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Background: Cardiovascular disease (CVD) significantly impacts Alzheimer’s Disease and Related Dementia (AD/ADRD) mortality. South Carolina has a high incidence of CVD and dementia mortality. The aim of this study, therefore, was to examine the neurological causes of death and the leading causes of death in the South Carolina Alzheimer’s Disease Registry (SCADR). Method: Data from 2005–2018 were extracted from the SCADR using ICD-9 and ICD-10 codes. The top 10 leading causes of death (LCOD) were identified using death certificates. Some neurological causes of death were operationalized by combining related ICD codes, such as CVD_C (I219, I251, I500, I64) and chronic obstructive pulmonary disease (COP_C), (J449, C349), and χ2 was used to compare socio-demographic characteristics and mortality. Adjusted hazard ratios (aHR) and 95% confidence intervals (CI) were estimated using extended Cox Proportional Hazard modeling, adjusting for socio-demographic factors. Results: A total of 207,093 registry cases were included in the analysis. About 70% of cases had Alzheimer’s Disease (AD) diagnosis, and 40% of all cases were 85 years and older. The LCOD was CVD_C (13.4%). The risk of death among cases with vascular dementia (VaD) was 1.17 times the risk of death among those with AD (aHR: 1.172, 95% CI: 1.148–1.196). Among all deaths, cases with COP_C had a significantly higher likelihood of death compared to those with CVD_C (aHR: 1.06, 95% CI: 1.025–1.090). Conclusions: The study highlights CVD_C as the LCOD in frequency, with survival analysis indicating COP_C risk of death as significantly higher compared to CVD_C deaths. There is a need to prioritize CVD and lung-related comorbidity prevention, assessment, and management programs for individuals living with ADRD.

Studies are increasingly examining the relationship between the neighborhood environment and cognitive decline; yet, few have investigated associations between multiple neighborhood features and Alzheimer's disease and related dementias (ADRD). We investigated the relationship between neighborhood features and ADRD cumulative incidence from 2010 to 2014 in the South Carolina Alzheimer's Disease Registry (SCADR). Diagnosed ADRD cases of age were ascertained from the SCADR by ZIP code and census tract. Neighborhood features from multiple secondary sources included poverty, air pollution [particulate matter with a diameter of 2.5 micrometers or less ( )], and rurality at the census-tract level and access to healthy food, recreation facilities, and diabetes screening at the county level. In addition to using Poisson generalized linear regression to estimate ADRD incident rate ratios (IRR) with 95% confidence intervals (CIs), we applied integrated nested Laplace approximations and stochastic partial differential equations (INLA-SPDE) to address disparate spatial scales. We estimated associations between neighborhood features and ADRD cumulative incidence. The average annual ADRD cumulative incidence was 690 per 100,000 people per census tract (95% CI: 660, 710). The analysis was limited to 98% of census tracts with a population old (i.e., 1,081 of 1,103). The average percent of families living below the federal poverty line per census tract was 18.8%, and of census tracts were considered rural. The average percent of households with limited access to healthy food was 6.4%. In adjusted models, every ) increase of was associated with 65% higher ADRD cumulative incidence ( ; 95% CI: 1.30, 2.09), where at or below is considered healthy. Compared to large urban census tracts, rural and small urban tracts had 10% ( ; 95% CI: 1.00, 1.23) and 5% ( ; 95% CI: 0.96, 1.16) higher ADRD, respectively. For every percent increase of the county population with limited access to healthy food, ADRD was 2% higher ( ; 95% CI: 1.01, 1.04). Neighborhood environment features, such as higher air pollution levels, were associated with higher neighborhood ADRD incidence. The INLA-SPDE method could have broad applicability to data collected across disparate spatial scales. https://doi.org/10.1289/EHP13183.

While studies have documented the influence of caregiver and care recipient factors on caregiver health, it is important to address the potential impact of neighborhood contexts. This study estimated the cross-sectional associations between neighborhood characteristics and mental health among caregivers cohabiting with Alzheimer’s disease care recipients that were experiencing severe or non-severe neuropsychiatric symptoms (NPSs) (e.g., aggression/anxiety). We obtained data collected in 2010 on caregivers and care recipients (n = 212) from a subset of South Carolina’s Alzheimer’s Disease Registry. Neighborhood measures (within 1 mile of the residence) came from the American Community Survey and the Rural-Urban Commuting Area Code. We categorized the neighborhood median household income into tertiles, namely, “low” (< $31,000), “medium” ($ 31,000–40,758), and “high” (>$40,758), and rurality as “large urban,” “small urban,” and “rural.” We used negative binomial regression to estimate the prevalence ratios (PRs) and 95% confidence intervals (CIs) for caregiver mental health using neighborhood characteristics. The mean age was 58 ± 10.3 years, 85% were women, and 55% were non-Hispanic Black. Among the caregivers cohabiting with a recipient experiencing severe NPS, higher distress was experienced by caregivers living in low- (PR = 1.61 (95% CI = 1.26–2.04)) and medium- (PR = 1.45 (95% CI = 1.17–1.78)) vs. high-income neighborhoods after an adjustment. These results suggest that neighborhood characteristics may amplify other social stressors experienced by caregivers.

