Necrolytic Migratory Erythema Associated With Glucagonoma Syndrome

Necrolytic migratory erythema (NME) is a rare cutaneous manifestation that may present as the first sign of glucagonoma syndrome. Glucagonoma syndrome is associated with alpha-cell pancreatic tumor, increased glucagon in blood and skin rash (NME). NME is usually the first and rather exclusive manifestation of this syndrome, but may occur in other diseases, which is called pseudoglucagonoma syndrome. We report a case of NME which is associated with glucagonoma syndrome

Investigation of the properties changes observed for plastic samples made by fused deposition modelling under radiation exposure

This work shows investigation of the transmission dynamics of polymer samples made of PLA plastic (polylactide) by fused deposition modelling for electron beam. Besides, results of tests for mechanical destruction (compression) of plastic samples with radiation exposure and without are shown. Article demonstrates that properties of plastic samples are stable up to 1.5 kGy, that proves the suitability of this material for sample production intended for depth dose distributions of electron beams

The development and characterization of a 60K SNP chip for chicken

<p>Abstract</p> <p>Background</p> <p>In livestock species like the chicken, high throughput single nucleotide polymorphism (SNP) genotyping assays are increasingly being used for whole genome association studies and as a tool in breeding (referred to as genomic selection). To be of value in a wide variety of breeds and populations, the success rate of the SNP genotyping assay, the distribution of the SNP across the genome and the minor allele frequencies (MAF) of the SNPs used are extremely important.</p> <p>Results</p> <p>We describe the design of a moderate density (60k) Illumina SNP BeadChip in chicken consisting of SNPs known to be segregating at high to medium minor allele frequencies (MAF) in the two major types of commercial chicken (broilers and layers). This was achieved by the identification of 352,303 SNPs with moderate to high MAF in 2 broilers and 2 layer lines using Illumina sequencing on reduced representation libraries. To further increase the utility of the chip, we also identified SNPs on sequences currently not covered by the chicken genome assembly (Gallus_gallus-2.1). This was achieved by 454 sequencing of the chicken genome at a depth of 12x and the identification of SNPs on 454-derived contigs not covered by the current chicken genome assembly. In total we added 790 SNPs that mapped to 454-derived contigs as well as 421 SNPs with a position on Chr_random of the current assembly. The SNP chip contains 57,636 SNPs of which 54,293 could be genotyped and were shown to be segregating in chicken populations. Our SNP identification procedure appeared to be highly reliable and the overall validation rate of the SNPs on the chip was 94%. We were able to map 328 SNPs derived from the 454 sequence contigs on the chicken genome. The majority of these SNPs map to chromosomes that are already represented in genome build Gallus_gallus-2.1.0. Twenty-eight SNPs were used to construct two new linkage groups most likely representing two micro-chromosomes not covered by the current genome assembly.</p> <p>Conclusions</p> <p>The high success rate of the SNPs on the Illumina chicken 60K Beadchip emphasizes the power of Next generation sequence (NGS) technology for the SNP identification and selection step. The identification of SNPs from sequence contigs derived from NGS sequencing resulted in improved coverage of the chicken genome and the construction of two new linkage groups most likely representing two chicken micro-chromosomes.</p

Trends in advanced materials for the fabrication of insulin electrochemical immunosensors

Diabetes is one of the main problems of our community that has been the reason for 1.5 million people in 2019. Raising blood sugar or hyperglycemia is an effect of uncontrolled diabetes which can lead to serious and dangerous health issues. Insulin is a hormone that regulates the level of sugar in the human blood. Therefore, reliable and fast quantification of insulin has a key role in the clinics. This review aims to provide a summary of the employed new advanced materials in the fabrication of insulin immunosensors for possible application in clinics. The importance and role of different types of nanomaterials in the fabrication of these types of insulin sensing probes were also highlighted. Carbon-based materials are concurrently the most used and sensitive materials for insulin immunosensor fabrication. Also, the employed techniques for the designing of insulin immunosensors have been discussed and their limitations and beneficial features for accurate and on-time quantification of insulin were explored in detail. Electrochemical-based methods have been widely utilized and usually provide more sensitive approaches than other methods which have been developed for insulin detection

The effects of anatomical location and distance from dental implants on the quality and quantity of metal artifacts in cone beam computed tomography scans: a cross-sectional study

Abstract Background Artifacts in cone beam computed tomography (CBCT) images can cause disruptions in diagnosis and treatment. Multiple factors influence the artifacts, including the quality and technology of devices, positions, patient-related factors, device settings, and bone density. Besides, anatomical area and distance from the implant affect the artifacts. This study aimed to investigate the effects of anatomical location and distance from the implant on the quality and quantity of artifacts. Methods A total of 200 CBCT images of patients with titanium implants and prostheses in the anterior and posterior regions of the maxilla and mandible were evaluated in this study. Four areas were assessed for each implant in three apical, middle, and cervical regions with distances of 3 mm, 4 mm, and 5 mm from the implant. Besides, the impact of adjacent implants on the artifacts was investigated. An ANOVA test with post hoc Bonferroni correction was used to analyze variable differences between subgroups. Results The differences were statistically significant, except for the difference between the posterior areas of the upper and lower jaws. A comparison of different areas revealed that most artifacts were related to the anterior maxilla, followed by anterior mandibular regions. The results of covariance analysis indicated that region and location had independent effects on the amount of artifacts. Conclusions Artifacts are more frequent in the anterior region compared to the posterior site. They are also more frequent in the maxilla than the mandible and cervical areas close to the implant than the middle and apical regions

