Abstract WMP40: Intra-arterial Mesenchymal Stem Cells in a Large Animal Endovascular Canine Stroke Model: Findings on Serial Diffusion Tensor Imaging
Bibliographic record
Abstract
Background: Cell therapy is promising in rodent pre-clinical studies. Confirmation of efficacy of this novel approach in large animals is recommended. Diffusion Tensor Imaging (DTI) assesses white matter microstructure in the CNS using Fractional anisotropy (FA) and apparent diffusion coefficient (ADC). Diffusion Tensor Tractography (DTT) DTT produces a 3D representation of the white matter tracts (WMT) in which data are displayed on a colored map that helps to identify the integrity of the tracts selected under the region of Interest (ROI). The purpose of this study was to use DTI-DTT analysis, and correlate it with the neuro-assessment of dogs over one month after ipsilateral intra-arterial (IA) Mesenchymal Stem Cell (MSCs) 48 hours after a brain-stroke was reversible middle cerebral artery occlusion (rMCAo) Methods: Mongrel Hounds (n=5), aged 12-36 months, were included in this pilot study. rMCAo was induced via endovascular approach using a detachable helical ultra-coil over 35-80 min. MSCs (1-20 millions) were infused intra-arterially 48hrs post stroke in the ipsilesional cervical internal carotid. DTI-T images were obtained at 48h post rMCAo pre-IA cell delivery, 15 & 30 days. DTT was also generated. FA-ADC values of the right & left hemisphere and CST were determined. Weekly neuro-evaluations were performed using our canine neuro-scale Results: Our data suggest a correlation between the neurological recovery and improvement in the FA-ADC values (see figure). An increase in the caliber of corticospinal tracts ipsilateral to the stroke by day 30 compared to pre-injection was observed on the DTT reconstruction for two canines that showed neurological improvement ( see figure) Conclusions: Serial DTI-DTT imaging after IA cell therapy shows improvement in white matter tracts correlating with neurologic recovery. This supports further development of DTI-DTT biomarkers to measure neurologic recovery in experimental models as well as early clinical trials.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".