Antibacterial and Analgesic Properties of Beta-Caryophyllene in a Murine Urinary Tract Infection Model
Bibliographic record
Abstract
Beta-caryophyllene has demonstrated anti-inflammatory effects in a variety of conditions, including interstitial cystitis. These effects are mediated primarily via the activation of the cannabinoid type 2 receptor. Additional antibacterial properties have recently been suggested, leading to our investigation of the effects of beta-caryophyllene in a murine model of urinary tract infection (UTI). Female BALB/c mice were intravesically inoculated with uropathogenic Escherichia coli CFT073. The mice received either beta-caryophyllene, antibiotic treatment using fosfomycin, or combination therapy. After 6, 24, or 72 h, the mice were evaluated for bacterial burden in the bladder and changes in pain and behavioral responses using von Frey esthesiometry. In the 24 h model, the anti-inflammatory effects of beta-caryophyllene were also assessed using intravital microscopy. The mice established a robust UTI by 24 h. Altered behavioral responses persisted 72 h post infection. Treatment with beta-caryophyllene resulted in a significant reduction in the bacterial burden in urine and bladder tissues 24 h post UTI induction and significant improvements in behavioral responses and intravital microscopy parameters, representing reduced inflammation in the bladder. This study demonstrates the utility of beta-caryophyllene as a new adjunct therapy for the management of UTI.
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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) | 0.000 | 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 | 0.001 |
| 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".