Baicalein Ameliorates Streptococcus suis-Induced Infection In Vitro and In Vivo
Bibliographic record
Abstract
As an important zoonotic pathogen, Streptococcus suis (S. suis) infection has been reported to be a causative agent for variety of diseases in humans and animals, especially Streptococcal toxic shock-like syndrome (STSLS), which is commonly seen in cases of severe S. suis infection. STSLS is often accompanied by excessive production of inflammatory cytokines, which is the main cause of death. This calls for development of new strategies to avert the damage caused by STSLS. In this study, we found for the first time that Baicalein, combined with ampicillin, effectively improved severe S. suis infection. Further experiments demonstrated that baicalein significantly inhibited the hemolytic activity of SLY by directly binding to SLY and destroying its secondary structure. Cell-based assays revealed that Baicalein did not exert toxic effects and conferred protection in S. suis-infected cells. Interestingly, compared with ampicillin alone, Baicalein combined with ampicillin resulted in a higher survival rate in mice severely infected with S. suis. At the same time, we found that baicalein can be combined with meropenem against MRSA. In conclusion, these results indicate that baicalein has a good application prospect.
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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 | 0.000 |
| 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 | 0.001 |
| 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".