Bibliographic record
Abstract
Cystatin C (CST3) is expressed ubiquitously and implicated in several neurological diseases. It can be posttranscriptionally modified. CST3 is usually quantified in a biological sample using antibody-based methods. Posttranscriptional modification can hamper antibody-based detection systems by altering antibody-binding epitope(s). To circumvent this problem, enzymatic digestion and liquid chromatography tandem mass spectrometry (LC-MS/MS) technique can be employed to identify and measure peptides of a target protein in a complex biological mixture. This chapter describes an LC-MS/MS-based method for accurate measurement of CST3 in cerebrospinal fluid (CSF). Here, CSF was directly subjected to trypsin digestion and digested peptides were extracted using a solid-phase extraction column. Extracted peptide samples were directly used for LC-MS/MS-based identification and quantification of CST3 peptides. Comparing the concentration in a set of samples measured by LC-MS/MS with that of immunoassay shows that it was significantly higher when measured by LC-MS/MS method, suggesting it a better quantification method. This approach is particularly well suited when posttranscriptional modification of CST3 is suspected and sample volume of CSF is small.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".