Changes in leukocyte subsets of pregnant gilts experimentally infected with porcine reproductive and respiratory syndrome virus and relationships with viral load and fetal outcome
Bibliographic record
Abstract
In spite of more than two decades of extensive research, the understanding of porcine reproductive and respiratory syndrome virus (PRRSv) immunity is still incomplete. A PRRSv infection of the late term pregnant female can result in abortions, early farrowings, fetal death, and the birth of weak, congenitally infected piglets. The objectives of the present study were to investigate changes in peripheral blood mononuclear cell populations in third trimester pregnant females infected with type 2 PRRSv (NVSL 97-7895) and to analyze potential relationships with viral load and fetal mortality rate. PRRSv infection caused a massive, acute drop in total leukocyte counts affecting all PBMC populations by two days post infection. Except for B cells, cell counts started to rebound by day six post infection. Our data also show a greater decrease of naïve B cells, T-helper cells and cytolytic T cells than their respective effector or memory counterparts. Absolute numbers of T cells and γδ T cells were negatively associated with PRRSv RNA concentration in gilt serum over time. Additionally, absolute numbers of T helper cells may be predictive of fetal mortality rate. The preceding three leukocyte populations may therefore be predictive of PRRSv-related pathological outcomes in pregnant gilts. Although many questions regarding the immune responses remain unanswered, these findings provide insight and clues that may help reduce the impact of PRRSv in pregnant gilts.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".