Building a Community Partnership for the Development of Health Ministries Within the African American Community: The Triad Pastors Network

African Americans continue to have worse health outcomes despite attempts to reduce health disparities. This is due, in part, to inadequate access to healthcare, but also to the health care and medical mistrust experienced by communities of color. Churches and worship centers have historically served as cultural centers of trusted resources for educational, financial, and health information within African American communities and a growing number of collaborations have developed between academic institutions and community/faith entities. Herein, we describe the infrastructure of a true and sustainable partnership developed with > 100 prominent faith leaders within the Piedmont Triad region of North Carolina for the purpose of developing or expanding existing health ministries within houses of worship, to improve health literacy and overall health long-term. The Triad Pastors Network is an asset-based partnership between the Maya Angelou Center for Health Equity at Wake Forest University School of Medicine and faith leaders in the Piedmont Triad region of North Carolina that was created under the guiding principles of community engagement to improve health equity and decrease health disparities experienced by African American communities. A partnership in which co-equality and shared governance are the core of the framework provides an effective means of achieving health-related goals in a productive and efficient manner. Faith-based partnerships are reliable approaches for improving the health literacy needed to address health disparities and inequities in communities of color.

The Experimentalist's Guide to Machine Learning for Small Molecule Design

Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML algorithms have accelerated scientific developments across a variety of research topics. The field of small molecule design is no exception, and an increasing number of researchers are applying ML techniques in their pursuit of discovering, generating, and optimizing small molecule compounds. The goal of this review is to provide simple, yet descriptive, explanations of some of the most commonly utilized ML algorithms in the field of small molecule design along with those that are highly applicable to an experimentally focused audience. The algorithms discussed here span across three ML paradigms: supervised learning, unsupervised learning, and ensemble methods. Examples from the published literature will be provided for each algorithm. Some common pitfalls of applying ML to biological and chemical data sets will also be explained, alongside a brief summary of a few more advanced paradigms, including reinforcement learning and semi-supervised learning.

Parenchymal border macrophages regulate tau pathology and tau-mediated neurodegeneration

Parenchymal border macrophages (PBMs) reside close to the central nervous system parenchyma and regulate CSF flow dynamics. We recently demonstrated that PBMs provide a clearance pathway for amyloid-β peptide, which accumulates in the brain in Alzheimer's disease (AD). Given the emerging role for PBMs in AD, we explored how tau pathology affects the CSF flow and the PBM populations in the PS19 mouse model of tau pathology. We demonstrated a reduction of CSF flow, and an increase in an MHCII+PBM subpopulation in PS19 mice compared with WT littermates. Consequently, we asked whether PBM dysfunction could exacerbate tau pathology and tau-mediated neurodegeneration. Pharmacological depletion of PBMs in PS19 mice led to an increase in tau pathology and tau-dependent neurodegeneration, which was independent of gliosis or aquaporin-4 depolarization, essential for the CSF-ISF exchange. Together, our results identify PBMs as novel cellular regulators of tau pathology and tau-mediated neurodegeneration.

Population-based correlates of COVID-19 infection: An analysis from the DFW COVID-19 prevalence study

BACKGROUND

COVID-19 has resulted in over 1 million deaths in the U.S. as of June 2022, with continued surges after vaccine availability. Information on related attitudes and behaviors are needed to inform public health strategies. We aimed to estimate the prevalence of COVID-19, risk factors of infection, and related attitudes and behaviors in a racially, ethnically, and socioeconomically diverse urban population.

METHODS

The DFW COVID-19 Prevalence Study Protocol 1 was conducted from July 2020 to March 2021 on a randomly selected sample of adults aged 18-89 years, living in Dallas or Tarrant Counties, Texas. Participants were asked to complete a 15-minute questionnaire and COVID-19 PCR and antibody testing. COVID-19 prevalence estimates were calculated with survey-weighted data.

RESULTS

Of 2969 adults who completed the questionnaire (7.4% weighted response), 1772 (53.9% weighted) completed COVID-19 testing. Overall, 11.5% of adults had evidence of COVID-19 infection, with a higher prevalence among Hispanic and non-Hispanic Black persons, essential workers, those in low-income neighborhoods, and those with lower education attainment compared to their counterparts. We observed differences in attitudes and behaviors by race and ethnicity, with non-Hispanic White persons being less likely to believe in the importance of mask wearing, and racial and ethnic minorities more likely to attend social gatherings.

CONCLUSION

Over 10% of an urban population was infected with COVID-19 early during the pandemic. Differences in attitudes and behaviors likely contribute to sociodemographic disparities in COVID-19 prevalence.