Regional mechanical properties of spinal cord gray and white matter in transverse section
Bibliographic record
Abstract
Understanding spinal cord injury requires a comprehensive knowledge of its mechanical properties, which remains debated due to the variability reported. This study aims to characterize the regional mechanical properties of the spinal cord in transverse sections using micro-indentation. Quasi-static indentations were performed on the entire surface of transverse slices obtained from 10 freshly harvested porcine thoracic spinal cords using a 0.5 mm diameter flat punch. No significant difference in average longitudinal elastic modulus was found between white matter (n = 183, E = 0.51 ± 0.21 kPa) and gray matter (n = 51, E = 0.53 ± 0.25 kPa). In the gray matter, the elastic modulus in the dorsal horn (0.48 ± 0.18 kPa) was significantly smaller than in the ventral horn (0.57 ± 0.24 kPa) (GLMM, p < 0.05). The elastic modulus in the dorsal horn was also significantly smaller than in the lateral (0.52 ± 0.22 kPa) and ventral funiculi (0.53 ± 0.18 kPa) of the white matter (GLMM, p < 0.05). However, there was no significant difference in the elastic modulus among the ventral, lateral and dorsal funiculi of the white matter (GLMM, p > 0.05). The average elastic modulus strongly varies between samples, ranging from 0.23 (±0.06) kPa to 0.79 (±0.18) kPa and the testing time postmortem was significantly associated with a decrease in elastic modulus (t = -5.2, p < 0.001). The spinal cord's white matter demonstrated significantly lower elastic modulus compared to published data on brain tissue tested under similar conditions. These findings enhance our comprehension of the mechanical properties of spinal cord white and gray matter, challenging the homogeneity assumption of current models.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".