Duodenal quantitative mucosal morphometry in children with environmental enteric dysfunction: a cross-sectional multi-country analysis.

BACKGROUND: Environmental enteric dysfunction (EED), a chronic inflammatory condition of the small intestine, is an important driver of childhood malnutrition globally. Quantifying intestinal morphology in EED allows for exploration of its association with functional and disease outcomes. OBJECTIVE: We sought to define morphometric characteristics of childhood EED and determine whether morphology features were associated with disease pathophysiology. METHODS: Morphometric measurements and histology were assessed on duodenal biopsy slides for this cross-sectional study from children with EED in Bangladesh, Pakistan, and Zambia (n=69), and those with no pathologic abnormality (NPA; n=8) or celiac disease (n=18) in North America. Immunohistochemistry was also conducted on 46, 8, and 18 biopsy slides, respectively. Linear mixed-effects regression models were used to reveal morphometric differences between EED compared to NPA or celiac disease, and identify associations between morphometry and histology or immunohistochemistry amongst children with EED. RESULTS: In duodenal biopsies, median EED villus height (248 µm), crypt depth (299 µm), and villus:crypt (V:C) ratio (0.9) values ranged between those of NPA (396 µm villus height; 246 µm crypt depth; 1.6 V:C ratio) and celiac disease (208 µm villus height; 365 µm crypt depth; 0.5 V:C ratio). Among EED biopsy slides, morphometric assessments were not associated with histologic parameters or immunohistochemical markers, other than pathologist determined subjective semi-quantitative villus architecture. CONCLUSIONS: Morphometric analysis of duodenal biopsy slides across geographies identified morphologic features of EED, specifically short villi, elongated crypts, and a smaller V:C ratio relative to NPA slides; although not as severe as in celiac slides. Morphometry did not explain other EED features, suggesting that EED histopathologic processes may be operating independently of morphology. While acknowledging the challenges with obtaining relevant tissue, these data form the basis for further assessments of the role of morphometry in EED.

Chylothorax related to acute SARS-CoV-2 infection in a patient with Noonan syndrome with prior uncomplicated cardiac surgeries.

SARS-CoV-2 is a novel coronavirus that has rarely been associated with chylothorax. Patients with Noonan syndrome are at risk for developing chylothorax, especially after cardiothoracic interventions. We present the case of SARS-CoV-2 infection triggering the underlying tendency of a patient with Noonan syndrome to develop chylothorax who did not develop it even after prior cardiothoracic interventions. Patient presented in respiratory distress without hypoxia and was found, on imaging, to have a large right-sided pleural effusion, which was eventually classified as chylothorax. The patient was then started on a low-fat diet. Chest tube drainage substantially reduced the effusion in size, and it remained stable. Our report highlights that SARS-CoV-2 infection can cause the development of a chylothorax or a chylous effusion in patients with Noonan syndrome or among populations with a similar predisposition. A high index of suspicion in vulnerable patients or those not responding to traditional therapy should exist with providers, thus leading to the testing of the fluid to confirm the diagnosis.

The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning.

Introduction: Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address. Challenge #1: VCE data are stochastic and contains significant artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are computationally cumbersome. Challenge #5: Clinicians are hesitant to accept AIMLT that cannot explain their process. Methods: An anatomic landmark detection model was used to test the application of convolutional neural networks (CNNs) to the task of classifying VCE data. We also created a tool that assists in expert annotation of VCE data. We then created more elaborate models using different approaches including a multi-frame approach, a CNN based on graph representation, and a few-shot approach based on meta-learning. Results: When used on full-length VCE footage, CNNs accurately identified anatomic landmarks (99.1%), with gradient weighted-class activation mapping showing the parts of each frame that the CNN used to make its decision. The graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of 91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity 90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity 94.8%) performed well. Discussion: Each of these five challenges is addressed, in part, by one of our AI-based models. Our goal of producing high performance using lightweight models that aim to improve clinician confidence was achieved.

