Tumor immune therapy: Lessons from infection and implications for cancer – Can IL‐7 help overcome immune inhibitory networks?
Bibliographic record
Abstract
The complexity that the immune system faces in distinguishing pathogens from self is manifested by the intricate immunological networks involved in initiation, promotion and abrogation of immunity. A substantially more complex algorithm is required to distinguish normal from aberrant self (e.g. in the form of cancers), and this is reflected by the apparent inefficiency of our immune system to eradicate tumors; however, with our expanding insights into the molecular networks that govern immunity, we can now consider therapies that transiently promote immunity and/or antagonize immune inhibitory networks. Cytokines that normally function to regulate immune responses hold much therapeutic promise in this regard. Translating this promise to tangible outcomes will require a thorough analysis of how, when and in what way these cytokines should be used to take advantage of synergistic and complementary effects of current cancer therapeutics. In this review, we focus on IL-7, as much data are emerging on the ability of this unique homeostatic cytokine to augment various anti-tumor immunotherapeutic modalities.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".