Fast and effective mitochondrial delivery of ω-Rhodamine-B-polysulfobetaine-PEG copolymers
Bibliographic record
Abstract
Abstract Mitochondrial targeting and entry, two crucial steps in fighting severe diseases resulting from mitochondria dysfunction, pose important challenges in current nanomedicine. Cell-penetrating peptides or targeting groups, such as Rhodamine-B (Rho), are known to localize in mitochondria, but little is known on how to enhance their effectiveness through structural properties of polymeric carriers. To address this issue, we prepared 8 copolymers of 3-dimethyl(methacryloyloxyethyl)ammonium propane sulfonate and poly(ethyleneglycol) methacrylate, p(DMAPS-ran-PEGMA) (molecular weight, 18.0 < M n < 74.0 kg/mol) with two different endgroups. We labeled them with Rho groups attached along the chain or on one of the two endgroups (α or ω). From studies by flow cytometry and confocal fluorescence microscopy of the copolymers internalization in HeLa cells in the absence and presence of pharmacological inhibitors, we established that the polymers cross the cell membrane foremost by translocation and also by endocytosis, primarily clathrin-dependent endocytosis. The most effective mitochondrial entry was achieved by copolymers of M n < 30.0 kg/mol, lightly grafted with PEG chains (< 5 mol %) labeled with Rho in the ω-position. Our findings may be generalized to the uptake and mitochondrial targeting of prodrugs and imaging agents with a similar polymeric scaffold.
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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".