ANTIMICROBIAL ACTIVITY OF PROSTANORM® AGAINST THE UROPATHOGENS ISOLATED FROM PATIENTS WITH CHRONIC PROSTATITIS
Bibliographic record
Abstract
Background . ProstaNorm® (0.2 g tablets and liquid extract for oral administration) is a complex medication (dry and liquid) extracted from a mixture of medicinal plants (Hyperici herb, Canadian goldenrod herb, Glycyrrhiza roots, Echinacea purpurea rhizome and roots). It has been successfully used during the last 20 years for the treatment of chronic prostatitis (CP) (especially the abacterial form), CP-associated infertility (spermatopathy), and for the restoration of normal urination. Previous studies have already demonstrated good antimicrobial activity of ProstaNorm®, which enables its use in the treatment of chronic bacterial prostatitis (CBP), especially taking into account other well-known effects of the drug (anti-inflammatory and analgesic activity, improvement of the prostatic tissue microcirculation and sperm characteristics). Objective : to evaluate in vitro bacteriostatic activity of ProstaNorm® against uropathogens isolated from patients with urinary tract infections (mostly with CP). Materials and methods. The following strains of gram-positive and gram-negative bacteria and fungi were isolated from urine, prostatic fluid, and ejaculate of patients with urinary tract infections: Streptococcus haemolyticus (n = 10), Streptococcus aureus (n = 3), D-group Streptococcus (n = 5), Enterococcus faecalis (n = 4), Enterococcus faecium (n = 5), Escherichia coli (n = 4), Klebsiella pneumonia (n = 3), Pseudomonas aeruginosa (n = 7), Candida albicans (n = 4), Candida glabrata (n = 2). We assessed antibacterial activity of ProstaNorm® (using the dilutions from 1:20 to 1:1280 of the initial 10% solution) against the isolated uropathogens. Results . We found that ProstaNorm® has a pronounced bacteriostatic effect against gram-negative bacteria (with the highest sensitivity shown by all Klebsiella pneumonia strains and 1 strain of Pseudomonas aeruginosa, when the microbial growth delay was 4.5–5.0 hours) and gram-positive bacteria (enterococci, hemolytic streptococci, and D-group streptococci). Additionally, the drug exhibits a bactericidal effect against some strains of gram-positive bacteria (three strains of streptococci). However, ProstaNorm® has no effect against Candida. Conclusion . Our results allow recommending ProstaNorm® for the treatment of chronic prostatitis accompanied by urinary tract infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".