Earthworms are important invertebrates that have been widely used as food and traditional medicine sources for thousands of years. Not only have researchers proven that earthworms are rich in proteins and other nutrients, they have also taken a keener interest in their unique pharmaceutical properties. Recent research has successfully discovered some beneficial functional components of earthworms due to the rapid development of biological technologies in the past decades. Therefore, earthworms could be a novel dietary supplement for human consumption. This review aims to summarize the current research about nutritional and therapeutic values of earthworms; and present a matured earthworm-derived product from Bocom Pharmaceuticals (USA) Corp as an example of its incorporation into a dietary supplement.

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Background Community-onset Staphylococcus aureus (CO-S. aureus) infections, methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus (MSSA), have a high burden on United States (US) Emergency Departments and urgent care, particularly within the pediatric population. Although individual risk factors have been well-studied, the influence of specific geographic locality and place-based risks for CO-S. aureus infections have not been well-characterized. Maximum entropy (MaxEnt) is a machine learning technique for ecological niche modeling, which predicts the distribution of disease vectors and their possible disease transmissions using environmental and other relevant risk factors. The aim of this study is to predict socioecological factors that contribute to the spread of CO-S. aureus in a major urban area. Method Electronic medical records from children with staphylococcal infections, who were treated at two pediatric hospitals from 2002 to 2016, were retrospectively reviewed. Children were included in the analyses if they had a confirmed S. aureus infection within 48 hours of hospital admission (i.e., CO-S. aureus), less than 19 years old, and a geo-referenced address within Atlanta’s metropolitan statistical area (MSA). The timespan was divided into two periods, 2002-2005 (early) and 2006-2010 (later), mimicking the trend of CO-S. aureus. Fourteen place-based factors, obtained at the US Census Bureau block group level, were included in the MaxEnt model: population under 18 years old, Caucasian, African American, ethnicity, poverty, low education (high school diploma), high education (Bachelor’s degree and above), crowding, nursery school enrollment, kindergarten enrollment, distance to K-12 school, distance to a children’s hospital, distance to a daycare center, and population density. These were based on factors previously determined a priori. A total of four models (CO-MRSA early, CO-MSSA early, CO-MRSA later, and CO-MSSA later) were run using the MaxEnt software (v.3.4.1). For each model, 75% and 25% of data was randomly assigned to training and testing groups, respectively. Models were assessed by jack-knife tests. Results 16,124 records met the eligibility criteria and were included in the MaxEnt models. Preliminary analyses of data from 2002-2010 suggest training Area Under the Curve (AUC) ranging from 0.802 to 0.828 and the test AUC ranging from 0.796 to 0.809, demonstrating these models are performing very well. Population density had the highest contribution in predicting CO-MRSA and CO-MSSA locations, which was confirmed by jack-knife tests. Conclusion By applying MaxEnt to pediatric CO-S. aureus infections in the Atlanta MSA, it was found that higher risks of CO-S. aureus infections may exist in more densely populated areas. MaxEnt can be utilized to identify potential future areas of CO-MRSA and CO-MSSA infections based on estimated or predicted changes to the place-based factors used to build these models, most notably population density. Predicted risk areas should have more frequent monitoring to prevent S. aureus infection outbreaks, which will also allow for more time to pool public health resources for these areas to quickly and effectively control outbreaks.

Background Community-onset Staphylococcus aureus (CO-S. aureus) pediatric infections, methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus (MSSA) continue to contribute to the burden of infections seen in the ambulatory setting in the US. Individual risk factors have been identified, but place-based factors and specific geographic locality have not been well-studied. The purpose of this study is to predict place-based factors that contribute to the spread of CO-S. aureus in a major urban area using maximum entropy (MaxEnt), a machine learning technique. Methods Electronic medical records from two pediatric hospitals (2002 to 2016) were retrospectively reviewed. Inclusion criteria: a confirmed S. aureus infection within 48 hours of hospital admission (CO-S. aureus), < 19 years old, and a geo-referenced address within Atlanta’s metropolitan statistical area (MSA). Fourteen place-based factors, at the US Census block group level, were included in the MaxEnt models: < 18 years old, Caucasian, African American, ethnicity, poverty, education attainment, crowding, daycare, kindergarten enrollment, distance to K-12 school, distance to a children’s hospital, distance to a daycare center, and population density. A total of four models (CO-MRSA early, CO-MSSA early, CO-MRSA later, and CO-MSSA later) were run using the MaxEnt software. For each model, 75% and 25% of data was randomly assigned to training and testing groups, respectively. Models were assessed by jack-knife tests. Results 16,124 records met eligibility criteria for MaxEnt models. The training Area Under the Curve (AUC) ranged from 0.771 to 0.837 and the test AUC ranged from 0.769 to 0.804. Population density had the highest contribution in predicting CO-MRSA and CO-MSSA locations, which was confirmed by jack-knife tests. Conclusion By applying MaxEnt to pediatric CO-S. aureus infections in the Atlanta MSA, it was found that higher risks of CO-S. aureus infections may exist in more densely populated areas. MaxEnt can be utilized to identify potential future areas of CO-MRSA and CO-MSSA infections based on estimated or predicted changes to the place-based factors used to build these models. Disclosures Lilly Immergluck, MD, MS, GSK: Clinical Trial- PI|Merck: Vaccine Trial Site- serve as PI|Moderna: Board Member|Novavax: Part of CoVID-19 Phase 3 Trial through US Covid Prevention Network.

Background: Into the third year of the COVID-19 pandemic and the second year of in-person learning for many K-12 schools in the United States, the benefits of mitigation strategies in this setting are still unclear. We compare COVID-19 cases in school-aged children and adolescents between a school district with a mandatory mask-wearing policy to one with an optional mask-wearing policy, during and after the peak period of the Delta variant wave of infection. Methods: COVID-19 cases during the Delta variant wave (August 2021) and post the wave (October 2021) were obtained from public health records. Cases of K-12 students, stratified by grade level (elementary, middle, and high school) and school districts across two counties, were included in the statistical and spatial analyses. COVID-19 case rates were determined and spatially mapped. Regression was performed adjusting for specific covariates. Results: Mask-wearing was associated with lower COVID-19 cases during the peak Delta variant period; overall, regardless of the Delta variant period, higher COVID-19 rates were seen in older aged students. Conclusion: This study highlights the need for more layered prevention strategies and policies that take into consideration local community transmission levels, age of students, and vaccination coverage to ensure that students remain safe at school while optimizing their learning environment.