Microbial community compositions in breast implant biofilms associated with contracted capsules
Bibliographic record
Abstract
Subclinical bacterial infections (biofilms) are strongly implicated in breast augmentation failure due to capsular contracture, and while these infections are generally ascribed to common skin commensals, this remains largely unsubstantiated through robust cultivation independent analyses. To determine capsule biofilm microbial community compositions, we employed amplicon sequencing of the 16S rRNA gene using DNA extracted from breast implant capsule samples. These cultivation independent analyses revealed that capsule associated biofilms are more diverse than canonical single-species infections, but have relatively low diversity (~ <100 species) compared to many host-associated microbial communities. In addition to taxa commonly associated with capsular contracture, the biofilms analyzed comprised a number of taxa that escaped detection in cultivation-dependent work. We have also isolated several key taxa identified through the culture-independent analyses. Together our analyses reveal that capsule biofilms are more diverse than cultivation studies suggest and can be heterogeneous within an individual capsule, between breasts of the same patient, across similar implant types, and over a range in severity of contracture. The complex nature of these communities requires further study across a broader suite of patients in addition to higher resolution analyses including metagenomics to better assess the fundamental role of microorganisms in capsular contracture.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".