Systematic Review of Survival Outcomes of Pregnancy-Associated Breast Cancer in Asian Countries.

Pregnancy-associated breast cancer (PABC) is a rare type of gestational cancer. It poses a significant challenge in diagnosis and management, especially in Asian countries with limited resources. We carried out a systematic literature review and narrative synthesis to identify survival outcomes for women with PABC in Asia. We searched MEDLINE, PubMed, Cochrane Library and the reference lists of the included English language articles for those conducted between January 2010 and August 2022. The search terms were pregnancy-associated breast cancer, breast cancer AND pregnancy, survival of PABC and prognosis of PABC patients. PABC is defined as breast cancer diagnosed either during pregnancy or 1 year-5 years postpartum. This review included observational studies conducted in Asian countries. The final 11 articles met the selection criteria and were analysed. Five of the studies had high quality methods as assessed using the Joanna Briggs Institute (JBI) checklist. We reported study design, year of diagnosis, country, definition of PABC, control group, age of participants, median follow-up time, survival outcomes and pregnancy as prognostic factors. Only five studies reported that PABC patients had a poor overall or disease-free survival rate compared to the control. Pregnancy was a significant independent prognostic factor of breast cancer in only two studies. This review highlights that pregnancy has an unconfirmed association with breast cancer survival in Asia. Most studies that found a non-significant association had small samples, thus there is a need for large-scale multinational epidemiological studies in Asia to establish the survival outcomes in PABC patients.

Machine and deep learning in inflammatory bowel disease.

PURPOSE OF REVIEW: The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial intelligence technologies like machine learning and deep learning presents another watershed moment in IBD treatment. Interest in these methods in IBD research has increased over the past 10 years, and they offer a promising path to better clinical outcomes for IBD patients. RECENT FINDINGS: Developing new tools to evaluate IBD and inform clinical management is challenging because of the expansive volume of data and requisite manual interpretation of data. Recently, machine and deep learning models have been used to streamline diagnosis and evaluation of IBD by automating review of data from several diagnostic modalities with high accuracy. These methods decrease the amount of time that clinicians spend manually reviewing data to formulate an assessment. SUMMARY: Interest in machine and deep learning is increasing in medicine, and these methods are poised to revolutionize the way that we treat IBD. Here, we highlight the recent advances in using these technologies to evaluate IBD and discuss the ways that they can be leveraged to improve clinical outcomes.

Placebo-Controlled Trial of an Oral BTK Inhibitor in Multiple Sclerosis.

BACKGROUND: Bruton's tyrosine kinase (BTK) regulates the functions of B cells and myeloid cells that are implicated in the pathogenesis of multiple sclerosis. Evobrutinib is a selective oral BTK inhibitor that has been shown to inhibit B-cell activation both in vitro and in vivo. METHODS: In this double-blind, randomized, phase 2 trial, we assigned patients with relapsing multiple sclerosis to one of five groups: placebo, evobrutinib (at a dose of 25 mg once daily, 75 mg once daily, or 75 mg twice daily), or open-label dimethyl fumarate (DMF) as a reference. The primary end point was the total (cumulative) number of gadolinium-enhancing lesions identified on T1-weighted magnetic resonance imaging at weeks 12, 16, 20, and 24. Key secondary end points included the annualized relapse rate and change from baseline in the score on the Expanded Disability Status Scale (EDSS). RESULTS: A total of 267 patients were randomly assigned to a trial group. The mean (±SD) total number of gadolinium-enhancing lesions during weeks 12 through 24 was 3.85±5.44 in the placebo group, 4.06±8.02 in the evobrutinib 25-mg group, 1.69±4.69 in the evobrutinib 75-mg once-daily group, 1.15±3.70 in the evobrutinib 75-mg twice-daily group, and 4.78±22.05 in the DMF group. The baseline adjusted rate ratios for the total number of lesions over time as compared with placebo were 1.45 in the evobrutinib 25-mg group (P = 0.32), 0.30 in the evobrutinib 75-mg once-daily group (P = 0.005), and 0.44 in the evobrutinib 75-mg twice-daily group (P = 0.06). The unadjusted annualized relapse rate at week 24 was 0.37 in the placebo group, 0.57 in the evobrutinib 25-mg group, 0.13 in the evobrutinib 75-mg once-daily group, 0.08 in the evobrutinib 75-mg twice-daily group, and 0.20 in the DMF group. There was no significant effect of trial group on the change from baseline in the EDSS score. Elevations in liver aminotransferase values were observed with evobrutinib. CONCLUSIONS: Patients with relapsing multiple sclerosis who received 75 mg of evobrutinib once daily had significantly fewer enhancing lesions during weeks 12 through 24 than those who received placebo. There was no significant difference with placebo for either the 25-mg once-daily or 75-mg twice-daily dose of evobrutinib, nor in the annualized relapse rate or disability progression at any dose. Longer and larger trials are required to determine the effect and risks of evobrutinib in patients with multiple sclerosis. (Funded by EMD Serono; ClinicalTrials.gov number, NCT02975349.).

