Analysis of Potential Biomarkers in Frontal Temporal Dementia: A Bioinformatics Approach.

Frontal temporal dementia (FTD) is a neurological disorder known to have fewer therapeutic options. So far, only a few biomarkers are available for FTD that can be used as potential comorbidity targets. For example, genes such as VCP, which has a role in breast cancer, and WFS1, which has a role in COVID-19, are known to show a role in FTD as well. To this end, in the present study, we aim to identify potential biomarkers or susceptible genes for FTD that show comorbidities with diseases such as COVID-19 and breast cancer. A dataset from Gene Expression Omnibus containing FTD expression profiles from African American and white ethnicity backgrounds was included in our study. In FTD samples of the GSE193391 dataset, we identified 305 DEGs, with 168 genes being up-regulated and 137 genes being down-regulated. We conducted a comorbidity analysis for COVID-19 and breast cancer, followed by an analysis of potential drug interactions, pathogenicity, analysis of genetic variants, and functional enrichment analysis. Our results showed that the genes AKT3, GFAP, ADCYAP1R1, VDAC1, and C4A have significant transcriptomic alterations in FTD along with the comorbidity status with COVID-19 and breast cancer. Functional pathway analysis revealed that these comorbid genes were significantly enriched in the pathways such as glioma, JAK/STAT signaling, systematic lupus erythematosus, neurodegeneration-multiple diseases, and neuroactive ligand-receptor interaction. Overall, from these results, we concluded that these genes could be recommended as potential therapeutic targets for the treatment of comorbidities (breast cancer and COVID-19) in patients with FTD.

Arizona Surge Line: An emergent statewide COVID-19 transfer service with equity as an outcome.

Introduction: The Arizona Surge Line was an emergent initiative during the COVID-19 pandemic to facilitate COVID-19 patient transfers and load-level hospitals on a statewide level. It was designed and implemented by the Arizona Department of Health Services in preparation for the first hospital surge due to COVID-19, recognizing the disproportionate impact that hospital surge would have on rural and tribal populations. Methods: We analyzed the Arizona Surge Line transfer data for the state's first two COVID-19 surges (4/16/2020-3/6/2021). Transfer data included transfer request characteristics, patient demographics and participating hospital characteristics. When applicable, we compared this data with Arizona census data, COVID-19 case data, and the CDC/ATSDR Social Vulnerability Index. The primary outcomes studied were the proportion of COVID-19 patient requests being successfully transferred, the median transfer time, and the proportion of vulnerable populations impacted. Results: During the period of study, 160 hospitals in Arizona made 6,732 requests for transfer of COVID-19 patients. The majority of these patients (84%, 95% CI: 83-85%) were placed successfully with a median transfer time of 59 min (inter-quartile range 33-116). Of all transfer requests, 58% originated from rural hospitals, 53% were for patients of American Indian/Alaska Native ethnicity, and 73% of patients originated from highly vulnerable areas. The majority (98%) of receiving facilities were in urban areas. The Arizona Surge Line matched the number of transfers with licensed market shares during the period of study. Conclusions: The Arizona Surge Line is an equity-enhancing initiative that disproportionately benefited vulnerable populations. This statewide transfer infrastructure could become a standard public health mechanism to manage hospital surges and enhance access to care during a health emergency.

Trends in COVID-19 Incidence After Implementation of Mitigation Measures - Arizona, January 22-August 7, 2020.

