Synergistic integration of hydrogels and cold plasma for biomedical applications and therapeutics
Bibliographic record
Abstract
The synergistic integration of hydrogels (HGs) and cold atmospheric plasma (CAP) represents a transformative advancement in biomaterials and plasma medicine, opening new pathways for next-generation therapeutics. HGs, as highly hydrated and biocompatible polymer networks, function as versatile platforms for tissue engineering, drug delivery, and wound management. CAP, a non-thermal ionized gas enriched with reactive oxygen and nitrogen species (RONS), exhibits potent antimicrobial, anti-inflammatory, and regenerative effects. The convergence of HGs and CAP enables the development of dynamic, localized therapeutic systems that support controlled and stimuli-responsive treatment strategies. This review critically examines the fundamental physicochemical principles of HGs and CAP, elucidates their interactive mechanisms, and highlights integrated applications in wound healing, cancer therapy, and regenerative medicine. Key challenges, including standardization, safety considerations, and mechanistic understanding, are discussed, alongside future perspectives for clinical translation and personalized therapeutics. Overall, the plasma activated HGs (PAHGs) interface holds immense potential to revolutionize biomedical interventions, offering multifunctionality, adaptability, and precision in therapeutic delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".