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Record W4362670298 · doi:10.54097/hset.v40i.6589

Nanotechnology in Cancer Diagnostics and Therapeutics

2023· article· en· W4362670298 on OpenAlex

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

VenueHighlights in Science Engineering and Technology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNanotechnologyCancerApplications of nanotechnologyNanomedicineCancer therapyNanoparticleMedicineRisk analysis (engineering)Computer scienceMaterials scienceInternal medicine

Abstract

fetched live from OpenAlex

In each and every nation on the planet, cancer continues to be one of the main causes of mortality and a serious impediment to the advancement of efforts to extend the human lifespan. Now, the growth of nanotechnology has led to new ideas and approaches in the detection and treatment of cancer. These new ideas and methods were developed by researching and developing the one-of-a-kind features of materials at the nanoscale. In terms of detection and therapy, the effects that nanotechnology has had on cancer are discussed in this research, including the use of gold nanoparticles, electronic noses and gadolinium (III) oxide nanoparticles in diagnostic imaging as well as analysis of tumor-targeted therapies and nanoparticle drug transport, and concludes with a summary of the advantages and potential risks of nanoparticles. In general, nanotechnology has the potential to improve the sensitivity of detection methods, the accuracy of diagnostic results, and significantly boost treatment outcomes, thus opening up a new research avenue for the field of cancer science.

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 score0.023
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.269
Teacher spread0.260 · 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