Beyond Inflammation: Decoding the Bacterial Landscape of Granulomatous Mastitis
Bibliographic record
Abstract
Background: Idiopathic granulomatous mastitis (IGM) is a rare, chronic inflammatory breast disease with an unclear etiology. This study aimed to investigate the potential microbial involvement in IGM by detecting bacterial DNA in biopsy samples. Methods: This cross-sectional study included 22 patients with histopathologically confirmed IGM, selected through convenience sampling from Besat Hospital, Sanandaj, Iran, in 2019. DNA was extracted from biopsy samples, and the 16S rRNA gene was amplified using universal primers. The amplified products were sequenced, and bacterial species were identified using NCBI BLAST. Results: The mean age of the patients was 35.23 years. DNA analysis revealed Escherichia coli in 21 of 22 samples (95.5%) and Staphylococcus lugdunensis in 1 sample (4.5%). The most common inflammatory symptom was erythema, observed in 8 patients (36.4%), while deep collections were the most frequent tissue abnormality, found in 10 patients (45.5%). Conclusion: The detection of E. coli in most samples suggests a potential bacterial role in IGM pathogenesis. Further research, including control samples from normal breast tissue, is needed to validate these findings and evaluate the potential benefits of molecular testing in clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".