The AP-1 repressor, JDP2, is a bona fide substrate for the c-Jun N-terminal kinase

AbstractThe Jun dimerization protein 2 (JDP2) is a novel member of the basic leucine zipper family of transcription factors. JDP2 binds DNA as a homodimer and heterodimer with ATF2 and Jun proteins but not with c-Fos proteins. JDP2 overexpression represses activating protein 1 transcription activity. Whereas JDP2 mRNA and protein levels are stable following different cell stimuli, JDP2 is rapidly phosphorylated upon UV irradiation, oxidative stress and low levels of translation inhibitor. The c-Jun N-terminal kinase phosphorylates JDP2 both in vitro and in vivo. JDP2 contains a putative consensus JNK docking-site and a corresponding phosphoacceptor site. Substitution of threonine 148 to an alanine residue blocks JNK-dependent JDP2 phosphorylation. Our data indicate that JDP2 is a bona fide substrate for the c-Jun N-terminal kinase. The precise role of JDP2 phosphorylation on its function is not yet known

Explainability Using Bayesian Networks for Bias Detection: FAIRness with FDO

In this paper we aim to provide an implementation of the FAIR Data Points (FDP) spec, that will apply our bias detection algorithm and automatically calculate a FAIRness score (FNS). FAIR metrics would be themselves represented as FDOs, and could be presented via a visual dashboard, and be machine accessible (Mons 2020, Wilkinson et al. 2016). This will enable dataset owners to monitor the level of FAIRness of their data. This is a step forward in making data FAIR, i.e., Findable, Accessible, Interoperable, and Reusable; or simply, Fully AI Ready data.First we may discuss the context of this topic with respect to Deep Learning (DL) problems. Why are Bayesian Networks (BN, explained below) beneficial for such issues?Explainability – Obtaining a directed acyclic graph (DAG) from a BN training provides coherent information about independence variables in the data base. In a generic DL problem, features are functions of these variables. Thus, one can derive which variables are dominant in our system. When customers or business units are interested in the cause of a neural net outcome, this DAG structure can be both a source to provide importance and clarify the model.Dimension Reduction — BN provides the joint distribution of our variables and their associations. The latter may play a role in reducing the features that we induce to the DL engine: If we know that for random variables X,Y the conditional entropy of X in Y are low, we may omit X since Y provides its nearly entire information. We have, therefore, a tool that can statistically exclude redundant variablesTagging Behavior – This section can be less evident for those who work in domains such as vision or voice. In some frameworks, labeling can be an obscure task (to illustrate, consider a sentiment problem with many categories that may overlap). When we tag the data, we may rely on some features within the datasets and generate conditional probability. Training BN, when we initialize an empty DAG, may provide outcomes in which the target is a parent of other nodes. Observing several tested examples, these outcomes reflect these “taggers’ manners”. We can therefore use DAGs not merely for the purpose of model development in machine learning but mainly learning taggers policy and improve it if needed.The conjunction of DL and Casual inference — Causal Inference is a highly developed domain in data analytics. It offers tools to resolve questions that on the one hand, DL models commonly do not and, on the other hand, the real-world raises. There is a need to find a framework in which these tools will work in conjunction. Indeed, such frameworks already exist (e.g., GNN). But a mechanism that merges typical DL problems causality is less common. We believe that the flow, as described in this paper, is a good step in the direction of achieving benefits from this conjunction.Fairness and Bias – Bayesian networks, in their essence, are not a tool for bias detection but they reveal which of the columns (or which of the data items) is dominant and modify other variables. When we discuss noise and bias, we address these faults to the column and not to the model or to the entire data base. However, assume we have a set of tools to measure bias (Purian et al. 2022). Bayesian networks can provide information about the prominence of these columns (as they are “cause” or “effect” in the data), thus allow us to assess the overall bias in the database.What are Bayesian Networks?The motivation for using Bayesian Networks (BN) is to learn the dependencies within a set of random variables. The networks themselves are directed acyclic graphs (DAG), which mimic the joint distribution of the random variables (e.g., Perrier et al. (2008)). The graph structure follows the probabilistic dependencies factorization of the joint distribution: a node V depends only on its parents (a r.v X independent of the other nodes will be presented as a parent free node).Real-World ExampleIn this paper we present a way of using the DL engine tabular data, with the python package bnlearn. Since this project is commercial, the variable names were masked; thus, they will have meaningless names.Constructing Our DAGWe begin by finding our optimal DAG.import bnlearn as bnDAG = bn.structure_learning.fit(dataframe) We now have a DAG. It has a set of nodes and an adjacency matrix that can be found as follow:print(DAG['adjmat']) The outcome has this form Fig. 1a.Where rows are sources (namely the direction of the arc is from the left column to the elements in the row) and columns are targets (i.e., the header of the column receives the arcs). When we begin drawing the obtained DAG, we get for one set of variables the following image: Fig. 1b.We can see that the target node in the rectangle is a source for many nodes. We can see that it still points arrows itself to two nodes. We will discuss this in the discussion (i.e., Rauber 2021). We have more variables, therefore I increased the number of nodes. Adding the information provided a new source for the target (i.e., its entire row is “False”). The obtained graph is the following: Fig. 1c.So, we know how to construct a DAG. Now we need to train its parameters. Code-wise we perform this as follows:model_mle = bn.parameter_learning.fit(DAG, dataframe, methodtype='maximumlikelihood')We can change ‘maximulikelihood’ with ‘bayes’ as described beyond. The outcome of this training is a set of factorized conditional distributions that reflect the DAG’s structure. It has this form for a given variable: Fig. 1d. The code to create DAG presentation is provided in Fig. 2. DiscussionIn this paper we have presented some of the theoretical concepts of Bayesian Networks and the usage they provide in constructing an approximated DAG for a set of variables. In addition, we presented a real-world example of end to end DAG learning: Constructing it using BN, training its parameters using maximum likelihood estimation (MLE) methods, and performing and inference.FAIR metrics, represented as FDOs, can also be visualised and monitored, taking care of data FAIRness

