Single Functional Group Platform for Multistimuli Responsivities: Tertiary Amine for CO<sub>2</sub>/pH/ROS-Triggered Cargo Release in Nanocarriers
Bibliographic record
Abstract
The design of multistimuli-responsive soft nanoparticles (NPs) often presents synthetic complexities and limited breadth in exploiting changes surrounding physiological environments. Nanocarriers that could collectively take advantage of several endogenous stimuli can offer a powerful tool in nanomedicine. Herein, we have capitalized on the chemical versatility of a single tertiary amine to construct miktoarm polymer-based nanocarriers that respond to dissolved CO 2, varied pH, reactive oxygen species (ROS), and ROS + CO 2 . Curcumin (Cur), an anti-inflammatory phytopharmaceutic, was loaded into micelles, and we validated the sensitivity of the tertiary amine in tuning Cur release. An in vitro evaluation indicated that Cur encapsulation strongly suppressed its toxicity at high concentrations, significantly inhibited nigericin-induced secretion of interleukin-1β by THP-1 macrophages, and the proportion of M2/M1 (anti-inflammatory/pro-inflammatory macrophages) was higher for Cur-loaded NPs than for free Cur. Our approach highlights the potential of a simple-by-design strategy in expanding the scope of polymeric NPs in drug delivery.
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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) | 0.002 | 0.001 |
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".