Engineering Murine Adipocytes and Skeletal Muscle Cells in Meat-like Constructs Using Self-Assembled Layer-by-Layer Biofabrication: A Platform for Development of Cultivated Meat
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
Global meat consumption has been growing on a per capita basis over the past 20 years resulting in ever-increasing devotion of resources in the form of arable land and potable water to animal husbandry which is unsustainable and inefficient. One approach to meet this insatiable demand is to use biofabrication methods used in tissue engineering in order to make skeletal muscle tissue-like constructs known as cultivated meat to be used as a food source. Here, we demonstrate the use of a scaffold-free biofabrication method that forms cell sheets composed of murine adipocytes and skeletal muscle cells and assembles these sheets in parallel to create a 3D meat-like construct without the use of any exogenous materials. This layer-by-layer self-assembly and stacking process is fast (4 days of culture to form sheets and few hours for assembly) and scalable (stable sheets with diameters >3 cm are formed). Tissues formed with only muscle cells were equivalent to lean meat with comparable protein and fat contents (lean beef had 1.5 and 0.9 times protein and fat, respectively, as our constructs) and incorporating adipocyte cells in different ratios to myoblasts and/or treatment with different media cocktails resulted in a 5% (low fat meat) to 35% (high fat meat) increase in the fat content. Not only such constructs can be used as cultivated meat, they can also be used as skeletal muscle models.
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