Statistical Fluctuations of Electromagnetic Transition Intensities and Electromagnetic Moments in pf-Shell Nuclei

We study the fluctuation properties of $\Delta T=0$ electromagnetic transition intensities and electromagnetic moments in $A \sim 60$ nuclei within the framework of the interacting shell model, using a realistic effective interaction for $pf$-shell nuclei with a $^{56}$Ni core. The distributions of the transition intensities and of the electromagnetic moments are well described by the Gaussian orthogonal ensemble of random matrices. In particular, the transition intensity distributions follow a Porter-Thomas distribution. When diagonal matrix elements (i.e., moments) are included in the analysis of transition intensities, we find that the distributions remain Porter-Thomas except for the isoscalar $M1$. The latter deviation is explained in terms of the structure of the isoscalar $M1$ operator.Comment: 11 pages, 4 figure

Artificial Intelligence Analysis of Gene Expression Data Predicted the Prognosis of Patients with Diffuse Large B-Cell Lymphoma

OBJECTIVE: We aimed to identify new biomarkers in Diffuse Large B-cell Lymphoma (DLBCL) using the deep learning technique. METHODS AND RESULTS: The multilayer perceptron (MLP) analysis was performed in the GSE10846 series, divided into discovery (n = 100) and validation (n = 414) sets. The top 25 gene-probes from a total of 54,614 were selected based on their normalized importance for outcome prediction (dead/alive). By Gene Set Enrichment Analysis (GSEA) the association to unfavorable prognosis was confirmed. In the validation set, by univariate Cox regression analysis, high expression of ARHGAP19, MESD, WDCP, DIP2A, CACNA1B, TNFAIP8, POLR3H, ENO3, SERPINB8, SZRD1, KIF23 and GGA3 associated to poor, and high SFTPC, ZSCAN12, LPXN and METTL21A to favorable outcome. A multivariate analysis confirmed MESD, TNFAIP8 and ENO3 as risk factors and ZSCAN12 and LPXN as protective factors. Using a risk score formula, the 25 genes identified two groups of patients with different survival that was independent to the cell-of-origin molecular classification (5-year OS, low vs. high risk): 65% vs. 24%, respectively (Hazard Risk = 3.2, P < 0.000001). Finally, correlation with known DLBCL markers showed that high expression of all MYC, BCL2 and ENO3 associated to the worst outcome. CONCLUSION: By artificial intelligence we identified a set of genes with prognostic relevance

Thyroid cancer incidences in the United Arab Emirates: a retrospective study on association with age and gender [version 1; peer review: 1 approved]

Background: Thyroid cancer is the ninth most common malignancy worldwide, but the third most common malignancy in the United Arab Emirates (UAE) . To our knowledge, this is the first UAE nationwide study aimed at presenting incidence rates of thyroid cancer at the national level of UAE based upon data from the national cancer registry and GLOBOCAN. Methods: Between 2011 and 2017, a total of 2036 thyroid cancer cases from UAE patients were registered, of which 75.3% were female and 24.7% male patients. Results: The results showed 6.6% increase in thyroid cancer cases in the UAE from 2011 to 2017 (p < 0.001) with a rise of approximately 400 cases per year from 2011 to 2040. Age standardized rate calculations showed increase in prevalence from 1.18 in 2011 to 4.32 in 2017 but decreases in incidence from 1.05 in 2011 to 0.15 in 2017. This trend is confirmed by the predictive model showing increase in incidence from 0.15 in 2017 to 0.64 by 2040. Gender was shown to be significantly associated with thyroid cancer. The female to male ratio was significantly higher in Emirati patients (4.86:1) (p < 0.001) than expat patients (2.47:1) (p < 0.01). Interestingly, expat patients contributed to the majority of thyroid cancer cases despite having lower female to male ratio. The age at diagnosis was significantly associated with thyroid cancer (p = 0.03) with the highest frequency diagnosed at 35-39 years of age. Globally, data from the predictive model showed that Asia had the highest rate of increase per year and UAE the lowest. Conclusions: The slight increase in thyroid cancer prevalence and incidence, together with the different female to male ratio and diagnosis at younger age warrants further investigation at the molecular level from UAE thyroid cancer patients to elucidate the molecular basis of thyroid cancer

Thyroid cancer incidence in the United Arab Emirates: a retrospective study on association with age and gender [version 2; peer review: 2 approved, 1 approved with reservations]

Background: Thyroid cancer is the ninth most common malignancy worldwide, but the third most common malignancy in the United Arab Emirates (UAE). To our knowledge, this is the first UAE nationwide study aimed at presenting incidence rates of thyroid cancer at the national level of UAE based upon data from the national cancer registry and GLOBOCAN. Methods: Between 2011 and 2017, a total of 2036 thyroid cancer cases from UAE patients were registered, of which 75.3% were female and 24.7% male patients. Results: The results showed 6.6% increase in thyroid cancer cases in the UAE from 2011 to 2017 (p < 0.001) with a rise of approximately 400 cases per year from 2011 to 2040. Age standardized rate calculations showed increase in prevalence from 1.18 in 2011 to 4.32 in 2017 but decreases in incidence from 1.05 in 2011 to 0.15 in 2017. This trend is confirmed by the predictive model showing increase in incidence from 0.15 in 2017 to 0.64 by 2040. Gender was shown to be significantly associated with thyroid cancer. The female to male ratio was significantly higher in Emirati patients (4.86:1) (p < 0.001) than expat patients (2.47:1) (p < 0.01). Interestingly, expat patients contributed to the majority of thyroid cancer cases despite having lower female to male ratio. The age at diagnosis was significantly associated with thyroid cancer (p = 0.03) with the highest frequency diagnosed at 35-39 years of age. Globally, data from the predictive model showed that Asia had the highest rate of increase per year and UAE the lowest. Conclusions: The slight increase in thyroid cancer prevalence and incidence, together with the different female to male ratio and diagnosis at younger age warrants further investigation at the molecular level from UAE thyroid cancer patients to elucidate the molecular basis of thyroid cancer

