Diverse immune cell profiles in ASFV-associated lymphopenia
Bibliographic record
Abstract
Abstract Pathogenic African swine fever virus (ASFV) remains a lethal causative agent in the domestic pig industry, which poses a burden on the swine market and causes substantial socioeconomic losses worldwide. Currently, there are no commercially efficacious vaccines or specific treatments available for ASF prevention and control. Unfortunately, little is known about the swine immune response upon ASFV infection. Here, we investigated the host immune response discrepancy induced by the field moderately virulent strain ASFV HB-2208 among healthy, diseased and asymptomatic pigs. In the peripheral blood of diseased swine, lymphopenia is caused by the massive loss of bystander lymphocytes, such as γδ T cells, B cells and CD4+ T cells. Conversely, ASFV has a strong tropism for the mononuclear phagocyte system (MPS) and partial dendritic cells (DCs), whose antigen-presenting ability is impeded by the downregulation of CD80 and MHC I. However, no significant difference in the number of CD8αhigh T cells was detected, whereas the frequencies of NK cells, NKT cells, and regulatory T cells (Tregs) were significantly increased. Additionally, an in vitro model was established with a coculture of primary pulmonary alveolar macrophages (PAMs) and peripheral blood mononuclear cells (PBMCs), which significantly reduced γδ T cells, B cells and CD4+ T cells and increased Tregs. The differentiated immune response might aid in enhancing the understanding of ASFV pathogenesis in suids and provide insights into the mechanism of ASFV-induced lymphopenia for further studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".