A novel dual-staining method for cost-effective visualization and differentiation of microbial biofilms
Bibliographic record
Abstract
Microbial biofilms are intricate communities that pose significant challenges in clinical and microbiological settings due to their resistance to antibiotics and immune responses. Advanced microscopy techniques, such as scanning electron microscopy (SEM), confocal laser scanning microscopy (CLSM), and fluorescence microscopy, are often employed to visualize and differentiate between these biofilms. However, these methods are not feasible in all laboratories because of their high cost and complexity. In contrast, simpler techniques like crystal violet and Congo red staining fail to differentiate bacterial cells from the biofilm matrix. This study introduces a novel dual-staining method using Maneval's stain for microbial biofilm detection and differentiation. This simple, cost-effective method requires only basic equipment and minimal reagents, making it suitable for routine use across various settings. We applied the dual-staining method to various microbial species, including Staphylococcus aureus, Enterococcus faecalis, Candida albicans, Escherichia coli, and Pseudomonas aeruginosa. When compared with the microtiter plate assay, results showed strong agreement, with the dual-staining method effectively differentiating between bacterial cells and the surrounding biofilm matrix, displaying a distinctive blue polysaccharide layer surrounding the magenta‒red bacterial cells. This technique offers a viable alternative to more expensive and complex biofilm detection methods, with potential applications in clinical diagnostics and biofilm research.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".