Islets of Langerhans are fundamental in understanding diabetes. A healthy human pancreas from a donor has been used to asses various islet parameters and their three-dimensional distribution. Here we show that islets are spread gradually from the head up to the tail section of the pancreas in the form of contracted or dilated islet routes. We also report a particular anatomical structure, namely the cluster of islets. Our observations revealed a total of 11 islet clusters which comprise of small islets that surround large blood vessels. Additional observations in the peripancreatic adipose tissue have shown lymphoid-like nodes and blood vessels captured in a local inflammatory process. Our observations are based on regional slice maps of the pancreas, comprising of 5,423 islets. We also devised an index of sphericity which briefly indicates various islet shapes that are dominant throughout the pancreas.

The main function of gene promoters appears to be the integration of different gene products in their biological pathways in order to maintain homeostasis. Generally, promoters have been classified in two major classes, namely TATA and CpG. Nevertheless, many genes using the same combinatorial formation of transcription factors have different gene expression patterns. Accordingly, we tried to ask ourselves some fundamental questions: Why certain genes have an overall predisposition for higher gene expression levels than others? What causes such a predisposition? Is there a structural relationship of these sequences in different tissues? Is there a strong phylogenetic relationship between promoters of closely related species?

Gene promoters have guided evolution processes for millions of years. It seems that they were the main engine responsible for the integration of different mutations favorable for the environmental conditions. In cooperation with different transcription factors and other biochemical components, these regulatory regions dictate the synthesis frequency of RNA molecules. Predominantly in the last decade, it has become clear that nuclear organization impacts upon gene regulation. To fully understand the connections between Homo sapiens chromosomes and their gene promoters, we analyzed 1200 promoter sequences using our Kappa Index of Coincidence method.

Genome-wide association studies (GWAS) published in the last decade raised the number of loci associated with type 1 (T1D) and type 2 diabetes (T2D) to more than 50 for each of these diabetes phenotypes. The environmental factors seem to play an important role in the expression of these genes, acting through transcription factors that bind to promoters. Using the available databases we examined the promoters of various genes classically associated with the two main diabetes phenotypes. Our comparative analyses have revealed significant architectural differences between promoters of genes classically associated with T1D and T2D. Nevertheless, five gene promoters (about 16%) belonging to T1D and six gene promoters (over 19%) belonging to T2D have shown some intermediary structural properties, suggesting a direct relationship to either LADA (Latent Autoimmune Diabetes in Adults) phenotype or to non-autoimmune type 1 phenotype. The distribution of these promoters in at least three separate classes seems to indicate specific pathogenic pathways. The image-based patterns (DNA patterns) generated by promoters of genes associated with these three phenotypes support the clinical observation of a smooth link between specific cases of typical T1D and T2D. In addition, a global distribution of these DNA patterns suggests that promoters of genes associated with T1D appear to be evolutionary more conserved than those associated with T2D. Though, the image based patterns obtained by our method might be a new useful parameter for understanding the pathogenetic mechanism and the diabetogenic gene networks.

Here, we describe a general-purpose prediction model. Our approach requires three matrices of equal size and uses two equations to determine the behavior against two possible outcomes. We use an example based on photon-pixel coupling data to show that in humans, this solution can indicate the predisposition to disease. An implementation of this model is made available in the supplementary material.