Blood cell assisted in vivo Particle Image Velocimetry using the confocal laser scanning microscope
Bibliographic record
Abstract
We demonstrated the feasibility of blood cell assisted in vivo Particle Image Velocimetry using confocal microscopy. Blood flow of skin vessel in a mouse was non-invasively imaged in vivo using a confocal microscopy. The video-rate confocal microscope was used to monitor the motion of the blood cells in the capillary of a live mouse ear. The home-built confocal laser scanning microscopy allowed us to take images at the acquisition rate of 30 frames per second. The individual blood cells could be distinguished from other cells and the trajectory of the each cell could be followed in the sequential images. The acquired confocal images were used to get the velocity profile of the in vivo blood flow in conjunction with the Particle Image Velocimetry (PIV), without injecting any exogenous nano/micro particles into the mouse. We were able to measure the blood velocity up to a few hundreds µm/sec for various vessels in a live mouse. Because there is no need for the injection of the exogenous tracing particles, it is expected that we could apply the current technology to the study of human capillary blood stream.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".