High-Throughput Quantitation of Metabolically Labeled Anionic Glycoconjugates by Scintillation Proximity Assay Utilizing Binding to Cationic Dyes
Bibliographic record
Abstract
Rapid, quantitative methods suited to a large number of samples are required for studies into the determination of disease etiology and in the evaluation of drugs and biological agents. This chapter describes an assay for anionic glycoconjugates (GCs), including glycosaminoglycans, which are major gene products of chondrocytes appearing in the extracellular matrix. The assay utilizes the electrostatic interaction between negatively charged sulfate and carboxyl groups of anionic GCs synthesized and secreted by chondrocytes with the cationic dye Alcian blue, immobilized to scintillant-coated 96-well plates. Metabolic labeling with D-[1, 6-3H (N)]-glucosamine allows all anionic GCs, including cartilage-specific and hyperglycosylated variants of fibronectin, to be quantitated. If Na235SO4 is used for the metabolic labeling instead, only glycosaminoglycans and proteoglycans will be quantitated. The samples are counted using a multi-detector instrument for scintillation proximity assays, such as the Wallac 1450 Microbeta Trilux, designed for detection of samples in 96-well plates and, as such, can be a high-throughput system. The bound anionic GCs can be visualized by sodium dodecyl sulfate-polyacrylamide gel electrophoresis after quantitation by elution with denaturing buffers. The method can be modified to include predigestion of the sample with a specific lyase, e.g., chondroitinase ABC or testicular hyaluronidase. To separate polyanions from other digested material after ethanol precipitation, the sample can be assayed as described in this chapter for a particular subtype of anionic GC. This assay addresses the need for high-throughput applications in arthritis and other medical and biological problems.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".