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Visualization of Invasion into the Body and Internal Diffusion of Nanoparticles

2008· article· en· W1986363048 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

VenueKey engineering materials · 2008
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsNanoparticleMaterials scienceVisualizationMicroscopyNanotechnologyBiomedical engineeringDiffusionMicroscopeComputer sciencePathologyArtificial intelligenceMedicinePhysics

Abstract

fetched live from OpenAlex

Nanoparticles may invade directly into the internal body through the respiratory or digestive system and diffuse inside body. The behavior of nanoparticles in the internal body is also essential to comprehend for the realization of DDS. Thus it is necessary to reveal the internal dynamics for the proper treatments and biomedical applications of nanoparticles. In the present study the plural methods with different principles such as X-ray scanning analytical microscope (XSAM), MRI and Fluorescent microscopy were applied to enable the observation of the internal diffusion of micro/nanoparticles in the (1) whole body level, (2) inner organ level and (3) tissue and intracellular level. Chemical analysis was also done by ICP-AES for organs and compared with the results of XSAM mapping.

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 score0.017
Threshold uncertainty score0.219

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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