So, you want to create a frog cell line? A guide to establishing frog skin cell lines from tissue explants
Bibliographic record
Abstract
Skin is an important interface with the external environment and investigating amphibian skin cell biology will improve our understanding of how environmental factors such as pathogens and pollutants are contributing to global amphibian declines. There is a critical need for in vitro systems to facilitate conservation research in model and non-model amphibians and the creation of new amphibian cell lines will play a significant role in reducing or even replacing the use of live animals for in vivo studies by providing an in vitro alternative. Here, we detail an adapted protocol for the generation of spontaneously arising cell lines from frog skin tissues, without the need for immortalization steps. Expanding the amphibian invitrome will foster and expedite new research in amphibian gene function, cellular responses, host-pathogen interactions, and toxicology. The following customizations to traditional tissue explant generation procedures have facilitated the successful generation of adherent skin epithelial-like cell lines from Xenopus laevis and can be further adapted for use with different frog species, such as Rana sylvatica, and different tissues: Osmotic adjustment of culture medium and solutions for different amphibian species. Use of small tissue explants, instead of enzymatic digestion of tissues, and gentle spotting of these tissue explants onto the growth surface of tissue culture flasks to promote better tissue adherence. Partial replacement of medium to allow accumulation of potential endogenous growth factors in cultures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.031 |
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".