Optical method to determine in vivo capillary hematocrit, hemoglobin concentration, and 3‐D network geometry in skeletal muscle
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop a tool to visualize and quantify hemodynamic information, such as hemoglobin concentration and hematocrit, within microvascular networks recorded in vivo using intravital video microscopy. Additionally, we aimed to facilitate the 3-D reconstruction of the microvascular networks. METHODS: Digital images taken from an intravital video microscopy preparation of the extensor digitorum longus muscle in rats for 25 capillary segments were used. The developed algorithm was used to delineate capillaries of interest, calculate the optical density for each pixel in the image, and reconstruct the 3-D capillary geometry using the calculated light path-lengths. Subsequently, the mean corpuscular hemoglobin concentration (MCHC), hemoglobin concentration, and hematocrit for these capillaries were calculated. We evaluated the hematocrit values determined by our methodology by comparing them to those obtained using a previously published method. RESULTS: (25) = .92, p < .001, and demonstrated excellent agreement with a mean difference of 1.3% and a coefficient of variation (CV) of 11%. The average MCHC, hemoglobin concentration, and light path-lengths were 23.83 g/dl, 8.06 g/dl, and 3.92 µm, respectively. CONCLUSION: The proposed methodology can quantify hemodynamic measurements and produce functional images for visualization of the microcirculation in vivo.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".