The link between impact-induced tensile strain and dendritic spine morphology in porcine brain tissue
Bibliographic record
Abstract
Brain tissue as a material presents unique properties with a multitude of cell types and densities, varying degrees of axonal fiber diameters and blood vessels. These neural components are contained within a very viscous environment that upon impact, can result in a variety of tensile, compressive and rotational forces. The depths of the sulcus appear to be particularly vulnerable to biomechanical forces following an impact. The movement and subsequent forces loaded on to the brain have been shown to produce a variety of biomechanical responses that impair neurophysiological functioning at the cellular level. We recently reported a decrease in microtubule associated protein 2 (MAP2) within the depths of the porcine sulcus in an ex vivo model, along with elevated tensile strain in this region within 1 hour after impact. In the current work, using the same impact model, we explored whether changes in spine morphology and density occurred within the same timeframe following impact. The Golgi-Cox method was used to visualize dendritic spine morphology. Cortical pyramidal neurons within the depths and the arms of the sulcus were reconstructed. One hour after impact, there was a change in the distribution of spine type resulting in an increased proportion of mushroom-type spines compared to nonimpacted tissue. The increased proportion of mushroom-type spines was proportional to tensile strain measurements in the apical dendrites. These results demonstrate the sensitivity of dendritic spine morphology to tensile strain within the porcine cortex and suggest a state of hyperexcitability during the hyperacute phase following an impact.
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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 | 0.000 |
| 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 | 0.000 | 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".