Self-Healing Guar Gum-Based Nanocomposite Hydrogel Promotes Infected Wound Healing through Photothermal Antibacterial Therapy
Bibliographic record
Abstract
Preventing bacterial infections is a crucial aspect of wound healing. There is an urgent need for multifunctional biomaterials without antibiotics to promote wound healing. In this study, we fabricated a guar gum (GG)-based nanocomposite hydrogel, termed GBTF, which exhibited photothermal antibacterial therapy for infected wound healing. The GBTF hydrogel formed a cross-linked network through dynamic borate/diol interactions between GG and borax, thereby exhibiting simultaneously self-healing, adaptable, and injectable properties. Additionally, tannic acid (TA)/Fe 3+ nanocomplexes (NCs) were incorporated into the hydrogel to confer photothermal antibacterial properties. Under the irradiation of an 808 nm near-infrared laser, the TA/Fe 3+ NCs in the hydrogel could rapidly generate heat, leading to the disruption of bacterial cell membranes and subsequent bacterial eradication. Furthermore, the hydrogels exhibited good cytocompatibility and hemocompatibility, making them a precandidate for preclinical and clinical applications. Finally, they could significantly promote bacteria-infected wound healing by reducing bacterial viability, accelerating collagen deposition, and promoting epithelial remodeling. Therefore, the multifunctional GBTF hydrogel, which was composed entirely of natural substances including guar gum, borax, and polyphenol/ferric ion NCs, showed great potential for regenerating infected skin wounds in clinical applications.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".