RGDSK/K rosette nanotubes induce inflammation and apoptosis through phosphorylation of p38 MAPK in human lung adenocarcinoma (Calu‐3) cells
Bibliographic record
Abstract
Rosette nanotubes (RNTs), a novel class of biologically inspired nanotubes, hold tremendous potential as targeted drug delivery shuttle. We synthesized hybrid RNTs that composed of RGDSK‐ and K‐functionalized RNTs in a ratio of 1:10 M (RGDSK/K‐RNTs). We noticed the expression of avb3 integrin on Calu‐3 cells and believe that these RNTs may bind to integrin. We investigated cell signaling events caused by these RNTs for induction of inflammation and apoptosis in Calu‐3 cells. RGDSK/K‐RNTs rapidly induced phosphorylation of p38 MAPK. RGDSK/K‐RNTs (>10:100 μM) promoted p38 MAPK‐dependent secretion of TNF‐α. All the tested concentrations of RGDSK/K‐RNTs resulted in an increase in caspase‐3 activity and DNA fragmentation in Calu‐3 cells at 18 hours of the exposure. Blocking phosphorylation of p38 MAPK strongly inhibited caspase‐3 activity and DNA fragmentation. Pro‐apoptotic properties of RGDSK/K‐RNTs were also supported by over‐expression of pro‐apoptotic genes. We therefore conclude that RGDSK/K‐RNTs induce phosphorylation of p38 MAPK, which regulates secretion of TNF‐α, activation of caspase‐3 and apoptosis in Calu‐3 cells. These results suggest that the RGDSK/K‐RNTs could be used as a drug to induce apoptosis in cancer cells or as a versatile platform to deliver a variety of biologically active molecules for cancer therapy. Support: NSERC, AARI. Grant Funding Source NSERC discovery, AARI
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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".