Carbon nanotube based targeted drug delivery systems for breast cancer and other drug delivery applications
Bibliographic record
Abstract
Breast cancer is the most commonly diagnosed cancer in women and the second leading cause of death among all cancers.Surgical removal of breast tumour tissues is the primary treatment for breast cancer.However, this does not rule out relapse at local or distant sites, so, chemotherapy is widely used as an adjuvant therapy.Although effective, chemotherapy drugs often cause severe side effects due to their non-specificity to cancer cells.Nanotechnology for drug delivery is an emerging field focused on targeting drugs to the desirable sites, such as tumour tissues, while minimizing the unwanted side effects of chemotherapy drugs on other tissues.Discovery of a new type of nanomaterial opens more opportunities for drug delivery.The carbon nanotube (CNT) is a novel type of synthetic material that has shown great potential for targeted delivery of anti-cancer agents.The initial hurdle for biomedical applications of CNT has been its hydrophobicity.Proper surface modification of CNT, or CNT functionalization, so as to P a g e | VI ACKNOWLEGEMENTS I would like to express my deep gratitude to my supervisor Dr. Satya Prakash who has provided me the opportunity to work in his lab.I am impressed with his enthusiasm for pursuing new science, which inspired me to explore various unfamiliar areas in my research.I am very grateful for his guidance, advice, encouragement and constant support in my project.On the other hand, I found it very invaluable that
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".