Perineural dexamethasone attenuates liposomal bupivacaine-induced delayed neural inflammation in mice in vivo
Bibliographic record
Abstract
BACKGROUND: Liposomal bupivacaine (Exparel®) is a sustained-release formulation of bupivacaine for use in surgical infiltration anaesthesia. We analysed the histological nerve toxicity and clinical effectiveness of perineural Exparel® alone or with added dexamethasone in a mouse model. METHODS: We assigned 98 mice receiving a perineural sciatic nerve injection into seven groups: sham (n=14, perineural saline), B (n=14, perineural bupivacaine), BDIP (n=14, perineural bupivacaine + intraperitoneal dexamethasone), BDPN (n=14, perineural bupivacaine + perineural dexamethasone), E (n=14, perineural Exparel®), EDIP (n=14, perineural Exparel® + intraperitoneal dexamethasone), and EDPN (n=14, perineural Exparel® + perineural dexamethasone). The duration of thermoalgesic and motor block was evaluated in 49 mice (seven mice randomly selected by group) every 30 min until recovery. Mice were killed for sciatic nerve histological assessment at 14 or 28 days. RESULTS: The median duration of motor block was 90, 120, 120, 120, 180, and 180 min and the duration of thermoalgesic block was 240, 300, 360, 360, 360, and 420 min for groups B, BDIP, BDPN, E, EDIP, and EDPN, respectively. The B group mice showed mild neural inflammation at 14 days and the E group mice showed mild neural inflammation at 28 days. Addition (intraperitoneal or perineural) of dexamethasone reduced neural inflammation induced by bupivacaine, whereas only perineural dexamethasone reduced neural inflammation induced by Exparel®. CONCLUSIONS: Perineural or systemic dexamethasone had a protective effect against the neural inflammation induced by bupivacaine, and perineural dexamethasone attenuated delayed inflammation induced by perineural Exparel®.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".