Genome-Wide Association Analysis and Genomic Prediction of <i>Mycobacterium avium</i> Subspecies <i>paratuberculosis</i> Infection in US Jersey Cattle

<div><p>Paratuberculosis (Johne’s disease), an enteric disorder in ruminants caused by <i>Mycobacterium avium</i> subspecies <i>paratuberculosis</i> (<i>MAP</i>), causes economic losses in excess of $200 million annually to the US dairy industry. To identify genomic regions underlying susceptibility to <i>MAP</i> infection in Jersey cattle, a case-control genome-wide association study (GWAS) was performed. Blood and fecal samples were collected from ∼5,000 mature cows in 30 commercial Jersey herds from across the US. Discovery data consisted of 450 cases and 439 controls genotyped with the Illumina BovineSNP50 BeadChip. Cases were animals with positive ELISA and fecal culture (FC) results. Controls were animals negative to both ELISA and FC tests that matched cases on birth date and herd. Validation data consisted of 180 animals including 90 cases (positive to FC) and 90 controls (negative to ELISA and FC), selected from discovery herds and genotyped by Illumina BovineLD BeadChip (∼7K SNPs). Two analytical approaches were used: single-marker GWAS using the GRAMMAR-GC method and Bayesian variable selection (Bayes C) using GenSel software. GRAMMAR-GC identified one SNP on BTA7 at 68 megabases (Mb) surpassing a significance threshold of 5×10⁻⁵. ARS-BFGL-NGS-11887 on BTA23 (27.7 Mb) accounted for the highest percentage of genetic variance (3.3%) in the Bayes C analysis. SNPs identified in common by GRAMMAR-GC and Bayes C in both discovery and combined data were mapped to BTA23 (27, 29 and 44 Mb), 3 (100, 101, 106 and 107 Mb) and 17 (57 Mb). Correspondence between results of GRAMMAR-GC and Bayes C was high (70–80% of most significant SNPs in common). These SNPs could potentially be associated with causal variants underlying susceptibility to <i>MAP</i> infection in Jersey cattle. Predictive performance of the model developed by Bayes C for prediction of infection status of animals in validation set was low (55% probability of correct ranking of paired case and control samples).</p></div

Posterior means of variance components in Bayesian genome-wide association study of susceptibility to <i>MAP</i> infection in US Jersey cattle.

1<p>Discovery and validation data.</p>2<p>95% highest posterior density of the estimates.</p

Manhattan plots displaying the results of Bayesian genome-wide association analysis (Bayes C) for susceptibility to <i>MAP</i> infection.

<p>A) Discovery data (2,657 windows) B) Combined data (2,656 windows). Y-axis represents the proportion of genetic variance explained by 1-Mb windows across the Bovine genome and X-axis represents the chromosomal location of windows.</p

Results of single-marker (GenABEL) and Bayesian (GenSel) genome-wide association analysis for susceptibility to <i>MAP</i> infection in Jersey cattle (combined data, N = 1,069)^1.

1<p>Only the twenty most significant SNPs are shown.</p>2<p>SNPs identified commonly by both GenABEL and GenSel analyses; order is based on P-values from GenABEL.</p>3<p>SNPs identified only by GenABEL.</p>4<p>SNPs identified only by GenSel.</p>5<p><i>Bos Taurus</i> chromosomes.</p>6<p>Position of SNP based on Bovine genome build UMD 3.1 (in base pair).</p>7<p>Major allele.</p>8<p>Minor allele.</p>9<p>Estimated effect of allele B (fitted allele) and the standard error of the estimated effect in the parenthesis.</p>10<p>P-value corrected by genomic control approach (GC).</p>11<p>Frequency of B allele in cases.</p>12<p>Frequency of B allele in controls.</p>13<p>Rank based on percentage of genetic variance among the twenty most significant windows by GenSel analysis.</p>14<p>Number of 1-Mb non-overlapping genome window and number of SNPs within each window in the parenthesis.</p>15<p>Percentage of total genetic variance explained by 1-Mb windows.</p>16<p>Proportion of models in which the corresponding window accounted for > 0.04% of genetic variance (expected variance if each window had the same effect: 1/total number of windows  = 2,657).</p>17<p>Proportion of MCMC iterations that included the corresponding SNP.</p

Receiver Operating Characteristic (ROC) curve for 10 fold cross-validation.

<p>A) Discovery data (32,587 SNPs) B) Combined data (32,375 SNPs). Ten sets of training and testing subsets were created. Multi-SNP models were developed by Bayes C analysis in training sets and were validated in testing set. Each broken line represents one model and solid bold line is the average area under curve (AUC) of all models. AUC is equivalent to the probability of correctly assigning a random pair of observations (positive and negative) to case and control. The diagonal represents a model with no predictive ability (AUC  = 0.5).</p