Cystatin C as a cerebrospinal fluid biomarker for pain in humans
Bibliographic record
Abstract
Through a process of subtraction cloning and differential hybridization, we previously identified several new genes whose expression was induced by peripheral inflammation. One of these coded for cystatin C, a secreted cysteine protease inhibitor in the cystatin superfamily. We hypothesized that, concurrent with increased expression in dorsal horn, increased secretion would elevate the cystatin C content in cerebrospinal fluid (CSF) during active pain states. Alterations were assessed by immunoassay and by surface enhanced laser desorption ionization (SELDI) mass spectrometry with either reverse phase or immobilized anti-cystatin C antibody surfaces using CSF from ten age-matched obstetrical patients at term. Five control subjects were scheduled for an elective caesarian section and were not in pain. Another five subjects were in labor for 8.9+/-1h and were in severe pain as assessed with a visual analog scale and the McGill short form questionnaire. The level of cystatin C as measured by immunoassay in the non-pain patients was 2.77+/-0.75 microg/ml and in the pain patients 5.36+/-0.92 microg/ml (P<0.02). The elevation occurred without significant change in total CSF protein or beta-endorphin content. The cystatin C increase also was detectable by SELDI with either raw CSF or after antibody capture. These data are consistent with our previous animal study and the idea that persistent pain induces the synthesis and release of cystatin C in dorsal spinal cord, the surplus of which overflows into the CSF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".