Aberrant expression of Human α-Defensin-5 levels using transcript, Western blot, and immunohistochemistry staining levels was significantly upregulated in Crohn’s colitis, p< 0.0001. In addition, immunohistochemistry for Paneth cells and Lysozyme, a Paneth cell marker, was also performed. Four patients groups were assessed via whole-transcriptome microarray, qPCR, Western blot, and immunohistochemistry for differential expression of Human α-Defensin-5. We sought to determine whether transcriptomic patterns could be used to develop diagnostic biomarker(s) to delineate inflammatory bowel disease more accurately. Approximately 30 percent of inflammatory bowel disease patients cannot be accurately diagnosed, increasing their risk of inappropriate treatment. This greatly effects medical and surgical care of the patient because treatments for the two diseases vary. Inability to distinguish Crohn’s colitis from ulcerative colitis leads to the diagnosis of indeterminate colitis. These factors are key stem cell niche regulators leading to DEFA5 secreting CCLCs differentiation ‘the colonic ectopy ileal metaplasia formation’ conspicuously of pathogenic importance in CC. Notably, we observed expansion of signatures after DEFA5 treatment on NCM460 and colonoids cells expressed at different times, intervals, and intensity. IHC staining of CC for MUC6 and DEFA5 stained in different locations indicating that DEFA5 is not co-expressed in UACL and is therefore NOT the genesis of CC, rather a secretagogue for specific signature(s) that underlie the distinct crypt pathobiology of CC. A fit logistic model with group CC and UC as the outcome and the DEFA5 as independent variable differentiator with a positive predictive value of 96 percent.
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DEFA5 bioassay discriminated CC and ulcerative colitis (UC) in a cohort of IC patients with accuracy. Three statistical methods were used: (i) Univariate analysis (ii) LASSO and (iii) Elastic net. Treated colonoids with/without enterotoxins for 14 days and the spent medium were collected and determined by quantitative expression of DEFA5, CCLCs and other biologic signatures. Treated human immortalized colonic epithelial cells (NCM460) and colonoids with pure DEFA5 on the secretion of signatures after 24hr. Differentiated ulcer-associated cell lineage (UACL) analysis by immunohistochemistry (IHC) of the CC patients, stained for Mucin 6 (MUC6) and DEFA5. Their initial biopsies were analyzed by DEFA5 bioassay. Identified 21 patients with indeterminate colitis (IC) between 2000–2007 and were reevaluation their final clinical diagnosis in 2014 after a followed-up for mean 8.7☓.7 (range, 4–14) years. These areas of ectopic ileal metaplasia, positive for Paneth cell (PC) markers are consistent with diagnosis of CC.
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Our results indicate that the SFDIA system can potentially be used as a multi-parameter islet mass assay that is superior in accuracy and consistency, when compared to conventional manual techniques.Įvidence link bacterial enterotoxins to apparent crypt-cell like cells (CCLCs), and Alpha Defensin 5 ( DEFA5 ) expansion in the colonic mucosa of Crohn’s colitis disease (CC) patients. Via analysis of dithizone-stained islets using SFDIA, we found that a higher islet tissue percentage is associated with top-layer islets as opposed to middle-layer islets (0.735 ± 0.213 and 0.576 ± 0.223, respectively).
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Using SFDIA on a representative islet sample, we measured an average diameter of 99.88 ± 53.91 µm, an average circularity of 0.591 ± 0.133, and an average solidity of 0.853 ± 0.107.
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Furthermore, our software can analyze and extract key human islet mass parameters, including quantity, size, volume, IEq, morphology, and purity, which are not fully obtainable from traditional manual counting methods. The counts from the SFDIA and manual counting showed an average difference of 2.91 ± 1.50%. We quantified islets by tracking multiple moving islets in a microfluidic channel using the SFDIA system, and we achieved a relatively consistent result. In this study, a Smartphone-Fluidic Digital Imaging Analysis (SFDIA) System, which incorporates microfluidic techniques and Python-based video processing software, was developed for islet mass assessment. Digital imaging analysis (DIA) system has shown its potential as an alternative, but it has some associated limitations. Islet mass is commonly assessed manually, which often leads to error and bias.
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Islet beta-cell viability, function, and mass are three decisive attributes that determine the efficacy of human islet transplantation for type 1 diabetes mellitus (T1DM) patients.