Tumor Necrosis Factor Alpha Enhances Glutamatergic Transmission onto Spinal Motoneurons
Bibliographic record
Abstract
The early stages of spinal cord injury (SCI) start with excitotoxic damage caused by a massive release of glutamate. However, glutamate release is not the only factor to consider. Inflammatory molecules like tumor necrosis factor alpha (TNFalpha), belonging to a group of cytokines initially identified and named for their ability to kill tumor cells, is also a key factor in neuronal death and inflammation. TNFalpha is released from macrophages and activated microglia following a SCI, reaching a peak 1 h after the primary injury. Motoneurons whose survival is necessary for successful rehabilitation are especially vulnerable to the effects of TNFalpha release. While TNFalpha has been postulated to increase glutamatergic synaptic transmission, evidence for this has been indirect. Here, we show using whole-cell recording from lumbar motoneurons that AMPA and NMDA receptor-mediated excitatory postsynaptic currents are rapidly increased following bath application of TNFalpha. Concurrently, the single-channel open probability of AMPA and NMDA channels were also augmented by TNFalpha. Overall, our data lead us to propose the idea that motoneuronal vulnerability to excitotoxicity is not only due to the excessive release of glutamate, but may also be attributable to the increased sensitivity of AMPARs and NMDARs to the proinflammatory factor, TNFalpha, released 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 | 0.000 | 0.000 |
| 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".