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Record W4312030490 · doi:10.1016/j.bea.2022.100070

Influence of clinical hemodialysis membrane morphology and chemistry on protein adsorption and inflammatory biomarkers released: In-situ synchrotron imaging, clinical and computational studies

2022· article· en· W4312030490 on OpenAlexafffundabout
Zhu Sishi, Jumanah Bahig, Denis Kalugin, Ahmed Shoker, Ning Zhu, Amira Abdelrasoul

Bibliographic record

VenueBiomedical Engineering Advances · 2022
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsCanadian Light Source (Canada)St. Paul's HospitalUniversity of Saskatchewan
FundersSaskatchewan Health Research Foundation
KeywordsMembraneProtein adsorptionChemistryPolyacrylonitrileAdsorptionBiophysicsHuman serum albuminBlood proteinsMembrane proteinChromatographyBiochemistryOrganic chemistryPolymerBiology

Abstract

fetched live from OpenAlex

Hemodialysis (HD) is often accompanied with activation of biochemical cascade reactions, caused by undesirable protein adsorption, which results in severe consequences for HD patients. Present research aims to study three hemodialysis membranes currently available in the Canadian hospitals of polyacrylonitrile (PAN), polyethersulfone (PES) and polyvinylidene fluoride (PVDF) and their interaction with three main human serum proteins: Human Serum Ablumin (HSA), Human Serum Fibrinogen (FB) and Human Serum Transferrin (TRF). In-situ synchrotron-based X-ray tomography (SR-μCT) was used to evaluate main membranes characteristics such as fiber diameter, pore size and their distribution, as well as to study the process of protein adsorption on the membranes surface and along membrane matrices. Scanning Electron Microscopy (SEM) was also used to investigate the morphology of proteins deposited on the membrane surface. Furthermore, interaction of membranes with inflammatory biomarkers was studied. Collected blood samples from HD patients were analyzed using Luminex assay for the inflammatory biomarkers of Serpin/Antitrombin-III, Properdin, C5a, 1L-1α, 1L-1β, IL-6 and TNF-α. Our results indicate the difference in the interaction of membranes with proteins is mainly attributed to a membrane surface charge and membrane chemistry. PAN and PES membranes, which are possessing high negative charge (-41.5mV and -68mV) and polar surface groups, would alter the morphology of adsorbed HAS and FB, while PVDF surface does not affect protein crystals growth. Furthermore, the molecular docking results showed that the highest interaction energy of human serum proteins was with PES membrane. The increased interaction of PES membrane with human serum proteins has triggered inflammation reactions. The release of C5a, IL-6 and Serpin cytokines for PES membrane was drastically higher compared to PAN and PVDF. The relative increase in those cytokine concentrations was 400-2000% for PES membrane, while it decreased by 10-40% for PAN and PVDF. It is worth noted that PVDF membrane had the lowest charge of -2.5 mV among investigated membranes, and it possessed the lowest interaction with human serum proteins and the least inflammation response.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.306
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2022
Admission routes3
Has abstractyes

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