<scp>CD</scp>19<sup>+</sup><scp>CD</scp>20<sup>−</sup><scp>CD</scp>27<sup>hi</sup><scp>IL</scp>‐s10‐producing B cells are overrepresented in R‐<scp>CHOP</scp>‐treated <scp>DLBCL</scp> patients in complete remission
Bibliographic record
Abstract
Treatment of diffuse large B cell lymphoma (DLBCL) with rituximab, an anti-CD20 monoclonal antibody, has resulted in significantly improved patient responses with longer event-free intervals and higher overall survival rates. However, since rituximab depletes all CD20-expressing cells, including noncancerous B cells, the effects of rituximab on the normal immunity of DLBCL patients under remission need to be examined. Here, we observed that DLBCL patients under remission contained significantly lower frequencies of total B cells, with a significantly overrepresented interleukin (IL)-10-producing B cell (B10) population in the peripheral blood. Further examination confirmed that a large fraction of B10 cells was CD20(-) CD27(hi) plasmablasts, possibly explaining the persistence of B10 cells after R-CHOP treatment. We also observed that the percentage of B10 cells in DLBCL patients in remission gradually reduced during the first year of achieving complete remission, primarily due to the replenishment of non-B10 B cells. Despite this, the percentage of B10 cells in DLBCL patients after 1 year of achieving complete remission was still higher than that in controls. CD4(+) and CD8(+) T cells cocultured with B10-enriched B cells secreted significantly lower levels of proinflammatory cytokines IFN-g and TNF-a, compared to those incubated with B10-depleted B cells. Together, our data observed a long-lasting overrepresentation of B10 cells in DLBCL patients under remission. Whether this change could impact on the overall anti-tumor immunity during remission requires 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 | 0.000 | 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) | 0.004 | 0.001 |
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".