Emerging nanomedicines for effective breast cancer immunotherapy
Bibliographic record
Abstract
Breast cancer continues to be the most frequently diagnosed malignancy among women, putting their life in jeopardy. Cancer immunotherapy is a novel approach with the ability to boost the host immune system to recognize and eradicate cancer cells with high selectivity. As a promising treatment, immunotherapy can not only eliminate the primary tumors, but also be proven to be effective in impeding metastasis and recurrence. However, the clinical application of cancer immunotherapy has faced some limitations including generating weak immune responses due to inadequate delivery of immunostimulants to the immune cells as well as uncontrolled modulation of immune system, which can give rise to autoimmunity and nonspecific inflammation. Growing evidence has suggested that nanotechnology may meet the needs of current cancer immunotherapy. Advanced biomaterials such as nanoparticles afford a unique opportunity to maximize the efficiency of immunotherapy and significantly diminish their toxic side-effects. Here we discuss recent advancements that have been made in nanoparticle-involving breast cancer immunotherapy, varying from direct activation of immune systems through the delivery of tumor antigens and adjuvants to immune cells to altering immunosuppression of tumor environment and combination with other conventional therapies.
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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 | 0.001 |
| 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 | 0.002 | 0.003 |
| 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".