Evidence map›Paper›PMID 36976062›Full record

ArticleJournal of functional biomaterials2023

A Novel Strategy for Enhanced Sequestration of Protein-Bound Uremic Toxins Using Smart Hybrid Membranes.

Madalena Lopes, Rita F Pires, Mónica Faria, Vasco D B Bonifácio

Abstract read
In one paragraph

Article in Journal of functional biomaterials, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Madalena LopesCenter of Physics and Engineering of Advanced Materials CeFEMA, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal.
Rita F PiresCenter of Physics and Engineering of Advanced Materials CeFEMA, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal.ORCID 0000-0003-3678-1462
Mónica FariaCenter of Physics and Engineering of Advanced Materials CeFEMA, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal.ORCID 0000-0003-3708-6242
Vasco D B BonifácioInstitute of Bioengineering and Biosciences (iBB) and Institute for Health and Bioeconomy (i4HB), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal.ORCID 0000-0003-2349-8473

Funding

Fundação para a Ciência e Tecnologia PTDC/MEC-ONC/29327/2017
6 · The paper itself

Abstract

Currently available hemodialysis (HD) membranes are unable to safely remove protein-bound uremic toxins (PBUTs), especially those bonded to human serum albumin (HSA). To overcome this issue, the prior administration of high doses of HSA competitive binders, such as ibuprofen (IBF), has been proposed as a complementary clinical protocol to increase HD efficiency. In this work, we designed and prepared novel hybrid membranes conjugated with IBF, thus avoiding its administration to end-stage renal disease (ESRD) patients. Two novel silicon precursors containing IBF were synthesized and, by the combination of a sol-gel reaction and the phase inversion technique, four monophasic hybrid integral asymmetric cellulose acetate/silica/IBF membranes in which silicon precursors are covalently bonded to the cellulose acetate polymer were produced. To prove IBF incorporation, methyl red dye was used as a model, thus allowing simple visual color control of the membrane fabrication and stability. These smart membranes may display a competitive behavior towards HSA, allowing the local displacement of PBUTs in future hemodialyzers.

Indexed as

chronic kidney diseasecompetitive bindinghemodialysismonophasic hybrid membranesprotein-bound uremic toxinssol-gel

Identifiers

PMID36976062
PMCPMC10059720

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.