Studying the Role of Novel Carbon Nano Tubes as a Therapeutic Agent to Treat Triple Negative Breast Cancer (TNBC) - an In Vitro and In Vivo Study
Bibliographic record
Abstract
Triple Negative Breast Cancer (TNBC) is a malignant cancer with a very high mortality rate around the world. African American(AA) women are 28% more likely to die from triple-negative breast cancer (TNBC) than white women with the same diagnosis. AA patients are also more likely to be diagnosed at a later stage of the disease and have the lowest survival rates for any stage of diagnosis; There are very few existing anti TNBC drugs with therapeutic efficacy hence newer anti TNBC drug design and investigation is needed. Carbon Nano Tubes(CNT) in recent years have shown effective anti-cancer properties in various types of cancers as reported in peer reviewed journals. Henceforth, we did an investigation to study the anticancer properties of a novel CNT in both in vitro and in vivo models of TNBC. We tested the CNT drug in vitro cytotoxicity studies on TNBC model MDA-MB-231 VIM RFP cell lines and Spheroid forming assays on the same cancer cells; we also did an in vivo study on TNBC model mice to study the therapeutic efficacy of this CNT drug in reducing the tumor load. Our initial studies showed increased cell death and reduction in spheroid numbers in the CNT treated cancer cells in comparison to control and a significant reduction in the tumor volume in the TNBC model mice than in untreated animals. Thus our initial studies have shown significant therapeutic efficacy of the novel CNT as an anti TNBC agent. Additional mechanistic studies need to be done to find out the cell death mechanisms, core canonical pathways involved, pharmacokinetic studies before translational research for this novel nanoparticle as a therapeutic agent from bench to bedside.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".