Aspirin-Based Organoiron Dendrimers as Promising Anti-Inflammatory, Anticancer, and Antimicrobial Drugs
Bibliographic record
Abstract
Designing nanocarriers with actions directed at a specific organ or tissue is a very promising strategy since it can significantly reduce the toxicity of a bioactive drug. In this study, an organometallic dendrimer was used to synthesize a biocompatible drug delivery system by attaching aspirin to the periphery of the dendrimer. Our goal is to enhance the bioavailability and anticancer activity of aspirin and reduce its toxicity through successive generations of organoiron dendrimers. The biological activity of aspirin-based dendrimer complexes was evaluated. The result of antimicrobial activity of the synthesized dendrimers also demonstrated an increase in their antimicrobial activity with increased generation of the dendrimers for most types of microorganisms. This study reveals for the first time that organoiron dendrimers linked with aspirin exhibit an excellent Gram-negative activity comparable to the reference drug Gentamicin. All synthesized dendrimers were tested for their anticancer activity against breast cancer cell lines (MCF-7), hepatocellular cell lines (Hep-G2), and a non-cancer cell line, Human Embryonic Kidney (HEK293), using the MTT cell viability assay and compared against a standard anticancer drug, Doxorubicin. Compounds G3-D9-Asp and G4-D12-Asp exhibited noticeable activity against both cell lines, both of which were more effective than aspirin itself. In addition, the in vivo anti-inflammatory activity and histopathology of swollen paws showed that the designed aspirin-based dendrimers displayed significant anti-inflammatory activity; however, G2-D6-Asp showed the best anti-inflammatory activity, which was more potent than the reference drug aspirin during the same period. Moreover, the coupling of aspirin to the periphery of organoiron dendrimers showed a significant reduction in the toxicity of aspirin on the stomach.
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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".