Still Not Sterile: Chlorhexidine gluconate treatment does not completely reduce skin microbial bioburden and promotes pathogen overabundance in patients undergoing elective surgeries
Bibliographic record
Abstract
Abstract Surgical site infections (SSI) continue to occur despite widespread adoption of surgical antiseptics. The effects of chlorhexidine gluconate (CHG)-based antiseptics on the skin microbiome also remains undefined due to confounding effects of CHG persistence on skin. Patients undergoing elective surgery were enrolled to characterize the immediate and long-term impact of pre-surgical preparation with CHG antiseptic on skin microbial communities. Due to the broad-spectrum antimicrobial activity of CHG and its propensity to bind extracellular DNA, methods to selectively identify live microorganisms are critical to this process and to fully elucidate the effectiveness of pre-surgical protocols and potential disruptions to the healthy skin microbiome. Swabs of the surgical site skin microbiome were collected at multiple timepoints before and after surgery. Microbial bioburden and community compositions were evaluated with viability qPCR and 16S ribosomal RNA gene profiling. Pre-operative CHG induced a measurable reduction in the viable microbial bioburden at the surgical site. On the day of surgery, surgical sites displayed a significant increase in the relative abundance of several SSI associated bacterial genera, including , Acinetobacter, Bacillus, Escherichia-Shigella, and Pseudomonas , compared to baseline . Bacillus species isolated from subjects at baseline showed resistance to CHG with MICs exceeding 1000 µg/ml. Despite major shifts in the skin microbiome upon exposure to CHG, they were transient in the majority of individuals. Skin microbial community structure recovered by the post-surgical follow-up. In short, this study shows that pre-surgical application of CHG can significantly reduce viable skin microbial bioburden, however, complete sterility is not achieved. While CHG induces temporary shifts in the skin microbiome, including enrichment for potentially pathogenic taxa, the skin microbiome recovers back to near baseline. Collectively, these findings identify tangible avenues for improving antiseptic formulations and offer further support that the skin microbiome is viable, stable, and resilient to chemical perturbation.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".