Directed differentiation and direct reprogramming: Applying stem cell technologies to hearing research
Bibliographic record
Abstract
Hearing loss is the most widely spread sensory disorder in our society. In the majority of cases, it is caused by the loss or malfunctioning of cells in the cochlea: the mechanosensory hair cells, which act as primary sound receptors, and the connecting auditory neurons of the spiral ganglion, which relay the signal to upper brain centers. In contrast to other vertebrates, where damage to the hearing organ can be repaired through the activity of resident cells, acting as tissue progenitors, in mammals, sensory cell damage or loss is irreversible. The understanding of gene and cellular functions, through analysis of different animal models, has helped to identify causes of disease and possible targets for hearing restoration. Translation of these findings to novel therapeutics is, however, hindered by the lack of cellular assays, based on human sensory cells, to evaluate the conservation of molecular pathways across species and the efficacy of novel therapeutic strategies. In the last decade, stem cell technologies enabled to generate human sensory cell types in vitro, providing novel tools to study human inner ear biology, model disease, and validate therapeutics. This review focuses specifically on two technologies: directed differentiation of pluripotent stem cells and direct reprogramming of somatic cell types to sensory hair cells and neurons. Recent development in the field are discussed as well as how these tools could be implemented to become routinely adopted experimental models for hearing research.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".