Artery-on-a-chip platform for automated, multimodal assessment of cerebral blood vessel structure and function
Bibliographic record
Abstract
We present a compact microfluidic platform for the automated, multimodal assessment of intact small blood vessels. Mouse olfactory artery segments were reversibly loaded onto a microfluidic device and kept under physiological (i.e., close to in vivo) environmental conditions. For immunohistochemical endpoint protein analysis, automated on chip fixation and staining of the artery eliminated the need for any subsequent tissue sectioning or processing outside the chip. In a first case study, we demonstrate the blood vessel abluminal structure based on the positions of smooth muscle cell nuclei, actin filaments and voltage gated calcium channels. In a second case study we incubated smooth muscle cells (SMCs) with a calcium-sensitive dye to simultaneously assess time-dependent, agonist-induced calcium and diameter changes of pressurized resistance arteries. We expect the presented microfluidic platform to promote routine on-chip staining and quantitative fluorescence imaging of intact blood vessels from different vascular beds, tissue engineered vascular constructs and vascularized microtissues. The at least tenfold reduction in required aliquot volumes for functional assessment and staining was achieved by on-board fluid manipulation of the syringe-pump free platform and may promote its applications for screening of newly synthesized compounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".