Hemocompatibility of Blood Purification Materials: Concepts, Mechanisms, and Characterization Techniques
Bibliographic record
Abstract
Since the mid-20th century, the development of different blood therapy modalities has ushered significant interest in blood-contacting materials. However, irrespective of the nature of the materials, whether they are synthetic or natural, most materials generate undesirable interactions with blood components unlike our blood vessels. These blood filtration materials often induce various forms of blood incompatibility, including hemolysis, platelet binding, coagulation, complement activation, and cell adhesion. These adverse reactions can further exacerbate patient outcomes as is evident from clinical studies. Thus, it is crucial to enhance the hemocompatibility of blood-contacting materials, and this effort begins with understanding how blood components interact with blood purification materials. In this chapter, we provide information on how blood-contacting materials can induce protein adsorption, hemolysis, coagulation, immune activation, cell adhesion, and the inter-connectivity between these biological pathways augmenting adverse blood interactions. Finally, we elaborate on the current standard methods used for assessing the hemocompatibility of blood-contacting materials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".