Current Situation of PC12 Cell Use in Neuronal Injury Study
Bibliographic record
Abstract
The nervous system diseases are easy to get and hard to cure.The mechanism is bound up with nervous cells injure, so it's significant to study medicine protect nervous cells injure.We need find an ideal model to study these diseases.PC12 cell is a pheochromocytoma cell line from RattusNorvegicus, because it has some characters of nerve cells and easy to cultivate and passage, these cells have been proved to be a useful cell model to study nervous physiology and pharmacology.There are several of PC12 cells, American type culture collection supply two kinds of PC12 cell named PC12 cell and PC12Adh.There are high differentiation, low differentiation and undifferentiating in domestic.Although they are very similar, there are still some differences, and not every PC12 cell is effective for every experimental model.After compared, we hold that PC12Adh cell line is more suitable for neurite outgrowth studies under ROCK inhibitor than the PC12 cell line, PC12 cells that induced by NGF and high differentiated PC12 cell are similar to cerebral cortical neurons, they are suitable for various physiological and pathological study of nervous system.Undifferentiated PC12 cells due to low levels of dopamine, therefore it is not suitable for study on neural cells.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".