The Role of Bacterial Biofilm in Adverse Soft-Tissue Filler Reactions: A Combined Laboratory and Clinical Study
Bibliographic record
Abstract
BACKGROUND: The development of chronic nodules and granulomatous inflammation after filler injections has been attributed to bacterial biofilm infection. The authors aimed to investigate the relationship between filler and bacterial biofilm using a combined in vitro and in vivo study. METHODS: In vitro assays to investigate the ability of filler materials to support the growth of Staphylococcus epidermidis biofilm and the effect of multiple needle passes through a biofilm-contaminated surface were designed. Analysis of clinical biopsy specimens from patients presenting with chronic granulomas following filler administration using a number of laboratory tests for biofilm was performed. RESULTS: All fillers (i.e., hyaluronic acid, polyacrylamide gel, and poly-L-lactic acid) supported the growth of S. epidermidis biofilm in vitro. Multiple needle passes through a biofilm-contaminated surface resulted in significantly increased contamination of filler material by a factor of 10,000 (p < 0.001). Six clinical samples from five patients all demonstrated bacterial biofilm. The mean number of bacteria was found to be 2.2 × 10 bacteria/mg tissue (range, 5.6 × 10 to 3.7 × 10 bacteria/mg tissue). Microbiome analysis detected a predominance of Pseudomonas, Staphylococcus, and Propionibacterium as present in these samples. CONCLUSIONS: Filler material can support the growth of bacterial biofilm in vitro. Multiple needle passes can significantly increase the risk of filler contamination. Biofilm appears to be associated with high numbers in clinical samples of patients presenting with chronic granulomatous inflammation. Strategies to reduce the risk of bacterial contamination need to be further studied and translated into clinical practice. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, V.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".