Exploring end of life priorities in Saudi males: usefulness of Q-methodology

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Analisis Spasial Tingkat Kerawanan Kasus Demam Berdarah Dengue Berbasis Sistem Informasi Geografis Di Kabupaten Magetan

Magetan Regency is one of the regencies in East Java Province which until now still has dengue hemorrhagic fever season. The incidence of dengue hemorrhagic fever occurs due to physical environmental factors in Magetan Regency which has a level of altitude, rainfall, humidity which varies from high, medium, low. Based on data recorded at the Health Service in 2021/2022, Magetan Regency had a large-scale number of cases of dengue fever, totaling 608 cases over the past three years, spread across Magetan Regency. The aims of this study were 1) to analyze the distribution of dengue hemorrhagic fever in Magetan district, 2) to analyze the level of vulnerability of dengue hemorrhagic fever cases in Magetan district, 3) to analyze the dominant factors affecting dengue hemorrhagic fever cases in Magetan district. The method used is a survey using residential block units as the unit of analysis, sampling using the Purposive Sampling Method with sub-district units. Methods of data analysis using tiered overlay analysis, dominant factor frequency table analysis, and spatial suitability overlay analysis. Based on the research results, 1) The distribution of dengue hemorrhagic fever cases in Magetan Regency was divided into 107 cases in the study area. 2) The high level of vulnerability is in Magetan, Maospati and Panekan Subdistricts and the dengue hemorrhagic fever case susceptibility zone in Magetan Regency is dominated by moderate and high susceptibility, 3) The dominant factors for DHF cases in Magetan Regency are rainfall and density of settlements in class high vulnerability

Working with an advisory group to co-create innovative intergenerational climate change research

This article discusses the opportunities of working with an Advisory Group on intergenerational climate change research. We co-created creative workshops to explore and articulate climate change perceptions and future imaginaries between younger and older people in Wales, UK. This 12-month programme of research activities led to a bilingual (Welsh and English) and bespoke comic, and a follow-up project that co-created an intergenerational activity book. Using a research diary format, we show how to practically follow the Responsible Research and Innovation dimensions of inclusion, reflexivity, anticipation, and responsiveness during the data collection stage. The opportunities for co-creation discussed here relate to two main areas: imagining and communicating futures through intergenerational workshops; and the extent to which the Advisory Group were co-creators. The voices of four members of the Advisory Group and the work of comic book artist show the benefits of an early involvement of time, resource and trust in a group who are potential critics, advocates, and bridge-builders. We make four recommendations: the importance of time and imagination in intergenerational climate research; the value of Advisory Groups in improving participatory methods; the need for sustained community-university partnerships and that Advisory Groups should be involved from the very beginning of research

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147–173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity. IPGS leads to an accuracy of 55%–60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into “Boolean quantum features,” inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores ((Formula presented.) and (Formula presented.)). By applying a logistic regression with both IPGS, ((Formula presented.) (or indifferently (Formula presented.)) and age as inputs, we reached an accuracy of 84%–86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147–173) by a factor of 10%

Clinical features and comorbidity pattern of HCV infected migrants compared to native patients in care in Italy: A real-life evaluation of the PITER cohort

Background: Direct-acting antivirals are highly effective for the treatment of hepatitis C virus (HCV) infection, regardless race/ethnicity. We aimed to evaluate demographic, virological and clinical data of HCV-infected migrants vs. natives consecutively enrolled in the PITER cohort. Methods: Migrants were defined by country of birth and nationality that was different from Italy. Mann-Whitney U test, Chi-squared test and multiple logistic regression were used. Results: Of 10,669 enrolled patients, 301 (2.8%) were migrants: median age 47 vs. 62 years, (p < 0.001), females 56.5% vs. 45.3%, (p < 0.001), HBsAg positivity 3.8% vs. 1.4%, (p < 0.05). Genotype 1b was prevalent in both groups, whereas genotype 4 was more prevalent in migrants (p < 0.05). Liver disease severity and sustained virologic response (SVR) were similar. A higher prevalence of comorbidities was reported for natives compared to migrants (p < 0.05). Liver disease progression cofactors (HBsAg, HIV coinfection, alcohol abuse, potential metabolic syndrome) were present in 39.1% and 47.1% (p > 0.05) of migrants and natives who eradicated HCV, respectively. Conclusion: Compared to natives, HCV-infected migrants in care have different demographics, HCV genotypes, viral coinfections and comorbidities and similar disease severity, SVR and cofactors for disease progression after HCV eradication. A periodic clinical assessment after HCV eradication in Italians and migrants with cofactors for disease progression is warranted

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, supporting their link with COVID-19 severity outcome

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death

The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10−8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10−8). A total of 113 variants were associated with survival at P-value < 1.0 × 10−5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways

Ultra-rare RTEL1 gene variants associate with acute severity of COVID-19 and evolution to pulmonary fibrosis as a specific long COVID disorder

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a novel coronavirus that caused an ongoing pandemic of a pathology termed Coronavirus Disease 19 (COVID-19). Several studies reported that both COVID-19 and RTEL1 variants are associated with shorter telomere length, but a direct association between the two is not generally acknowledged. Here we demonstrate that up to 8.6% of severe COVID-19 patients bear RTEL1 ultra-rare variants, and show how this subgroup can be recognized. Methods: A cohort of 2246 SARS-CoV-2-positive subjects, collected within the GEN-COVID Multicenter study, was used in this work. Whole exome sequencing analysis was performed using the NovaSeq6000 System, and machine learning methods were used for candidate gene selection of severity. A nested study, comparing severely affected patients bearing or not variants in the selected gene, was used for the characterisation of specific clinical features connected to variants in both acute and post-acute phases. Results: Our GEN-COVID cohort revealed a total of 151 patients carrying at least one RTEL1 ultra-rare variant, which was selected as a specific acute severity feature. From a clinical point of view, these patients showed higher liver function indices, as well as increased CRP and inflammatory markers, such as IL-6. Moreover, compared to control subjects, they present autoimmune disorders more frequently. Finally, their decreased diffusion lung capacity for carbon monoxide after six months of COVID-19 suggests that RTEL1 variants can contribute to the development of SARS-CoV-2-elicited lung fibrosis. Conclusion: RTEL1 ultra-rare variants can be considered as a predictive marker of COVID-19 severity, as well as a marker of pathological evolution in pulmonary fibrosis in the post-COVID phase. This notion can be used for a rapid screening in hospitalized infected people, for vaccine prioritization, and appropriate follow-up assessment for subjects at risk. Trial Registration NCT04549831 (www.clinicaltrial.org