Capsaicin potently blocks <i>Salmonella typhimurium</i> invasion of Vero cells
Bibliographic record
Abstract
Abstract As at 2021, the center for disease control (CDC) reported that Salmonella causes 1.2 million illness in the United States each year, with a mortality rate approaching 500 deaths per annum. Infants, the elderly, and persons with compromised immunity are the population with higher risk of mortality from this infection. At present there is no commercially available, safe and efficacious vaccine for the control and management of Salmonella typhimurium ( S. typhimurium) . More so, S. typhimurium has been shown to develop resistance against most antibiotics used for treatment of the infection. Capsaicin, a bioactive compound from Capsicum chinense (C. chinenses) is undoubtedly one of the most widely used spice in the world. This heat producing compound is not only been used as food additive but have been demonstrated to possess unique properties that have pharmacological, physiological, and antimicrobial applications. In this work, the antimicrobial property of pure capsaicin or capsaicin extract against S. typhimurium is tested to determine the compounds effectiveness in S. typhimurium inhibition. 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 the bacteria. Furthermore, both capsaicin extract and pure capsaicin potently blocked S. typhimurium invasion of an animal cell line in vitro . Taken together, this work revealed 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.
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 | 0.001 |
| 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".