Injectable and Self-Healing Nanocomposite Hydrogels with Ultrasensitive pH-Responsiveness and Tunable Mechanical Properties: Implications for Controlled Drug Delivery
Why this work is in the frame
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Bibliographic record
Abstract
Injectable, self-healing, and pH-responsive hydrogels are great intelligent drug delivery systems for controlled and localized therapeutic release. Hydrogels that show pH-sensitive behaviors in the mildly acidic range are ideal to be used for the treatment of regions showing local acidosis like tumors, wounds and infections. In this work, we present a facile preparation of an injectable, self-healing, and supersensitive pH-responsive nanocomposite hydrogel based on Schiff base reactions between aldehyde-functionalized polymers and amine-modified silica nanoparticles. The hydrogel shows fast gelation within 10 s, injectability, and rapid self-healing capability. Moreover, the hydrogel demonstrates excellent stability under neutral physiological conditions, while a sharp gel-sol transition is observed, induced by a faintly acidic environment, which is desirable for controlled drug delivery. The pH-responsiveness of the hydrogel is ultrasensitive, where the mechanical properties, hydrolytic degradation, and drug release behaviors can alter significantly when subjected to a slight pH change of 0.2. Additionally, the hydrogel's mechanical and pH-responsive properties can be readily tuned by its composition. Its excellent biocompatibility is confirmed by cytotoxicity tests toward human dermal fibroblast cells (HDFa). The novel injectable, self-healing, and sensitive pH-responsive hydrogel serves as a promising candidate as a localized drug carrier with controlled delivery capability, triggered by acidosis, holding great promise for cancer therapy, wound healing, and infection treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it