Association Between APOL1 Genotypes and Risk of Cardiovascular Disease in MESA (Multi-Ethnic Study of Atherosclerosis).

BACKGROUND:APOL1 genetic variants confer an increased risk for kidney disease. Their associations with cardiovascular disease (CVD) are less certain. We aimed to compare the prevalence of subclinical CVD and incidence of atherosclerotic CVD and heart failure by APOL1 genotypes among self-identified black participants of MESA (Multi-Ethnic Study of Atherosclerosis). METHODS AND RESULTS:Cross-sectional associations of APOL1 genotypes (high-risk=2 alleles; low-risk=0 or 1 allele) with coronary artery calcification, carotid-intimal media thickness, and left ventricular mass were evaluated using logistic and linear regression. Longitudinal associations of APOL1 genotypes with incident myocardial infarction, stroke, coronary heart disease, and congestive heart failure were examined using Cox regression. We adjusted for African ancestry, age, and sex. We also evaluated whether hypertension or kidney function markers explained the observed associations. Among 1746 participants with APOL1 genotyping (mean age 62 years, 55% women, mean cystatin C-based estimated glomerular filtration rate 89 mL/min per 1.73 m2, 12% with albuminuria), 12% had the high-risk genotypes. We found no difference in prevalence or severity of coronary artery calcification, carotid-intimal media thickness, or left ventricular mass by APOL1 genotypes. The APOL1 high-risk group was 82% more likely to develop incident heart failure compared with the low-risk group (95% confidence interval, 1.01-3.28). Adjusting for hypertension (hazard ratio, 1.80; 95% confidence interval, 1.00-3.24) but not markers of kidney function (hazard ratio, 1.86; 95% confidence interval, 1.03-3.35) slightly attenuated this association. The APOL1 high-risk genotypes were not significantly associated with other clinical CVD outcomes. CONCLUSIONS:Among blacks without baseline CVD, the APOL1 high-risk variants may be associated with increased risk for incident heart failure but not subclinical CVD or incident clinical atherosclerotic CVD

Association of urinary uromodulin with kidney function decline and mortality: the health ABC study .

BackgroundUrine uromodulin (uUMOD) is a protein secreted by the kidney tubule. Recent studies have suggested that higher uUMOD may be associated with improved kidney and mortality outcomes.MethodsUsing a case-cohort design, we evaluated the association between baseline uUMOD levels and ≥ 30% estimated glomerular filtration rate (eGFR) decline, incident chronic kidney disease (CKD), rapid kidney function decline, and mortality using standard and modified Cox proportional hazards regression.ResultsThe median value of uUMOD was 25.8 µg/mL, mean age of participants was 74 years, 48% were women, and 39% were black. Persons with higher uUMOD had lower prevalence of diabetes and coronary artery disease (CAD), and had lower systolic blood pressure. Persons with higher uUMOD also had higher eGFR, lower urinary albumin to creatinine ratio (ACR), and lower C-reactive protein (CRP). There was no association of uUMOD with > 30% eGFR decline. In comparison to those in the lowest quartile of uUMOD, those in the highest quartile had a significantly (53%) lower risk of incident CKD (CI 73%, 18%) and a 51% lower risk of rapid kidney function decline (CI 76%, 1%) after multivariable adjustment. Higher uUMOD was associated with lower risk of mortality in demographic adjusted models, but not after multivariable adjustment.ConclusionHigher levels of uUMOD are associated with lower risk of incident CKD and rapid kidney function decline. Additional studies are needed in the general population and in persons with advanced CKD to confirm these findings.


