A polymer chip‐based technology for the investigation of small resistance arteries
Bibliographic record
Abstract
Growing consensus links hypertension, a primary cardiovascular risk factor, to elevated peripheral resistance. Advanced research on peripheral resistance arteries (RA) is therefore essential to improve our knowledge regarding hypertension and existing treatment strategies. Current experimental approaches (i.e., pressure myography) are time intensive, not high throughput ready and require highly skilled personnel. To overcome these limitations, we have designed and fabricated a microfluidic device that allows RAs to be reversibly loaded, fixed, and perfused on a polymer chip (AoC). The current study aims to validate the AoC in relation to the classical cannulation approach. Dose response curves for phenylephrine (PE) and acetylcholine (ACh) were virtually identical for mesenteric RAs tested on the AoC and those studied on a conventional pressure myograph. Arteries kept in culture on the AoC for 24h prior to functional testing showed fully maintained responses to PE and ACh. On‐chip vessel loading with Fura‐2 revealed irregular Ca2+ oscillations that were synchronized following PE treatment. This study presents a fast, easy to manage, low cost, scalable and high throughput‐ready technology that will drastically facilitate microvascular research. Based on its innovative features, the AoC has great potential for application in drug development and on‐site clinical use (personalized medicine).
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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 | 0.001 | 0.000 |
| 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 | 0.002 |
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".