Alteration of Primary Afferent Activity following Inferior Alveolar Nerve Transection in Rats
Bibliographic record
Abstract
BACKGROUND: In order to evaluate the neural mechanisms underlying the abnormal facial pain that may develop following regeneration of the injured inferior alveolar nerve (IAN), the properties of the IAN innervated in the mental region were analyzed. RESULTS: Fluorogold (FG) injection into the mental region 14 days after IAN transection showed massive labeling of trigeminal ganglion (TG). The escape threshold to mechanical stimulation of the mental skin was significantly lower (i.e. mechanical allodynia) at 11-14 days after IAN transection than before surgery. The background activity, mechanically evoked responses and afterdischarges of IAN Adelta-fibers were significantly higher in IAN-transected rats than naive. The small/medium diameter TG neurons showed an increase in both tetrodotoxin (TTX)-resistant (TTX-R) and -sensitive (TTX-S) sodium currents (INa) and decrease in total potassium current, transient current (IA) and sustained current (IK) in IAN-transected rats. The amplitude, overshoot amplitude and number of action potentials evoked by the depolarizing pulses after 1 muM TTX administration in TG neurons were significantly higher, whereas the threshold current to elicit spikes was smaller in IAN-transected rats than naive. Resting membrane potential was significantly smaller in IAN-transected rats than that of naive. CONCLUSIONS: These data suggest that the increase in both TTX-S INa and TTX-R INa and the decrease in IA and Ik in small/medium TG neurons in IAN-transected rats are involved in the activation of spike generation, resulting in hyperexcitability of Adelta-IAN fibers innervating the mental region after IAN transection.
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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 | 0.000 | 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".