Identification of brain-enriched proteins in the cerebrospinal fluid proteome by LC-MS/MS profiling and mining of the Human Protein Atlas
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Cerebrospinal fluid (CSF) is a proximal fluid which communicates closely with brain tissue, contains numerous brain-derived proteins and thus represents a promising fluid for discovery of biomarkers of central nervous system (CNS) diseases. The main purpose of this study was to generate an extensive CSF proteome and define brain-related proteins identified in CSF, suitable for development of diagnostic assays. METHODS: Six non-pathological CSF samples from three female and three male individuals were selected for CSF analysis. Samples were first subjected to strong cation exchange chromatography, followed by LC-MS/MS analysis. Secreted and membrane-bound proteins enriched in the brain tissues were retrieved from the Human Protein Atlas. RESULTS: In total, 2615 proteins were identified in the CSF. The number of proteins identified per individual sample ranged from 1109 to 1421, with inter-individual variability between six samples of 21 %. Based on the Human Protein Atlas, 78 brain-specific proteins found in CSF samples were proposed as a signature of brain-enriched proteins in CSF. CONCLUSION: A combination of Human Protein Atlas database and experimental search of proteins in specific body fluid can be applied as an initial step in search for disease biomarkers specific for a particular tissue. This signature may be of significant interest for development of novel diagnostics of CNS diseases and identification of drug targets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it