioeconomic status. RESULTS: Researchers reported medium to large correlations between MCAT total scores and medical student outcomes. Correlations between total UGPAs and medical student outcomes were similar but slightly lower. When MCAT scores and UGPAs were used together, they predicted student performance and progress better than either alone. Despite differences in average MCAT scores and UGPAs between students who self-identified as White or Asian and those from underrepresented racial and ethnic groups, predictive validity results were comparable. The same was true for students from different socioeconomic backgrounds, and for males and females. CONCLUSIONS: These data demonstrate that MCAT scores add value to the prediction of medical student performance and progress and that applicants from different backgrounds who enter medical school with similar ranges of MCAT scores and UGPAs perform similarly in the curriculum.

Ulcerative Colitis: Novel Epithelial Insights Provided by Single Cell RNA Sequencing.

Ulcerative Colitis (UC) is a chronic inflammatory disease of the intestinal tract for which a definitive etiology is yet unknown. Both genetic and environmental factors have been implicated in the development of UC. Recently, single cell RNA sequencing (scRNA-seq) technology revealed cell subpopulations contributing to the pathogenesis of UC and brought new insight into the pathways that connect genome to pathology. This review describes key scRNA-seq findings in two major studies by Broad Institute and University of Oxford, investigating the transcriptomic landscape of epithelial cells in UC. We focus on five major findings: (1) the identification of BEST4 + cells, (2) colonic microfold (M) cells, (3) detailed comparison of the transcriptomes of goblet cells, and (4) colonocytes and (5) stem cells in health and disease. In analyzing the two studies, we identify the commonalities and differences in methodologies, results, and conclusions, offering possible explanations, and validated several cell cluster markers. In systematizing the results, we hope to offer a framework that the broad scientific GI community and GI clinicians can use to replicate or corroborate the extensive new findings that RNA-seq offers.

Stochastic analysis and modeling of pharmaceutical screw feeder mass flow rates.

Screw feeders, as the initial operation in continuous manufacturing of drug product processes, greatly influence the mass flow rate of pharmaceutical powders downstream. Existing flowsheet models can quickly simulate the average powder mass flow rate while custom Discrete Element Method models require prohibitively long times to simulate a minute of realistic, high-variance particle flow. We propose a hybrid deterministic-stochastic feeder flowsheet model that leverages time series analysis and an Autoregressive Moving Average (ARMA) model to quantify and simulate the observed non-random variation in feeder powder flow. To allow for improved process and controller design, our approach is quick-to-solve, high-variance, and has a low experimental overhead. By examining the deterministic model errors of three different volumetrically fed excipients, we demonstrate that the errors are leptokurtic, heavy-tailed, and display a linear dependence on their prior two seconds of state. These errors are all reasonably modeled by an ARMA(2,1) model and are parametrically distinct from each other. Furthermore, we show that refilling the feeder online significantly alters the error distribution, autocorrelation structure, and ARMA parameters. These findings lay the groundwork necessary to model and predict the realistic feeder dynamics of a much broader range of powders and operating conditions.

Educational Outcomes of a 4-Year MD-MPH Dual-Degree Program: High Completion Rates and Higher Likelihood of Primary Care Residency.

PURPOSE: In 2007, University of Texas Health Science Center Houston School of Public Health at San Antonio (UTHealth SPH) and UT Health San Antonio Long School of Medicine (LSOM) designed and implemented a 4-year dual MD and Master of Public Health (MPH) program. Dual MD-MPH programs wherein students can receive both degrees within 4 years are unique, and programmatic evaluation may have generalizable implications for accredited MD-MPH programs. METHOD: Demographic information was collected from UTHealth SPH and LSOM student data. The primary outcome variable was MD-MPH program completion in 4 years. Comprehensive Basic Science Examination (CBSE) scores, United States Medical Licensing Examination Step 1 and Step 2 scores, and successful primary care residency match data were compared between MD-MPH and MD-only students. Family medicine, internal medicine, obstetrics-gynecology, and pediatrics were considered primary care residencies, and an analysis excluding obstetrics-gynecology was also conducted. RESULTS: Of 241 MD-MPH students enrolled 2007-2017, 66% were women, 22% Hispanic, and 10% African American. Four-year MD-MPH program completion occurred for 202 (93% of eligible) students; 9 (4.1%) received MD only, 3 (1.4%) received MPH only; and 4 (1.8%) received neither. MD-MPH students' median CBSE score was 2 points lower than for MD-only students (P = .035), but Step 1 and 2 scores did not differ. Primary care residency match was more likely compared with MD-only students, both including and excluding obstetrics-gynecology (odds ratio [OR]: 1.75; 95% confidence interval [CI]: 1.31, 2.33; and OR: 1.36; 95% CI: 1.02, 1.82, respectively). CONCLUSIONS: The 4-year MD-MPH program retains and graduates a socioeconomically and racial/ethnically diverse group of students with a 93% success rate. MD-MPH graduates were more likely to pursue primary care residency than non-dual-degree students, which may have implications for addressing population health disparities.

