Risk profile indicators and Spanish banks’ probability of default from a regulatory approach

This paper analyses the relationships between the traditional bank risk profile indicators and a new measure of banks’ probability of default that considers the Basel regulatory framework. First, based on the SYstemic Model of Bank Originated Losses (SYMBOL), we calculated the individual probabilities of default (PD) of a representative sample of Spanish credit institutions during the period of 2008–2016. Then, panel data regressions were estimated to explore the influence of the risk indicators on the PD. Our findings on the Spanish banking system could be important to regulatory and supervisory authorities. First, the PD based on the SYMBOL model could be used to analyse bank risk from a regulatory approach. Second, the results might be useful for designing new regulations focused on the key factors that affect the banks’ probability of default. Third, our findings reveal that the emphasis on regulation and supervision should differ by type of entity

Factors Influencing the European Bank’s Probability of Default: An Application of SYMBOL Methodology

This paper analyses European banks’ probability of default (PD) by estimating a new measure that is based on the SYstemic Model of Bank Originated Losses (SYMBOL). First, we calculate the individual PD of a sample of European credit institutions during the period of 2011–2016. Then, dynamic panel data models are estimated to analyse the influence of several bank-specific and macroeconomic variables on the PD. We conclude that capital adequacy, liquidity, asset quality and profitability indicators influence the European banks’ PD. The macroeconomic scenario, the industry concentration and the size of banks also appear to have an impact on their risk.Fundación de la Universidad de Cantabria para el Estudio y la Investigación del sector Financiero (UCEIF) y el Banco Santander

Soluble AXL is a novel blood marker for early detection of pancreatic ductal adenocarcinoma and differential diagnosis from chronic pancreatitis

Background: Early diagnosis is crucial for patients with pancreatic ductal adenocarcinoma (PDAC). The AXL receptor tyrosine kinase is proteolytically processed releasing a soluble form (sAXL) into the blood stream. Here we explore the use of sAXL as a biomarker for PDAC. Methods: AXL was analysed by immunohistochemistry in human pancreatic tissue samples. RNA expression analysis was performed using TCGA/GTEx databases. The plasma concentrations of sAXL, its ligand GAS6, and CA19-9 were studied in two independent cohorts, the HMar cohort (n = 59) and the HClinic cohort (n = 142), including healthy controls, chronic pancreatitis (CP) or PDAC patients, and in a familial PDAC cohort (n = 68). AXL expression and sAXL release were studied in PDAC cell lines and murine models. Findings: AXL is increased in PDAC and precursor lesions as compared to CP or controls. sAXL determined in plasma from two independent cohorts was significantly increased in the PDAC group as compared to healthy controls or CP patients. Patients with high levels of AXL have a lower overall survival. ROC analysis of the plasma levels of sAXL, GAS6, or CA19-9 in our cohorts revealed that sAXL outperformed CA19-9 for discriminating between CP and PDAC. Using both sAXL and CA19-9 increased the diagnostic value. These results were validated in murine models, showing increased sAXL specifically in animals developing PDAC but not those with precursor lesions or acinar tumours. Interpretation: sAXL appears as a biomarker for early detection of PDAC and PDAC–CP discrimination that could accelerate treatment and improve its dismal prognosis. Funding: This work was supported by grants PI20/00625 (PN), RTI2018-095672-B-I00 (AM and PGF), PI20/01696 (MG) and PI18/01034 (AC) from MICINN-FEDER and grant 2017/SGR/225 (PN) from Generalitat de Catalunya. © 2021 The Author(s

COVID-19 testing, infection, and vaccination among deported Mexican migrants: Results from a survey on the Mexico-U.S. border

BackgroundMigrants detained and held in immigration and other detention settings in the U.S. have faced increased risk of COVID-19 infection, but data on this population is scarce. This study sought to estimate rates of COVID-19 testing, infection, care seeking, and vaccination among Mexican migrants detained by U.S. immigration authorities and forcibly returned to Mexico.MethodsWe conducted a cross-sectional probability survey of Mexican migrants deported from the U.S. to three Mexican border cities: Tijuana, Ciudad Juárez, and Matamoros (N = 306). Deported migrants were recruited at Mexican migration facilities after being processed and cleared for departure. A two-stage sampling strategy was used. Within each city, a selection of days and shifts were selected during the operating hours of these deportation facilities. The probability of selection was proportional to the volume of migrants deported on each day of the month and during each time period. During the selected survey shifts, migrants were consecutively approached, screened for eligibility, and invited to participate in the survey. Survey measures included self-reported history of COVID-19 testing, infection, care seeking, vaccination, intentions to vaccinate, and other prevention and risk factors. Weighted data were used to estimate population-level prevalence rates. Bivariate tests and adjusted logistic regression models were estimated to identify associations between these COVID-19 outcomes and demographic, migration, and contextual factors.ResultsAbout 84.1% of migrants were tested for COVID-19, close to a third were estimated to have been infected, and, among them, 63% had sought care for COVID-19. An estimated 70.1% had been vaccinated against COVID-19 and, among those not yet vaccinated, 32.5% intended to get vaccinated. Close to half (44.3%) of respondents had experienced crowdedness while in detention in the U.S. Socio-demographic (e.g. age, education, English fluency) and migration-related (e.g. type of detention facility and time in detention) variables were significantly associated with COVID-19 testing, infection, care seeking, and vaccination history. Age, English fluency, and length of detention were positively associated with testing and vaccination history, whereas detention in an immigration center and length of time living in the U.S. were negatively related to testing, infection, and vaccination history. Survey city and survey quarter also showed adjusted associations with testing, infection, and vaccination history, reflecting potential variations in access to services across geographic regions and over time as the pandemic unfolded.ConclusionThese findings are evidence of increased risk of COVID-19 infection, insufficient access to testing and treatment, and missed opportunities for vaccination among Mexican migrants detained in and deported from the U.S. Deportee receiving stations can be leveraged to reduce disparities in testing and vaccination for deported migrants. In addition, decarceration of migrants and other measures informed by public health principles must be implemented to reduce COVID-19 risk and increase access to prevention, diagnostic, and treatment services among this underserved population

