Fast and Slow Skeletal Troponin I in Serum from Patients with Various Skeletal Muscle Disorders: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Detection of skeletal muscle injury is hampered by a lack of commercially available assays for serum markers specific for skeletal muscle; serum concentrations of skeletal troponin I (sTnI) could meet this need. Moreover, because sTnI exists in 2 isoforms, slow (ssTnI) and fast (fsTnI), corresponding to slow- and fast-twitch muscles, respectively, it could provide insight into differential injury/recovery of specific fiber types. The purpose of this study was to investigate whether the 2 isoforms of sTnI and their modified forms are present in the blood of patients with various skeletal muscle disorders. METHODS: Serial serum samples were obtained from 25 patients with various skeletal muscle injuries. Serum proteins were separated by a modified sodium dodecyl sulfate-polyacrylamide gel electrophoresis protocol followed by Western blotting for sTnI with monoclonal antibodies specific to ssTnI and fsTnI. RESULTS: We observed (a) intact and, in some cases, degraded sTnI products; (b) evidence of posttranslational modifications in addition to proteolysis; and (c) differential detectability of both skeletal isoforms in the same patient. CONCLUSIONS: It is possible to monitor both sTnI isoforms; this could lead to the development of new diagnostic assays for skeletal muscle damage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".