Development and validation of a novel microfluidic device for the manipulation of skeletal muscle microvascular blood flow in vivo
Bibliographic record
Abstract
Abstract Objective To develop and validate a novel liquid microfluidic approach to deliver drugs to microscale regions of tissue while simultaneously allowing for visualization and quantification of microvascular blood flow. Methods Microfluidic devices were fabricated using soft lithographic techniques, molded in polydimethylsiloxane, and bound to a coverslip with a 600 × 300 μm micro‐outlet. Sprague‐Dawley rats, anesthetized with pentobarbital, were instrumented to monitor systemic parameters. The extensor digitorum longus muscle was dissected, externalized, and reflected across the device mounted on the stage of an inverted microscope. Doses (10−8 to 10−3 M) of adenosine triphosphate (ATP), acetylcholine, and phenylephrine (PE) were administered to the muscle via perfusion through the device. Microvascular blood flow directly overlying the micro‐outlet was recorded at multiple focal depths. Red blood cell (RBC) velocity, supply rate, and hematocrit were measured from recordings. Results ATP significantly increased RBC velocity and supply rate. Increasing concentrations of PE caused a decrease in RBC velocity and supply rate. Perfusion changes were restricted to areas directly overlying the micro‐outlet and within 500 μm. Conclusions This novel microfluidic device allows for a controlled delivery of dissolved substances to constrained regions of microvasculature while simultaneously allowing for visualization and measurement of blood flow within discrete vessels and networks.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".