Identification of Candidate Parkinson Disease Genes by Integrating Genome-Wide Association Study, Expression, and Epigenetic Data Sets

Importance Substantial genome-wide association study (GWAS) work in Parkinson disease (PD) has led to the discovery of an increasing number of loci shown reliably to be associated with increased risk of disease. Improved understanding of the underlying genes and mechanisms at these loci will be key to understanding the pathogenesis of PD. / Objective To investigate what genes and genomic processes underlie the risk of sporadic PD. / Design and Setting This genetic association study used the bioinformatic tools Coloc and transcriptome-wide association study (TWAS) to integrate PD case-control GWAS data published in 2017 with expression data (from Braineac, the Genotype-Tissue Expression [GTEx], and CommonMind) and methylation data (derived from UK Parkinson brain samples) to uncover putative gene expression and splicing mechanisms associated with PD GWAS signals. Candidate genes were further characterized using cell-type specificity, weighted gene coexpression networks, and weighted protein-protein interaction networks. / Main Outcomes and Measures It was hypothesized a priori that some genes underlying PD loci would alter PD risk through changes to expression, splicing, or methylation. Candidate genes are presented whose change in expression, splicing, or methylation are associated with risk of PD as well as the functional pathways and cell types in which these genes have an important role. / Results Gene-level analysis of expression revealed 5 genes (WDR6 [OMIM 606031], CD38 [OMIM 107270], GPNMB [OMIM 604368], RAB29 [OMIM 603949], and TMEM163 [OMIM 618978]) that replicated using both Coloc and TWAS analyses in both the GTEx and Braineac expression data sets. A further 6 genes (ZRANB3 [OMIM 615655], PCGF3 [OMIM 617543], NEK1 [OMIM 604588], NUPL2 [NCBI 11097], GALC [OMIM 606890], and CTSB [OMIM 116810]) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell types compared with neurons. The weighted gene coexpression performed on the GTEx data set showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes associated with protein ubiquitination and in the ubiquitin-dependent protein catabolic process in the nucleus accumbens, caudate, and putamen. TMEM163 and ZRANB3 were both important in modules in the frontal cortex and caudate, respectively, indicating regulation of signaling and cell communication. Protein interactor analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known mendelian PD and parkinsonism proteins than would be expected by chance. / Conclusions and Relevance Together, these results suggest that several candidate genes and pathways are associated with the findings observed in PD GWAS studies

Identification of sixteen novel candidate genes for late onset Parkinson’s disease

Background Parkinson’s disease (PD) is a neurodegenerative movement disorder affecting 1–5% of the general population for which neither effective cure nor early diagnostic tools are available that could tackle the pathology in the early phase. Here we report a multi-stage procedure to identify candidate genes likely involved in the etiopathogenesis of PD. Methods The study includes a discovery stage based on the analysis of whole exome data from 26 dominant late onset PD families, a validation analysis performed on 1542 independent PD patients and 706 controls from different cohorts and the assessment of polygenic variants load in the Italian cohort (394 unrelated patients and 203 controls). Results Family-based approach identified 28 disrupting variants in 26 candidate genes for PD including PARK2, PINK1, DJ-1(PARK7), LRRK2, HTRA2, FBXO7, EIF4G1, DNAJC6, DNAJC13, SNCAIP, AIMP2, CHMP1A, GIPC1, HMOX2, HSPA8, IMMT, KIF21B, KIF24, MAN2C1, RHOT2, SLC25A39, SPTBN1, TMEM175, TOMM22, TVP23A and ZSCAN21. Sixteen of them have not been associated to PD before, were expressed in mesencephalon and were involved in pathways potentially deregulated in PD. Mutation analysis in independent cohorts disclosed a significant excess of highly deleterious variants in cases (p = 0.0001), supporting their role in PD. Moreover, we demonstrated that the co-inheritance of multiple rare variants (≥ 2) in the 26 genes may predict PD occurrence in about 20% of patients, both familial and sporadic cases, with high specificity (> 93%; p = 4.4 × 10− 5). Moreover, our data highlight the fact that the genetic landmarks of late onset PD does not systematically differ between sporadic and familial forms, especially in the case of small nuclear families and underline the importance of rare variants in the genetics of sporadic PD. Furthermore, patients carrying multiple rare variants showed higher risk of manifesting dyskinesia induced by levodopa treatment. Conclusions Besides confirming the extreme genetic heterogeneity of PD, these data provide novel insights into the genetic of the disease and may be relevant for its prediction, diagnosis and treatment

Voxel Selection in MRI through Bagging and Conformal Analysis: Application to Detection of Obsessive Compulsive Disorder

In this work we apply a multivariate feature selection method based on bagging linear SVMs to construct a classifier able to differentiate among control subjects and patients with obsessive compulsive disorder (OCD). Our method selects sets of voxels that are relevant for the detection of the disease. The voxel selection is completed with a conformal analysis based refinement that controls over fitting and dramatically reduces the test error rate of the final classifier. Furthermore, the resulting discrimination pattern is organized in regions that show great agreement with those found by traditional methods used in OCD problems, achieving cleaner and more accurate region maps. © 2012 IEEE

Finding genetically-supported drug targets for Parkinson’s disease using Mendelian randomization of the druggable genome

Parkinson’s disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson’s disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson’s disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson’s disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson’s disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson’s disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson’s disease drug development