Detection of bacterial fluorescence from in vivo wound biofilms using a point‐of‐care fluorescence imaging device
Bibliographic record
Abstract
Abstract Wound biofilms must be identified to target disruption and bacterial eradication but are challenging to detect with standard clinical assessment. This study tested whether bacterial fluorescence imaging could detect porphyrin‐producing bacteria within a biofilm using well‐established in vivo models. Mouse wounds were inoculated on Day 0 with planktonic bacteria (n = 39, porphyrin‐producing and non‐porphyrin‐producing species, 107 colony forming units (CFU)/wound) or with polymicrobial biofilms (n = 16, 3 biofilms per mouse, each with 1:1:1 parts Staphylococcus aureus/Escherichia coli/Enterobacter cloacae, 107 CFU/biofilm) that were grown in vitro. Mouse wounds inoculated with biofilm underwent fluorescence imaging up to Day 4 or 5. Wounds were then excised and sent for microbiological analysis. Bacteria‐matrix interaction was assessed with scanning electron microscopy (SEM) and histopathology. A total of 48 hours after inoculation with planktonic bacteria or biofilm, red fluorescence was readily detected in wounds; red fluorescence intensified up to Day 4. Red fluorescence from biofilms persisted in excised wound tissue post‐wash. SEM and histopathology confirmed bacteria‐matrix interaction. This pre‐clinical study is the first to demonstrate the fluorescence detection of bacterial biofilm in vivo using a point‐of‐care wound imaging device. These findings have implications for clinicians targeting biofilm and may facilitate improved visualisation and removal of biofilms.
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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 | 0.001 | 0.001 |
| 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".