High Wall Shear Incites Cerebral Aneurysm Formation and Low Wall Shear Stress Propagates Cerebral Aneurysm Growth
Bibliographic record
Abstract
This review discusses mechanisms for the development of cerebral aneurysms. Endothelial cells exhibit a variety of structural and functional changes when they come into contact with normal laminar flow. In response to laminar shear stress, endothelial cells modify their potassium ion channels, go through cytoskeletal rearrangements and shape modifications and create prostacyclin. In cerebral arteries, aneurysmal dilatation most frequently starts at locations with substantial wall shear stress, which include arterial bifurcations and vascular branch sites, where blood flow abruptly switches to turbulent flow. At this point, high shear stress frequently arises, placing increased strain on the vasculature. As the vascular branch points and arterial bifurcations are the initial sites of cerebral aneurysm genesis, this helps confirm the role of high wall shear stress in the development of cerebral aneurysms. Low wall shear stress increases the initial proinflammatory effect already present in the vasculature, which furthers the formation of cerebral aneurysms. In fact, regions of aneurysmal regions with low wall shear stress grow more quickly and are more prone to rupture compared to regions with high wall shear stress. Therefore, it seems plausible to assume that turbulent blood flow inside a dilated cerebral aneurysm causes low wall shear stress, thereby encouraging aneurysmal growth. J Neurol Res. 2023;13(1):1-11 doi: https://doi.org/10.14740/jnr749
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".