Dairy byproducts as sustainable alternatives to FCS in 2D and 3D skeletal muscle cell cultures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Skeletal muscle tissue engineering is a rapidly developing field with applications in disease modelling, tissue replacement, biorobotics, and cultivated meat. The need for more sustainable and ethical biotechnologies has grown due to concerns about resource scarcity, climate change, and animal welfare. One major challenge is replacing fetal calf serum (FCS), a widely used but ethically and environmentally highly problematic media supplement. A promising alternative is the utilization of natural byproducts such as whey and colostrum from the dairy industry, which provide essential nutrients and growth factors. In this study, wheys produced by microfiltration of raw milk and colostrum were investigated as FCS replacements for culturing C2C12 skeletal muscle cells. Composition analysis confirmed a variety of pro-proliferative compounds in both substances. Cell culture experiments led to the development of an optimized medium formulation based on colostrum whey. Colostrum whey medium (CM) supported cell proliferation and maintained the myogenic differentiation potential for over 30 days. Additionally, a CM-based freezing solution enabled effective cryopreservation throughout culture. In 3D static suspension culture, CM sustained viable spheroids for over 14 days. Spheroids showed significantly higher proliferation compared to those in serum-containing medium, making CM suitable for 3D modelling and scale-up of biomass production. These findings highlight CM as a sustainable, cost-effective, and ethical alternative for skeletal muscle tissue engineering, particularly in cultivated meat production.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it