Author response: Neuronal sources of hedgehog modulate neurogenesis in the adult planarian brain
Bibliographic record
Abstract
Most animals can continue to generate and add new neurons in their nervous system into adulthood, though the process is often tightly regulated. In adult humans, only a small number of neurons are made or lost, such that the fewer than 2% of the neurons in the nervous will change over, or “turnover”, the course of a year. The turnover of neurons in some other animals is much higher than it is in humans. A freshwater flatworm, called Schmidtea mediterranea, is one example of such an animal that can even regenerate an entirely new brain if its head is decapitated. These flatworms have a large population of adult stem cells, which makes these high rates of neuron production and regeneration possible. However, it is largely unknown if this population contains stem cells that can only become new neurons, in other words “dedicated neuronal stem cells”. Moreover, it is also not clear what kinds of signals communicate with these stem cells to promote the production of new neurons. In animals from flies to humans, a signaling molecule encoded by a gene called hedgehog forms part of a signaling pathway that can promote neuron production during development. Therefore, Currie et al. asked if the hedgehog signaling molecule might communicate with the stem cells in adult flatworms to control how many new neurons they produce. The experiments revealed that the hedgehog signaling molecule is almost exclusively produced by the flatworm’s brain and the pair of nerve cords that run the length of the flatworm. Currie et al. then found a smaller group of cells close to the flatworm’s brain that looked like dedicated neural stem cells. These cells can receive the hedgehog signals, and further experiments showed that flatworm’s brain requires hedgehog signaling to be able to produce new neurons at its normal level. The hedgehog signaling molecule is likely only one of many signaling molecules that regulate the production of new neurons in flatworms. It will be important to uncover these additional signals and understand how they work in concert. In the future, a better understanding of this process will help efforts to devise ways to induce humans to replace neurons that are lost following injury or neurodegenerative diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.140 | 0.032 |
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".