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Record W2167495017 · doi:10.1109/iembs.2006.260413

Towards MRI-Controlled Ferromagnetic and MC-1 Magnetotactic Bacterial Carriers for Targeted Therapies in Arteriolocapillar Networks Stimulated by Tumoral Angiogenesis

2006· article· en· W2167495017 on OpenAlexaff
Sylvain Martel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsMagnetotactic bacteriaAngiogenesisMagnetosomeFerromagnetismCancer researchBiomagnetismMaterials scienceChemistryNanotechnologyNuclear magnetic resonanceCell biologyOptoelectronicsMedicinePhysicsBiologyMagnetic fieldCondensed matter physics

Abstract

fetched live from OpenAlex

The delivery of a therapeutic agent through controlled carriers directly to the tumoral lesion can enhance treatment efficacy by reducing dosage while minimizing systemic circulation of toxic compounds through healthy tissues. As such, the induction of a feedback controlled steering force on ferromagnetic carriers from magnetic gradients generated by an upgraded clinical magnetic resonance imaging (MRI) system has been demonstrated by our group. But the gradient strengths required in some sections of the capillary network may be technologically very difficult to achieve for human due mainly to the size and cooling issues of additional gradient coils embedded in the MRI bore. As such, the use of MC-1 magnetotactic bacteria (MTB) pushing microbeads with a therapeutic agent may provide complementary means of propulsion in smaller capillaries. Based on preliminary experimental results, carriers and a new method combining the induction of force in a ferromagnetic material with the thrust provided by MTB for direct targeting to the tumor mass are proposed.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.904

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 head0.003
GPT teacher head0.183
Teacher spread0.179 · 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 designSimulation or modeling
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

Citations17
Published2006
Admission routes1
Has abstractyes

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