Revealing heterogeneity and damage response in the adult human utricle
Bibliographic record
Abstract
The human utricle is a vestibular organ essential for balance, a function that declines with age. With the aging population projected to double to 2 billion by 2050 and no pharmaceutical or biological treatments available, balance disorders represent a significant unmet medical need. The utricle is composed of sensory and non-sensory cells, which are closely related. Non-sensory cells have limited capacity to regenerate sensory cells and, therefore, are a relevant therapeutic target. In this work, we profile the cellular and transcriptional landscape of the adult human utricle and its early response to ototoxic damage using bulk and single-cell RNA-sequencing of patient-derived samples. We identify six transcriptionally distinct non-sensory cell types, including a previously uncharacterized supporting cell-like population, demonstrating utricular heterogeneity. Following aminoglycoside-induced damage, we detect early transcriptional changes consistent with a capacity to respond to ototoxic damage within 24 hours and potentially initiate a regenerative response via an early-responding cell population, providing a foundation for regenerative strategies for balance recovery. Utricle, a component of the inner ear vestibular system, is essential for balance. This study maps the cells and genes in the adult human utricle. It identifies distinct non-sensory cell types and reveals early responses to damage, providing insight into potential strategies for restoring balance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".