Influence of clinical hemodialysis membrane morphology and chemistry on protein adsorption and inflammatory biomarkers released: In-situ synchrotron imaging, clinical and computational studies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".