Quantitative Morphometry and Machine Learning Model to Explore Duodenal and Rectal Mucosal Tissue of Children with Environmental Enteric Dysfunction.

Environmental enteric dysfunction (EED) is a subclinical enteropathy prevalent in resource-limited settings, hypothesized to be a consequence of chronic exposure to environmental enteropathogens, resulting in malnutrition, growth failure, neurocognitive delays, and oral vaccine failure. This study explored the duodenal and colonic tissues of children with EED, celiac disease, and other enteropathies using quantitative mucosal morphometry, histopathologic scoring indices, and machine learning-based image analysis from archival and prospective cohorts of children from Pakistan and the United States. We observed villus blunting as being more prominent in celiac disease than in EED, as shorter lengths of villi were observed in patients with celiac disease from Pakistan than in those from the United States, with median (interquartile range) lengths of 81 (73, 127) µm and 209 (188, 266) µm, respectively. Additionally, per the Marsh scoring method, celiac disease histologic severity was increased in the cohorts from Pakistan. Goblet cell depletion and increased intraepithelial lymphocytes were features of EED and celiac disease. Interestingly, the rectal tissue from cases with EED showed increased mononuclear inflammatory cells and intraepithelial lymphocytes in the crypts compared with controls. Increased neutrophils in the rectal crypt epithelium were also significantly associated with increased EED histologic severity scores in duodenal tissue. We observed an overlap between diseased and healthy duodenal tissue upon leveraging machine learning image analysis. We conclude that EED comprises a spectrum of inflammation in the duodenum, as previously described, and the rectal mucosa, warranting the examination of both anatomic regions in our efforts to understand and manage EED.

Identifying metabolic shifts in Crohn's disease using' omics-driven contextualized computational metabolic network models.

Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract. A clear gap in our existing CD diagnostics and current disease management approaches is the lack of highly specific biomarkers that can be used to streamline or personalize disease management. Comprehensive profiling of metabolites holds promise; however, these high-dimensional profiles need to be reduced to have relevance in the context of CD. Machine learning approaches are optimally suited to bridge this gap in knowledge by contextualizing the metabolic alterations in CD using genome-scale metabolic network reconstructions. Our work presents a framework for studying altered metabolic reactions between patients with CD and controls using publicly available transcriptomic data and existing gene-driven metabolic network reconstructions. Additionally, we apply the same methods to patient-derived ileal enteroids to explore the utility of using this experimental in vitro platform for studying CD. Furthermore, we have piloted an untargeted metabolomics approach as a proof-of-concept validation strategy in human ileal mucosal tissue. These findings suggest that in silico metabolic modeling can potentially identify pathways of clinical relevance in CD, paving the way for the future discovery of novel diagnostic biomarkers and therapeutic targets.

Spectrum of germline pathogenic variants using a targeted next generation sequencing panel and genotype-phenotype correlations in patients with suspected hereditary breast cancer at an academic medical centre in Pakistan.

