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Record W1965281948 · doi:10.1155/2011/130721

Pilot Study of Inhaled Aerosols Targeted via Magnetic Alignment of High Aspect Ratio Particles in Rabbits

2010· article· en· W1965281948 on OpenAlexafffund
Gillian Redman, Andrew R. Martin, Paul Waszak, Richard B. Thompson, Po‐Yin Cheung, Bernard Thébaud, Warren H. Finlay

Bibliographic record

VenueJournal of Nanomaterials · 2010
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceLungTargeted drug deliveryMagnetic resonance imagingDrug deliveryMagnetic nanoparticlesInhalationNanoparticleNanotechnologyBiomedical engineeringMedicineInternal medicineRadiologyAnatomy

Abstract

fetched live from OpenAlex

Recently, inhaled pharmaceutical aerosols have seen increased investigation in the treatment of lung cancer, where the inability to deliver adequate therapeutic drug concentrations to tumour sites may be overcome with improved targeted delivery to the site of the tumour. In this study, the feasibility of magnetically targeted delivery of high aspect ratio particles loaded with iron oxide nanoparticles was studied in 19 New Zealand White rabbits. Half of the exposed rabbits had a magnetic field placed externally over their right lung. Iron sensitive magnetic resonance images of the lungs were acquired to determine the iron concentrations in the right and left lung of each animal. The right/left ratio increased in the middle and basal regions of the lung where, due to the morphology of the rabbit lung, this method of targeting is most effective. With further optimization, this technique could be an effective method for increasing the dose of drug delivered to a specific site within the lung.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.264
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations75
Published2010
Admission routes2
Has abstractyes

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