A Deep Learning Convolutional Neural Network Can Differentiate Between Helicobacter Pylori Gastritis and Autoimmune Gastritis With Results Comparable to Gastrointestinal Pathologists.

CONTEXT.­: Pathology studies using convolutional neural networks (CNNs) have focused on neoplasms, while studies in inflammatory pathology are rare. We previously demonstrated a CNN that differentiates reactive gastropathy, Helicobacter pylori gastritis (HPG), and normal gastric mucosa. OBJECTIVE.­: To determine whether a CNN can differentiate the following 2 gastric inflammatory patterns: autoimmune gastritis (AG) and HPG. DESIGN.­: Gold standard diagnoses were blindly established by 2 gastrointestinal (GI) pathologists. One hundred eighty-seven cases were scanned for analysis by HALO-AI. All levels and tissue fragments per slide were included for analysis. The cases were randomized, 112 (60%; 60 HPG, 52 AG) in the training set and 75 (40%; 40 HPG, 35 AG) in the test set. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The test set was blindly reviewed by pathologists with different levels of GI pathology expertise as follows: 2 GI pathologists, 2 general surgical pathologists, and 2 residents. Each pathologist rendered their preferred diagnosis, HPG or AG. RESULTS.­: At the HALO-AI AD percentage cutoff of 50% or more, the CNN results were 100% concordant with the gold standard diagnoses. On average, autoimmune gastritis cases had 84.7% HALO-AI autoimmune gastritis AD and HP cases had 87.3% HALO-AI HP AD. The GI pathologists, general anatomic pathologists, and residents were on average, 100%, 86%, and 57% concordant with the gold standard diagnoses, respectively. CONCLUSIONS.­: A CNN can distinguish between cases of HPG and autoimmune gastritis with accuracy equal to GI pathologists.

High Effectiveness of Broad Access Direct-Acting Antiviral Therapy for Hepatitis C in an Australian Real-World Cohort: The REACH-C Study.

Australia was one of the first countries with unrestricted access to government subsidized direct-acting antiviral (DAA) therapy for adults with chronic hepatitis C virus. This study assessed real-world DAA treatment outcomes across a diverse range of Australian clinical services and evaluated factors associated with successful treatment and loss to follow-up. Real-world Effectiveness of Antiviral therapy in Chronic Hepatitis C (REACH-C) consisted a national observational cohort of 96 clinical services including specialist clinics and less traditional settings such as general practice. Data were obtained on consecutive individuals who commenced DAAs from March 2016 to June 2019. Effectiveness was assessed by sustained virological response ≥12 weeks following treatment (SVR) using intention-to-treat (ITT) and per-protocol (PP) analyses. Within REACH-C, 10,843 individuals initiated DAAs (male 69%; ≥50 years 52%; cirrhosis 22%). SVR data were available in 85% (9,174 of 10,843). SVR was 81% (8,750 of 10,843) by ITT and 95% (8,750 of 9,174) by PP. High SVR (≥92%) was observed across all service types and participant characteristics. Male gender (adjusted odds ratio [aOR] 0.56, 95% confidence interval [CI] 0.43-0.72), cirrhosis (aOR 0.52, 95% CI 0.41-0.64), recent injecting drug use (IDU; aOR 0.64, 95% CI 0.46-0.91) and previous DAA treatment (aOR 0.50, 95% CI 0.28-0.90) decreased the likelihood of achieving SVR. Multiple factors modified the likelihood of loss to follow-up including IDU ± opioid agonist therapy (OAT; IDU only: aOR 1.75, 95% CI 1.44-2.11; IDU + OAT: aOR 1.39, 95% CI 1.11-1.74; OAT only, aOR 1.36; 95% CI 1.13-1.68) and age (aOR 0.97, 95% CI 0.97-0.98). Conclusion: Treatment response was high in a diverse population and through a broad range of services following universal access to DAA therapy. Loss to follow-up presents a real-world challenge. Younger people who inject drugs were more likely to disengage from care, requiring innovative strategies to retain them in follow-up.

Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence.

BACKGROUND: The presence of nodal metastases is important in the treatment of papillary thyroid carcinoma (PTC). We present our experience using a convolutional neural network (CNN) to predict the presence of nodal metastases in a series of PTC patients using visual histopathology from the primary tumor alone. METHODS: 174 cases of PTC were evaluated for the presence or absence of lymph metastases. The artificial intelligence (AI) algorithm was trained and tested on its ability to discern between the two groups. RESULTS: The best performing AI algorithm demonstrated a sensitivity and specificity of 94% and 100%, respectively, when identifying nodal metastases. CONCLUSION: A CNN can be used to accurately predict the likelihood of nodal metastases in PTC using visual data from the primary tumor alone.