Mitigating the spread of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), requires individual, community, and state public health actions to prevent person-to-person transmission. Community mitigation measures can help slow the spread of COVID-19; these measures include wearing masks, social distancing, reducing the number and size of large gatherings, pausing operation of businesses where maintaining social distancing is challenging, working from or staying at home, and implementing certain workplace and educational institution controls (1-4). The Arizona Department of Health Services' (ADHS) recommendations for mitigating exposure to SARS-CoV-2 were informed by continual monitoring of patient demographics, SARS-CoV-2 community spread, and the pandemic's impacts on hospitals. To assess the effect of mitigation strategies in Arizona, the numbers of daily COVID-19 cases and 7-day moving averages during January 22-August 7, 2020, relative to implementation of enhanced community mitigation measures, were examined. The average number of daily cases increased approximately 151%, from 808 on June 1, 2020 to 2,026 on June 15, 2020 (after stay-at-home order lifted), necessitating increased preventive measures. On June 17, local officials began implementing and enforcing mask wearing (via county and city mandates),* affecting approximately 85% of the state population. Statewide mitigation measures included limitation of public events; closures of bars, gyms, movie theaters, and water parks; reduced restaurant dine-in capacity; and voluntary resident action to stay at home and wear masks (when and where not mandated). The number of COVID-19 cases in Arizona peaked during June 29-July 2, stabilized during July 3-July 12, and further declined by approximately 75% during July 13-August 7. Widespread implementation and enforcement of sustained community mitigation measures informed by state and local officials' continual data monitoring and collaboration can help prevent transmission of SARS-CoV-2 and decrease the numbers of COVID-19 cases.

The role of community context in planning and implementing community-based health promotion projects.

The current study examines how community context affected collaborative planning and implementation in eight sites participating in a healthy cities and communities initiative in California. Data are from 23 focus groups conducted with coalition members, and 76 semi-structured interviews with local coordinators and community leaders. Multiple case study methods were used to identify major themes related to how five contextual domains influenced collaborative planning and implementation. Results showed that history of collaboration can influence resources and interpersonal and organizational connections available for planning and implementation, as well as priorities selected for action. Community politics and history can affect which segments of the community participate in a planning process and what issues are prioritized, as well as the pool of partners willing to aid in implementation. Some community norms and values bring people together and others appear to limit involvement from certain groups. Community demographics and economic conditions may shape outreach strategies for planning and implementation, and may also shape priorities. Geography can play a role in assessment methods, priority selection, partners available to aid in implementation, and participation in activities and events. Results suggest that community context plays a substantive role in shaping how community-based health promotion projects unfold.

Using network analysis to assess the evolution of organizational collaboration in response to a major environmental health threat.

Effective inter-organizational collaboration is essential to a community's ability to leverage social and material resources for community problem solving, particularly in the face of complex public health problems. This study used network analysis to document the evolution of collaboration among 21 organizations in the Tar Creek Superfund site in northeastern Oklahoma from 1997 to 2005. The Tar Creek Superfund site was part of a major lead and zinc mining operation and suffers from widespread heavy metal contamination. An organizational network of 21 organizations and a subset of eight tribes were assessed through interviews at three points in time for density and centrality. In addition to collaboration on any topic, we examined information exchange and joint planning related to lead. Density scores were consistently higher in 2005 than in 1997 for both the full and tribal networks. Centralization indices for information exchange showed a marked reduction in the hierarchical structure of information exchange over time. Of particular note is that tribal linkages with local, state and federal agencies increased over time, as did inter-tribal linkages to address the lead issue.

How does community context influence coalitions in the formation stage? A multiple case study based on the Community Coalition Action Theory.

BACKGROUND: Community coalitions are rooted in complex and dynamic community systems. Despite recognition that environmental factors affect coalition behavior, few studies have examined how community context impacts coalition formation. Using the Community Coalition Action theory as an organizing framework, the current study employs multiple case study methodology to examine how five domains of community context affect coalitions in the formation stage of coalition development. Domains are history of collaboration, geography, community demographics and economic conditions, community politics and history, and community norms and values. METHODS: Data were from 8 sites that participated in an evaluation of a healthy cities and communities initiative in California. Twenty-three focus groups were conducted with coalition members, and 76 semi-structured interviews were conducted with local coordinators and coalition leaders. Cross-site analyses were conducted to identify the ways contextual domains influenced selection of the lead agency, coalition membership, staffing and leadership, and coalition processes and structures. RESULTS: History of collaboration influenced all four coalition factors examined, from lead agency selection to coalition structure. Geography influenced coalition formation largely through membership and staffing, whereas the demographic and economic makeup of the community had an impact on coalition membership, staffing, and infrastructure for coalition processes. The influence of community politics, history, norms and values was most noticeable on coalition membership. CONCLUSIONS: Findings contribute to an ecologic and theory-based understanding of the range of ways community context influences coalitions in their formative stage.