BACKGROUND: Breast cancer is the most common malignancy in women, affecting over 1.5 million women every year, which accounts for the highest number of cancer-related deaths in women globally. Hereditary breast cancer (HBC), an important subset of breast cancer, accounts for 5-10% of total cases. However, in Low Middle-Income Countries (LMICs), the population-specific risk of HBC in different ethnicities and the correlation with certain clinical characteristics remain unexplored. METHODS: Retrospective chart review of patients who visited the HBC clinic and proceeded with multi-gene panel testing from May 2017 to April 2020. Descriptive and inferential statistics were used to analyze clinical characteristics of patients. Fisher's exact, Pearson's chi-squared tests and Logistic regression analysis were used for categorical variables and Wilcoxon rank-sum test were used for quantitative variables. For comparison between two independent groups, Mann-Whitney test was performed. Results were considered significant at a p value of < 0.05. RESULTS: Out of 273 patients, 22% tested positive, 37% had a VUS and 41% had a negative genetic test result. Fifty-five percent of the positive patients had pathogenic variants in either BRCA1 or BRCA2, while the remaining positive results were attributed to other genes. Patients with a positive result had a younger age at diagnosis compared to those having a VUS and a negative result; median age 37.5 years, IQR (Interquartile range) (31.5-48). Additionally, patients with triple negative breast cancer (TNBC) were almost 3 times more likely to have a positive result (OR = 2.79, CI = 1.42-5.48 p = 0.003). Of all patients with positive results, 25% of patients had a negative family history of breast and/or related cancers. CONCLUSIONS: In our HBC clinic, we observed that our rate of positive results is comparable, yet at the higher end of the range which is reported in other populations. The importance of expanded, multi-gene panel testing is highlighted by the fact that almost half of the patients had pathogenic or likely pathogenic variants in genes other than BRCA1/2, and that our test positivity rate would have only been 12.8% if only BRCA1/2 testing was done. As the database expands and protocol-driven referrals are made across the country, our insight about the genetic architecture of HBC in our population will continue to increase.

Association of Anti-Rotavirus IgA Seroconversion with Growth, Environmental Enteric Dysfunction and Enteropathogens in Rural Pakistani Infants.

BACKGROUND: The underperformance of oral vaccines in children of low- and middle-income countries is partly attributable to underlying environmental enteric dysfunction (EED). METHODOLOGY: We conducted a longitudinal, community-based study to evaluate the association of oral rotavirus vaccine (Rotarix®) seroconversion with growth anthropometrics, EED biomarkers and intestinal enteropathogens in Pakistani infants. Children were enrolled between three to six months of their age based on their nutritional status. We measured serum anti-rotavirus immunoglobulin A (IgA) at enrollment and nine months of age with EED biomarkers and intestinal enteropathogens. RESULTS: A total of 391 infants received two doses of rotavirus (RV) vaccine. 331/391 provided paired blood samples. Of these 331 children, 45% seroconverted at 9 months of age, 35% did not seroconvert and 20% were seropositive at baseline. Non-seroconverted children were more likely to be stunted, wasted and underweight at enrollment. In univariate analysis, insulin-like growth factor (IGF) concentration at 6 months were higher in seroconverters, median (25th, 75th percentile): 26.3 (16.5, 43.5) ng/ml vs. 22.5 (13.6, 36.3) ng/ml for non-seroconverters, p-value = 0.024. At nine months, fecal myeloperoxidase (MPO) concentrations were significantly lower in seroconverters, 3050(1250, 7587) ng/ml vs. 4623.3 (2189, 11650) ng/ml in non-seroconverted children, p-value = 0.017. In multivariable logistic regression analysis, alpha-1 acid glycoprotein (AGP) and IGF-1 concentrations were positively associated with seroconversion at six months. The presence of sapovirus and rotavirus in fecal samples at the time of rotavirus administration, was associated with non-seroconversion and seroconversion, respectively. CONCLUSION: We detected high baseline RV seropositivity and impaired RV vaccine immunogenicity in this high-risk group of children. Healthy growth, serum IGF-1 and AGP, and fecal shedding of rotavirus were positively associated with RV IgA seroconversion following immunization, whereas the presence of sapovirus was more common in non-seroconverters. TRIAL REGISTRATION: Clinical Trials ID: NCT03588013.

Challenges and opportunities in the establishment of a hereditary breast cancer clinic at an academic medical center in a low-middle income country.