BACKGROUND: Studies are increasingly examining the relationship between the neighborhood environment and cognitive decline; yet, few have investigated associations between multiple neighborhood features and Alzheimer's disease and related dementias (ADRD). OBJECTIVE: We investigated the relationship between neighborhood features and ADRD cumulative incidence from 2010 to 2014 in the South Carolina Alzheimer's Disease Registry (SCADR). METHODS: Diagnosed ADRD cases =50 years of age were ascertained from the SCADR by ZIP code and census tract. Neighborhood features from multiple secondary sources included poverty, air pollution [particulate matter with a diameter of 2.5 micrometers or less (PM2:5)], and rurality at the census-tract level and access to healthy food, recreation facilities, and diabetes screening at the county level. In addition to using Poisson generalized linear regression to estimate ADRD incident rate ratios (IRR) with 95% confidence intervals (CIs), we applied integrated nested Laplace approximations and stochastic partial differential equations (INLA-SPDE) to address disparate spatial scales. We estimated associations between neighborhood features and ADRD cumulative incidence. RESULTS: The average annual ADRD cumulative incidence was 690 per 100,000 people per census tract (95% CI: 660, 710). The analysis was limited to 98% of census tracts with a population =50 years old (i.e., 1,081 of 1,103). The average percent of families living below the federal poverty line per census tract was 18.8%, and ~20% of census tracts were considered rural. The average percent of households with limited access to healthy food was 6.4%. In adjusted models, every 5 lg=m3) increase of PM2:5 was associated with 65% higher ADRD cumulative incidence (IRR = 1:65; 95% CI: 1.30, 2.09), where PM2:5 at or below 12 lg=m3 is considered healthy. Compared to large urban census tracts, rural and small urban tracts had 10% (IRR = 1:10; 95% CI: 1.00, 1.23) and 5% (IRR = 1:05; 95% CI: 0.96, 1.16) higher ADRD, respectively. For every percent increase of the county population with limited access to healthy food, ADRD was 2% higher (IRR = 1:02; 95% CI: 1.01, 1.04). CONCLUSIONS: Neighborhood environment features, such as higher air pollution levels, were associated with higher neighborhood ADRD incidence. The INLA-SPDE method could have broad applicability to data collected across disparate spatial scales.

We describe the development, optimization, and validation of a multiplex droplet digital PCR (ddPCR) assay for the simultaneous detection of Babesia, Bartonella, and Borrelia spp. DNA from several sample matrices, including clinical blood samples from animals and humans, vectors, in-vitro infected human and animal cell lines, and tissues obtained from animal models (infected with Bartonella and/or B. burgdorferi). The multiplex ddPCR assay was able to detect 31 Bartonella, 13 Borrelia, and 24 Babesia species, including Theileria equi, T. cervi, and Cytauxzoon felis. No amplification of Treponema or Leptospira spp. was observed. Sensitivity of 0.2–5 genome equivalent DNA copies per microliter was achieved for different members of the Bartonella and Borrelia genus, depending on the species or matrix type (water or spiked blood DNA) tested. The ddPCR assay facilitated the simultaneous detection of co-infections with two and three vector-borne pathogens comprising four different genera (Babesia, Bartonella, Borrelia, and Theileria) from clinical and other sample sources.

Bartonella bacilliformis (B. bacilliformis), Bartonella henselae (B. henselae), and Bartonella quintana (B. quintana) are bacteria known to cause verruga peruana or bacillary angiomatosis, vascular endothelial growth factor (VEGF)-dependent cutaneous lesions in humans. Given the bacteria’s association with the dermal niche and clinical suspicion of occult infection by a dermatologist, we determined if patients with melanoma had evidence of Bartonella spp. infection. Within a one-month period, eight patients previously diagnosed with melanoma volunteered to be tested for evidence of Bartonella spp. exposure/infection. Subsequently, confocal immunohistochemistry and PCR for Bartonella spp. were used to study melanoma tissues from two patients. Blood from seven of the eight patients was either seroreactive, PCR positive, or positive by both modalities for Bartonella spp. exposure. Subsequently, Bartonella organisms that co-localized with VEGFC immunoreactivity were visualized using multi-immunostaining confocal microscopy of thick skin sections from two patients. Using a co-culture model, B. henselae was observed to enter melanoma cell cytoplasm and resulted in increased vascular endothelial growth factor C (VEGFC) and interleukin 8 (IL-8) production. Findings from this small number of patients support the need for future investigations to determine the extent to which Bartonella spp. are a component of the melanoma pathobiome.

