Computational Analysis of a Microfluidic Device for Measuring Oxygen‐Dependent ATP Release from Erythrocytes
Bibliographic record
Abstract
Release of adenosine triphosphate (ATP) from erythrocytes is believed to be a key component of the system for regulating microvascular blood flow. Recently, shear rate‐dependant ATP release from erythrocytes was measured using a microfluidic device (Wan et al, PNAS, 2008). These authors found ATP release occurred within 25–75ms following increased shear. The present work seeks to apply a similar approach to oxygen‐dependant ATP release, the dynamics of which are thought to be important in regulation of microvascular O2 delivery. Our computational model allows for the analysis of such a system to: i. design the optimal device to measure ATP release and ii. quantitatively interpret the resulting output signal. A computational model was constructed based on hemodynamics, convective‐diffusive transport of O2 and ATP, ATP/luciferin‐luciferase reaction kinetics, and optics. The computational model shows erythrocyte O2 levels can be altered rapidly enough to measure a delay in ATP release of 25ms. We show that this computational model is appropriate for analyzing properties associated with erythrocyte ATP release including the relationship between hemoglobin O2 saturation and magnitude of ATP release.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".