Covalent Binding of Nanoliposomes to the Surface of Magnetotactic Bacteria for the Synthesis of Self-Propelled Therapeutic Agents
Bibliographic record
Abstract
The targeted and effective delivery of therapeutic agents remains an unmet goal in the field of controlled release systems. Magnetococcus marinus MC-1 magnetotactic bacteria (MTB) are investigated as potential therapeutic carriers. By combining directional magnetotaxis-microaerophilic control of these self-propelled agents, a larger amount of therapeutics can be delivered surpassing the diffusion limits of large drug molecules toward hard-to-treat hypoxic regions in solid tumors. The potential benefits of these carriers emphasize the need to develop an adequate method to attach therapeutic cargos, such as drug-loaded nanoliposomes, without substantially affecting the cell's ability to act as delivery agents. In this study, we report on a strategy for the attachment of liposomes to MTB (MTB-LP) through carbodiimide chemistry. The attachment efficacy, motility, and magnetic response of the MTB-LP were investigated. Results confirm that a substantial number of nanoliposomes (∼70) are efficiently linked with MTB without compromising functionality and motility. Cytotoxicity assays using three different cell types (J774, NIH/3T3, and Colo205) reveal that liposomal attachments to MTB formulation improve the biocompatibility of MTB, whereas attachment does not interfere with liposomal uptake.
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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 | 0.000 | 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".