Bovine immunodeficiency virus and bovine leukemia virus and their mixed infection in Iranian Holstein cattle
Bibliographic record
Abstract
INTRODUCTION: Bovine immunodeficiency virus (BIV) and bovine leukemia virus (BLV) have worldwide distributions, but their prevalences in Iran are unknown. We investigated the presence of infections in Iranian Holstein cattle and determined changes in hematological values for infected animals. METHODOLOGY: Nested PCR was used on blood samples from 143 animals Holstein cattle to detect proviral BIV and BLV gag sequences. Flow cytometric analysis was performed using monoclonal antibodies (mAbs) against CD4, CD8, and CD21 bovine T lymphocyte subsets. RESULTS: Proviral BIV and BLV gag sequences were detected in 20.3% and 17% of the animals, respectively. BIV-BLV confection was also detected in 4.2% of the study population but this was not statistically significant. Flow cytometric analysis showed that both BIV-infected cows and non-infected ones had CD4/CD8 ratios of 2.45 and 1.43, respectively, and this difference was significant. BLV infected and non-infected animals had no significant differences in their CD4/CD8 ratio. In comparison to non-infected cattle, those with both BIV and BLV had a significant decrease in their CD4/CD8 ratios (1.5 % vs. 2.3; P = 0.01). CONCLUSION: This is the first report of BIV and BLV infections in Iran. We found no evidence that infection with one agent predisposed an animal to infection with the other. BIV infection may have a role in decreasing T CD8 counts, but this may depend on the genetics of the cattle and virus strains involved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".