Interferon-Stimulated Exonuclease Gene 20 is A Prognosis and Predictive Biomarker and Correlated with Macrophages M2 Polarization in Glioblastoma: A Pan-Cancer Analysis

Background: Since the mutation of isocitrate dehydrogenase 1 was confirmed to be different in the tumor microenvironment of multiple cancer types, several researchers have included it in the study of tumor-infiltrating immune cells. Interferon-stimulated exonuclease gene 20 (ISG20) plays a role in the modulation of immunity and inflammation, and its abnormally high expression is conducive for the progression of tumor malignancy. However, whether ISG20 is associated with isocitrate dehydrogenase 1 mutation during tumorigenesis and cancer progression remains unknown to date. Methods: TIMER2.0, ONCOMINE, GEPIA2, TCGA and CGGA were applied to assess the clinical significance of ISG20 and its correlation with tumor-infiltrating immune cells in glioma. cBioPortal and MethSurv databases were used to observe the genetic and DNA methylation changes of ISG20, respectively. Visualization of data was mostly achieved by R language. Quantitative real-time PCR (qRT-PCR) and Immunohistochemistry (IHC) was performed to evaluate the mRNA and protein expression.Results: ISG20 expression was significantly different in most cancers. However, when we combined ISG20 with isocitrate dehydrogenase 1 mutation, we found significant differences only in glioblastoma (GBM). The clinical values of ISG20 in glioblastoma showed that the ISG20 overexpression was strongly associated with a worse overall survival (OS). Additionally, ISG20 was altered in 9% of samples of patients with GBM, and ISG20 expression was negatively correlated with its DNA methylation level. More importantly, ISG20 expression was associated with macrophage alternatively activated (M2) polarization in glioblastoma. Conclusions: ISG20 overexpression is conducive to malignant phenotype but adverse to OS, suggesting that ISG20 is a potential therapeutic target and prognosis and predictive biomarker in patients with GBM.

Meta-analysis of HNF1A-MODY3 Variants Among Human Population

BackgroundPreviously, numerous case-control studies have highlighted variants responsible for Maturity onset diabetes of young (MODY). However, these studies have been conducted among diverse populations and hence yielded contradictory results. We, therefore, performed a meta-analysis to precisely find the association of SNPs with the disease for the HNF1A gene.ObjectiveMeta-analysis of clinically defined studies deciphering mutations in the HNF1A gene responsible for the development of MODY3 was conducted among various populations to determine associations using statistical approaches. MethodsThe curation of 505 research articles published between the years 2000-2021 was carried out. Visualization of data-related protocols and statistical-analysis were conducted, which led to the identification of highly prevalent mutations among different populations (majorly Europe). Further comparison between the frequencies of the control (healthy population) and test (diseased population) dataset generated through curation was performed.ResultsWe identified nine MODY3 mutations (rs587776825, rs1169288, rs1800574, rs2464196, rs137853244, rs137853238, rs587780357, rs137853240 and rs137853243) at the genome-wide significance level (p<5.0×10–8). The present study confirmed that the data does not follow a normal distribution. Further, the data was confirmed to be a more homogenous type with frequencies having a significant association with the disease.ConclusionThis meta-analysis found significant associations of mutations in HNF1A with MODY3, consistent with previous studies. Our findings should help elucidate the mutations in a compiled form responsible for causing MODY3.

Data visualisation in software supporting qualitative analysis

This text results from research developed in the Postgraduate Program in Education at the Tiradentes University (Unit), in partnership with the University of Aveiro, Portugal, in 2019 and 2020. The objective sought to describe how the Visualization of Data (VD) is represented in the analysis of qualitative data with the support of Qualitative Data Analysis Software (QDAS). To achieve this objective, we reached the inclusion/exclusion criteria. Seven software frequently used today, trying to understand the most frequent representations of HV in QDAS, their structuring, and how they can contribute to the phases of organisation and analysis in a scenario that can vary from small to large amounts of data. The results show that the QDAS can help the researcher visualise the qualitative data analysed with transparency through data visualisation representations that stood out in tables, charts, maps, and representations with movements. During the analysis, it was also observed that each software offers representations in different ways. The type of user/researcher interaction with the generated representations has been an exclusive phenomenon of digital technologies, which visually improves how scientific production knowledge can better circulate knowledge production.

Expansion of a Web-Based Platform to Support Caregivers in California Using CareNav

About one in five Americans is engaged in providing care to a family member. Caregivers (unpaid family members or friends) support older adults and persons with disability with a variety of conditions, including challenges in physical, cognitive, and mental health. In California, 4.5 million family caregivers are assisting individuals over the age of 18. The CA Department of Health Care Services funds 11 Caregiver Resource Centers (CRC) to support caregivers and, in 2019, provided support to expand information technology services through adoption of a statewide online assessment platform and client portal, CareNav™, to serve as a client record and referral tool. CareNav™ facilitates collection of consistent state-wide data that can inform program improvement and policy. This study evaluated the implementation process from the perspective of 35 CRC team members in guided focus group discussions. CRC staff identified several potential benefits to adopting CareNav™, including ease of client access and convenience, the ability to aggregate data to inform planning and policy, and a streamlined process for resource sharing. Challenges included customizing site-specific data needs, as well as concerns about equitable access to internet services. Ongoing evaluation will focus on validation and visualization of data, and translation of data into actionable information to improve quality and reach.

Cubic Spline Interpolation for Data Infections of COVID-19 Pandemic in Iraq

In this paper, we use an empirical equation and cubic spline interpolation to fit Covid-19 data available for accumulated infections and deaths in Iraq. For Scientific visualization of data interpretation, it is useful to use interpolation methods for purposes fitting by data interpolation. The data used is from 3 January 2020 to 21 January 2021 in order to obtain graphs to analysing the rate of increasing the pandemic and then obtain predicted values for the data infections and deaths in that period of time. Stochastic fit to the data of daily infections and deaths of Covid-19 is also discussed and showed in figures. The results of the cubic splines and the empirical equation used will be numerically compared. The principle of least square errors will be used for both these interpolations. The numerical results will be indicated that the cubic spline gives an accurate fitting to data.

Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign

Use of a multi-sensor approach can provide citizens with holistic insights into the air quality of their immediate surroundings and their personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from seven European cities, discusses the data fusion and harmonization of a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants. Harmonizing the data streams identified issues with the sensor devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate visualizations and reports, and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization of a diverse set of multi-sensor data streams considerably improved the quality and quantity of distilled data that a research participant received. Though automation considerably accelerated the production of the reports, manual and structured double checks are strongly recommended.