Determination of protein concentration in skeletal muscle using two spectrophotometric assays: the Lowry and the Bradford
Bibliographic record
Abstract
Background: Laboratory research often involves protein analysis, particularly for the comparison of protein expression and activity. Therefore, determination of protein concentration is an important first step prior to biochemical analyses. The Lowry and Bradford methods for protein determination are the most commonly used today, yet vary in several aspects. To date, no comparisons have been made in skeletal muscle tissue. Objective: We compared protein concentrations of mouse red and white gastrocnemius (N = 52), range of linearity, reagent stability and protein stability, using both the Lowry and Bradford assays. Results: Protein concentration determined by the Lowry (mean ± SD: 5.95 ± 1.45 mg/ml) was on average 15% higher than the Bradford (5.08 ± 1.31 mg/ml). We found a moderate correlation (r = 0.36, P = 0.01) with a slope of 0.39 ± 0.15 between the two methods. However, the Bland‐Altman test revealed a considerable bias (15.8 ± 29.7%; range: −42% to +74%). The linear range of concentration was smaller for the Lowry (0.05–0.50 mg/ml) than the Bradford (0–2.0 mg/ml). Lowry reagents were more stable over an hour than those of the Bradford (5.6% vs. 14.6% difference, respectively). Peak protein concentrations were reached immediately with the Lowry assay, and between 7–10 min with the Bradford assay. Conclusion: We have determined that although both the Lowry and Bradford assays measure protein concentration in skeletal muscle, the two methods are not interchangeable. Both methods have various strengths and weaknesses and should be considered before analysis. (This research was supported by the Hamilton Health Sciences Foundation and Faculty of Health‐York University).
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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".