Hearing Loss: Reestablish the Neural Plasticity in Regenerated Spiral Ganglion Neurons and Sensory Hair Cells 2018
Bibliographic record
Abstract
Hearing loss is a common sensory disorder that has been a serious concern globally.It is recently estimated that around 466 million people worldwide have disabling hearing loss, including 34 million children, and this number will increase to over 900 million by 2050 (according to WHO report 2018).The majority of hearing disorders occur due to the death of either inner ear hair cells (HCs) or spiral ganglion neurons (SGNs), thus leading to sensorial neural hearing loss (SNHL).SNHL is known as the most common form of hearing disorder that comprises about 85% of all hearing loss cases.This type of damage is induced by a variety of reasons such as inner ear trauma, ischemia, ototoxic drugs, noise exposure, inflammation, viral infections, genetic deficits, autoimmunologic reaction, and aging.SNHL is generally not reversible due to the lack of regeneration capacity of HCs and SGNs.However, various recent studies have determined that the HCs and SGNs hold a regenerative potential and it is possible to find the cure for SNHL in the near future.This is supported by the clear understanding of the genetical control and signaling pathways involved in the development of HCs and SGNs and their functions, the regenerative potential of residing adult stem cells, and the development of gene therapy and the clinical trials of new pharmaceutical compounds on damaged cochleae.Last year, we have published the first special issue of "Hearing Loss: Reestablish the Neural Plasticity in Regenerated Spiral Ganglion Neurons and Sensory Hair Cells", and this year in this second special issue, we are presenting a new series of articles to report the most recent advances in several major areas as summarized below: HC development, HC damage and protection, HC regeneration, SGN development and protection, inherited hearing loss, and inner ear drug delivery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".