Bibliographic record
Abstract
OBJECTIVE: Despite advances in conventional therapy, the prognosis for most glioma patients remains dismal. This has prompted an intensive search for effective treatment alternatives. Immunotherapy, one such alternative, has long been recognized as a potentially potent cancer treatment but has been limited by an inadequate understanding of the immune system. Now, increased insight into immunology is suggesting more rational approaches to immunotherapy. In this article, we explore key aspects of modern immunology and discuss their implications for glioma therapy. METHODS: A thorough literature review of glioma immunology and immunotherapy was undertaken to inquire into the basic immunology, central nervous system immunology, glioma immunobiology, standard glioma immunotherapy, and recent immunotherapeutic advances in glioma treatment. RESULTS: Although gliomas express tumor-associated antigens and appear potentially sensitive to immune responses, many factors work together to inhibit antiglioma immunity. Not surprisingly, most clinical attempts at glioma immunotherapy have met with little success to date. However, novel immunostimulatory strategies, such as immunogene therapy, directed cytokine delivery, and dendritic cell manipulation, have recently yielded dramatic preclinical results in glioma models. This suggests that glioma-derived immunosuppression can be overcome. CONCLUSION: Modern molecular biology and immunology techniques have yielded a wealth of new data about glioma immunobiology. Armed with this information, many investigators have proposed novel means to stimulate antiglioma immune responses. Although definitive clinical results remain to be seen, the current renaissance in glioma immunology and immunotherapy shows great promise for the future.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".