It is now standard of care to offer genetic testing to patients at risk of hereditary breast cancer and make management decisions based on these results. Although great strides have been made in ensuring access to genetic testing and genetic counseling by establishing hereditary breast cancer clinics in well-resourced countries, these are essentially non-existent in low-middle income countries like Pakistan. We established a hereditary breast cancer clinic involving a multidisciplinary team, including a medical geneticist and a genetic counselor. Our efforts were based on consensus guidelines and included educating medical providers about the importance of genetic testing in breast cancer care and the mandatory presence of a genetics team member at the weekly Breast Tumor Board meeting. This resulted in an increase in the number of referrals of breast cancer patients for genetic testing. In this report, we describe the challenges we faced in setting up such a system in Pakistan and the measures to overcome them. There is a need to establish such hereditary breast cancer clinics, which can also be replicated at other centers in low-resource settings, to improve standardized assessment and management of the patients with hereditary breast cancer according to consensus guidelines.

Distance from Healthcare Facilities Is Associated with Increased Morbidity of Acute Infection in Pediatric Patients in Matiari, Pakistan.

The relationship between environmental factors and child health is not well understood in rural Pakistan. This study characterized the environmental factors related to the morbidity of acute respiratory infections (ARIs), diarrhea, and growth using geographical information systems (GIS) technology. Anthropometric, address and disease prevalence data were collected through the SEEM (Study of Environmental Enteropathy and Malnutrition) study in Matiari, Pakistan. Publicly available map data were used to compile coordinates of healthcare facilities. A Pearson correlation coefficient (r) was used to calculate the correlation between distance from healthcare facilities and participant growth and morbidity. Other continuous variables influencing these outcomes were analyzed using a random forest regression model. In this study of 416 children, we found that participants living closer to secondary hospitals had a lower prevalence of ARI (r = 0.154, p < 0.010) and diarrhea (r = 0.228, p < 0.001) as well as participants living closer to Maternal Health Centers (MHCs): ARI (r = 0.185, p < 0.002) and diarrhea (r = 0.223, p < 0.001) compared to those living near primary facilities. Our random forest model showed that distance has high variable importance in the context of disease prevalence. Our results indicated that participants closer to more basic healthcare facilities reported a higher prevalence of both diarrhea and ARI than those near more urban facilities, highlighting potential public policy gaps in ameliorating rural health.

Self-Attentive Adversarial Stain Normalization.

Hematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) are utilized for biopsy visualization-based diagnostic and prognostic assessment of diseases. Variation in the H&E staining process across different lab sites can lead to significant variations in biopsy image appearance. These variations introduce an undesirable bias when the slides are examined by pathologists or used for training deep learning models. Traditionally proposed stain normalization and color augmentation strategies can handle the human level bias. But deep learning models can easily disentangle the linear transformation used in these approaches, resulting in undesirable bias and lack of generalization. To handle these limitations, we propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain. This unsupervised generative adversarial approach includes self-attention mechanism for synthesizing images with finer detail while preserving the structural consistency of the biopsy features during translation. SAASN demonstrates consistent and superior performance compared to other popular stain normalization techniques on H&E stained duodenal biopsy image data.

Deep Learning Methods for Anatomical Landmark Detection in Video Capsule Endoscopy Images.

Video capsule endoscope (VCE) is an emerging technology that allows examination of the entire gastrointestinal (GI) tract with minimal invasion. While traditional endoscopy with biopsy procedures are the gold standard for diagnosis of most GI diseases, they are limited by how far the scope can be advanced in the tract and are also invasive. VCE allows gastroenterologists to investigate GI tract abnormalities in detail with visualization of all parts of the GI tract. It captures continuous real time images as it is propelled in the GI tract by gut motility. Even though VCE allows for thorough examination, reviewing and analyzing up to eight hours of images (compiled as videos) is tedious and not cost effective. In order to pave way for automation of VCE-based GI disease diagnosis, detecting the location of the capsule would allow for a more focused analysis as well as abnormality detection in each region of the GI tract. In this paper, we compared four deep Convolutional Neural Network models for feature extraction and detection of the anatomical part within the GI tract captured by VCE images. Our results showed that VGG-Net has superior performance with the highest average accuracy, precision, recall and, F1-score compared to other state of the art architectures: GoogLeNet, AlexNet and, ResNet.

