Differential Dynamics of Regulatory T-Cell and Th17 Cell Balance in Mesenteric Lymph Nodes and Blood following Early Antiretroviral Initiation during Acute Simian Immunodeficiency Virus Infection
Bibliographic record
Abstract
Tregs contribute to SIV/HIV disease progression by inhibition of antiviral specific responses and effector T-cell proliferation. Tregs also cause tissue fibrosis via transforming growth factor β1 production and collagen deposition, which are associated with microbial translocation and generalized immune activation. Early ARV initiation upon viral exposure is recommended globally and results in improved immune function recovery and reduced viral persistence. Here, using an acute SIV infection model of rhesus macaques, we demonstrated for the first time that despite clear improvements in mucosal CD4 T cells, in contrast to blood, Treg frequencies in MLNs remained elevated following early ARV initiation. The particular Th17/Treg balance observed in MLNs can contribute, in part, to the maintenance of mucosal fibrosis during suppressive ARV treatment. Our results provide a better understanding of gut mucosal immune dynamics following early ARV initiation. These findings suggest that Treg-based treatments could serve as a novel immunotherapeutic approach to decrease gut mucosal damage during SIV/HIV infections.
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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 | 0.000 | 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 | 0.001 |
| 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".