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

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

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

miR-27a-3p regulates expression of intercellular junctions at the brain endothelium and controls the endothelial barrier permeability

Background The brain endothelial barrier permeability is governed by tight and adherens junction protein complexes that restrict paracellular permeability at the blood-brain barrier (BBB). Dysfunction of the inter-endothelial junctions has been implicated in neurological disorders such as multiple sclerosis, stroke and Alzheimer’s disease. The molecular mechanisms underlying junctional dysfunction during BBB impairment remain elusive. MicroRNAs (miRNAs) have emerged as versatile regulators of the BBB function under physiological and pathological conditions, and altered levels of BBB-associated microRNAs were demonstrated in a number of brain pathologies including neurodegeneration and neuroinflammatory diseases. Among the altered micro-RNAs, miR-27a-3p was found to be downregulated in a number of neurological diseases characterized by loss of inter-endothelial junctions and disruption of the barrier integrity. However, the relationship between miR-27a-3p and tight and adherens junctions at the brain endothelium remains unexplored. Whether miR-27a-3p is involved in regulation of the junctions at the brain endothelium remains to be determined. Methods Using a gain-and-loss of function approach, we modulated levels of miR-27a-3p in an in-vitro model of the brain endothelium, key component of the BBB, and examined the resultant effect on the barrier paracellular permeability and on the expression of essential tight and adherens junctions. The mechanisms governing the regulation of junctional proteins by miR-27a-3p were also explored. Results Our results showed that miR-27a-3p inhibitor increases the barrier permeability and causes reduction of claudin-5 and occludin, two proteins highly enriched at the tight junction, while miR-27a-3p mimic reduced the paracellular leakage and increased claudin-5 and occludin protein levels. Interestingly, we found that miR-27-3p induces expression of claudin-5 and occludin by downregulating Glycogen Synthase Kinase 3 beta (GSK3ß) and activating Wnt/ ß-catenin signaling, a key pathway required for the BBB maintenance. Conclusion For the first time, we showed that miR-27a-3p is a positive regulator of key tight junction proteins, claudin-5 and occludin, at the brain endothelium through targeting GSK3ß gene and activating Wnt/ß-catenin signaling. Thus, miR-27a-3p may constitute a novel therapeutic target that could be exploited to prevent BBB dysfunction and preserves its integrity in neurological disorders characterized by impairment of the barrier’s function

Identifying Patterns of Breast Cancer Genetic Signatures using Unsupervised Machine Learning

Deploying machine learning to improve medical diagnosis is a promising area. The purpose of this study is to identify and analyze unique genetic signatures for breast cancer grades using publicly available gene expression microarray data. The classification of cancer types is based on unsupervised feature learning. Unsupervised clustering use matrix algebra based on similarity measures which made it suitable for analyzing gene expression. The main advantage of the proposed approach is the ability to use gene expression data from different grades of breast cancer to generate features that automatically identify and enhance the cancer diagnosis. In this paper, we tested different similarity measures in order to find the best way that identifies the sets of genes with a common function using expression microarray data

Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed very good performance with a coefficient of determination R2 of 49.8% and a median absolute deviation of 2.85 days. Furthermore, another DT-based model was constructed to predict COVID-19 risk of death. The model showed excellent performance with sensitivity and specificity of 96.5 and 87.8%, respectively, and overall prediction accuracy of 96%. Further validation using unsupervised learning methods showed similar separation patterns, and a receiver operator characteristic approach suggested stable and robust DT model performance. The results show that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used anticoagulant medications. Fortunately, intubated patients who are using anticoagulant and dexamethasone medications with an international normalized ratio of <1.69 have zero risk of death from COVID-19. In conclusion, we constructed artificial intelligence–based models to accurately predict the length of hospital stay and risk of death in COVID-19 cases. These smart models will arm physicians on the front line to enhance management strategies to save lives