Computational Model of Protein-Bound Uremic Toxins Kinetics During Hemodialysis and Dose Evaluation of Accurate Dialysis Using Deep Learning Techniques

Notice of Violation of IEEE Publication Principles

“Computational Model of Protein-Bound Uremic Toxins Kinetics During Hemodialysis and Dose Evaluation of Accurate Dialysis Using Deep Learning Techniques”
by Jing Huang
in IEEE Access, April 2020

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper has copied content from the papers cited below. The original content was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title).

“Removal of Protein-Bound Uremic Toxins during Hemodialysis Using a Binding Competitor”
by Magdalena Madero, Karla B. Cano, Israel Campos, Xia Tao, Vaibhav Maheshwari, Jillian Brown, Beatriz Cornejo, Garry Handelman, Stephan Thijssen, and Peter Kotanko
in Clinical Journal of the American Society of Nephrology, 14, 2019

“A Novel Mathematical Model of Protein-bound Uremic Toxin Kinetics during Hemodialysis”
by Vaibhav Maheshwari, Stephan Thijssen, Xia Tao, Doris Fuertinger, Franz Kappel, Peter Kotanko
in Scientific Reports, 7, 2017


The protein-bound uremic toxins (PBUT), evolving to avoid conventional hemodialysis, decreases the toxin-free dissemination by a higher degree of binding of proteins, in turn increasing the dialyzer redirected membranes from it. Therefore, PBUT kinetics mechanical understanding can open ways of improving dialytic removal. A robust PBUT kinetic model has been developed, which consists of the various levels of compartment and dialyzer. This model represents a dynamic balance between protein, toxin and complex protein toxins. Further, this model has been calibrated and validated through literature, clinical evidence and studies is presented in this paper with numerical results. This anticipates key aspects of PBUT kinetic, which includes free and binding PBUT concentration profiles, where the dialytic variance PBUT has the elimination of dialysis rate effect. A popular Deep learning (DL) integrated PBUT kinetic model has been used in the elimination of non-dialysis dose evaluation conditions. The new DL-PBUT algorithm removes interruptions, helps to estimate the dialysis quality parameter (MR/Y), online with considerable precision with minimized delay and more efficiently than the known algorithms. The test results have been computed for various datasets which has been analyzed from the patients at lab scale shows promising outcomes.