FILTER-PAPER BLOOD SAMPLES FOR ELISA DETECTION OF BRUCELLA ANTIBODIES IN CARIBOU
Bibliographic record
Abstract
We evaluated blood collected on Nobuto filter-paper (FP) strips for use in detecting Brucella spp. antibodies in caribou. Whole blood (for serum) and blood-saturated FP strips were obtained from 185 killed arctic caribou (Rangifer tarandus groenlandicus). Sample pairs (serum and FP eluates) were simultaneously tested in duplicate using competitive enzyme-linked immunosorbent assay (c-ELISA) and indirect ELISA (i-ELISA) for Brucella spp. Prior work based on isolation of Brucella spp. revealed sensitivity (SE) and specificity (SP) of 100% and 99%, respectively, for both these serum assays in caribou. Infection status of the animals in the current study was unknown but recent sampling had revealed clinical brucellosis and >40% Brucella antibody prevalence in the herd. To assess the performance of FP relative to serum in these assays, serum was used as the putative gold standard. On both assays, the findings for duplicate runs (A and B) were similar. For c-ELISA run A, the FP Brucella prevalence (47%) was lower than serum prevalence (52%), with SE 89% (95% confidence interval [CI]: 82-95%) and SP 99% (97-100%). For i-ELISA run A, serum and FP Brucella prevalence rates were identical (43%), and the SE and SP of FP testing were 100% and 99% (97-100%), respectively. The findings suggest better FP test performance with i-ELISA than with c-ELISA; however, i-ELISA does not distinguish cross-reacting antibodies induced by Brucella vaccination or exposure to certain other Gram-negative pathogens. Results for duplicate FP eluates (prepared using separate FP strips from each animal) were strongly correlated for both protocols (r=0.996 and 0.999 for c-ELISA and i-ELISA, respectively), indicating minimal variability among FPs from any individual caribou. Dried caribou FP blood samples stored for 2 mo at room temperature are comparable with serum for use in Brucella spp. c-ELISA and i-ELISA. Hunter-based FP sampling can facilitate detection of disease exposure in remote regions and under adverse conditions, and can expand wildlife disease surveillance across temporospatial scales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".