Optimizing protein solubility for two‐dimensional gel electrophoresis analysis of human myocardium
Bibliographic record
Abstract
In order to maximize the myocardial proteome observed by two-dimensional gel electrophoresis (2-DE), the effect of (1) either an ionic or different zwitterionic detergents during tissue homogenization and (2) altering the "standard" detergent for isoelectric focusing (3-[(3-cholamidopropyl)dimethylamino]-1-propane sulfonate (CHAPS)) to either the zwitterionic detergent amidosulfobetaine-14 (ASB-14) or N-decyl-N-N'-dimethyl-3-ammonio-1-propane sulfonate (SB3-10) was investigated. Sodium dodecyl sulfate was shown to be a superior detergent for extraction of proteins during homogenization of cardiac tissue compared to the detergents ASB-14, SB3-10 or CHAPS. Additionally, both ASB-14 and SB3-10 exhibited better extraction than CHAPS for distinct regions of two-dimensional gels. In most cases, the best combination of homogenization and focusing conditions did not involve the use of the same detergent. Specifically, it was found that the ability to mix homogenization and focusing conditions can allow one to obtain an optimum balance between the resolution and number of protein spots obtained in 2-DE analysis of cardiac tissue. An excellent initial combination of buffers to utilize for the general examination of cardiac proteins was determined to be initial homogenization in a buffer containing ASB-14 followed by focusing in a buffer containing CHAPS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".