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).

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.

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.

Mechanical Strain of the Trilobed Transposition Flap in Artificial Skin Models: Pivotal Restraint Decreases With Decreasing Rotational Angles.

BACKGROUND: In transposition flaps, thicker tissue and higher degrees of rotation are associated with increased pivotal restraint; however, limited experimental data exist quantifying the degree to which these affect flap biomechanics. The use of artificial skin models in conjunction with digital image correlation technology allows for investigation into biomechanical properties of skin flaps. OBJECTIVE: To quantify the effects of tissue thickness and rotational angles on pivotal restraint within transposition flaps using artificial skin models. METHODS: Ninety degree bilobed and trilobed flaps were used to close defects in artificial skin models of increasing thicknesses. Digital image correlation was used to quantify strain. Quantitative and qualitative differences in strain were assessed in increasing flap thicknesses and between flap designs. RESULTS: Increasing flap thickness was associated with decreasing strain. In the bilobed flap, increasing thickness was associated with displacement of the flap pivot point away from the distal flap edge. Comparatively, lower angles of rotation in the trilobed flap were not associated with migration of the flap pivot point. CONCLUSION: Increased pivotal restraint observed in higher degrees of rotation is due to migration of the flap pivot point. This model supports the common practice of decreasing flap angles to compensate for pivotal restraint.

Artificial Intelligence Applied to Gastrointestinal Diagnostics: A Review.

Artificial intelligence (AI), a discipline encompassed by data science, has seen recent rapid growth in its application to healthcare and beyond, and is now an integral part of daily life. Uses of AI in gastroenterology include the automated detection of disease and differentiation of pathology subtypes and disease severity. Although a majority of AI research in gastroenterology focuses on adult applications, there are a number of pediatric pathologies that could benefit from more research. As new and improved diagnostic tools become available and more information is retrieved from them, AI could provide physicians a method to distill enormous amounts of data into enhanced decision-making and cost saving for children with digestive disorders. This review provides a broad overview of AI and examples of its possible applications in pediatric gastroenterology.

Correction to: Celiac Disease Screening for High-Risk Groups: Are We Doing It Right?

The original version of the article is unfortunately missing the funding information. Funding note is given below.

Celiac Disease Screening for High-Risk Groups: Are We Doing It Right?

Celiac disease (CD) is an immune-mediated enteropathy triggered by dietary ingestion of gluten in genetically susceptible patients. CD is often diagnosed by a "case-finding" approach of symptomatic patients. In recent times, the diagnostic paradigm has shifted to investigate patients who may be asymptomatic, but are at high risk of developing CD due to shared genetic susceptibilities. These high-risk groups include first-degree relatives of CD patients and patients with Type 1 diabetes mellitus, autoimmune thyroid disease, Down's syndrome, and Turner syndrome. Moreover, CD is often diagnosed as the cause of iron deficiency anemia or unexplained chronic diarrhea. Although screening for CD with serological tests is not recommended for the general population, it should be considered in these special populations. In this review, we explore screening for CD among high-risk groups in light of recent research and development in the CD arena.

Artificial Intelligence and Its Role in Identifying Esophageal Neoplasia.

Randomized trials have demonstrated that ablation of dysplastic Barrett's esophagus can reduce the risk of progression to cancer. Endoscopic resection for early stage esophageal adenocarcinoma and squamous cell carcinoma can significantly reduce postoperative morbidity compared to esophagectomy. Unfortunately, current endoscopic surveillance technologies (e.g., high-definition white light, electronic, and dye-based chromoendoscopy) lack sensitivity at identifying subtle areas of dysplasia and cancer. Random biopsies sample only approximately 5% of the esophageal mucosa at risk, and there is poor agreement among pathologists in identifying low-grade dysplasia. Machine-based deep learning medical image and video assessment technologies have progressed significantly in recent years, enabled in large part by advances in computer processing capabilities. In deep learning, sequential layers allow models to transform input data (e.g., pixels for imaging data) into a composite representation that allows for classification and feature identification. Several publications have attempted to use this technology to help identify dysplasia and early esophageal cancer. The aims of this reviews are as follows: (a) discussing limitations in our current strategies to identify esophageal dysplasia and cancer, (b) explaining the concepts behind deep learning and convolutional neural networks using language appropriate for clinicians without an engineering background, (c) systematically reviewing the literature for studies that have used deep learning to identify esophageal neoplasia, and (d) based on the systemic review, outlining strategies on further work necessary before these technologies are ready for "prime-time," i.e., use in routine clinical care.

