Anti-Mycobacterial Activity of Flavonoid and Pyrimidine Compounds
Bibliographic record
Abstract
We evaluated the anti-mycobacterial effect of a flavonoid 5,7-dihydroxy-2-(4-hydroxyphenyl) 4H-chromen-4-one (1) and two pyrimidines, 4-hydroxy-2-dimethylamino-5-nitroso-6-aminopyrimidine (2) and 2-chloro-5-n-nonylpyrimidine (3) in vitro against Mycobacterium tuberculosis (M. tuberculosis, H37Ra) and Mycobacterium avium (M. avium), using a Microplate Alamar Blue Assay (MABA). The effects of the compounds 1–3 in combination with first- and second-line anti-TB drugs isoniazid, rifampicin, cycloserine, and clarithromycin on the growth of M. tuberculosis and M. avium were also evaluated in in vitro assays. As a single agent, compounds 1 and 2 exhibited modest activity while compound 3 was the most effective against M. tuberculosis and M. avium. When compounds 1–3 were evaluated at lower than 50% of their inhibitory concentrations in a two-drug combination with isoniazid or rifampicin, they showed additive to synergistic interactions. This inhibitory effect was improved when each of the three compounds was tested together in a three-drug combination with two of the first-line anti-TB drugs. Compounds 1–3 also demonstrated strong synergistic interaction in combination with cycloserine and clarithromycin in inhibiting the growth of M. tuberculosis and M. avium, respectively. This study demonstrated that compounds 1–3 have potential to be developed as effective anti-TB agents with combined use.
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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 | 0.001 | 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".