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Record W1987554255 · doi:10.1109/iembs.2010.5627106

Effect of the chain of magnetosomes embedded in magnetotactic bacteria and their motility on Magnetic Resonance imaging

2010· article· en· W1987554255 on OpenAlexaff
Ouajdi Felfoul, N Mokrani, Mojtaba Mohammadi, Sylvain Martel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsPolytechnique Montréal
FundersNational Institute of Biomedical Imaging and Bioengineering
KeywordsMagnetotactic bacteriaMagnetosomeMagnetic resonance imagingNuclear magnetic resonanceMagnetic nanoparticlesBacteriaNanoparticleChemistryBiophysicsMaterials scienceBiologyNanotechnologyPhysicsMedicine

Abstract

fetched live from OpenAlex

This paper investigates the influence of the magnetosome's chain, the motility, and the bacterial cell of MC-1 magnetotactic bacteria (MTB) on the Magnetic Resonance imaging (MRI) contrast. Because of its embedded magnetic nanoparticles, that allow magnetic guidance and imaging contrast generation under MRI, magnetotactic bacteria are being considered for therapeutic drug delivery to tumors. In order to separately investigate the different potential sources of contrast in MRI, we used three samples of MC-1 MTB. The first sample was constituted of magnetic bacteria that successfully synthesize magnetic nanoparticles. MC-1 bacteria that do not synthesize magnetosomes form the second sample while the third sample is constituted from dead MC-1 magnetic bacteria containing magnetic nanoparticle. T(2)-weighted magnetic resonance images were obtained for multiple echo times. T(2) was then estimated by fitting the signal intensity data for different echo time values to a monoexponential decay curve. It is found that nanoparticles synthesized by MC-1 MTB are the predominant source of contrast in MRI over motion and the cell body.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.621

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 head0.207
Teacher spread0.204 · 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

Citations14
Published2010
Admission routes1
Has abstractyes

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