Surfactants: Role in biofilm management and cellular behaviour
Bibliographic record
Abstract
Appropriate and effective wound cleaning represents an important process that is necessary for preparing the wound for improved wound healing and for helping to dislodge biofilms. Wound cleaning is of paramount importance to wound bed preparation for helping to enhance wound healing. Surfactant applications in wound care may represent an important area in the cleaning continuum. However, understanding of the role and significance of surfactants in wound cleansing, biofilm prevention and control, and enhancing cellular viability and proliferation is currently lacking. Despite this, some recent evidence on poloxamer-based surfactants where the surfactants are present in high concentration have been shown to have an important role to play in biofilm management; matrix metalloproteinase modulation; reducing inflammation; and enhancing cellular proliferation, behaviour, and viability. Consequently, this review aims to discuss the role, mode of action, and clinical significance of the use of medically accepted surfactants, with a focus on concentrated poloxamer-based surfactants, to wound healing but, more specifically, the role they may play in biofilm management and effects on cellular repair.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".