In search of Broad Applicable, Small Molecule Inhibitors of Salmonella Biofilm Formation
Bibliographic record
Abstract
Salmonella is an important cause of foodborne infections worldwide. A major difficulty in the battle against Salmonella is the fact that Salmonella can form biofilms on various biotic and abiotic surfaces, both in- and outside the host. Therefore, the inhibition of these biofilms could be an effective way to combat Salmonella. We are currently conducting a high-throughput screening (using the ‘Calgary Biofilm device’) of a compound library consisting of > 20.000 small molecules, in search of Salmonella biofilm inhibitors which are active at a broad temperature range and therefore have potential to be used both in- and outside the host. The compounds have a molecular weight between 200 and 500 dalton and are selected on their possible drug ability. The screening is executed both at 16 °C and 37 °C. We aim at identifying compounds that inhibit biofilm formation, without killing the bacteria. This way, the development of resistance is less likely. After screening of 16.000 compounds (80%), we identified 133 possible biofilm inhibitors. Subsequently the dose-response relationship of the ‘hits’ was determined, as well as the growth-influences of the compounds. The compounds with maximum biofilm inhibitory capacities and minimal growth influences, were studied further, both on prevention and destruction of biofilms from Salmonella and Pseudomonas. Using these results we identified 8 ‘lead’ families off which the “structure-activity relationship” will be determined aswell as the activity in different in vitro and in vivo testsystems. Finally the ‘Mode of Action’ of will be determined using reportergene studies and/or microarray analysis.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".