Circumferential Alignment of Muscle Cells in the Tunica media of the Human Brain Artery
Bibliographic record
Abstract
Our purpose was to measure how structurally coordinated is the network of muscle cells in the brain artery. Vessels from 7 autopsies were fixed with glutaraldehyde and formalin at physiologic pressure. We embedded each artery alongside a block of liver, formed into a rectangular prism, prepared it for light microscopy and stained the sections with haematoxylin and eosin (HE). The angle of cutting the arterial segments was determined with the aid of the block of liver tissue as a Cartesian reference. We measured the directional alignment of vascular smooth muscle using the centrally located nucleus as a vector of orientation. The end coordinates of the profiles of the nuclei (appearing dark with the HE stain) were recorded on a digitizer tablet, and analysis was done as suggested by a previous modelling study. The method provides an average alignment from the collective measurements on the hundreds of nuclei in each histological section. Data from 10 arteries (approximately 22,000 nuclei) from 17 sections showed that brain arteries have highly oriented medial muscle cells aligned circumferentially (average magnitude of 1.3 +/- [SD] 1.5 degrees from true cross section), with a helical variation (along the artery) of +/- 7.9 degrees and a variation in the spiral direction of +/- 5.4 degrees, i.e. a three-dimensional variation from nucleus to nucleus of +/- 10 degrees.
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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 | 0.000 | 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".