Hearing Loss: Reestablish the Neural Plasticity in Regenerated Spiral Ganglion Neurons and Sensory Hair Cells 2020
Bibliographic record
Abstract
Hearing loss in one of the most common sensory disorders among people around the world, and it has often been referred to as an "invisible disability." Globally, over 1.5 billion people are currently experiencing hearing loss to some degree in 2021, accounting for 20% of the world's population, of which, an estimated 430 million have hearing loss of moderate or higher severity in the better hearing ear, and this number could increase to 2.5 billion by 2050 according to WHO report. Multiple risk factors could contribute to one's hearing capacity during his/her lifetime course, including genetic factors, ototoxic chemicals, noise exposure, trauma to the ear or head, and age-related degeneration. Among all cases of hearing disorders, 85% of them are under the category of sensorineural hearing loss (SNHL). Although SNHL is induced by various factors through different approaches and mechanisms, the major cause for SNHL is irreversible loss of either inner ear hair cells (HCs) or degeneration of spiral ganglion neurons (SGNs). At present, there is no effective treatments for SNHL available, thus, it is not curable yet. In recent years, many studies are dedicated to working on the regenerative capacity of developing and functional HCs and SGNs, and promising results on animal models present us with the potential of regenerating HCs and SGNs by gene therapy, stem cell induction, and signaling pathway manipulation. This raises the possibility of curing SNHL in the foreseeable future. In 2017 and 2018, we have publishes two special issues of "Hearing Loss:
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".