Faculty Bibliography

OBJECTIVE:Heightened inflammation, dysregulated immunity, and thrombotic events are characteristic of hospitalized COVID-19 patients. Given that platelets are key regulators of thrombosis, inflammation, and immunity they represent prime candidates as mediators of COVID-19-associated pathogenesis. The objective of this study was to understand the contribution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the platelet phenotype via phenotypic (activation, aggregation) and transcriptomic characterization. APPROACH AND RESULTS/UNASSIGNED:In a cohort of 3915 hospitalized COVID-19 patients, we analyzed blood platelet indices collected at hospital admission. Following adjustment for demographics, clinical risk factors, medication, and biomarkers of inflammation and thrombosis, we find platelet count, size, and immaturity are associated with increased critical illness and all-cause mortality. Bone marrow, lung tissue, and blood from COVID-19 patients revealed the presence of SARS-CoV-2 virions in megakaryocytes and platelets. Characterization of COVID-19 platelets found them to be hyperreactive (increased aggregation, and expression of P-selectin and CD40) and to have a distinct transcriptomic profile characteristic of prothrombotic large and immature platelets. In vitro mechanistic studies highlight that the interaction of SARS-CoV-2 with megakaryocytes alters the platelet transcriptome, and its effects are distinct from the coronavirus responsible for the common cold (CoV-OC43). CONCLUSIONS:Platelet count, size, and maturity associate with increased critical illness and all-cause mortality among hospitalized COVID-19 patients. Profiling tissues and blood from COVID-19 patients revealed that SARS-CoV-2 virions enter megakaryocytes and platelets and associate with alterations to the platelet transcriptome and activation profile.

PURPOSE:Residency programs face overwhelming numbers of residency applications, limiting holistic review. Artificial intelligence techniques have been proposed to address this challenge but have not been created. Here, a multidisciplinary team sought to develop and validate a machine learning (ML)-based decision support tool (DST) for residency applicant screening and review. METHOD:Categorical applicant data from the 2018, 2019, and 2020 residency application cycles (n = 8,243 applicants) at one large internal medicine residency program were downloaded from the Electronic Residency Application Service and linked to the outcome measure: interview invitation by human reviewers (n = 1,235 invites). An ML model using gradient boosting was designed using training data (80% of applicants) with over 60 applicant features (e.g., demographics, experiences, academic metrics). Model performance was validated on held-out data (20% of applicants). Sensitivity analysis was conducted without United States Medical Licensing Examination (USMLE) scores. An interactive DST incorporating the ML model was designed and deployed that provided applicant- and cohort-level visualizations. RESULTS:The ML model areas under the receiver operating characteristic and precision recall curves were 0.95 and 0.76, respectively; these changed to 0.94 and 0.72, respectively, with removal of USMLE scores. Applicants' medical school information was an important driver of predictions-which had face validity based on the local selection process-but numerous predictors contributed. Program directors used the DST in the 2021 application cycle to select 20 applicants for interview that had been initially screened out during human review. CONCLUSIONS:The authors developed and validated an ML algorithm for predicting residency interview offers from numerous application elements with high performance-even when USMLE scores were removed. Model deployment in a DST highlighted its potential for screening candidates and helped quantify and mitigate biases existing in the selection process. Further work will incorporate unstructured textual data through natural language processing methods.

Cutaneous T cell lymphoma (CTCL) is a heterogeneous group of mature T cell neoplasms characterized by the accumulation of clonal malignant CD4+ T cells in the skin. The most common variant of CTCL, Mycosis Fungoides, is confined to the skin in early stages but can be accompanied by extracutaneous dissemination of malignant T cells to the blood and lymph nodes in advanced stages of disease. Sézary Syndrome, a leukemic form of disease is characterized by significant blood involvement. Little is known about the transcriptional and genomic relationship between skin and blood residing malignant T cells in CTCL. To identify and interrogate malignant clones in matched skin and blood from leukemic MF and SS patients, we combine T cell receptor clonotyping, with quantification of gene expression and cell surface markers at the single cell level. Our data reveals clonal evolution at a transcriptional and genetic level within the malignant populations of individual patients. We highlight highly consistent transcriptional signatures delineating skin-derived and blood-derived malignant T cells. Analysis of these two populations suggests that environmental cues, along with genetic aberrations, contribute to transcriptional profiles of malignant T cells. Our findings indicate that the skin microenvironment in CTCL promotes a transcriptional response supporting rapid malignant expansion, as opposed to the quiescent state observed in the blood, potentially influencing efficacy of therapies. These results provide insight into tissue-specific characteristics of cancerous cells and underscore the need to address the patients' individual malignant profiles at the time of therapy to eliminate all sub-clones.

