Longitudinal Changes of Peripheral Blood DC Subsets and Regulatory T Cells in Chinese Chronic HIV-1-Infected Patients during Antiretroviral Therapy
Bibliographic record
Abstract
It has been emphasized that chronic generalized immune dysfunction is the leading event in the pathogenesis of HIV infection, in which the contribution of dendritic cells (DCs) and regulatory T cells (Tregs) should not be underestimated. In current study, we assessed the longitudinal changes of peripheral blood DC subsets and Tregs in chronically asymptomatic treatment-naive HIV-1-infected patients during 60 weeks of antiretroviral therapy (ART), and compared with those in healthy controls and long term non-progressors (LTNPs). Blood samples were collected at week 0, 4, 12, 24, 48 and 60 of treatment to measure the counts of DC subsets and Tregs by flow cytometry and IFN-a plasma levels by ELISA. The counts of myeloid dendritic cells (mDCs) increased during ART, reaching similar levels to healthy controls at week 60 post ART but still lower than those of LTNPs. In HIV-1-infected patients, the mDCs counts were directly correlated with CD4 counts during ART. Changes in mDCs at week 8 were positively correlated with the changes in CD4 counts at week 60 post ART. However, the counts and function of plasmacytoid dendritic cells (pDCs) remained relatively stable during ART, and similar to those in healthy controls and LTNPs. The percentage of Tregs increased before ART and normalized after ART. Importantly, we found pDCs counts were associated with percentage of Tregs during ART, which may help in understanding of the role of these cells in HIV infection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".