In vitro Experiments of Dialysate Regeneration Unit on Waste Dialysis Fluid

Putrya, B. M.; Zhilo, Nikita M.; Baklanova, Anastasia V.; Bazaev, N. A.

doi:10.1109/eiconrus.2019.8656711

Cited by 2 publications

(1 citation statement)

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“…The regeneration unit for dialysates is an important part of the portable artificial renal, where its principal purpose being to purify dialysates from uremic contaminants, to regulate pH, to correct the electrolyte concentration and osmotic concentration [19], [20]. Here, authors proposed to combine sorbent and electrochemical techniques for dialysis regeneration.…”

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confidence: 99%

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

Huang

2020

IEEE Access

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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. INDEX TERMS Deep learning, uremic toxins, kinetic, complex protein toxins, clinical evidence.

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confidence: 99%