Capsaicin Potently Blocks Salmonella typhimurium Invasion of Vero Cells
Bibliographic record
Abstract
Salmonella typhimurium (S. typhimurium) is one of the major food and waterborne bacteria that causes several health outbreaks in the world. Although there are few antibiotics against this bacterium, some of these drugs are challenged with resistance and toxicity. To mitigate this challenge, our group explored the ethnomedicinal/herbalism knowledge about a certain spice used in Northern Ghana in West Africa against bacterial and viral infection. This plant is Capsicum chinense (C. chinense). The plant is one of the commonest food spices consumed across the world. The seed of the plant contains both capsaicin and dihydrocapsaicin. Apart from capsaicin and dihydrocapsaicin, other major capsaicinoids in C. chinense include nordihydrocapsaicin, homodihydrocapsaicin, and homocapsaicin. In this pilot work, we investigated the antibacterial activity of pure capsaicin and capsaicin extract obtained from C. chinense against S. typhimurium in vitro. Capsaicin extract showed potent inhibition of S. typhimurium growth at concentrations as low as 100 ng/mL, whereas pure capsaicin comparatively showed poorer inhibition of bacteria growth at such a concentration. Interestingly, both capsaicin extract and pure capsaicin were found to potently block a S. typhimurium invasion of the Vero cell in vitro. Taken together, we believed that capsaicin might work synergistically with dihydrocapsaicin or the other capsaicinoids to inhibit S. typhimurium growth, whereas individually, capsaicin or dihydrocapsaicin could potently block the bacteria entry and invasion of Vero cells.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".