In Vivo Two-Photon Imaging of Axonal Dieback, Blood Flow and Calcium Influx withMethylprednisolone Therapy after Spinal Cord Injury
Bibliographic record
Abstract
Severe spinal cord injury (SCI) can cause neurological dysfunction and paralysis. However, the early dynamic changes of neurons and their surrounding environment after SCI are poorly understood. Although methylprednisolone (MP) is currently the standard therapeutic agent for treating SCI, its efficacy remains controversial. The purpose of this project was to investigate the early dynamic changes and MP's efficacy on axonal damage, blood flow, and calcium influx into axons in a mouse SCI model. YFP H-line and Thy1-GCaMP transgenic mice were used in this study. Two-photon microscopy was used for imaging of axonal dieback, blood flow, and calcium influx post-injury. We found that MP treatment attenuated progressive damage of axons, increased blood flow, and reduced calcium influx post-injury. Furthermore, microglia/macrophages accumulated in the lesion site after SCI and expressed the proinflammatory mediators iNOS, MCP-1 and IL-1β. MP treatment markedly inhibited the accumulation of microglia/macrophages and reduced the expression of the proinflammatory mediators. MP treatment also improved the recovery of behavioral function post-injury. These findings suggest that MP exerts a neuroprotective effect on SCI treatment by attenuating progressive damage of axons, increasing blood flow, reducing calcium influx, and inhibiting the accumulation of microglia/macrophages after 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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".