Neutrophils as immune effector cells in antibody therapy in cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tumor-targeting monoclonal antibodies are available for a number of cancer cell types (over)expressing the corresponding tumor antigens. Such antibodies can limit tumor progression by different mechanisms, including direct growth inhibition and immune-mediated mechanisms, in particular complement-dependent cytotoxicity, antibody-dependent cellular phagocytosis, and antibody-dependent cellular cytotoxicity (ADCC). ADCC can be mediated by various types of immune cells, including neutrophils, the most abundant leukocyte in circulation. Neutrophils express a number of Fc receptors, including Fcγ- and Fcα-receptors, and can therefore kill tumor cells opsonized with either IgG or IgA antibodies. In recent years, important insights have been obtained with respect to the mechanism(s) by which neutrophils engage and kill antibody-opsonized cancer cells and these findings are reviewed here. In addition, we consider a number of additional ways in which neutrophils may affect cancer progression, in particular by regulating adaptive anti-cancer immunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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 it