[<sup>18</sup>F]SynVesT-1 PET Detects SV2A Changes in the Spinal Cord and Brain of Rats with Spinal Cord Injury
Bibliographic record
Abstract
Traumatic spinal cord injury (SCI) is a devastating neurologic condition lacking effective prognostic and treatment methods. PET imaging of synaptic vesicle glycoprotein 2A (SV2A) has been used in measuring synapse changes. We explore the feasibility of using [18F]SynVesT-1 PET to detect the synaptic changes in a rat model of SCI. Methods: [18F]SynVesT-1 PET scans were performed on rats with T7 moderate contusion injury (n = 9) and sham controls (n = 7) on day 1 and days 9–11 after injury. The simplified reference region method 2 was used to compute the distribution volume ratios (DVRs) for the spinal cord (SC) and the brain, with the cervical cord and brain stem as the reference region, respectively. The averaged SUV ratio 30–60 min after injection was calculated as an alternative outcome measure. Diffusion tensor imaging (DTI) was used to evaluate axonal changes on post mortem SCs. Western blotting, immunohistochemical staining, and immunofluorescence staining were used to confirm the imaging results. Results: [18F]SynVesT-1 showed the highest uptake in the cervical SC. Notably, the DVR at the injury epicenter in SCI rats showed a 61% decrease on day 1 and a 53% decrease on days 9–11, compared with sham controls. The changes in SUV ratio 30–60 min after injection were consistent with the changes in DVR. The fiber damage in the epicenter was identified by DTI, and the loss of SV2A was confirmed by immunohistochemical staining and Western blotting. Further, the amygdala, limbic insular cortex, and cerebellum were found to be significantly affected by the SCI on day 1 by PET. The DTI analysis revealed fiber damage in the internal capsule and somatosensory cortex. Conclusion: [18F]SynVesT-1 PET effectively identified synapse loss in the contusion SCI rat model. The quantification of synaptic density through SV2A PET presents a promising objective metric for evaluating novel therapeutics for SCI.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".