Emerging nanomedicines for effective breast cancer immunotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it