Bile Acid Profiling Reveals Distinct Signatures in Undernourished Children with Environmental Enteric Dysfunction.

BACKGROUND: Intestinal inflammation and malabsorption in environmental enteric dysfunction (EED) are associated with early childhood growth faltering in impoverished settings worldwide. OBJECTIVES: The goal of this study was to identify candidate biomarkers associated with inflammation, EED histology, and as predictors of later growth outcomes by focusing on the liver-gut axis by investigating the bile acid metabolome. METHODS: Undernourished rural Pakistani infants (n = 365) with weight-for-height Z score (WHZ) < -2 were followed up to the age of 24 mo and monitored for growth, infections, and EED. Well-nourished local children (n = 51) were controls, based on consistent WHZ > 0 and height-for-age Z score (HAZ) > -1 on 2 consecutive visits at 3 and 6 mo. Serum bile acid (sBA) profiles were measured by tandem MS at the ages of 3-6 and 9 mo and before nutritional intervention. Biopsies and duodenal aspirates were obtained following upper gastrointestinal endoscopy from a subset of children (n = 63) that responded poorly to nutritional intervention. BA composition in paired plasma and duodenal aspirates was compared based on the severity of EED histopathological scores and correlated to clinical and growth outcomes. RESULTS: Remarkably, >70% of undernourished Pakistani infants displayed elevated sBA concentrations consistent with subclinical cholestasis. Serum glycocholic acid (GCA) correlated with linear growth faltering (HAZ, r = -0.252 and -0.295 at the age of 3-6 and 9 mo, respectively, P <0.001) and biomarkers of inflammation. The proportion of GCA positively correlated with EED severity for both plasma (rs = 0.324 P = 0.02) and duodenal aspirates (rs = 0.307 P = 0.06) in children with refractory wasting that underwent endoscopy, and the proportion of secondary BA was low in both undernourished and EED children. CONCLUSIONS: Dysregulated bile acid metabolism is associated with growth faltering and EED severity in undernourished children. Restoration of intestinal BA homeostasis may offer a novel therapeutic target for undernutrition in children with EED. This trial was registered at clinicaltrials.gov as NCT03588013.

Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision.

Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. The diagnostic gold-standard involves manual review of a patient's biopsy tissue sample by a clinical pathologist for the presence of 15 or greater eosinophils within a single high-power field (400× magnification). Diagnosing EoE can be a cumbersome process with added difficulty for assessing the severity and progression of disease. We propose an automated approach for quantifying eosinophils using deep image segmentation. A U-Net model and post-processing system are applied to generate eosinophil-based statistics that can diagnose EoE as well as describe disease severity and progression. These statistics are captured in biopsies at the initial EoE diagnosis and are then compared with patient metadata: clinical and treatment phenotypes. The goal is to find linkages that could potentially guide treatment plans for new patients at their initial disease diagnosis. A deep image classification model is further applied to discover features other than eosinophils that can be used to diagnose EoE. This is the first study to utilize a deep learning computer vision approach for EoE diagnosis and to provide an automated process for tracking disease severity and progression.

Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy.

Probe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett's esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between pathologists. We deployed deep-learning-based image and video analysis in order to improve diagnostic accuracy of pCLE videos and biopsy images. Blinded experts categorized biopsies and pCLE videos as squamous, non-dysplastic BE, or dysplasia/cancer, and deep learning models were trained to classify the data into these three categories. Biopsy classification was conducted using two distinct approaches-a patch-level model and a whole-slide-image-level model. Gradient-weighted class activation maps (Grad-CAMs) were extracted from pCLE and biopsy models in order to determine tissue structures deemed relevant by the models. 1970 pCLE videos, 897,931 biopsy patches, and 387 whole-slide images were used to train, test, and validate the models. In pCLE analysis, models achieved a high sensitivity for dysplasia (71%) and an overall accuracy of 90% for all classes. For biopsies at the patch level, the model achieved a sensitivity of 72% for dysplasia and an overall accuracy of 90%. The whole-slide-image-level model achieved a sensitivity of 90% for dysplasia and 94% overall accuracy. Grad-CAMs for all models showed activation in medically relevant tissue regions. Our deep learning models achieved high diagnostic accuracy for both pCLE-based and histopathologic diagnosis of esophageal dysplasia and its precursors, similar to human accuracy in prior studies. These machine learning approaches may improve accuracy and efficiency of current screening protocols.

