Proteomic Signature of the Murine Intervertebral Disc
Bibliographic record
Abstract
Low back pain is the most common musculoskeletal problem and the single most common cause of disability, often attributed to degeneration of the intervertebral disc. Lack of effective treatment is directly related to our limited understanding of the pathways responsible for maintaining disc health. While transcriptional analysis has permitted initial insights into the biology of the intervertebral disc, complete proteomic characterization is required. We therefore employed liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) protein/peptide separation and mass spectrometric analyses to characterize the protein content of intervertebral discs from skeletally mature wild-type mice. A total of 1360 proteins were identified and categorized using PANTHER. Identified proteins were primarily intracellular/plasma membrane (35%), organelle (30%), macromolecular complex (10%), extracellular region (9%). Molecular function categorization resulted in three distinct categories: catalytic activity (33%), binding (molecule interactions) (29%), and structural activity (13%). To validate our list, we confirmed the presence of 14 of 20 previously identified IVD-associated markers, including matrix proteins, transcriptional regulators, and secreted proteins. Immunohistochemical analysis confirmed distinct localization patterns of select protein with the intervertebral disc. Characterization of the protein composition of healthy intervertebral disc tissue is an important first step in identifying cellular processes and pathways disrupted during aging or disease progression.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".