Author response: DIPPER, a spatiotemporal proteomics atlas of human intervertebral discs for exploring ageing and degeneration dynamics
Bibliographic record
Abstract
The backbone of vertebrate animals consists of a series of bones called vertebrae that are joined together by disc-like structures that allow the back to move and distribute forces to protect it during daily activities. It is common for these intervertebral discs to degenerate with age, resulting in back pain and severely reducing quality of life. The mechanical features of intervertebral discs are the result of their proteins. These include extracellular matrix proteins, which form the external scaffolding that binds cells together in a tissue, and signaling proteins, which allow cells to communicate. However, how the levels of different proteins in each region of the disc vary with time has not been fully examined. To establish how protein composition changes with age, Tam, Chen et al. quantified the protein levels and gene activity (which leads to protein production) of intervertebral discs from young and old deceased individuals. They found that the position of different mixtures of proteins in the intervertebral disc changes with age, and that young people have high levels of extracellular matrix proteins and signaling proteins. Levels of these proteins decreased as people got older, as did the amount of proteins produced. To determine which region of the intervertebral disc different proteins were in, Tam, Chen et al. also performed magnetic resonance imaging (MRI) of the samples to correlate image intensity (which represents water content) with the corresponding protein signature. The data obtained provides a high-quality map of how the location of different proteins changes with age, and is available online under the name DIPPER. This database is an informative resource for research into skeletal biology, and it will likely advance the understanding of intervertebral disc degeneration in humans and animals, potentially leading to the development of new treatment strategies for this condition.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.216 | 0.092 |
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".