A Multivalent Approach to Triggerable-Release Cancer Drug Delivery Systems
Bibliographic record
Abstract
Cancer continues to be one of the largest health concerns in Canada with approximately 43% of Canadians expected to be diagnosed in their lifetime. However, traditional chemotherapy methods often create complications from nonspecific drug distribution and poor penetration into tumors, providing an inefficient method for suppressing tumor growth and metastasis, and causing indiscriminate harm to healthy cells in the body. The damage that is caused to healthy cells is the root of most destructive and painful side-effects associated with chemotherapy, including nausea, fatigue, hair loss, mouth sores, fertility issues, and organ damage . Nanodiamonds, microscopic diamond particles, have recently gained popularity in medical applications due to their low cost and negligible toxicity. Additionally, their large surface area allows them to be easily modified with biocompatible attachments like polyethylene glycol (PEG) chains and a self-immolative drug linker, which acts as an efficient drug carrier due to its increased loading site. The Trant Team seeks to design and characterize a selective drug delivery system utilizing the pH-sensitive linker property to release the drug in the cancer cell's acidic environment, reducing harm to not-as-acidic healthy cells. Previous work within the team used nanodiamond single valent carriers in preliminary studies. This presentation will describe multivalent modifications to further increase the loading capacity. Once synthesized and characterized, this drug delivery system is to be tested in vivo on zebrafish to observe its safety and efficacy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".