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Record W2286859030 · doi:10.1371/journal.pone.0149723

Engineering Multi-Walled Carbon Nanotube Therapeutic Bionanofluids to Selectively Target Papillary Thyroid Cancer Cells

2016· article· en· W2286859030 on OpenAlex
Idit Dotan, Philip J. R. Roche, Miltiadis Paliouras, Elliot J. Mitmaker, Mark Trifiro

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldEngineering
TopicLaser-Ablation Synthesis of Nanoparticles
Canadian institutionsJewish General HospitalMcGill University
FundersIsrael Cancer Research Fund
KeywordsThyroid cancerCarbon nanotubePapillary thyroid cancerCancerNanotechnologyCancer researchMedicineChemistryBiophysicsMaterials scienceBiologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.203
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it