Response of cartilage explants to LPS cultured in normoxic and hypoxic conditions is modulated by Spirulina: implications for exercise recovery in vivo
Bibliographic record
Abstract
Exercise-induced inflammation and free radical production are crucial for recovery, yet excess inflammation poses risks to equine athletes, leading to conditions like arthritis. Spirulina, recognized for its antioxidant and anti-inflammatory properties, could mitigate degenerative diseases without hindering post-exercise recovery. This study investigates Spirulina’s direct impact on cartilage responses to LPS-induced inflammation in normoxic and hypoxic conditions, focusing on outcomes relevant to cartilage matrix turnover and exercise-induced inflammation. Spirulina underwent simulated digestion and liver metabolism, yielding a simulated biological extract (SPsim). In the normoxic experiment, porcine cartilage explants were cultured with SPsim (0, 30, or 90 μg/mL) for 72 h after 24 h in basal media, with LPS (0 or 10 μg/mL) added for the final 48 h. The hypoxic experiment mirrored this, with explants transferred to a hypoxia chamber for the final 48 h. Media samples collected at 0, 24, and 48 h were analyzed for biomarkers related to cartilage turnover (GAG), and exercise-induced inflammation (IL-6 and NO). Cell viability, assessed by live:dead staining, remained > 97% and unaffected by oxygen tension. In normoxic conditions, SPsim (30 μg/mL) significantly reduced GAG release at 48 h. Under hypoxia, SPsim (30 and 90 μg/mL) inhibited LPS-induced GAG release. SPsim (90 μg/mL) increased IL-6 and NO production in LPS-stimulated explants in normoxia, and a similar effect was observed with the lower SPsim dose (30 μg/mL) in hypoxic conditions. These results suggest that Spirulina may enhance cartilage mediators, potentially promoting healthy cartilage turnover during exercise recovery.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".