Non-functional pancreatic neuroendocrine tumours: ATRX/DAXX and alternative lengthening of telomeres (ALT) are prognostically independent from ARX/PDX1 expression and tumour size

OBJECTIVE: Recent studies have found aristaless-related homeobox gene (ARX)/pancreatic and duodenal homeobox 1 (PDX1), alpha-thalassemia/mental retardation X-linked (ATRX)/death domain-associated protein (DAXX) and alternative lengthening of telomeres (ALT) to be promising prognostic biomarkers for non-functional pancreatic neuroendocrine tumours (NF-PanNETs). However, they have not been comprehensively evaluated, especially among small NF-PanNETs (≤2.0 cm). Moreover, their status in neuroendocrine tumours (NETs) from other sites remains unknown. DESIGN: An international cohort of 1322 NETs was evaluated by immunolabelling for ARX/PDX1 and ATRX/DAXX, and telomere-specific fluorescence in situ hybridisation for ALT. This cohort included 561 primary NF-PanNETs, 107 NF-PanNET metastases and 654 primary, non-pancreatic non-functional NETs and NET metastases. The results were correlated with numerous clinicopathological features including relapse-free survival (RFS). RESULTS: ATRX/DAXX loss and ALT were associated with several adverse prognostic findings and distant metastasis/recurrence (p\u3c0.001). The 5-year RFS rates for patients with ATRX/DAXX-negative and ALT-positive NF-PanNETs were 40% and 42% as compared with 85% and 86% for wild-type NF-PanNETs (p\u3c0.001 and p\u3c0.001). Shorter 5-year RFS rates for ≤2.0 cm NF-PanNETs patients were also seen with ATRX/DAXX loss (65% vs 92%, p=0.003) and ALT (60% vs 93%, p\u3c0.001). By multivariate analysis, ATRX/DAXX and ALT status were independent prognostic factors for RFS. Conversely, classifying NF-PanNETs by ARX/PDX1 expression did not independently correlate with RFS. Except for 4% of pulmonary carcinoids, ATRX/DAXX loss and ALT were only identified in primary (25% and 29%) and NF-PanNET metastases (62% and 71%). CONCLUSIONS: ATRX/DAXX and ALT should be considered in the prognostic evaluation of NF-PanNETs including ≤2.0 cm tumours, and are highly specific for pancreatic origin among NET metastases of unknown primary

Infection and inflammation stimulate expansion of a CD74+ Paneth cell subset to regulate disease progression

Paneth cells (PCs), a specialized secretory cell type in the small intestine, are increasingly recognized as having an essential role in host responses to microbiome and environmental stresses. Whether and how commensal and pathogenic microbes modify PC composition to modulate inflammation remain unclear. Using newly developed PC-reporter mice under conventional and gnotobiotic conditions, we determined PC transcriptomic heterogeneity in response to commensal and invasive microbes at single cell level. Infection expands the pool of CD7

Development and validation of a deep learning model to quantify glomerulosclerosis in kidney biopsy specimens

Importance: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. Objective: To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. Design, Setting, and Participants: This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. Main Outcomes and Measures: Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists\u27 estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. Results: The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. Conclusions and Relevance: The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard

HOIL1 regulates group 2 innate lymphoid cell numbers and type 2 inflammation in the small intestine

Patients with mutations in HOIL1 experience a complex immune disorder including intestinal inflammation. To investigate the role of HOIL1 in regulating intestinal inflammation, we employed a mouse model of partial HOIL1 deficiency. The ileum of HOIL1-deficient mice displayed features of type 2 inflammation including tuft cell and goblet cell hyperplasia, and elevated expression of Il13, Il5 and Il25 mRNA. Inflammation persisted in the absence of T and B cells, and bone marrow chimeric mice revealed a requirement for HOIL1 expression in radiation-resistant cells to regulate inflammation. Although disruption of IL-4 receptor alpha (IL4Rα) signaling on intestinal epithelial cells ameliorated tuft and goblet cell hyperplasia, expression of Il5 and Il13 mRNA remained elevated. KLRG

Long-term culture captures injury-repair cycles of colonic stem cells

The colonic epithelium can undergo multiple rounds of damage and repair, often in response to excessive inflammation. The responsive stem cell that mediates this process is unclear, in part because of a lack of in vitro models that recapitulate key epithelial changes that occur in vivo during damage and repair. Here, we identify a Hop

Deep learning quantification of percent steatosis in donor liver biopsy frozen sections

BACKGROUND: Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies. METHODS: We developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequently calculates the percent steatosis. Ninety-six WSI of frozen donor liver sections from our transplant pathology service were annotated for steatosis and used to train (n = 30 WSI) and test (n = 66 WSI) the deep learning model. FINDINGS: The model had good correlation and agreement with the annotation in both the training set (r of 0.88, intraclass correlation coefficient [ICC] of 0.88) and novel input test sets (r = 0.85 and ICC=0.85). These measurements were superior to the estimates of the on-service pathologist at the time of initial evaluation (r = 0.52 and ICC=0.52 for the training set, and r = 0.74 and ICC=0.72 for the test set). INTERPRETATION: Use of this deep learning algorithm could be incorporated into routine pathology workflows for fast, accurate, and reproducible donor liver evaluation. FUNDING: Mid-America Transplant Society

Determinants of pro-environmental behavior among excessive smartphone usage children and moderate smartphone usage children in Taiwan

Introduction Although there is evidence linking the relationships between smartphone usage with health, stress, and academic performance, there is still inadequate knowledge about the influence on pro-environmental behaviors. This study seeks to bridge this gap by adapting the theory of attribution framework to examine the effects of personal norms, social norms, perceived behavioral control on pro-environmental behavior of smartphone usage in children. Methods A total of 225 children aged between 11 to 12 from eight selected public primary schools at the Hsinchu Science and Industrial Park in Taiwan were surveyed. Two distinct groups (excessive versus moderate usage) were purposefully selected for comparison, of which 96 participants were excessive smartphone users while the remaining 129 were moderate smartphone users. Results Findings revealed significant differences between excessive and moderate smartphone usage children groups in personal norms (p  0.05), as well as social norms and pro-environmental behavior for moderate smartphone usage children (β = 0.181, t = 1.924, p > 0.05), but such a relationship could be developed through the mediating effect of perceived behavioral control (β = 0.497, t = 4.471***, p < 0.001). Discussion The results suggested that excessive smartphone usage children lack positive perceived behavioral control, and their pro-environmental behavior could only be predicted through explicit social norms, whereas pro-environmental behavior of moderate smartphone usage children was implicitly influenced by personal norms through perceived behavioral control