Identifying Diagnostic and Prognostic targets for Papillary Thyroid Carcinoma through mining Gene Expression BIG Datasets using Adaptive Filtering and Advanced Bioinformatics Algorithms

Thyroid Cancer is the most common endocrine malignancy. Although the mortality rate of thyroid cancer is considered to be low, however the reoccurrence and persistence of the disease is still considered high. The most common type of thyroid cancer is papillary thyroid carcinoma consisting of >70% of all types of thyroid cancer. Thyroid cancer is heterogeneous and complex. BIG data in the form of publicly available gene expression (transcriptomics) datasets can provide valuable source to gain deeper understanding of complex diseases such as papillary thyroid carcinoma (PTC). In this study, we used a novel bioinformatics method based on adaptive filtering to reduce the number of genes expressed eliminating genes that are invariant across the various disease stages. In order to shed light on some of the mechanisms involved in PTC, the filtered genes were used in systematic pathway analysis searches across 20,500 annotated cellular pathways using modified Kolmogorov-Smirnov algorithm to identify the relevant differentially activated cellular pathways across the various stages of the disease. Our analysis from 95 PTC patient biopsies consisting of 41 normal, 28 nonaggressive and 26 metastatic papillary thyroid carcinoma revealed 2193 differential activated cellular pathways among non-aggressive samples and 1969 among metastatic samples compared to normal tissue. The key pathways for non-aggressive PTC includes calcium and potassium ion transport, hormone signaling pathways, protein tyrosine phosphatase activity and protein tyrosine kinase activity. The key pathways for metastatic PTC include growth, apoptosis, activation of MAPK activity and regulation of serine threonine kinase activity. The most frequent genes across the enriched pathways were KCNQ1, CACNA1D, KCNN4, BCL2, and PTK2B for non-aggressive PTC, and EGFR, PTK2B, KCNN4 and BCL2 for metastatic PTC. Survival analysis results showed that PTK2B, CACNA1D and BCL2 contributed to poor survival of PTC patients. The study identified insights into mechanisms of PTC

Variations in pre-analytical FFPE sample processing and bioinformatics: challenges for next generation molecular diagnostic testing in clinical pathology

Advances in cellular pathology techniques will improve diagnostic medicine. However, such improvements have to overcome many challenges including variations in pre-analytical sample processing, bioinformatics data analysis and clinical interpretation of data. In order to resolve such challenges, bioinformatics needs to become more tightly coupled to the experimental methodology development

Identifying Asthma genetic signature patterns by mining Gene Expression BIG Datasets using Image Filtering Algorithms

Asthma is a treatable but incurable chronic inflammatory disease affecting more than 14% of the UAE population. Asthma is still a clinical dilemma as there is no proper clinical definition of asthma, unknown definitive underlying mechanisms, no objective prognostic tool nor bedside noninvasive diagnostic test to predict complication or exacerbation. Big Data in the form of publicly available transcriptomics can be a valuable source to decipher complex diseases like asthma. Such an approach is hindered by technical variations between different studies that may mask the real biological variations and meaningful, robust findings. A large number of datasets of gene expression microarray images need a powerful tool to properly translate the image intensities into truly differential expressed genes between conditioned examined from the noise. Here we used a novel bioinformatic method based on the coefficient of variance to filter nonvariant probes with stringent image analysis processing between asthmatic and healthy to increase the power of identifying accurate signals hidden within the heterogeneous nature of asthma. Our analysis identified important signaling pathways members, namely NFKB and TGFB pathways, to be differentially expressed between severe asthma and healthy controls. Those vital pathways represent potential targets for future asthma treatment and can serve as reliable biomarkers for asthma severity. Proper image analysis for the publicly available microarray transcriptomics data increased its usefulness to decipher asthma and identify genuine differentially expressed genes that can be validated across different datasets

Semi-Automated Image Analysis Methodology to Investigate Intracellular Heterogeneity in Immunohistochemical Stained Sections

The discovery of tissue heterogeneity revolutionized the existing knowledge regarding the cellular, molecular, and pathophysiological mechanisms in biomedicine. Therefore, basic science investigations were redirected to encompass observation at the classical and quantum biology levels. Various approaches have been developed to investigate and capture tissue heterogeneity; however, these approaches are costly and incompatible with all types of samples. In this paper, we propose an approach to quantify heterogeneous cellular populations through combining histology and images processing techniques. In this approach, images of immunohistochemically stained sections are processed through color binning of DAB-stained cells (in brown) and non-stained cells (in blue) to select cellular clusters expressing biomarkers of interest. Subsequently, the images were converted to a binary format through threshold modification (threshold 60%) in the grey scale. The cell count was extrapolated from the binary images using the particle analysis tool in ImageJ. This approach was applied to quantify the level of progesterone receptor expression levels in a breast cancer cell line sample. The results of the proposed approach were found to closely reflect those of manual counting. Through this approach, quantitative measures can be added to qualitative observation of subcellular targets expression