Redistribution of the astrocyte phenotypes in the medial vestibular nuclei after unilateral labyrinthectomy
Bibliographic record
Abstract
Astrocytes are highly heterogeneous and involved in different aspects of fundamental functions in the central nervous system (CNS). However, whether and how this heterogeneous population of cells reacts to the pathophysiological challenge is not well understood. To investigate the response status of astrocytes in the medial vestibular nucleus (MVN) after vestibular loss, we examined the subtypes of astrocytes in MVN using single-cell sequencing technology in a unilateral labyrinthectomy mouse model. We discovered four subtypes of astrocytes in the MVN with each displaying unique gene expression profiles. After unilateral labyrinthectomy, the proportion of the astrocytic subtypes and their transcriptional features on the ipsilateral side of the MVN differ significantly from those on the contralateral side. With new markers to detect and classify the subtypes of astrocytes in the MVN, our findings implicate potential roles of the adaptive changes of astrocyte subtypes in the early vestibular compensation following peripheral vestibular damage to reverse behavioral deficits.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".