Immuno-Isolation in Oncology - A Mini-Review
Bibliographic record
Abstract
Immuno-isolation provides a potentially safe and effective method of delivering recombinant therapeutic molecules. Its application as a drug-delivery platform for the treatment of cancer has shown promising developments recently. This review will summarize the principle and current progress of this novel therapy paradigm in oncology. In this approach, a non-autologous cell line is genetically modified to secrete a recombinant product with potential for tumor suppression. Such a cell line may be implanted without graft rejection into all patients with similar neoplastic disease. The immune protection is conferred by enclosure within immuno-isolating devices such as microcapsules whose permeability would allow passage of smaller molecules such as oxygen, nutrients and waste products as well as the desired therapeutic transgene product. However, large immune mediators such as complement, macrophages and lymphocytes responsible for graft rejection would be excluded. In this review, we will consider how this technology may be applied as a novel genetic tool for cancer treatment to deliver antibodies, cytokines, enzymes and growth factors for treatment of various types of cancer. These molecules can be delivered at low constitutive levels, thereby permitting long-term systemic delivery, maintaining biological activity over extended periods, and eliminating the costs of product purification. The current success of this strategy in cancer treatment will be reviewed in in vitro systems, in animal models of cancer, and in human clinical trials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".