Cellular Trafficking of Helical Rosette Nanotubes in Dendritic Cells
Bibliographic record
Abstract
Self‐assembling helical rosette nanotubes (HRNs), composed of cytosine and guanine, have multiple applications. HRN interactions with dendritic cells (DCs), sentinels of the immune system, are key in determining HRN safety and viability. Splenic classical and plasmacytoid DCs were isolated from female C57/BL6 mice using magnetic cell sorting. HRN were conjugated with the RGD (arginine, glycine, aspartic acid) peptide and the fluorophore FITC in a ratio of 1:10μM respectively. Confocal microscopy was used to visualize the endocytic pathways used, HRN intracellular localization and the real‐time uptake process. HRNs used multiple pathways to take residence within clathrin and caveolin‐coated vesicles, early endosomes, and lysosomes. Able to recognize RGD, the integrin αVβ3 is a potential receptor for receptor‐mediated endocytosis. HRNs co‐localized with major histocompatibility complexes indicating possible direct interaction leading to antigen presentation. HRN exposure stimulated DC maturation as shown by dendrite formation and maturation marker (CD40, CD83) expression. Cell viability was confirmed through microscopy and caspase 3 and 9 expression. These data demonstrate that HRN engage αVβ3 to enter DCs through multiple, presumably redundant, pathways, and interact with antigen‐presenting molecules. Grant Funding Source : National Sciences and Engineering Research Council of Canada
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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) | 0.000 | 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".