Optimisation of the Microplate Resazurin Assay for Screening and Bioassay‐guided Fractionation of Phytochemical Extracts against <i>Mycobacterium tuberculosis</i>
Bibliographic record
Abstract
INTRODUCTION: Because of increased resistance to current drugs, there is an urgent need to discover new anti-mycobacterial compounds for the development of novel anti-tuberculosis drugs. The microplate resazurin assay (MRA) is commonly used to evaluate natural products and synthetic compounds for anti-mycobacterial activity. However, the assay can be problematic and unreliable when screening methanolic phytochemical extracts. OBJECTIVE: To optimise the MRA for the screening and bioassay-guided fractionation of phytochemical extracts using Mycobacterium tuberculosis H37Ra. METHODS: The effects of varying assay duration, resazurin solution composition, solvent (dimethyl sulphoxide - DMSO) concentration and type of microtitre plate used on the results and reliability of the MRA were investigated. The optimal bioassay protocol was applied to methanolic extracts of medicinal plants that have been reported to possess anti-mycobacterial activity. RESULTS: The variables investigated were found to have significant effects on the results obtained with the MRA. A standardised procedure that can reliably quantify anti-mycobacterial activity of phytochemical extracts in as little as 48 h was identified. The optimised MRA uses 2% aqueous DMSO, with an indicator solution of 62.5 µg/mL resazurin in 5% aqueous Tween 80 over 96 h incubation. CONCLUSION: The study has identified an optimal procedure for the MRA when used with M. tuberculosis H37Ra that gives rapid, reliable and consistent results. The assay procedure has been used successfully for the screening and bioassay-guided fractionation of anti-mycobacterial compounds from methanol extracts of Canadian medicinal plants.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".