Magnetic Resonance Imaging of Fe<inf>3</inf>O<inf>4</inf> Nanoparticles Embedded in Living Magnetotactic Bacteria for Potential Use as Carriers for In Vivo Applications
Bibliographic record
Abstract
MC-1 Magnetotactic Bacteria (MTB) are studied for their potential use as bio-carriers for drug delivery. The exploitation of the flagella combined with nanoparticles magnetite or magnetosomes chain embedded in each bacterium and used to change the swimming direction of each MTB through magnetotaxis provide both propulsion and steering in small diameters blood vessels. But for guiding these MTB towards a target, being capable to image these living bacteria in vivo using an existing medical imaging modality is essential. Here, it is shown that the magnetosomes embedded in each MTB can be used to track the displacement of these bacteria using an MRI system. In fact, these magnetosomes disturb the local magnetic field affecting T1 and T2-relaxation times during MRI. MR T1-weighted and T2-weighted images as well as T2-relaxivity of MTB are studied in order to validate the possibility of monitoring MTB drug delivery operations using a clinical MR scanner. This study proves that MTB affect much more the T2-relaxation than T1-relaxation rate and can be though as a negative contrast agent. The signal decay in the T2-weighted images is found to change proportionally to the bacterial concentration. These results show that a bacterial concentration of 2.2x10(7) cells/mL can be detected using a T2-weighted image, which is very encouraging to further investigate the application of MTB for in vivo applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".