Integrating socio-economic vulnerability factors improves neighborhood-scale wastewater-based epidemiology for public health applications.

Wastewater Based Epidemiology (WBE) of COVID-19 is a low-cost, non-invasive, and inclusive early warning tool for disease spread. Previously studied WBE focused on sampling at wastewater treatment plant scale, limiting the level at which demographic and geographic variations in disease dynamics can be incorporated into the analysis of certain neighborhoods. This study demonstrates the integration of demographic mapping to improve the WBE of COVID-19 and associated post-COVID disease prediction (here kidney disease) at the neighborhood level using machine learning. WBE was conducted at six neighborhoods in Seattle during October 2020 - February 2022. Wastewater processing and RT-qPCR were performed to obtain SARS-CoV-2 RNA concentration. Census data, clinical data of COVID-19, as well as patient data of acute kidney injury (AKI) cases reported during the study period were collected and the distribution across the city was studied using Geographic Information System (GIS) mapping. Further, we analyzed the data set to better understand socioeconomic impacts on disease prevalence of COVID-19 and AKI per neighborhood. The heterogeneity of eleven demographic factors (such as education and age among others) was observed within neighborhoods across the city of Seattle. Dynamics of COVID-19 clinical cases and wastewater SARS-CoV-2 varied across neighborhood with different levels of demographics. Machine learning models trained with data from the earlier stages of the pandemic were able to predict both COVID-19 and AKI incidence in the later stages of the pandemic (Spearman correlation coefficient of 0·546 - 0·904), with the most predictive model trained on the combination of wastewater data and demographics. The integration of demographics strengthened machine learning models' capabilities to predict prevalence of COVID-19, and of AKI as a marker for post-COVID sequelae. Demographic-based WBE presents an effective tool to monitor and manage public health beyond COVID-19 at the neighborhood level.

Innervation of an Ultrasound-Mediated PVDF-TrFE Scaffold for Skin-Tissue Engineering.

In this work, electrospun polyvinylidene-trifluoroethylene (PVDF-TrFE) was utilized for its biocompatibility, mechanics, and piezoelectric properties to promote Schwann cell (SC) elongation and sensory neuron (SN) extension. PVDF-TrFE electrospun scaffolds were characterized over a variety of electrospinning parameters (1, 2, and 3 h aligned and unaligned electrospun fibers) to determine ideal thickness, porosity, and tensile strength for use as an engineered skin tissue. PVDF-TrFE was electrically activated through mechanical deformation using low-intensity pulsed ultrasound (LIPUS) waves as a non-invasive means to trigger piezoelectric properties of the scaffold and deliver electric potential to cells. Using this therapeutic modality, neurite integration in tissue-engineered skin substitutes (TESSs) was quantified including neurite alignment, elongation, and vertical perforation into PVDF-TrFE scaffolds. Results show LIPUS stimulation promoted cell alignment on aligned scaffolds. Further, stimulation significantly increased SC elongation and SN extension separately and in coculture on aligned scaffolds but significantly decreased elongation and extension on unaligned scaffolds. This was also seen in cell perforation depth analysis into scaffolds which indicated LIPUS enhanced perforation of SCs, SNs, and cocultures on scaffolds. Taken together, this work demonstrates the immense potential for non-invasive electric stimulation of an in vitro tissue-engineered-skin model.