Patterns of beverages consumed and risk of incident kidney disease

© 2019 by the American Society of Nephrology. Background and objectives Selected beverages, such as sugar-sweetened beverages, have been reported to influence kidney disease risk, although previous studies have been inconsistent. Further research is necessary to comprehensively evaluate all types of beverages in association with CKD risk to better inform dietary guidelines. Design, setting, participants, & measurements We conducted a prospective analysis in the Jackson Heart Study, a cohort of black men and women in Jackson, Mississippi. Beverage intake was assessed using a food frequency questionnaire administered at baseline (2000–2004). Incident CKD was defined as onset of eGFR\u3c60 ml/min per 1.73 m 2 and ≥30% eGFR decline at follow-up (2009–13) relative to baseline among those with baseline eGFR ≥60 ml/min per 1.73 m 2 . Logistic regression was used to estimate the association between the consumption of each individual beverage, beverage patterns, and incident CKD. Beverage patterns were empirically derived using principal components analysis, in which components were created on the basis of the linear combinations of beverages consumed. Results Among 3003 participants, 185 (6%) developed incident CKD over a median follow-up of 8 years. At baseline, mean age was 54 (SD 12) years, 64% were women, and mean eGFR was 98 (SD 18) ml/min per 1.73 m 2 . After adjusting for total energy intake, age, sex, education, body mass index, smoking, physical activity, hypertension, diabetes, HDL cholesterol, LDL cholesterol, history of cardiovascular disease, and baseline eGFR, a principal components analysis–derived beverage pattern consisting of higher consumption of soda, sweetened fruit drinks, and water was associated with significantly greater odds of incident CKD (odds ratio tertile 3 versus 1 =1.61; 95% confidence interval, 1.07 to 2.41). Conclusions Higher consumption of sugar-sweetened beverages was associated with an elevated risk of subsequent CKD in this community-based cohort of black Americans

Relationship of Metabolic Syndrome With Incident Aortic Valve Calcium and Aortic Valve Calcium Progression: The Multi-Ethnic Study of Atherosclerosis (MESA)*

ObjectiveMetabolic syndrome (MetS) has been associated with increased prevalence of aortic valve calcium (AVC) and with increased progression of aortic stenosis. The purpose of this study was to determine whether MetS is associated with increased risks for the development of new ("incident") AVC or for progression of established AVC as assessed by CT.Research design and methodsThe relationships of MetS or its components as well as of diabetes to risks for incident AVC or AVC progression were studied among participants with CT scans performed at baseline and at either year 2 or year 3 examinations in the Multi-Ethnic Study of Atherosclerosis (MESA).ResultsOf 5,723 MESA participants meeting criteria for inclusion, 1,674 had MetS by Adult Treatment Panel III criteria, whereas 761 had diabetes. Among the 5,123 participants without baseline AVC, risks for incident AVC, adjusted for time between scans, age, sex, race/ethnicity, LDL cholesterol, lipid-lowering medications, and smoking, were increased significantly for MetS (odds ratio [OR] 1.67 [95% CI 1.21-2.31]) or diabetes (2.06 [1.39-3.06]). In addition, there was an increase in incident AVC risk with increasing number of MetS components. Similar results were found using the International Diabetes Federation MetS criteria. Among the 600 participants (10.5%) with baseline AVC, neither MetS nor diabetes was associated with AVC progression.ConclusionsIn the MESA cohort, MetS was associated with a significant increase in incident ("new") AVC, raising the possibility that MetS may be a potential therapeutic target to prevent AVC development