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

OBJECTIVES: Striking histopathological overlap between distinct but related conditions poses a 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 the secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using CNNs including one with multizoom architecture. Gradient-weighted class activation mappings (Grad-CAMs) were used to visualize the models' decision-making process for classifying each disease. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAMs to confirm structural preservation and biomedical relevance, respectively. RESULTS: Four hundred and sixty-one high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37.5 (19.0-121.5) 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 models' ability to learn different microscopic morphological features for EE, CD, and controls. CONCLUSIONS: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features and emulating human pathologist decision-making process. Grad-CAMs illuminated the otherwise "black box" of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.

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

BACKGROUND & AIMS: Environmental enteric dysfunction (EED) limits the Sustainable Development Goals of improved childhood growth and survival. We applied mucosal genomics to advance our understanding of EED. METHODS: The Study of Environmental Enteropathy and Malnutrition (SEEM) followed 416 children from birth to 24 months in a rural district in Pakistan. Biomarkers were measured at 9 months and tested for association with growth at 24 months. The duodenal methylome and transcriptome were determined in 52 undernourished SEEM participants and 42 North American controls and patients with celiac disease. RESULTS: After accounting for growth at study entry, circulating insulin-like growth factor-1 (IGF-1) and ferritin predicted linear growth, whereas leptin correlated with future weight gain. The EED transcriptome exhibited suppression of antioxidant, detoxification, and lipid metabolism genes, and induction of anti-microbial response, interferon, and lymphocyte activation genes. Relative to celiac disease, suppression of antioxidant and detoxification genes and induction of antimicrobial response genes were EED-specific. At the epigenetic level, EED showed hyper-methylation of epithelial metabolism and barrier function genes, and hypo-methylation of immune response and cell proliferation genes. Duodenal coexpression modules showed association between lymphocyte proliferation and epithelial metabolic genes and histologic severity, fecal energy loss, and wasting (weight-for-length/height Z < -2.0). Leptin was associated with expression of epithelial carbohydrate metabolism and stem cell renewal genes. Immune response genes were attenuated by giardia colonization. CONCLUSIONS: Children with reduced circulating IGF-1 are more likely to experience stunting. Leptin and a gene signature for lymphocyte activation and dysregulated lipid metabolism are implicated in wasting, suggesting new approaches for EED refractory to nutritional intervention. ClinicalTrials.gov, Number: NCT03588013. (https://clinicaltrials.gov/ct2/show/NCT03588013).

Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth - a four-year prospective study.