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.

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

BACKGROUND: Environmental Enteropathy (EE), characterized by alterations in intestinal structure, function, and immune activation, is believed to be an important contributor to childhood undernutrition and its associated morbidities, including stunting. Half of all global deaths in children < 5 years are attributable to under-nutrition, making the study of EE an area of critical priority. METHODS: Community based intervention study, divided into two sub-studies, 1) Longitudinal analyses and 2) Biopsy studies for identification of EE features via omics analyses. Birth cohorts in Matiari, Pakistan established: moderately or severely malnourished (weight for height Z score (WHZ) < - 2) children, and well-nourished (WHZ > 0) children. Blood, urine, and fecal samples, for evaluation of potential biomarkers, will be collected at various time points from all participants (longitudinal analyses). Participants will receive appropriate educational and nutritional interventions; non-responders will undergo further evaluation to determine eligibility for further workup, including upper gastrointestinal endoscopy. Histopathological changes in duodenal biopsies will be compared with duodenal biopsies obtained from USA controls who have celiac disease, Crohn's disease, or who were found to have normal histopathology. RNA-Seq will be employed to characterize mucosal gene expression across groups. Duodenal biopsies, luminal aspirates from the duodenum, and fecal samples will be analyzed to define microbial community composition (omic analyses). The relationship between histopathology, mucosal gene expression, and community configuration will be assessed using a variety of bioinformatic tools to gain better understanding of disease pathogenesis and to identify mechanism-based biomarkers. Ethical review committees at all collaborating institutions have approved this study. All results will be made available to the scientific community. DISCUSSION: Operational and ethical constraints for safely obtaining intestinal biopsies from children in resource-poor settings have led to a paucity of human tissue-based investigations to understand and reverse EE in vulnerable populations. Furthermore, EE biomarkers have rarely been correlated with gold standard histopathological confirmation. The Study of Environmental Enteropathy and Malnutrition (SEEM) is designed to better understand the pathophysiology, predictors, biomarkers, and potential management strategies of EE to inform strategies to eradicate this debilitating pathology and accelerate progress towards the 2030 Sustainable Development Goals. TRIAL REGISTRATION: Retrospectively registered; clinicaltrials.gov ID NCT03588013 .

Social-emotional aspects of quality of life in multiple sclerosis.

Multiple sclerosis (MS) is an inflammatory auto-immune disease of the central nervous system. It leads to many impairments including physical, cognitive, psychological, and social challenges. Our study examined gender and cultural associations with quality of life (QoL), personal characteristics, and benefits from having MS among those with MS. The study was conducted in Austria and the United States. The sample included 128 participants, 64 in each country, of whom 78 were women and 50 were men aged between 20 and 57 years. We used standard statistical tests, including analyses of covariance (ANCOVA) and partial correlations for the analysis of quantitative data. For the qualitative part of the survey we used semi-structured interviews, which we transcribed and coded to identify categories in the answers for qualitative analyses. Austrian participants with MS perceived a higher social-emotional QoL in comparison to American participants. American participants expressed a higher self-esteem in comparison to Austrian participants. Men reported a lower ability to express love than women. Independent of sex/gender and nationality, participants reported benefits through the disease, especially with regard to improved compassion, mindfulness, improved family relations and lifestyle gains. The qualitative interviews revealed additional gender differences for coping with the illness; and in experiences, expectations, and challenges related to MS. These insights can be used to develop targeted psychological and social support interventions aimed toward improving social-emotional QoL for persons with MS.

Use of Reticulocyte Hemoglobin Content in the Assessment of Iron Deficiency in Children With Inflammatory Bowel Disease.

