Dynamic loading leads to increased metabolic activity and spatial redistribution of viable cell density in nucleus pulposus tissue
Bibliographic record
Abstract
Abstract Background Nucleus pulposus (NP) cell density is orchestrated by an interplay between nutrient supply and metabolite accumulation. Physiological loading is essential for tissue homeostasis. However, dynamic loading is also believed to increase metabolic activity and could thereby interfere with cell density regulation and regenerative strategies. The aim of this study was to determine whether dynamic loading could reduce the NP cell density by interacting with its energy metabolism. Methods Bovine NP explants were cultured in a novel NP bioreactor with and without dynamic loading in milieus mimicking the pathophysiological or physiological NP environment. The extracellular content was evaluated biochemically and by Alcian Blue staining. Metabolic activity was determined by measuring glucose and lactate in tissue and medium supernatants. A lactate‐dehydrogenase staining was performed to determine the viable cell density (VCD) in the peripheral and core regions of the NP. Results The histological appearance and tissue composition of NP explants did not change in any of the groups. Glucose levels in the tissue reached critical values for cell survival (≤0.5 mM) in all groups. Lactate released into the medium was increased in the dynamically loaded compared to the unloaded groups. While the VCD was unchanged on Day 2 in all regions, it was significantly reduced in the dynamically loaded groups on Day 7 (p ≤ 0.01) in the NP core, which led to a gradient formation of VCD in the group with degenerated NP milieu and dynamic loading (p ≤ 0.05). Conclusion It was demonstrated that dynamic loading in a nutrient deprived environment similar to that during IVD degeneration can increase cell metabolism to the extent that it was associated with changes in cell viability leading to a new equilibrium in the NP core. This should be considered for cell injections and therapies that lead to cell proliferation for treatment of IVD degeneration.
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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 | 0.000 | 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) | 0.001 | 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 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".