Endocytosis of pro‐apoptotic twin‐base RGDSK helical rosette nanotubes in human U937 macrophage cell line
Bibliographic record
Abstract
Twin‐base helical rosette nanotubes (RNTs), a novel class of biologically inspired nanotubes, hold tremendous potential as targeted drug delivery shuttle. To realize biomedical potential of RNT, there is a critical need to understand the interaction of RNT with the cells. Therefore, we synthesized hybrid RNT that composed of RGDSK and FITC to gain ability to track them during in vitro experiments with human differentiated macrophages. Scanning electron microscopy confirmed nano‐scale dimensions of RGDSK RNTs. FITC‐RGDSK RNT was co‐localized with integrin avb3‐Cy5 on the macrophages within 2 minutes of incubation at 37 deg C. Integrin avb3 and flotallin‐1, a lipid rafts protein, showed fever co‐localization in non‐stimulated cells. FITC‐RGDSK RNT also co‐localizes with EEA1, an early endosome marker. The inhibition of the co‐localization of FITC‐RGDSK RNT with the integrin and translocation into endosomes at 4 deg C demonstrated energy‐dependence of the process. The incubation of differentiated macrophages with RGDSK‐TBL RNT (>5 μM) resulted in nearly 100 % apoptosis within 12 hours. These results suggest that the FITC/RGDSK‐TBL RNT could be used as a versatile platform to deliver a variety of biologically active molecules for cancer therapy. Support: NSERC, AARI. Grant Funding Source: AARI, NSERC
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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".