[Figure: see text].

INTRODUCTION/BACKGROUND:Finite element (FE) mechanics models of the heart are becoming more sophisticated. However, there is lack of consensus about optimal element type and coupling of FE models to the circulation. We describe biventricular (left (LV) and right (RV) ventricles) FE mechanics model creation using hexahedral elements, airbags and a functional mockup interface (FMI) to lumped-parameter models of the circulation. METHODS:Cardiac MRI (CMR) was performed in two healthy volunteers and a single patient with ischemic heart disease (IHD). CMR images were segmented and surfaced, meshing with hexahedral elements was performed with a "thin butterfly with septum" topology. LV and RV inflow and outflow airbags were coupled to lumped-parameter circulation models with an FMI interface. Pulmonary constriction (PAC) and vena cava occlusion (VCO) were simulated and end-systolic pressure-volume relations (ESPVR) were calculated. RESULTS:Mesh construction was prompt with representative contouring and mesh adjustment requiring 32 and 26 min Respectively. The numbers of elements ranged from 4104 to 5184 with a representative Jacobian of 1.0026 ± 0.4531. Agreement between CMR-based surfaces and mesh was excellent with root-mean-squared error of 0.589 ± 0.321 mm. The LV ESPVR slope was 3.37 ± 0.09 in volunteers but 2.74 in the IHD patient. The effect of PAC and VCO on LV ESPVR was consistent with ventricular interaction (p = 0.0286). CONCLUSION/CONCLUSIONS:Successful co-simulation using a biventricular FE mechanics model with hexahedral elements, airbags and an FMI interface to lumped-parameter model of the circulation was demonstrated. Future studies will include comparison of element type and study of cardiovascular pathologies and device therapies.

Early-life antibiotic exposure perturbs the intestinal microbiota and accelerates type 1 diabetes (T1D) development in the NOD mouse model. Here, we found that maternal cecal microbiota transfer (CMT) to NOD mice after early-life antibiotic perturbation largely rescued the induced T1D enhancement. Restoration of the intestinal microbiome was significant and persistent, remediating the antibiotic-depleted diversity, relative abundance of particular taxa, and metabolic pathways. CMT also protected against perturbed metabolites and normalized innate and adaptive immune effectors. CMT restored major patterns of ileal microRNA and histone regulation of gene expression. Further experiments suggest a gut-microbiota-regulated T1D protection mechanism centered on Reg3γ, in an innate intestinal immune network involving CD44, TLR2, and Reg3γ. This regulation affects downstream immunological tone, which may lead to protection against tissue-specific T1D injury.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

OBJECTIVES/OBJECTIVE:Our discussant's thoughtful consideration of the patient's case allows for review of three maxims of medicine: Occam's razor (the simplest diagnosis is the most likely to be correct), Hickam's dictum (multiple disease entities are more likely than one), and Crabtree's bludgeon (the tendency to make data fit to an explanation we hold dear). CASE PRESENTATION/METHODS:A 66-year-old woman with a history of hypertension presented to our hospital one day after arrival to the United States from Guinea with chronic daily vomiting, unintentional weight loss and progressive shoulder pain. Her labs are notable for renal failure, nephrotic range proteinuria and normocytic anemia while her shoulder X-ray shows osseous resorption in the lateral right clavicle. Multiple myeloma became the team's working diagnosis; however, a subsequent shoulder biopsy was consistent with follicular thyroid carcinoma. Imaging suggested the patient's renal failure was more likely a result of a chronic, unrelated process. CONCLUSIONS:It is tempting to bludgeon diagnostic possibilities into Occam's razor. Presumption that a patient's signs and symptoms are connected by one disease process often puts us at a cognitive advantage. However, atypical presentations, multiple disease processes, and unique populations often lend themselves more to Hickam's dictum than to Occam's razor. Diagnostic aids include performing a metacognitive checklist, engaging analytic thinking, and acknowledging the imperfections of these axioms.

In the setting of long-standing structural racism in health care, it is imperative to highlight inequities in the medical school-to-residency transition. In obstetrics and gynecology, the percentage of Black residents has decreased in the past decade. The etiology for this troubling decrease is unknown, but racial and ethnic biases inherent in key residency application metrics are finally being recognized, while the use of these metrics to filter applicants is increasing. Now is the time for action and for transformational change to rectify the factors that are detrimentally affecting the racial diversity of our residents. This will benefit our patients and learners with equitable health care and better outcomes.