Effect of intercellular collisions on red blood cell membrane damage
Bibliographic record
Abstract
Modelling blood flow and particularly cellular damage induced by supra- and non-physiological blood-flow conditions is crucial when developing novel blood-exposed biomedical devices and treatments. Blood is composed of 30-50% red blood cells (RBCs) by volume, yet the mechanisms and effects of intercellular collisions are frequently neglected in red blood cell damage models. These effects are investigated by employing fully coupled 3D fluid structure-interaction simulations to simulate the collision processes in a Couette shear flow and to gauge their effect on the strain experienced by the RBC membrane as well as the transmembrane hemoglobin diffusion rate. Intercellular collisions are found to nearly double the membrane strain at hemolytic shear rates, with declining effect as the shear rate increases, and have a similar effect on sublethal hemoglobin diffusion. Viscoelastic simulations were conducted to examine the effect of incorporating membrane viscosity on the strain experienced by red blood cell membrane during collisions, and find minimal impact of incorporating viscoelasticity at high shear. Incorporating the effect of intercellular collisions is found to be a crucial factor for predicting stress-induced cellular damage under dynamic conditions.
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 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".