Vestibular compensation after unilateral labyrinthectomy: normal versus cerebellar dysfunctional mice.
Bibliographic record
Abstract
INTRODUCTION: Loss of vestibular information from one labyrinth produces marked asymmetries of postural and ocular motor control, which resolve over time. Recent developments in mouse genetic engineering, which allow the generation of transgenic and knockout mutant mice, provide a unique opportunity to bridge the gap between the molecular mechanisms that underlie compensation and behaviour. METHOD: We compared compensation following unilateral labyrinthectomy in wild-type mice and a cerebellar-dysfunctional mouse (the Lurcher mutant). The Lurcher mutant is characterized by a point mutation in the ionotropic glutamate receptor delta 2 subunit gene that results in loss of all Purkinje cells. To further investigate this question, we characterized vestibular compensation in a strain of mutant mice that completely lack cerebellar Purkinje cells. RESULTS: Static signs resolved within 24 hours in wild-type mice but did not fully resolve in Lurcher mice. Dynamic signs were evaluated by the quantitative analysis of vestibulo-ocular (VOR) and vestibulocollic (VCR) reflexes. The VOR assessed at 0.5 Hz exhibited increasing gain from day 1 to day 5, reaching control levels by day 20 for the wild-type mice. In contrast, Lurcher mutant mice showed significantly less compensation over this same period. VOR compensation in the mutant mice was slightly more robust in response to high acceleration thrusts but again never reached control levels. Similarly, VCR gains showed limited compensation and remained subnormal in mutant mice. CONCLUSION: Compensation for dynamic signs starts at day 5 after unilateral labyrinthectomy in normal mice. Cerebellar dysfunctional mutant mice do not compensate for static signs and show limited vestibular compensation for dynamic signs only. We conclude that other noncerebellar pathways for vestibular compensation exist, and our findings emphasize the need for these to be further explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".