Still not sterile: viability-based assessment of the skin microbiome following pre-surgical application of a broad-spectrum antiseptic reveals transient pathogen enrichment and long-term recovery
Bibliographic record
Abstract
ABSTRACT Broad-spectrum antiseptics such as chlorhexidine gluconate (CHG) have widespread use as pre-surgical tools to lower skin microbial burden and reduce the risk of surgical site infection. However, the short- and long-term effects of CHG on healthy skin microbial communities remain undefined due to the confounding effects of CHG binding with persistent bacterial DNA on the skin surface. Here, we aim to accurately characterize the immediate and long-term impact of pre-surgical preparation with CHG-based antiseptics on the human skin microbiome. Twenty-eight patients undergoing elective surgeries were enrolled. Swabs of the surgical site and a control site skin microbiome were collected at multiple time points before and up to 2 weeks after surgery. A propidium monoazide (PMAxx)-based viability assay was optimized to selectively evaluate DNA from live microbes in complex skin microbial communities with viability-qPCR and viable 16S ribosomal RNA gene profiling. Pre-operative CHG induces 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 also demonstrate resistance to CHG with minimum inhibitory concentrations exceeding 1,000 µg/mL. Although there are major skin microbiome shifts upon exposure to CHG, we also find that these shifts are largely transient. For the majority of individuals, skin microbial bioburden and community structure recover to near baseline by post-surgical follow-up. IMPORTANCE Surgical site infections continue to occur despite widespread adoption of surgical antiseptics. Before surgery, patients often wash their whole body multiple times with chlorhexidine gluconate (CHG)-based antiseptic soap and have CHG applied to the surgical site in the operating room. However, the effects of CHG antiseptics on the healthy skin microbiome are undefined due to CHG persisting and binding DNA from dead cells on the skin. We optimized a viability assay to selectively target DNA from live microbes on the skin before and after exposure to CHG. Our findings demonstrate that pre-surgical application of CHG significantly reduces the bioburden on skin; however, potentially pathogenic bacteria remain. Post-surgery, the skin microbiome eventually recovers to resemble its pre-CHG exposed state. Collectively, these findings identify tangible avenues for improving antiseptic formulations and further support that the skin microbiome is viable, stable, and resilient to chemical perturbation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".