Adhesion of <i>Bacillus</i> spores and <i>Escherichia coli</i> cells to inert surfaces: role of surface hydrophobicity
Bibliographic record
Abstract
The ability of bacterial spores and vegetative cells to adhere to inert surfaces was investigated by means of the number of adherent spores (Bacillus cereus and Bacillus subtilis spores) and Escherichia coli cells and their resistance to cleaning or rinsing procedures (adhesion strength). Six materials (glass, stainless steel, polyethylene high density (PEHD), polyamide-6, polyvinyl chloride, and Teflon) were tested. Slight differences in the number of adherent spores (less than 1 log unit) were observed between materials, but a higher number of adherent E. coli cells was found on the hydrophobic materials PEHD and Teflon. Conversely, the resistance of both B. cereus and B. subtilis spores to a cleaning procedure was significantly affected by the material. Hydrophobic materials were harder to clean. The topography parameter derived from the Abbott-Firestone curve, RVK, and, to a lesser extent, the widely used roughness parameters RA (average roughness) and Rz (maximal roughness), were related to the number of adherent cells. Lastly, the soiling level as well as the adhesion strength were shown to depend largely on the microorganism. The number of adhering B. cereus hydrophobic spores and their resistance to a cleaning procedure were found to be 10 times greater than those of the B. subtilis hydrophilic spores. Escherichia coli was loosely bound to all the materials tested, even after 24 h biofilm formation.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".