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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".