Bibliographic record
Abstract
Mechanical damage to the skin due to an injury provoked by a cut or other impact facilitates the penetration, invasion, and colonization of microorganisms into the epithelial layer, favoring the development of infections. This situation is exacerbated when opportunistic microorganisms or pathogens reach deeper layers in the skin, where bacteria can proliferate under aerobic or anaerobic conditions. This sort of infection can compromise healing by delaying the scarring process. More complex situations occur when the wound is caused by burning, leading to further complications because more necrotized tissue is exposed, allowing bacteria to replicate on a greater surface. This situation is exacerbated when the wound starts to exudate during its healing process. Exudates or wound drainage develop a rich environment for epithelial cells to migrate to the wound. It is an aqueous fluid containing nutrients, inflammatory mediators, proteins, leukocytes, etc. Unfortunately, this enriched environment also favors the growth of bacteria that can lead to infected acute or chronic wounds. Therefore, considering the problems that might be derived from a skin injury, the development of effective wound dressing is a need for wound care. This chapter will discuss the applications of lignin in combination with nanocomposites for wound care. These applications include the use of lignin combined with nanoparticles, hydrogels, nanofibers, etc. This chapter will also discuss the toxicity, anti-inflammatory, antioxidant, and antimicrobial properties, and physiological concerns of lignin-based nanocomposites.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".