Treatment of Ligament Constructs with Exercise-conditioned Serum: A Translational Tissue Engineering Model
Bibliographic record
Abstract
In vitro experiments are essential to understand biological mechanisms; however, the gap between monolayer tissue culture and human physiology is large, and translation of findings is often poor. Thus, there is ample opportunity for alternative experimental approaches. Here we present an approach in which human cells are isolated from human anterior cruciate ligament tissue remnants, expanded in culture, and used to form engineered ligaments. Exercise alters the biochemical milieu in the blood such that the function of many tissues, organs and bodily processes are improved. In this experiment, ligament construct culture media was supplemented with experimental human serum that has been 'conditioned' by exercise. Thus the intervention is more biologically relevant since an experimental tissue is exposed to the full endogenous biochemical milieu, including binding proteins and adjunct compounds that may be altered in tandem with the activity of an unknown agent of interest. After treatment, engineered ligaments can be analyzed for mechanical function, collagen content, morphology, and cellular biochemistry. Overall, there are four major advantages versus traditional monolayer culture and animal models, of the physiological model of ligament tissue that is presented here. First, ligament constructs are three-dimensional, allowing for mechanical properties (i.e., function) such as ultimate tensile stress, maximal tensile load, and modulus, to be quantified. Second, the enthesis, the interface between boney and sinew elements, can be examined in detail and within functional context. Third, preparing media with post-exercise serum allows for the effects of the exercise-induced biochemical milieu, which is responsible for the wide range of health benefits of exercise, to be investigated in an unbiased manner. Finally, this experimental model advances scientific research in a humane and ethical manner by replacing the use of animals, a core mandate of the National Institutes of Health, the Center for Disease Control, and the Food and Drug Administration.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".