Background: Hepatitis B vaccination is recommended for chronic kidney disease (CKD) patients before starting dialysis. We performed an analyis aimed to describe the clinical and biological parameters related to the success of vaccination in CKD patients before starting dialysis.
Methods: We extracted data of 170 non-dialyzed patients who were offered hepatitis B vaccination from a register. They received a first vaccination of 40μg followed by boosters after one, two and six months. Patients were considered protected if their hepatitis B antibody level was >10IU/L, three months apart. A logistic regression and a Bayesian model were used to describe the relationships between variables and the success of vaccination.
Results: Vaccination protected 50.6% of the patients. Model adjustment to the data was higher using the Bayesian model compared to the logistic regression (with area under the ROC curve of 0.955±0.007 vs 0.775±0.066 respectively). The Bayesian model's robustness studied using a 10 fold cross validation showed a percentage of misclassified subjects of 12.4±1.8%, a sensitivity of 87.7±0.3%, a specificity of 87.5±0.3%, a positive predictive value of 87.8±0.3% and negative predictive value of 87.4±0.2%. As classified by the Bayesian model, the variables most related to successful vaccination were, in descending order: age, eGFR, protidemia, albuminemia, cause of renal failure, gender, previous vaccination and weight.
Conclusion: The Bayesian network confirmed that both kidney function and nutritional status of patients are important factors to explain the success of vaccination against hepatitis B in CKD patients before dialysis. For research purposes, before an external validation, the network can be used online at www.hed.cc/?s=Bhepatitis&n=ReseauhepatiteBsup10.neta.
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http://dx.doi.org/10.1016/j.nephro.2019.02.010 | DOI Listing |
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The proliferation of sophisticated counterfeiting poses critical challenges to global security and commerce, with annual losses exceeding $2.2 trillion. This paper presents a novel physics-constrained deep learning framework for high-precision security ink colorimetry, integrating three key innovations: a physics-informed neural architecture achieving unprecedented color prediction accuracy (CIEDE2000 (ΔE00): 0.
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Airborne transient electromagnetic (ATEM) surveys provide a fast, flexible approach for identifying conductive metal deposits across a variety of intricate terrains. Nonetheless, the secondary electromagnetic response signals captured by ATEM systems frequently suffer from numerous noise interferences, which impede effective data processing and interpretation. Traditional denoising methods often fall short in addressing these complex noise backgrounds, leading to less-than-optimal signal extraction.
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Institute of Autmatic Control, University of Kaiserslautern-Landau, 67653 Kaiserslautern, Germany.
Harsh operating conditions imposed by vehicular applications significantly limit the utilization of proton exchange membrane fuel cells (PEMFCs) in electric propulsion systems. Improper/poor management and supervision of rapidly varying current demands can lead to undesired electrochemical reactions and critical cell failures. Among other failures, flooding and catalytic degradation are failure mechanisms that directly impact the composition of the membrane electrode assembly and can cause irreversible cell performance deterioration.
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