Recent Developments and Current Challenges of Heparin-Grafted Hemodialysis Membranes
Bibliographic record
Abstract
Hemodialysis (HD) is a life-sustaining extracorporeal blood purifying treatment for end-stage renal disease (ESRD) patients. However, this membrane-based therapy is associated with acute side effects, life-threatening chronic conditions, and unacceptably high morbidity and mortality rates. Numerous surface coatings have been developed to improve the blood compatibility of biomaterials. Heparin is a widely used anticoagulant substance that increases the clotting time and increases the membrane hemocompatibility in terms of platelet adhesion and protein adsorption and anti-clotting activity. However, using heparin is challenging due to its severe or life-threatening side effects such as heparin-induced thrombocytopenia (HIT), in addition to heparin induced thrombocytopenia and thrombosis (HITT). In addition, heparin is strongly electronegative and exhibits a binding affinity for the positive active sites of human serum proteins, which is an additional challenge. Consequently, covalently immobilized heparin would create a more charged surface to induce more blood–membrane interactions, and consequently more adsorbed human serum proteins and biochemical pathway activations, which can negatively affect dialysis patients. Therefore, the current critical review has thoroughly focused on different heparin HD membrane systems, the challenges of heparin-coated dialysis membranes, and the factors affecting its hemocompatibility, in addition to the methods that can be used to enhance its hemocompatibility. Furthermore, this review summarizes the advantages and disadvantages of heparin-grafted methods. Furthermore, the influence of the heparin-immobilization method on the hemocompatibility and performance of the HD membrane was comprehensively analyzed. Finally, we conclude with the future perspectives for the strategies toward the heparinization and heparin-like/mimicking modification of membrane surfaces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".