Intra-hospital microbiome variability is driven by accessibility and clinical activities.

The hospital environmental microbiome, which can affect patients' and healthcare workers' health, is highly variable and the drivers of this variability are not well understood. In this study, we collected 37 surface samples from the neonatal intensive care unit (NICU) in an inpatient hospital before and after the operation began. Additionally, healthcare workers collected 160 surface samples from five additional areas of the hospital. All samples were analyzed using 16S rRNA gene amplicon sequencing, and the samples collected by healthcare workers were cultured. The NICU samples exhibited similar alpha and beta diversities before and after opening, which indicated that the microbiome there was stable over time. Conversely, the diversities of samples taken after opening varied widely by area. Principal coordinate analysis (PCoA) showed the samples clustered into two distinct groups: high alpha diversity [the pediatric intensive care unit (PICU), pathology lab, and microbiology lab] and low alpha diversity [the NICU, pediatric surgery ward, and infection prevention and control (IPAC) office]. Least absolute shrinkage and selection operator (LASSO) classification models identified 156 informative amplicon sequence variants (ASVs) for predicting the sample's area of origin. The testing accuracy ranged from 86.37% to 100%, which outperformed linear and radial support vector machine (SVM) and random forest models. ASVs of genera that contain emerging pathogens were identified in these models. Culture experiments had identified viable species among the samples, including potential antibiotic-resistant bacteria. Though area type differences were not noted in the culture data, the prevalences and relative abundances of genera detected positively correlated with 16S sequencing data. This study brings to light the microbial community temporal and spatial variation within the hospital and the importance of pathogenic and commensal bacteria to understanding dispersal patterns for infection control. IMPORTANCE: We sampled surface samples from a newly built inpatient hospital in multiple areas, including areas accessed by only healthcare workers. Our analysis of the neonatal intensive care unit (NICU) showed that the microbiome was stable before and after the operation began, possibly due to access restrictions. Of the high-touch samples taken after opening, areas with high diversity had more potential external seeds (long-term patients and clinical samples), and areas with low diversity and had fewer (short-term or newborn patients). Classification models performed at high accuracy and identified biomarkers that could be used for more targeted surveillance and infection control. Though culturing data yielded viability and antibiotic-resistance information, it disproportionately detected the presence of genera relative to 16S data. This difference reinforces the utility of 16S sequencing in profiling hospital microbiomes. By examining the microbiome over time and in multiple areas, we identified potential drivers of the microbial variation within a hospital.

Aerosol Acidity Sensing via Polymer Degradation.

The acidity of atmospheric aerosols is a critical property that affects the chemistry and composition of the atmosphere. Many key multiphase chemical reactions are pH-dependent, impacting processes like secondary organic aerosol formation, and need to be understood at a single particle level due to differences in particle-to-particle composition that impact both climate and health. However, the analytical challenge of measuring aerosol acidity in individual particles has limited pH measurements for fine (<2.5 µm) and coarse (2.5-10 µm) particles. This has led to a reliance on indirect methods or thermodynamic modeling, which focus on average, not individual, particle pH. Thus, new approaches are needed to probe single particle pH. In this study, a novel method for pH measurement was explored using degradation of a pH-sensitive polymer, poly(ε-caprolactone), to determine the acidity of individual submicron particles. Submicron particles of known pH (0 or 6) were deposited on a polymer film (21-25 nm thick) and allowed to react. Particles were then rinsed off, and the degradation of the polymer was characterized using atomic force microscopy and Raman microspectroscopy. After degradation, holes in the PCL films exposed to pH 0 were observed, and the loss of the carbonyl stretch was monitored at 1723 cm-1. As particle size decreased, polymer degradation increased, indicating an increase in aerosol acidity at smaller particle diameters. This study describes a new approach to determine individual particle acidity and is a step toward addressing a key measurement gap related to our understanding of atmospheric aerosol impacts on climate and health.