Bibliographic record
Abstract
Lab coats are widely used in biohazard laboratories and healthcare facilities as protective garments to prevent direct exposure to pathogens, spills, and burns. These cotton-based protective coats provide ideal conditions for microbial growth and attachment sites due to their porous nature, moisture-holding capacity, and retention of warmth from the user's body. Several studies have demonstrated the survival of pathogenic bacteria on hospital garments and lab coats, acting as vectors of microbial transmission. A common approach to fix these problems is the application of antimicrobial agents in textile finishing, but concerns have been raised due to the toxicity and environmental effects of many synthetic chemicals. The ongoing pandemic has also opened a window for the investigation of effective antimicrobials and eco-friendly and toxic-free formulations. This study uses two natural bioactive compounds, carvacrol and thymol, encapsulated in chitosan nanoparticles, which guarantee effective protection against four human pathogens with up to a 4-log reduction (99.99%). These pathogens are frequently detected in lab coats used in biohazard laboratories. The treated fabrics also resisted up to 10 wash cycles with 90% microbial reduction, which is sufficient for the intended use. We made modifications to the existing standard fabric tests to better represent the typical scenarios of lab coat usage. These refinements allow for a more accurate evaluation of the effectiveness of antimicrobial lab coats and for the simulation of the fate of any accidental microbial spills that must be neutralized within a short time. Further studies are recommended to investigate the accumulation of pathogens over time on antimicrobial lab coats compared to regular protective coats.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.471 | 0.315 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".