Engineering Multi-Walled Carbon Nanotube Therapeutic Bionanofluids to Selectively Target Papillary Thyroid Cancer Cells
Bibliographic record
Abstract
BACKGROUND: The incidence of papillary thyroid carcinoma (PTC) has risen steadily over the past few decades as well as the recurrence rates. It has been proposed that targeted ablative physical therapy could be a therapeutic modality in thyroid cancer. Targeted bio-affinity functionalized multi-walled carbon nanotubes (BioNanofluid) act locally, to efficiently convert external light energy to heat thereby specifically killing cancer cells. This may represent a promising new cancer therapeutic modality, advancing beyond conventional laser ablation and other nanoparticle approaches. METHODS: Thyroid Stimulating Hormone Receptor (TSHR) was selected as a target for PTC cells, due to its wide expression. Either TSHR antibodies or Thyrogen or purified TSH (Thyrotropin) were chemically conjugated to our functionalized Bionanofluid. A diode laser system (532 nm) was used to illuminate a PTC cell line for set exposure times. Cell death was assessed using Trypan Blue staining. RESULTS: TSHR-targeted BioNanofluids were capable of selectively ablating BCPAP, a TSHR-positive PTC cell line, while not TSHR-null NSC-34 cells. We determined that a 2:1 BCPAP cell:α-TSHR-BioNanofluid conjugate ratio and a 30 second laser exposure killed approximately 60% of the BCPAP cells, while 65% and >70% of cells were ablated using Thyrotropin- and Thyrogen-BioNanofluid conjugates, respectively. Furthermore, minimal non-targeted killing was observed using selective controls. CONCLUSION: A BioNanofluid platform offering a potential therapeutic path for papillary thyroid cancer has been investigated, with our in vitro results suggesting the development of a potent and rapid method of selective cancer cell killing. Therefore, BioNanofluid treatment emphasizes the need for new technology to treat patients with local recurrence and metastatic disease who are currently undergoing either re-operative neck explorations, repeated administration of radioactive iodine and as a last resort external beam radiation or chemotherapy, with fewer side effects and improved quality of life.
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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) | 0.000 | 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 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".