Nanotechnology for improving response to cancer chemo and immunotherapy
Bibliographic record
Abstract
C is the number one cause of death in Canada, surpassing cardiovascular disease. Despite significant strides in understanding the mechanisms behind cancer, efficient and curative therapies are still missing. Chemotherapy is the treatment of choice in many cancers. However, it rarely cures cancer and mostly becomes ineffective by drug resistance. Moreover, emergence of intolerable toxicities by chemotherapeutic agents lowers the quality of life significantly, in cancer patients. Immunotherapy is the more recent and unconventional form therapy for cancer. Recent research has provided a strong case for the potential benefit of combined immuno and chemotherapy in the eradication of cancer. In both cases achieving high therapeutic efficacy requires targeted delivery -to cancer cells and its microenvironment in case of chemotherapy and to antigen presenting cells in case of cancer vaccines. The objective of our research is to design targeted vaccine and drug nano-delivery systems that can enhance the efficacy and reduce the toxicity of immunotherapy and chemotherapy in cancer. In this presentation, a brief update on the progress made by our research group in the design and development of engineered block-copolymer micelles as targeted nano-therapeutics that can enhance efficacy and reduce toxicity of chemotherapeuty in sensitive and resistant cancer phenotypes will be described. Development of nanoparticlulate delivery systems that can enhance immune responses against cancer leading to the rejection of established tumors in animal models by body’s defense mechanism will also be described.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".