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BackgroundThe risk of severe coronavirus disease 2019 (COVID-19) varies significantly among persons of similar age and is higher in males. Age-independent, sex-biased differences in susceptibility to severe COVID-19 may be ascribable to deficits in a sexually dimorphic protective attribute that we termed immunologic resilience (IR).ObjectiveWe sought to examine whether deficits in IR that antedate or are induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection independently predict COVID-19 mortality.MethodsIR levels were quantified with 2 novel metrics: immune health grades (IHG-I [best] to IHG-IV) to gauge CD8+ and CD4+ T-cell count equilibrium, and blood gene expression signatures. IR metrics were examined in a prospective COVID-19 cohort (n = 522); primary outcome was 30-day mortality. Associations of IR metrics with outcomes in non-COVID-19 cohorts (n = 13,461) provided the framework for linking pre-COVID-19 IR status to IR during COVID-19, as well as to COVID-19 outcomes.ResultsIHG-I, tracking high-grade equilibrium between CD8+ and CD4+ T-cell counts, was the most common grade (73%) among healthy adults, particularly in females. SARS-CoV-2 infection was associated with underrepresentation of IHG-I (21%) versus overrepresentation (77%) of IHG-II or IHG-IV, especially in males versus females (P < .01). Presentation with IHG-I was associated with 88% lower mortality, after controlling for age and sex; reduced risk of hospitalization and respiratory failure; lower plasma IL-6 levels; rapid clearance of nasopharyngeal SARS-CoV-2 burden; and gene expression signatures correlating with survival that signify immunocompetence and controlled inflammation. In non-COVID-19 cohorts, IR-preserving metrics were associated with resistance to progressive influenza or HIV infection, as well as lower 9-year mortality in the Framingham Heart Study, especially in females.ConclusionsPreservation of immunocompetence with controlled inflammation during antigenic challenges is a hallmark of IR and associates with longevity and AIDS resistance. Independent of age, a male-biased proclivity to degrade IR before and/or during SARS-CoV-2 infection predisposes to severe COVID-19.

* 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., Deep learning is a new and emerging paradigm of artificial intelligence (AI) research. Convolutional neural networks (CNN) are an algorithmic form of deep learning that are highly accurate in image-based [...]

The α-helical (AH) domain of the hepatitis C virus nonstructural protein NS5A, anchored at the cytoplasmic leaflet of the endoplasmic reticulum, plays a role in viral replication. However, the peptides derived from this domain also exhibit remarkably broad-spectrum virocidal activity, raising questions about their modes of membrane association. Here, using giant lipid vesicles, we show that the AH peptide discriminates between membrane compositions. In cholesterol-containing membranes, peptide binding induces microdomain formation. By contrast, cholesterol-depleted membranes undergo global softening at elevated peptide concentrations. Furthermore, in mixed populations, the presence of ∼100 nm vesicles of viral dimensions suppresses these peptide-induced perturbations in giant unilamellar vesicles, suggesting size-dependent membrane association. These synergistic composition- and size-dependent interactions explain, in part, how the AH domain might on the one hand segregate molecules needed for viral assembly and on the other hand furnish peptides that exhibit broad-spectrum virocidal activity.

Context.--Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched. Objective.--To investigate the use of DL for nonneoplastic gastric biopsies. Design.--Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion. Results.--For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] = 99.7%) and H pylori (AUC = 100%), and 40% for reactive gastropathy (AUC = 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/ specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%). Conclusions.--A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis. (Arch Pathol Lab Med. 2020;144:370-378; doi: 10.5858/arpa.2019-0004-OA), The recent approval of whole-slide images for primary diagnosis has rapidly accelerated the adoption of digital pathology in diagnostic settings. In addition, there are now large, publicly available data sets [...]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

* Context.--Metastatic mucinous tumors present a diagnostic challenge for pathologists as tumor histomorphology is often nonspecific and optimal immunoprofiles are still under investigation. Objective.--To present a head-to-head comparison of special AT-rich sequence-binding protein 2 (SATB2) and caudal type homeobox 2 (CDX2) expression in a diverse array of primary mucinous tumors. Design.--SATB2 and CDX2 immunohistochemical stains were performed on whole sections from 44 mucinous colorectal carcinomas and 175 noncolorectal mucinous tumors. A nuclear scoring system measuring intensity (0-3+) and percentage staining (0 = Results.--SATB2 demonstrated acceptable accuracy at low to moderate expression levels (H-scores of 1-4). With these H-score cutoffs, overall accuracy was greater than 90%. In contrast, CDX2's accuracy rivaled that of SATB2 only at an H-score of 5 (89.0%), as its specificity suffered at lower expression levels ( 3 and CDX2 = 5). Conclusions.--SATB2 is a more accurate marker of colorectal origin across a variety of expression levels compared with CDX2 when applied to mucinous tumors from a host of primary sites. However, these stains are near equivalent when each is interpreted at its optimal expression level. (Arch Pathol Lab Med. 2019;143:1119-1125; doi: 10.5858/arpa.2018-0337-OA), Metastatic mucinous tumors pose a unique diagnostic challenge for the surgical pathologist as their morphologic and clinical characteristics are often nonspecific. The colorectum is the most common site of origin. [...]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]