A novel histological index for evaluation of environmental enteric dysfunction identifies geographic-specific features of enteropathy among children with suboptimal growth

Liu, Ta‐Chiang; VanBuskirk, Kelley; Ali, Syed Asad; Kelly, Paul; Holtz, Lori R.; Yılmaz, Ömer H.; Sadiq, Kamran; Iqbal, Najeeha Talat; Amadi, Beatrice; Syed, Sana; Ahmed, Tahmeed; Moore, Sean R.; Ndao, I. Malick; Isaacs, Michael H.; Pfeifer, John D.; Atlas, Hannah E; Tarr, Phillip I.; Denno, Donna M.; Moskaluk, Christopher A.

doi:10.1371/journal.pntd.0007975

Cited by 54 publications

(102 citation statements)

References 53 publications

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“…The lack of correlation between the histopathologic scoring system used here and LAZ is consistent with reports from other studies that employed more elaborate scoring schemes ( 31 ). Our findings underscore the value of characterizing the duodenal proteome and linking its features to components of the microbiota and in turn to linear growth.…”

Section: Discussionsupporting

confidence: 90%

Duodenal Microbiota in Stunted Undernourished Children with Enteropathy

et al. 2020

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BACKGROUND Environmental enteric dysfunction (EED) is an enigmatic disorder of the small intestine postulated to play a role in childhood undernutrition, a pressing global health problem. Defining the incidence of EED, its pathophysiology, and its contribution to impaired linear and ponderal growth has been hampered by the difficulty in directly sampling the small intestinal mucosa and microbial community (microbiota). METHODS Slum-dwelling Bangladeshi children aged 18±2 months, with linear growth-faltering (stunting) who failed a nutritional intervention underwent endoscopy to obtain duodenal biopsies and aspirates. Levels of 4077 plasma proteins and 2619 duodenal proteins were quantified in 80 children with histopathologic evidence of EED, and the abundances of bacterial strains in their duodenal microbiota were determined using culture-independent methods. Young germ-free mice, fed a Bangladeshi diet, were colonized with bacterial strains cultured from the duodenal aspirates. RESULTS The absolute abundances of a shared group of 14 bacterial strains recovered from the duodenums of children with EED and not typically classified as enteropathogens were negatively correlated with linear growth (length-for-age Z-score;β=-0.38±0.12(SEM);ρ=-0.49;p=0.003), and positively correlated with duodenal proteins involved in immunoinflammatory responses. Representation of these 14 duodenal taxa was significantly different in fecal microbiota from EED versus healthy children (p<0.001;PERMANOVA). Gnotobiotic mice colonized with cultured EED-donor duodenal strains develop a small intestinal enteropathy. CONCLUSIONS These results provide evidence of a causal relationship between components of the small intestinal microbiota, enteropathy and stunting and offer a rationale for developing therapeutics that target what must no longer remain terra incognita-the small intestinal microbiota. ClinicalTrials.gov identifier: NCT02812615

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Section: Discussionsupporting

confidence: 90%

Duodenal Microbiota in Stunted Undernourished Children with Enteropathy

et al. 2020

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Section: Discussionmentioning

confidence: 99%

“…In addition, human EED is a complex syndrome with multiple phenotypes depending on various adverse exposure 45 and geographic differences 46 and no single model captures the complexity of human EED 8 . Nevertheless, the present model provides valuable insights into key EED features to better understand mechanisms behind the human disease.…”

Section: Discussionmentioning

confidence: 99%

Modeling undernutrition with enteropathy in mice

Salameh

Jarbeau

Morel³

et al. 2020

Sci Rep

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Undernutrition is a global health issue leading to 1 out 5 all deaths in children under 5 years. Undernutrition is often associated with environmental enteric dysfunction (EED), a syndrome associated with increased intestinal permeability and gut inflammation. We aimed to develop a novel murine model of undernutrition with these EED features. Post-weaning mice were fed with low-protein diet (LP) alone or combined with a gastrointestinal insult trigger (indomethacin or liposaccharides). Growth, intestinal permeability and inflammation were assessed. LP diet induced stunting and wasting in post-weaning mice but did not impact gut barrier. We therefore combined LP diet with a single administration of indomethacin or liposaccharides (LPS). Indomethacin increased fecal calprotectin production while LPS did not. To amplify indomethacin effects, we investigated its repeated administration in addition to LP diet and mice exhibited stunting and wasting with intestinal hyperpermeability and gut inflammation. The combination of 3-weeks LP diet with repeated oral indomethacin administration induced wasting, stunting and gut barrier dysfunction as observed in undernourished children with EED. As noninvasive methods for investigating gut function in undernourished children are scarce, the present pre-clinical model provides an affordable tool to attempt to elucidate pathophysiological processes involved in EED and to identify novel therapeutic strategies.

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“…(30) While scoring systems such as the EE score and modified Marsh score for EE and CD, respectively, have the potential to diagnose and classify the severity of these diseases, there is a need to more accurately define the cellular parameters that play an important role in diagnosis. (27) We developed an AI-based image analysis platform, based on our preliminary results, (30) for the diagnosis of small bowel enteropathies. To the best of our knowledge, similar small bowel enteropathy image analysis platforms that include EE do not exist.…”

Section: Discussionmentioning

confidence: 99%

“…CD) is an essential task performed by pathologists. (26) Even though an EE scoring system has been proposed, it will benefit from approaches narrowing down specific cellular parameters important for an accurate EE diagnosis. (27) CD is an immune-mediation condition with sensitivity to gluten leading to small bowel injury.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection

Syed

Ehsan

Shrivastava

et al. 2020

Preprint

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Objectives: Striking histopathological overlap between distinct but related conditions poses a significant disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; Environmental Enteropathy (EE) and Celiac Disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies. Methods: Data for secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using convolutional neural networks (CNNs: ResNet and custom Shallow CNN). Gradient-weighted Class Activation Mappings (Grad-CAMs) were used to visualize the decision making process of the models. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAM visualizations to confirm structural preservation and biological relevance, respectively. Results: 461 high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37.5 (19.0 to 121.5) months with a roughly equal sex distribution; 77 males (51.3%). ResNet50 and Shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98.3% with an ensemble. Grad-CAMs demonstrated ability of the models to learn distinct microscopic morphological features. Conclusion: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features, emulating human pathologist decision making process, performing in the case of suboptimal computational environment, and being modified for improving disease classification accuracy. Grad-CAMs that were employed illuminated the otherwise black box of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.

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A novel histological index for evaluation of environmental enteric dysfunction identifies geographic-specific features of enteropathy among children with suboptimal growth

Cited by 54 publications

References 53 publications

Duodenal Microbiota in Stunted Undernourished Children with Enteropathy

Duodenal Microbiota in Stunted Undernourished Children with Enteropathy

Modeling undernutrition with enteropathy in mice

Artificial Intelligence-Based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection

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