Rapid Screening Method for Mycobactericidal Activity of Chemical Germicides That Uses <i>Mycobacterium terrae</i> Expressing a Green Fluorescent Protein Gene
Bibliographic record
Abstract
The slow growth of mycobacteria in conventional culture methods impedes the testing of chemicals for mycobactericidal activity. An assay based on expression of the green fluorescent protein (GFP) by mycobacteria was developed as a rapid alternative. Plasmid pBEN, containing the gene encoding a red-shifted, high-intensity GFP mutant, was incorporated into Mycobacterium terrae (ATCC 15755), and GFP expression was observed by epifluorescence microscopy. Mycobactericidal activity was assessed by separately exposing a suspension of M. terrae(pBEN) to several dilutions of test germicides based on 7.5% hydrogen peroxide, 2.4% alkaline glutaraldehyde, 10% acid glutaraldehyde, and 15.5% of a phenolic agent for contact times ranging from 10 to 20 min (22 degrees C), followed by culture of the exposed cells in broth (Middlebrook 7H9) and measurement of fluorescence every 24 h. When the fluorescence was to be compared with CFU, the samples were plated on Middlebrook 7H11 agar and incubated for 4 weeks. No increase in fluorescence or CFU occurred in cultures in which the cells had been inactivated by the germicide concentrations tested. Where the test bacterium was exposed to ineffective levels of the germicides, fluorescence increased after a lag period of 1 to 7 days, corresponding to the level of bacterial inactivation. In untreated controls, fluorescence increased rapidly to reach a peak in 2 to 4 days. A good Pearson correlation coefficient (r > or =0.85) was observed between the intensity of fluorescence and the number of CFU. The GFP-based fluorescence assay reduced the turnaround time in the screening of chemical germicides for mycobactericidal activity to < or =7 days.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".