ABSTRACT
Mathematical models have played a crucial role in exploring and guiding pandemic responses. University campuses present a particularly well-documented case for institutional outbreaks, thereby providing a unique opportunity to understand detailed patterns of pathogen spread. Here, we present descriptive and modeling analyses of SARS-CoV-2 transmission on the Princeton University (PU) campus-this model was used throughout the pandemic to inform policy decisions and operational guidelines for the university campus. Epidemic patterns between the university campus and surrounding communities exhibit strong spatiotemporal correlations. Mathematical modeling analysis further suggests that the amount of on-campus transmission was likely limited during much of the wider pandemic until the end of 2021. Finally, we find that a superspreading event likely played a major role in driving the Omicron variant outbreak on the PU campus during the spring semester of the 2021-2022 academic year. Despite large numbers of cases on campus in this period, case levels in surrounding communities remained low, suggesting that there was little spillover transmission from campus to the local community.
ABSTRACT
Plant natural products remain a good resource for the discovery of novel pharmaceuticals. A mouse macrophage-based quantitative, reverse transcription polymerase chain reaction (qRT-PCR) system was optimized to screen plant extracts for antiinflammatory activities using three well known genetic markers of inflammation. Plants used for extraction were taxonomically identified and vouchered species from two Central Asian countries, Uzbekistan and Kyrgyzstan, collected through the International Cooperative Biodiversity Groups (ICBG) program. The mRNA expression of cyclooxygenase-2, interleukin 1beta and inducible nitric oxide synthase genes in RAW macrophages was determined quantitatively in response to treatment with plant extracts applied at 100 microg/mL. The screening of 1000 extracts from 449 plant species belonging to 68 plant families resulted in 75 extracts (7.5%) showing strong (75% or higher inhibition) activity against at least one target gene. Many extracts showed qualitative and quantitative differences in the levels of activities against each target gene. Extracts identified from this screen were able to reduce inflammatory symptoms in vivo, thereby validating the screening approach.
