Characterization of the sensory, affective, cognitive, biochemical, and neuronal alterations in a modified chronic constriction injury model of neuropathic pain in mice
Bibliographic record
Abstract
The chronic constriction injury (CCI) of the sciatic nerve is a nerve injury-based model of neuropathic pain (NP). Comorbidities of NP such as depression, anxiety, and cognitive deficits are associated with a functional reorganization of the medial prefrontal cortex (mPFC). Here, we have employed an adapted model of CCI by placing one single loose ligature around the sciatic nerve in mice for investigating the alterations in sensory, motor, affective, and cognitive behavior and in electrophysiological and biochemical properties in the prelimbic division (PrL) of the mPFC. Our adapted model of CCI induced mechanical allodynia, motor, and cognitive impairments and anxiety- and depression-like behavior. In the PrL division of mPFC was observed an increase in GABA and a decrease in d-aspartate levels. Moreover an increase in the activity of neurons responding to mechanical stimulation with an excitation, mPFC (+), and a decrease in those responding with an inhibition, mPFC (-), was found. Altogether these findings demonstrate that a single ligature around the sciatic nerve was able to induce sensory, affective, cognitive, biochemical, and functional alterations already observed in other neuropathic pain models and it may be an appropriate and easily reproducible model for studying neuropathic pain mechanisms and treatments.
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".