Evaluation of Competitive ELISA For Detection of Antibodies to Rift Valley Fever Virus in Cattle and Sheep Sera
Bibliographic record
Abstract
Rift Valley fever virus (RVFV) is a mosquito‐borne, zoonotic pathogen that causes mass abortions and deaths in ruminants. In humans, it can cause illnesses ranging from fever to occasionally fatal hemorrhagic fever, encephalitis and liver failure. RVFV is an enveloped, single‐stranded, negative‐sense tripartite RNA virus of the Phlebovirus genus (family: Phenuiviridae , order: Bunyavirales ). It is categorized as a Select Agent by the CDC and USDA. Originally endemic to sub‐Saharan Africa only, it has recently spread beyond the continent to the Arabian Peninsula. Thus, the high likelihood of RVFV's spread to non‐endemic countries spurs the need for rapid diagnostics and surveillance tests. In this study, we assessed the efficacy of the recombinant RVFV nucleoprotein based competitive ELISA (cELISA) assay to detect RVFV antibodies in cattle and sheep sera. This cELISA is a new prototype packaged by Veterinary Medical Research & Development (VMRD). We used heat inactivated serum samples from ruminants (cattle=66, sheep=99) that were experimentally infected with either the MP‐12 RVFV vaccine or a virulent strain as well as known RVFV negative sera (cattle=330, sheep=179). We compared the cELISA assay with a plaque reduction neutralization test (PRNT 80 ), the gold standard method for the detection of anti‐RVFV neutralizing antibodies. The recommended cut‐off value for the cELISA was 60%. Additionally, using experimental sera determined positive or negative by PRNT 80 (at cut‐offs <1:10 and 1:40) and ROC analysis, we determined the optimal cut‐offs to be 46% and 68% respectively. With the cut‐off of 60% for cELISA and 1:40 titer for PRNT 80 , the sensitivity and specificity of the cELISA assay was determined to be 95.1% and 91.8% respectively. Antibodies to RVFV were first detected at 5 days post inoculation (dpi) in both sheep and cattle. Interestingly, at 5 dpi, the cELISA detected antibodies in 2/4 samples from RVFV inoculated cattle in comparison to 0/4 by PRNT 80 . We found the prototype cELISA to be an easy, sensitive, specific, and safe test for the detection of antibodies to RVFV in cattle and sheep. The prototype cELISA thus, could be used for early diagnosis and surveillance that will help in diminish the disease burden. Support or Funding Information This project was funded by the Science and Technology Directorate of the U.S. Department of Homeland Security under Award Number HSHQDC‐13‐J‐00278 to the Institute for Infectious Animal Diseases within the Texas A&M University System, the USDA, Agricultural Research Service Project # 5430‐050‐009‐00D, and Department of Homeland Security Center of Excellence for Emerging and Zoonotic Animal Diseases (CEEZAD), Grant No. 2010‐ST061‐AG0001. The views and conclusions expressed in this publication are those of authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security or the USDA. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".