Study of Environmental Enteropathy and Malnutrition (SEEM) in Pakistan: protocols for biopsy based biomarker discovery and validation

Iqbal, Najeeha Talat; Syed, Sana; Sadiq, Kamran; Khan, Marium; Iqbal, Junaid; Jennie, Z.; Umrani, Fayaz; Ahmed, Sheraz; Maier, Elizabeth A.; Denson, Lee A.; Haberman, Yael; McNeal, Monica; Setchell, Kenneth D.R.; Zhao, Xueheng; Qureshi, Shahida; Shen, Lanlan; Moskaluk, Christopher A.; Liu, Ta‐Chiang; Yılmaz, Ömer H.; Brown, Donald E.; Barratt, Michael J.; Kung, Vanderlene L.; Gordon, Jeffrey I.; Moore, Sean R.; Ali, Syed Asad

doi:10.1186/s12887-019-1564-x

Cited by 33 publications

(40 citation statements)

References 68 publications

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“…SEEM is an Aga Khan University (AKU) prospective cohort study that enrolled children at birth in Matiari, Pakistan between 2016 and 2019 undergoing evaluation for EED and growth up to 24 months of age. 8 The SEEM cohort consisted of 416 children (365 malnourished cases and 51 well-nourished healthy controls) with a median enrollment time of 5 days after birth. Anthropometry data were collected monthly, and participants received nutritional education.…”

Section: Methodsmentioning

confidence: 99%

“…Anthropometry data were collected monthly, and participants received nutritional education. 8 Child length was measured from birth to 24 months, and we refer to length/height throughout. Blood, urine, and fecal samples were collected at 9 months of age.…”

Section: Methodsmentioning

confidence: 99%

“…To better characterize the longitudinal development of EED and growth failure during early childhood and to define key gut factors in children with severe EED refractory to nutritional intervention, we established the SEEM Pakistan cohort (Study of Environmental Enteropathy and Malnutrition). 8 SEEM is an inception cohort following 416 at-risk children from birth up to 24 months of age. SEEM aimed to define longitudinal growth trajectories during the first 2 years of life and identify severe cases unresponsive to nutritional intervention that require endoscopic evaluation, define EED pathogenesis using histology, transcriptome, and epigenome (methylome) of duodenal biopsy specimens, and use data including noninvasive biomarkers collected up to 9 months of age to predict growth at 24 months.…”

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confidence: 99%

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Mucosal Genomics Implicate Lymphocyte Activation and Lipid Metabolism in Refractory Environmental Enteric Dysfunction

et al. 2021

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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Mucosal Genomics Implicate Lymphocyte Activation and Lipid Metabolism in Refractory Environmental Enteric Dysfunction

et al. 2021

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“…UVA biopsies were from archival specimens with clinical diagnoses of CD and histologically normal controls (referred to as controls) from participants who had undergone esophagogastroduodenoscopy in the past 25 years (data accessed: 1992 to 2017) as part of an archival controls sub-study, methods reported elsewhere. (33) Biopsies available from each site for this project were archival and had been previously processed per local institutional H&E slide staining, tissue sectioning, and tissue paraffin embedding protocols. Our timeline for secondary data collection and analysis was from November 2017 to December 2019.…”

Section: Methodsmentioning

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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“…On the other hand, environmental enteric dysfunction (EED) with findings suggestive of impaired gut function (i.e., alterations in intestinal structure, function, and immune activation and poor growth) is considered as an important contributor to childhood malnutrition and stunting across geographically widespread resource-limited settings (5,(11)(12)(13). In fact, impaired absorption of available nutrients provided during treatment and refeeding-mediated diarrhea risk are considered likely to affect the recovery from malnutrition.…”

Section: Introductionmentioning

confidence: 99%

Nutritional Support in Malnourished Children With Compromised Gastrointestinal Function: Utility of Peptide-Based Enteral Therapy

et al. 2021

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This review focuses on nutritional support in malnourished children with compromised gastrointestinal function addressing the interplay between malnutrition and gastrointestinal dysfunction, and the specific role of peptide-based enteral therapy in pediatric malnutrition. Malnutrition is associated with impaired gut functions such as increased intestinal permeability, malabsorption, and diarrhea, while pre-existing functional gastrointestinal disorders may also lead to malnutrition. Presence of compromised gastrointestinal function in malnourished children is critical given that alterations such as malabsorption and increased intestinal permeability directly interfere with efficacy of nutritional support and recovery from malnutrition. Appropriate nutritional intervention is the key step in the management of malnutrition, while alterations in gastrointestinal functions in malnourished children are likely even in those with mild degree malnutrition. Therefore, nutritional therapy in children with compromised gastrointestinal function is considered to involve gut-protective interventions that address the overlapping and interacting effects of diarrhea, enteropathy and malnutrition to improve child survival and developmental potential in the long-term. Peptide-based enteral formulas seem to have clinical applications in malnourished children with compromised gastrointestinal function, given their association with improved gastrointestinal tolerance and absorption, better nitrogen retention/ balance, reduced diarrhea and bacterial translocation, enhanced fat absorption, and maintained/restored gut integrity as compared with free amino acid or whole-protein formulas.

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Study of Environmental Enteropathy and Malnutrition (SEEM) in Pakistan: protocols for biopsy based biomarker discovery and validation

Cited by 33 publications

References 68 publications

Mucosal Genomics Implicate Lymphocyte Activation and Lipid Metabolism in Refractory Environmental Enteric Dysfunction

Mucosal Genomics Implicate Lymphocyte Activation and Lipid Metabolism in Refractory Environmental Enteric Dysfunction

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

Nutritional Support in Malnourished Children With Compromised Gastrointestinal Function: Utility of Peptide-Based Enteral Therapy

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