Factors affecting the accuracy of urine-based biomarkers of BSE
Bibliographic record
Abstract
BACKGROUND: Transmissible spongiform encephalopathy diseases are untreatable, uniformly fatal degenerative syndromes of the central nervous system that can be transmitted both within as well as between species. The bovine spongiform encephalopathy (BSE) epidemic and the emergence of a new human variant of Creutzfeldt-Jakob disease (vCJD), have profoundly influenced beef production processes as well as blood donation and surgical procedures. Simple, robust and cost effective diagnostic screening and surveillance tools are needed for both the preclinical and clinical stages of TSE disease in order to minimize both the economic costs and zoonotic risk of BSE and to further reduce the risk of secondary vCJD. OBJECTIVE: Urine is well suited as the matrix for an ante-mortem test for TSE diseases because it would permit non-invasive and repeated sampling. In this study urine samples collected from BSE infected and age matched control cattle were screened for the presence of individual proteins that exhibited disease specific changes in abundance in response to BSE infection that might form the basis of such an ante-mortem test. RESULTS: Two-dimensional differential gel electrophoresis (2D-DIGE) was used to identify proteins exhibiting differential abundance in two sets of cattle. The known set consisted of BSE infected steers and age matched controls throughout the course of the disease. The blinded unknown set was composed of BSE infected and control samples of both genders, a wide range of ages and two different breeds. Multivariate analyses of individual protein abundance data generated classifiers comprised of the proteins best able to discriminate between the samples based on disease state, breed, age and gender. CONCLUSION: Despite the presence of confounding factors, the disease specific changes in abundance exhibited by a panel of urine proteins permitted the creation of classifiers able to discriminate between control and infected cattle with a high degree of accuracy.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".