RELi protocol: Optimization for protein extraction from white, brown and beige adipose tissues
Bibliographic record
Abstract
Global obesity rates have reached pandemic proportions, increasing the risk of metabolic complications for hundreds of millions of individuals worldwide. Gaining insight on adipose tissue biology and understanding how fat pads behave during obesity is critical to investigate metabolic syndromes. Elucidation of cellular signaling pathways engaged by adipose tissue both in health and disease requires standardized protocols for protein extraction that yield consistently pure samples. A recurrent problem of currently available protocols is lipid or detergent contamination in extracted protein samples, which renders protein quantification inaccurate and, as a consequence, consistency and reproducibility of protein loading become unreliable. To overcome this problem, we improved the process of adipose tissue protein extraction by improving tissue lysis and decreasing lipid contamination. Here we describe the Removal of Excess Lipids (RELi) protocol to obtain increased yields of total proteins extracted from adipose tissue. The RELi protocol allows accurate and reproducible adipose tissue sample preparation for Western blot analysis and other investigative techniques requiring adipose tissue-derived proteins.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.023 |
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".