Clinical applications of virosomes in cancer immunotherapy
Bibliographic record
Abstract
Cancer immunotherapy is increasingly accepted as a treatment option for advanced stage disease. The identification of tumour-associated antigens in 1991 has prompted the development of antigen-specific immunotherapeutic strategies for a variety of cancers. Many of them result in some immunological responses in cancer patients; however, clinical results were not observed concomitantly with immunological responses; therefore, further improvements in the field of immunotherapy are urgently needed. Virosomes are lipidic envelopes devoid of genetic information, but which retain the antigenic profile and fusogenic properties from their viral origin. Virosomes are versatile antigen carriers and can be engineered to perform various tasks in cancer immunotherapy. Preclinical data have fostered the development of innovative clinical protocols. Hence, immunopotentiating reconstituted influenza virosomes will be assessed in breast and melanoma immunotherapy, and may contribute to the development of clinically effective cancer vaccines and ultimately improve patient outcomes. The objective of this review is to provide an overview of the potential clinical applications of virosomes as innovative and potentially effective reagents in active specific cancer immunotherapy.
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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 | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| 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".