BACKGROUND: Iron deficiency and anemia affect up to 50% to 75% of patients with inflammatory bowel disease (IBD). Iron deficiency in IBD may be difficult to diagnose because of the effect of inflammation on iron status biomarkers. Thus, there is a need for better methods to accurately determine iron status in IBD. OBJECTIVE: The aim of the study was to investigate the association of inflammation with hemoglobin content of reticulocytes (CHr) and the utility of CHr in comparison to standard iron biomarkers. METHODS: We conducted a cross-sectional study of children with IBD. Iron biomarkers (CHr, ferritin, soluble transferrin receptor [sTfR], hepcidin, hemoglobin) were measured along with systemic biomarkers of inflammation (C-reactive protein, α1-acid glycoprotein]. Spearman correlations were used to evaluate the relation of inflammation and iron biomarkers. The criterion standard for iron deficiency was defined as inflammation-corrected ferritin <15 µg/L or sTfR >8.3 mg/L. Receiver operating characteristic curves were used to estimate the prognostic values of all iron biomarkers to identify patients with iron deficiency. RESULTS: We analyzed data in 62 children ages 5 to 18 years. Sixty-nine percent of our subjects had Crohn disease and 31% had ulcerative colitis, of which 42% were girls and 53% African American. The prevalence of anemia was 32%, of iron deficiency was 52% using ferritin <15 µg/L or sTfR >8.3 mg/L, 39% using red blood cell distribution width of >14.5%, 26% using body iron stores of <0 mg/kg body weight, 25% using CHr of <28 pg, and 11% using mean corpuscular volume of <75 fL/cell. The prevalence of elevated CRP or AGP was 48%. After correcting ferritin and sTfR levels for inflammation, the prevalence of iron deficiency was 68%. CHr was correlated with C-reactive protein (rs -0.44, P < 0.001) and α1-acid glycoprotein (rs -0.37, P < 0.05). The optimal prognostic value for inflammation-adjusted CHr to predict iron deficiency was 34 pg (area under the receiver operating characteristic of 0.70), with 88% sensitivity and 30% specificity. CONCLUSIONS: Iron deficiency and anemia are common in this pediatric IBD cohort. All explored iron biomarkers, including CHr, were affected by inflammation and should be adjusted. A single iron biomarker is unlikely to best predict iron deficiency in pediatric IBD. Iron intervention studies are needed to examine the response of iron biomarkers to iron supplementation in the setting of inflammation.

Environmental Enteric Dysfunction in Children.

Diarrheal diseases are a major cause of childhood death in resource-poor countries, killing approximately 760,000 children younger than 5 years each year. Although deaths due to diarrhea have declined dramatically, high rates of stunting and malnutrition have persisted. Environmental enteric dysfunction (EED) is a subclinical condition caused by constant fecal-oral contamination with resultant intestinal inflammation and villous blunting. These histological changes were first described in the 1960s, but the clinical effect of EED is only just being recognized in the context of failure of nutritional interventions and oral vaccines in resource-poor countries. We review the existing literature regarding the underlying causes of and potential interventions for EED in children, highlighting the epidemiology, clinical and histologic classification of the entity, and discussing novel biomarkers and possible therapies. Future research priorities are also discussed.

Pilot Study Evaluating Efficacy of 2 Regimens for Hypovitaminosis D Repletion in Pediatric Inflammatory Bowel Disease.

OBJECTIVES: Vitamin D is critical for skeletal health; hypovitaminosis D is common in pediatric inflammatory bowel disease (IBD), yet optimal repletion therapy is not well studied. We aimed to conduct a pilot trial comparing the efficacy of 2 vitamin D regimens of weekly dosing for the repletion of hypovitaminosis D in pediatric IBD. METHODS: Subjects identified from our IBD clinic with 25-hydroxyvitamin D (25[OH]D) concentrations <30 ng/mL were randomized to 10,000 (n = 18) or 5000 (n = 14) IU of oral vitamin D3/10 kg body weight per week for 6 weeks. Serum 25(OH)D, Ca, and parathyroid hormone concentrations were measured at baseline, week 8, and week 12. RESULTS: In the higher dosing group, serum 25(OH)D increased from 23.7 ±â€Š8.5 ng/mL at baseline to 49.2 ±â€Š13.6 ng/mL at 8 weeks; P < 0.001. In the lower dosing group, serum 25(OH)D increased from 24.0 ±â€Š7.0 ng/mL at baseline to 41.5 ±â€Š9.6 ng/mL at 8 weeks; P < 0.001. At 12 weeks, serum 25(OH)D concentrations were 35.1 ±â€Š8.4 and 30.8 ±â€Š4.2 ng/mL for the higher and lower dose regimens, respectively. Mean serum Ca and parathyroid hormone concentrations did not significantly change during the study. No patient exhibited hypercalcemia, and no serious adverse events occurred. CONCLUSIONS: Both treatment arms were safe and effective at normalizing vitamin D nutriture in pediatric IBD. Although significant repletion of 25(OH)D concentration was achieved in both dosing groups at 8 weeks, this effect was lost by the 12-week follow-up. Maintenance vitamin D therapy following initial repletion is likely required to maintain long-term normalized vitamin D status.