Automated Method for Tracking Individual Red Blood Cells Within Capillaries to Compute Velocity and Oxygen Saturation
Bibliographic record
Abstract
OBJECTIVE: The authors present a new method to track individual red blood cells (RBCs) as they move through capillaries. This method uses a recently developed Measurement and Analysis System for Capillary Oxygen Transport (MASCOT) and the concept of space-time images to track RBCs between consecutive frames of video recordings of the microcirculation. METHODS: A space-time image displays in a single static image for a single capillary the location of all RBCs as a function of time. Analysis is performed on video tapes of RBC flow through capillaries to obtain velocity of individual cells as they traverse the capillary of interest. A space-time image is generated to track RBCs from one frame to the next and their velocities are computed. Based on the optical density values of each cell obtained from synchronized videotapes at two wavelengths, the oxygen saturation of a cell can be determined. In this manner, oxygen saturation can be tracked for the same cells as they move through the capillary. RESULTS AND CONCLUSIONS: These measurements, taken together, allow one to determine how much and how fast oxygen is being delivered to the surrounding tissue. This method provides, for the first time, a way to track individual RBCs flowing through capillary networks and study their RBC dynamics and oxygenation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.004 | 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".