BACKGROUND: Stunting affects up to one-third of the children in low-to-middle income countries (LMICs) and has been correlated with decline in cognitive capacity and vaccine immunogenicity. Early identification of infants at risk is critical for early intervention and prevention of morbidity. The aim of this study was to investigate patterns of growth in infants up through 48 months of age to assess whether the growth of infants with stunting eventually improved as well as the potential predictors of growth. METHODS: Height-for-age z-scores (HAZ) of children from Matiari (rural site, Pakistan) at birth, 18 months, and 48 months were obtained. Results of serum-based biomarkers collected at 6 and 9 months were recorded. A descriptive analysis of the population was followed by assessment of growth predictors via traditional machine learning random forest models. RESULTS: Of the 107 children who were followed up till 48 months of age, 51% were stunted (HAZ < - 2) at birth which increased to 54% by 48 months of age. Stunting status for the majority of children at 48 months was found to be the same as at 18 months. Most children with large gains started off stunted or severely stunted, while all of those with notably large losses were not stunted at birth. Random forest models identified HAZ at birth as the most important feature in predicting HAZ at 18 months. Of the biomarkers, AGP (Alpha- 1-acid Glycoprotein), CRP (C-Reactive Protein), and IL1 (interleukin-1) were identified as strong subsequent growth predictors across both the classification and regressor models. CONCLUSION: We demonstrated that children most children with stunting at birth remained stunted at 48 months of age. Value was added for predicting growth outcomes with the use of traditional machine learning random forest models. HAZ at birth was found to be a strong predictor of subsequent growth in infants up through 48 months of age. Biomarkers of systemic inflammation, AGP, CRP, IL1, were also strong predictors of growth outcomes. These findings provide support for continued focus on interventions prenatally, at birth, and early infancy in children at risk for stunting who live in resource-constrained regions of the world.

Real-World Experience Measurement of Liver Iron Concentration by R2 vs. R2 Star MRI in Hemoglobinopathies.

BACKGROUND: Non-invasive determination of liver iron concentration (LIC) is a valuable tool that guides iron chelation therapy in transfusion-dependent patients. Multiple methods have been utilized to measure LIC by MRI. The purpose of this study was to compare free breathing R2* (1/T2*) to whole-liver Ferriscan R2 method for estimation of LIC in a pediatric and young adult population who predominantly have hemoglobinopathies. METHODS: Clinical liver and cardiac MRI scans from April 2016 to May 2018 on a Phillips 1.5 T scanner were reviewed. Free breathing T2 and T2* weighted images were acquired on each patient. For T2, multi-slice spin echo sequences were obtained. For T2*, a single mid-liver slice fast gradient echo was performed starting at 0.6 ms with 1.2 ms increments with signal averaging. R2 measurements were performed by Ferriscan analysis. R2* measurements were performed by quantitative T2* map analysis. RESULTS: 107 patients underwent liver scans with the following diagnoses: 76 sickle cell anemia, 20 Thalassemia, 9 malignancies and 2 Blackfan Diamond anemia. Mean age was 12.5 ± 4.5 years. Average scan time for R2 sequences was 10 min, while R2* sequence time was 20 s. R2* estimation of LIC correlated closely with R2 with a correlation coefficient of 0.94. Agreement was strongest for LIC < 15 mg Fe/g dry weight. Overall bias from Bland-Altman plot was 0.66 with a standard deviation of 2.8 and 95% limits of agreement -4.8 to 6.1. CONCLUSION: LIC estimation by R2* correlates well with R2-Ferriscan in the pediatric age group. Due to the very short scan time of R2*, it allows imaging without sedation or anesthesia. Cardiac involvement was uncommon in this cohort.

The Enteric Nervous System and Its Emerging Role as a Therapeutic Target.

The gastrointestinal (GI) tract is innervated by the enteric nervous system (ENS), an extensive neuronal network that traverses along its walls. Due to local reflex circuits, the ENS is capable of functioning with and without input from the central nervous system. The functions of the ENS range from the propulsion of food to nutrient handling, blood flow regulation, and immunological defense. Records of it first being studied emerged in the early 19th century when the submucosal and myenteric plexuses were discovered. This was followed by extensive research and further delineation of its development, anatomy, and function during the next two centuries. The morbidity and mortality associated with the underdevelopment, infection, or inflammation of the ENS highlight its importance and the need for us to completely understand its normal function. This review will provide a general overview of the ENS to date and connect specific GI diseases including short bowel syndrome with neuronal pathophysiology and current therapies. Exciting opportunities in which the ENS could be used as a therapeutic target for common GI diseases will also be highlighted, as the further unlocking of such mechanisms could open the door to more therapy-related advances and ultimately change our treatment approach.