Novel insights in hemodialysis: Most recent theories on membrane hemocompatibility improvement
Bibliographic record
Abstract
Kidney failure patients or patients with end-stage renal diseases (ESRD), need hemodialysis (HD) as a life-sustaining service. However, the morbidity and mortality rates are still high and unacceptable. Several academic efforts and various types of research have targeted the improvement of blood purification technologies in general and HD technologies more specifically. Major concerns are attributed to the incompatibility of the materials used in membrane filters as the main element of the HD systems. Elevated levels of cytokines and inflammatory markers as the result of bloodstream-membrane incompatibility, are attributed to several side-effects and cardiovascular shocks in the patients. Computational efforts along with new hypothesizes are published recently. The novel efforts in the field are suggesting a different behavior of water as the reason of hemocompatibility of the membrane. Accordingly, the membranes’ hydrophilicity or the thickness of the hydration layer (as the previous understanding of the field) is not responsible for the compatibility with the blood. New findings are suggesting that the stability of the water is more important to protect human serum proteins from interaction with the polymeric membranes. This critical review intends to cover the most recent understandings in the field with the goal of shedding light on the futures direction. This short review covers the new insight to hemocompatibility measure, i.e., intermediate water molecule and the mechanism of intermediate water-assisted hemocompatibility improvement. Furthermore, the review covers the nature of the human serum proteins from the dialysis point of view and how they get denatured by uremic metabolites. The role of hydration layer of both human serum proteins and the polymeric membranes is discussed. In addition, the probable hemocompatibility improvement framework is suggested.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".