<i>In Vitro</i> Detection of Porphyrin-Producing Wound Bacteria with Real-Time Fluorescence Imaging
Bibliographic record
Abstract
Aim: Fluorescence imaging can visualize polymicrobial populations in chronic and acute wounds based on porphyrin fluorescence. We investigated the fluorescent properties of specific wound pathogens and the fluorescence detected from bacteria in biofilm. Methods: Utilizing Remel Porphyrin Test Agar, 32 bacterial and four yeast species were examined for red fluorescence under 405 nm violet light illumination. Polymicrobial biofilms, supplemented with δ-aminolevulinic acid, were investigated similarly. Results: A total of 28/32 bacteria, 1/4 yeast species and polymicrobial biofilms produced red fluorescence, in agreement with their known porphyrin production abilities. Conclusion: These results identify common wound pathogens capable of producing porphyrin-specific fluorescence and support clinical observations using fluorescence imaging to detect pathogenic bacteria in chronic wounds.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".