Assessing B cell subsets changes in HIV subjects receiving a dendritic cell immunotherapy
Bibliographic record
Abstract
Dendritic cells (DC) have a central role in cell-mediated immunity and they are today in the middle of many immunotherapy strategies. Recently, a clinical trial was initiated at the Montreal Chest Institute, to evaluate the effect of an immunotherapy (AGS-004) containing autologous DC to amplify the T cell immune responses of HIV-1-infected subjects. However, concerns have been raised that B cell activation following DC immunotherapy may lead to the development of autoimmune diseases. Here, we studied the safety, patient tolerance and changes in total B cells and B cell subsets following administration of AGS-004 in ten HIV-1 subjects receiving antiretroviral therapy (ART). Clinically, AGS-004 was safe, well tolerated and caused few mild side effects. Moreover, CD4 and CD8 cell counts and HIV-1 viral load were unchanged throughout the 48-weeks follow-up study period. In addition, total B cells and B cell subsets, which were measured as an indicator of the immune activation status, did not change over time, except that the proportion of B memory cells significantly increased after receiving the AGS-004 immunotherapy (P=0.005). Collectively, these data show that the AGS-004 is relatively well tolerable and induces an increase in B memory cells. Further investigations would need to be done to confirm the presence of an activated immune status including functional properties of these B memory cells and antibody measurements.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".