Investigation of wild-type and mycolactone-negative mutant<i>Mycobacterium ulcerans</i>on skeletal muscle: IGF-1 protects against mycolactone-induced muscle catabolism
Bibliographic record
Abstract
Buruli ulcer (BU), which is caused by Mycobacterium ulcerans (MU), is an endemic and neglected tropical disease that affects mostly subcutaneous tissues. Skeletal muscle under infected skin is also subject to serious dysfunctions and contractures. The goal of this study was to investigate the effects of an infection with the wild-type M. ulcerans (WT-MU) or the mycolactone-negative Mycobacterium ulcerans (M(neg)-MU) mutant strains on myotubes or fully differentiated skeletal muscles. WT-MU infection decreased by 22% and 29% the maximal muscle force at days 7 and 42 postinfection, respectively, while M(neg)-MU induced no decrease at day 7 postinfection and a small but significant 13% decrease in muscle force at day 42. A 13.2-fold and 4.3-fold increase in neutrophil and macrophage concentrations, respectively, was observed on day 42 following the injection of WT-MU. However, the increases in neutrophil and macrophage concentrations were 2.4-fold and 5.5-fold in M(neg)-MU. Myoblast proliferation decreased by 20%, myotube diameter by 45%, MyHC levels by 32%, while MuRF-1 levels increased by 22.8% when C2C12 cells and WT-MU were cocultured for 48 h at a multiplicity of infection of 5:1. In contrast, M(neg)-MU had no significant effect. Interestingly, the addition of 1,000 ng/ml of IGF-1 to the WT-MU/C2C12 coculture significantly improved all of these biological parameters. The present investigation clearly established that muscle dysfunction and chronic inflammation in the presence of WT-MU are largely caused by the release of mycolactone, and the addition of recombinant IGF-1 was sufficient to alleviate some of the antiproliferative and atrophic effects of mycolactone.
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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".