Culturing characteristics of Hanwoo myosatellite cells and C2C12 cells incubated at 37°C and 39°C for cultured meat
Bibliographic record
Abstract
To improve culture efficiency of Hanwoo myosatellite cells, these cells were cultured at different temperatures. Hanwoo myosatellite cells were compared with C2C12 cells to observe proliferation and differentiation at culture temperatures of 37℃ and 39℃ and determine the possibility of using them as cultured meat. Immunofluorescence staining using Pax7 and Hoechst, both cells cultured at 37℃ proliferated better than cultured at 39℃ (<italic>p</italic> &lt; 0.05). When differentiated cells were stained with myosin and Hoechst, there was no significant difference in myotube thickness and Fusion index (<italic>p</italic> &gt; 0.05). In Western blotting analysis, Hanwoo myosatellite cells were no significant difference in the expression of myosin between cells differentiated at the two temperatures (<italic>p</italic> &gt; 0.05). C2C12 cells were no significant difference in the expression of myosin between cells differentiated at the two temperatures (<italic>p</italic> &gt; 0.05). In RT-qPCR analysis, Hanwoo myosatellite cells cultured at 39℃ had significantly (p &lt; 0.05) higher expression levels of MyHC, MYF6, and MB than those cultured at 37℃. C2C12 cells cultured at 39℃ showed significantly (p &lt; 0.05) higher expression levels of MYOG and MB than those cultured at 37℃. To increase culture efficiency of Hanwoo myosatellite cells, proliferating at 37℃ and differentiating at 39℃ are appropriate. Since results of temperature differences of Hanwoo myosatellite cells were similar to those of C2C12 cells, they could be used as a reference for producing cultured meat using Hanwoo satellite cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".