Refinement of Protein Extraction Protocols for Human Peripheral Nerve Tissue
Bibliographic record
Abstract
Our aim was to establish an effective method for protein extraction from freshly frozen human peripheral nerves, determine the minimum amount required for consistent protein extraction outcomes, and assess which method produced the highest number of protein identities. Five extraction methods were compared using 8 M urea and Ripa buffer using either the Bullet Blender or Bioruptor. Out of the total 2619 identified proteins, protein extraction using the Ripa buffer combined with either Bioruptor or Bullet Blender resulted in the identification of 1582 (60%) and 1615 (62%) proteins, respectively. In contrast, using 8 M urea and Bioruptor for protein extraction resulted in 1022 proteins (39%), whereas employing Bullet Blender yielded 1446 proteins (55%). Sample amounts, ranging from 0.6 to 10 mg, were prepared with consistent protein extraction outcome obtained for samples ≥1.2 mg. Combining Ripa and 8 M urea with Bullet Blender increased protein identification to 2126 (81%). Proteins were classified by their cell components, molecular functions, and biological processes. Furthermore, a subclassification of proteins involved in the extracellular matrix (ECM) was introduced. We recommend the use of Ripa buffer, in combination with 8 M urea and Bullet Blender for extracting proteins from fresh-frozen human nerves weighing ≥1.2 mg.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".