Population-Based Limits of Urine Creatinine Excretion

Introduction: The validity of a timed urine collection is typically judged by measurement of urine creatinine excretion, but prevailing limits may be unreliable. We sought to empirically derive population-based limits of excretion for evaluating the validity of a timed urine collection. Methods: Covariate and 24-hour urine data were obtained from 3582 participants in the Chronic Renal Insufficiency Cohort (CRIC) study, 814 participants in the Modification of Diet in Renal Disease (MDRD) study, 1010 participants in the Jackson Heart Study (JHS), and 8536 participants in the Prevention of Renal Vascular End Stage Disease (PREVEND) study. Weight, height, age, sex, and serum creatinine concentrations were evaluated as potential predictors of urine creatinine excretion using Akaike Information Criteria, R-squared values, and deviance. Bias and precision of the fitted models were assessed by analyses of residuals. Agreement between 24-hour creatinine clearance and 125I-iothalamate clearance was assessed before and after exclusion of potentially invalid urine samples. Results: A best-fitting model to predict 24-hour urine creatinine excretion among the 9199 discovery cohort members included sex-specific terms for weight, height, and age (R-squared = 0.328). This model had a median bias of +4.3 mg creatinine/day (95% confidence interval −5.6, +13.3 mg/day) in 4599 validation cohort members, and 82% of observed values were within 30% of predicted model. Serum creatinine concentrations only marginally improved model precision but reduced bias in persons with advanced chronic kidney disease (CKD). Conclusion: The limits of urine creatinine excretion derived here represent the most valid and representative data for appraising the adequacy of a timed urine collection

Kidney Function and Cognitive Health in Older Adults: The Cardiovascular Health Study

Recent evidence has demonstrated the importance of kidney function in healthy aging. We examined the association between kidney function and change in cognitive function in 3,907 participants in the Cardiovascular Health Study, recruited from 4 U.S. communities, and studied from 1992 - 1999. Kidney function was measured by cystatin C-based estimated glomerular filtration rate (eGFR[subscript cys]). Cognitive function was assessed using the Modified Mini-Mental State Exam and the Digit Symbol Substitution Test administered up to 7 times during annual visits. There was an association between eGFR[subscript cys] and change in cognitive function after adjustment for confounders; persons with eGFR[subscript cys] < 60 ml/min/1.73m² had a 0.64 (95% confidence interval: 0.51, 0.77) point/year faster decline in Modified Mini-Mental State Exam score and a 0.42 (95% confidence interval: 0.28, 0.56) point/year faster decline in Digit Symbol Substitution Test score compared with persons with eGFR[subscript cys] ≥ 90 ml/min/1.73m². Additional adjustment for intermediate cardiovascular events modestly impacted these associations. Participants with eGFR[subscript cys] < 60 ml/min/1.73m² had fewer cognitive impairment-free life-years on average compared with those with eGFR[subscript cys] ≥ 90 ml/min/1.73m², independent of confounders and mediating cardiovascular events (-0.44, 95% confidence interval: -0.62, -0.26). Older adults with reduced kidney function are at increased risk of worsening cognitive function.This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by the author(s) and published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. It can be found at: http://aje.oxfordjournals.org/Keywords: stroke, aging, myocardial infarction, successful aging, chronic kidney disease, cognitive function, prospective study, congestive heart failur

A Two-Biomarker Model Predicts Mortality in the Critically Ill with Sepsis.

RATIONALE: Improving the prospective identification of patients with systemic inflammatory response syndrome (SIRS) and sepsis at low risk for organ dysfunction and death is a major clinical challenge. OBJECTIVES: To develop and validate a multibiomarker-based prediction model for 28-day mortality in critically ill patients with SIRS and sepsis. METHODS: A derivation cohort (n = 888) and internal test cohort (n = 278) were taken from a prospective study of critically ill intensive care unit (ICU) patients meeting two of four SIRS criteria at an academic medical center for whom plasma was obtained within 24 hours. The validation cohort (n = 759) was taken from a prospective cohort enrolled at another academic medical center ICU for whom plasma was obtained within 48 hours. We measured concentrations of angiopoietin-1, angiopoietin-2, IL-6, IL-8, soluble tumor necrosis factor receptor-1, soluble vascular cell adhesion molecule-1, granulocyte colony-stimulating factor, and soluble Fas. MEASUREMENTS AND MAIN RESULTS: We identified a two-biomarker model in the derivation cohort that predicted mortality (area under the receiver operator characteristic curve [AUC], 0.79; 95% confidence interval [CI], 0.74-0.83). It performed well in the internal test cohort (AUC, 0.75; 95% CI, 0.65-0.85) and the external validation cohort (AUC, 0.77; 95% CI, 0.72-0.83). We determined a model score threshold demonstrating high negative predictive value (0.95) for death. In addition to a low risk of death, patients below this threshold had shorter ICU length of stay, lower incidence of acute kidney injury, acute respiratory distress syndrome, and need for vasopressors. CONCLUSIONS: We have developed a simple, robust biomarker-based model that identifies patients with SIRS/sepsis at low risk for death and organ dysfunction