The use of DNPH‐derivatized protein carbonyls as a marker of oxidative stress in mouse heart and liver
Bibliographic record
Abstract
Reactive oxygen species (ROS) have been shown to modify proteins, lipids and DNA. These modified biomolecules serve as markers for the quantification of oxidative stress in various physiological and diseased conditions. Identification and quantification of a marker of protein oxidation (i.e., protein carbonyls) in tissues continue to be time consuming and/or costly. To identify and quantify this biomarker, we utilized a mouse model of ALS (G93A) having a mutant human Cu/Zn‐superoxide dismutase gene resulting in elevated levels of ROS. Using spectroscopic methods, we previously reported that the G93A mouse has 53% higher protein carbonyls in the skeletal muscle as compared with wild‐type littermates. However, these methods require large amounts of sample. In this study, we present a comparison of immunochemical methods, using DNPH‐derivatized carbonyls (Oxyblot, Chemicon International vs. ELISA), to assess oxidative stress. A protein with an apparent molecular weight of 28 kDa proved to associate well with established patterns of oxidative stress in these animals. Higher carbonyl content (80%) was found in the liver of G93A versus wild‐type mice (5165 ± 741 AU vs. 2869 ± 762 AU, mean ± SEM, P = 0.044). Most importantly, this analysis required, at most, 3 μg of sample and is a more sensitive and biologically relevant measure of oxidative stress as compared with ELISA‐based methods. In summary, we have identified and quantified a 28 kDa protein that can serve as a measure of protein oxidative 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".