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Record W2071969511 · doi:10.1504/ijnt.2008.016551

Medical nanotechnology using genetic material and the need for precaution in design and risk assessments

2008· article· en· W2071969511 on OpenAlexaff
Michael G. Tyshenko

Bibliographic record

VenueInternational Journal of Nanotechnology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsInstitute of Population and Public HealthUniversity of Ottawa
Fundersnot available
KeywordsNanotechnologyDendrimerDrug deliveryNanomedicineRisk analysis (engineering)Computer scienceData scienceMaterials scienceNanoparticleMedicine

Abstract

fetched live from OpenAlex

As a new field of research, medical nanotechnology promises a suite of potential applications for drug delivery, diagnostics and gene therapy. Deoxyribonucleic acid (DNA) is ideal at the nanoscale for use as a backbone when making scaffolded structures and more complex dendrimer constructions owing to its properties as a rigid, linear molecule. Much attention has focused on benefits from scaffolded nanoscale constructs but little has been mentioned about the risks associated with such material once inside the human body. The public is starting to focus on the health impacts of nanotechnology including the toxic effects of nanoparticles to living systems. Even though degraded nano-scale genetic material is unlikely to present any significant problems when used therapeutically, the potential risks may be avoided by using bioinformatics database resources when designing these kinds of nanotechnology-based therapeutics.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.001
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 score0.134
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.311
Teacher spread0.296 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2008
Admission routes1
Has abstractyes

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