Endpoint Quaking-Induced Conversion: a Sensitive, Specific, and High-Throughput Method for Antemortem Diagnosis of Creutzfeldt-Jacob Disease
Bibliographic record
Abstract
The Prion Laboratory Section of the Public Health Agency of Canada supports heath care professionals dealing with patients suspected to have Creutzfeldt-Jakob disease (CJD) by testing cerebrospinal fluid (CSF) for protein markers of CJD. To better serve Canadian diagnostic requirements, a quaking-induced conversion (QuIC)-based assay has been added to the test panel. The QuIC tests exploit the ability of disease-associated prion protein, found in the CSF of a majority of CJD patients, to convert a recombinant prion protein (rPrP) into detectable amounts of a misfolded, aggregated form of rPrP. The rPrP aggregates interact with a specific dye, causing a measurable change in the dye's fluorescence emission spectrum. Optimal test and analysis parameters were empirically determined. Taking both practical and performance considerations into account, an endpoint QuIC (EP-QuIC) configuration was chosen. EP-QuIC uses a thermo-mixer to perform the shaking necessary to produce the quaking-induced conversions. Fluorescence readings are obtained from a microwell fluorescence reader only at the beginning and the end of EP-QuIC reactions. Samples for which the relative fluorescence unit ratio between the initial and final readings represent a ≥4 increase in signal intensity in at least two of the three replicates are classified as positive. A retrospective analysis of 91 CSF samples that included 45 confirmed cases of CJD and 46 non-CJD cases was used to estimate the performance characteristics of the EP-QuIC assay. The diagnostic sensitivity and specificity of the EP-QuIC test of this set of samples were 98 and 91%, respectively.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".