This work used the broth microdilution method to investigate the antimicrobial activity of the essential oil obtained from the leaves of Plectranthus neochilus (PN-EO) against a representative panel of oral pathogens. We assessed the antimicrobial activity of this oil in terms of the minimum inhibitory concentration (MIC). PN-EO displayed moderate activity against Enterococcus faecalis (MIC = 250 μg/mL) and Streptococcus salivarus (MIC = 250 μg/mL), significant activity against Streptococcus sobrinus (MIC = 62.5 μg/mL), Streptococcus sanguinis (MIC = 62.5 μg/mL), Streptococcus mitis (MIC = 31.25 μg/mL), and Lactobacillus casei (MIC = 31.25 μg/mL), and interesting activity against Streptococcus mutans (MIC = 3.9 μg/mL). GC-FID and GC-MS helped to identify thirty-one compounds in PN-EO; α-pinene (1, 14.1%), β-pinene (2, 7.1%), trans-caryophyllene (3, 29.8%), and caryophyllene oxide (4, 12.8%) were the major chemical constituents of this essential oil. When tested alone, compounds 1, 2, 3, and 4 were inactive (MIC > 4000 μg/mL) against all the microorganisms. These results suggested that the essential oil extracted from the leaves of Plectranthus neochilus displays promising activity against most of the evaluated cariogenic bacteria, especially S. mutans.

Introduction Body mass index (BMI) and mortality in old adults from general population have been related in a U-shaped or J-shaped curve.

In recent years, concern has grown over the contribution of nitrogen (N) fertilizer use to nitrate (NO3 ) water pollution and nitrous oxide (N2O), nitric oxide (NO), and ammonia (NH3) atmospheric pollution. Characterizing soil N effluxes is essential in developing a strategy to mitigate N leaching and emissions to the atmosphere. In this paper, a previously described and tested mechanistic N cycle model (TOUGHREACT-N) was successfully tested against additional observations of soil pH and N2O emissions after fertilization and irrigation and before plant emergence. We used TOUGHREACT-N to explain the significantly different N gas emissions and nitrate leaching rates resulting from the different N fertilizer types, application methods, and soil properties. The N2O emissions from NH4 +-N fertilizer were higher than from urea and NO3 -N fertilizers in coarse-textured soils. This difference increased with decreases in fertilization application rate and increases in soil buffering capacity. In contrast to methods used to estimate global terrestrial gas emissions, we found strongly nonlinear N2O emissions as a function of fertilizer application rate and soil calcite content. Speciation of predicted gas N flux into N2O and N2 depended on pH, fertilizer form, and soil properties. Our results highlighted the need to derive emission and leaching factors that account for fertilizer type, application method, and soil properties.

Rhabdomyosarcoma, one of the most common childhood sarcomas, is comprised of two main subtypes, embryonal and alveolar (ARMS). ARMS, the more aggressive subtype, is primarily characterized by the t(2;13)(p35;p14) chromosomal translocation, which fuses two transcription factors, PAX3 and FOXO1 to generate the oncogenic fusion protein PAX3-FOXO1. Patients with PAX3-FOXO1-postitive tumors have a poor prognosis, in part due to the enhanced local invasive capacity of these cells, which leads to the increased metastatic potential for this tumor. Despite this knowledge, little is known about the role that the oncogenic fusion protein has in this increased invasive potential. In this report we use large-scale comparative transcriptomic analyses in physiologically relevant primary myoblasts to demonstrate that the presence of PAX3-FOXO1 is sufficient to alter the expression of 70 mRNA and 27 miRNA in a manner predicted to promote cellular invasion. In contrast the expression of PAX3 alters 60 mRNA and 23 miRNA in a manner predicted to inhibit invasion. We demonstrate that these alterations in mRNA and miRNA translate into changes in the invasive potential of primary myoblasts with PAX3-FOXO1 increasing invasion nearly 2-fold while PAX3 decreases invasion nearly 4-fold. Taken together, these results allow us to build off of previous reports and develop a more expansive molecular model by which the presence of PAX3-FOXO1 alters global gene regulatory networks to enhance the local invasiveness of cells. Further, the global nature of our observed changes highlights the fact that instead of focusing on a single-gene target, we must develop multi-faceted treatment regimens targeting multiple genes of a single oncogenic phenotype or multiple genes that target different oncogenic phenotypes for tumor progression.