Spinal Cord Blood Flow and Blood Vessel Permeability Measured by Dynamic Computed Tomography Imaging in Rats after Localized Delivery of Fibroblast Growth Factor
Bibliographic record
Abstract
Following spinal cord injury, profound vascular changes lead to ischemia and hypoxia of spinal cord tissue. Since fibroblast growth factor 2 (FGF2) has angiogenic effects, its delivery to the injured spinal cord may attenuate the tissue damage associated with ischemia. To limit systemic mitogenic effects, FGF2 was delivered to the spinal cord via a gel of hyaluronan and methylcellulose (HAMC) injected into the intrathecal space, and compared to controls receiving HAMC alone and artificial cerebrospinal fluid (aCSF) alone. Dynamic perfusion computed tomography (CT) was employed for the first time in small animals to serially measure blood flow and permeability in the injured and uninjured spinal cord. Spinal cord blood flow (SCBF) and permeability-surface area (PS) measurements were obtained near the injury epicenter, and at two regions rostral to the epicenter in animals that received a 26-g clip compression injury. As predicted, SCBF measurements decreased and PS increased after injury. FGF2 delivered via HAMC after injury restored SCBF towards pre-injury values in all regions, and increased blood flow rates at 7 days post-injury compared to pre-injury measurements. PS was stabilized at regions rostral to the epicenter of injury when FGF2 was delivered with HAMC, with significantly lower values than aCSF controls at 7 days in the region farthest from the epicenter. Laminin staining for blood vessels showed a qualitative increase in vessel density after 7 days when FGF2 was locally delivered. Additionally, permeability stains showed that FGF2 moderately decreased permeability at 7 days post-injury. These data demonstrate that localized delivery of FGF2 improves spinal cord hemodynamics following injury, and that perfusion CT is an important technique to serially measure these parameters in small animal models of spinal cord injury.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".