Student Competition (Knowledge Generation) ID 1987548
Bibliographic record
Abstract
Background Degenerative cervical myelopathy (DCM) is the most common form of spinal impairment worldwide and is caused by a series of degenerative changes that compress the spinal cord. DCM can be treated with surgical decompression (DEC), but recovery is impaired by secondary injury, such that 44% of patients who undergo DEC never regain lost function, and 9% experience persistent decline. The fractalkine receptor, CX3CR1, is expressed by monocyte-derived macrophages and CNS-resident microglia, where it mediates communication with neurons expressing its ligand, CX3CL1, and maintains phagocytic, migration, and injury response functions. Objective The purpose of this study is to determine the role of fractalkine in degeneration and post-surgical injury. We hypothesize that CX3CR1 expression is elevated by DCM and DEC and that inhibiting it may attenuate inflammation and improve functional outcomes. Methods To determine this, I am using a mouse model of C5-6 myelopathy in Cx3cr1-knockout and wildtype mice to produce functional degeneration and neuropathic pain across a 12-week period. Results Immunostaining of spinal tissue from myelo-pathic wildtype mice indicates upregulation of CX3CR1 throughout DCM and confirms this expression to be mediated by resident microglia. Inhibiting fractalkine signalling attenuates neuropathic pain across DCM progression. Following DEC, these benefits are reversed, and knockouts experience more pain up to 5 weeks post-surgery, suggesting a critical role for CX3CR1 in mediating secondary injury. Conclusions My findings thus far indicate a significant role played by fractalkine signaling in DCM and DEC recovery and posit microglia as a target for therapeutic intervention.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.967 | 0.896 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".