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Aedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this invasive species is rapidly expanding into North American temperate areas and urbanized places causing major concerns for public health. Previous analyses show that warm temperatures and high humidity during the mosquito season are ideal conditions for A. albopictus development, while its distribution is correlated with population density. To better understand A. albopictus expansion into urban places it is important to consider the role of both environmental and neighborhood factors. The present study aims to assess the relative importance of both environmental variables and neighborhood factors in the prediction of A. albopictus' presence in Southeast Pennsylvania using MaxEnt (version 3.4.1) machine-learning algorithm. Three models are developed that include: (1) exclusively environmental variables, (2) exclusively neighborhood factors, and (3) a combination of environmental variables and neighborhood factors. Outcomes from the three models are compared in terms of variable importance, accuracy, and the spatial distribution of predicted A. albopictus' presence. All three models predicted the presence of A. albopictus in urban centers, however, each to a different spatial extent. The combined model resulted in the highest accuracy (74.7%) compared to the model with only environmental variables (73.5%) and to the model with only neighborhood factors (72.1%) separately. Although the combined model does not essentially increase the accuracy in the prediction, the spatial patterns of mosquito distribution are different when compared to environmental or neighborhood factors alone. Environmental variables help to explain conditions associated with mosquitoes in suburban/rural areas, while neighborhood factors summarize the local conditions that can also impact mosquito habitats in predominantly urban places. Overall, the present study shows that MaxEnt is suitable for integrating neighborhood factors associated with mosquito presence that can complement and improve species distribution modeling.

Numbers of aggressive prostate cancer (aPC) cases are rising, but only a few risk factors have been identified. In this study, we introduce a systematic approach to integrate geospatial data into external exposome research using aPC cases from Pennsylvania. We demonstrate the association between several area-level exposome measures across five Social Determinants of Health domains (SDOH) and geographic areas identified as having elevated odds of aPC. Residential locations of Pennsylvania men diagnosed with aPC from 2005 to 2017 were linked to 37 county-/tract-level SDOH exosome measures. Variable reduction processes adopted from neighborhood-wide association study along with Bayesian geoadditive logistic regression were used to identify areas with elevated odds of aPC and exposome factors that significantly attenuated the odds and reduced the size of identified areas. Areas with significantly higher odds of aPC were explained by various SDOH exposome measures, though the extent of the reduction depended on geographic location. Some areas were associated with race (social context), health insurance (access), or tract-level poverty (economics), while others were associated with either county-level water quality or a combination of factors. Area-level exposome measures can guide future patient-level external exposome research and help design targeted interventions to reduce local cancer burden.

Background This study is the first to examine associations between several area-based socioeconomic factors and human papillomavirus (HPV) vaccine uptake among boys in the United States (U.S.). Methods Data from the 2012-2013 National Immunization Survey-Teen restricted-use data were analyzed to examine associations of HPV vaccination initiation (receipt of ≥1 dose) and series completion (receipt of three doses) among boys aged 13-17 years ( N  = 19,518) with several individual-level and ZIP Code Tabulation Area (ZCTA) census measures. Multivariable logistic regression was used to estimate the odds of HPV vaccination initiation and series completion separately. Results In 2012-2013 approximately 27.9% (95% CI 26.6%-29.2%) of boys initiated and 10.38% (95% CI 9.48%-11.29%) completed the HPV vaccine series. Area-based poverty was not statistically significantly associated with HPV vaccination initiation. It was, however, associated with series completion, with boys living in high-poverty areas (≥20% of residents living below poverty) having higher odds of completing the series (AOR 1.22, 95% CI 1.01-1.48) than boys in low-poverty areas (0-4.99%). Interactions between race/ethnicity and ZIP code-level poverty indicated that Hispanic boys living in high-poverty areas had a statistically significantly higher odds of  HPV vaccine initiation (AOR 1.43, 95% CI 1.03-1.97) and series completion (AOR 1.56, 95% CI 1.05-2.32)  than Hispanic boys in  low-poverty areas. Non-Hispanic Black boys in high poverty areas had higher odds of initiation (AOR 2.23, 95% CI 1.33-3.75) and completion (AOR 2.61, 95% CI 1.06-6.44) than non-Hispanic Black boys in low-poverty areas. Rural/urban residence and population density were also significant factors, with boys from urban or densely populated areas having higher odds of initiation and completion compared to boys living in non-urban, less densely populated areas. Conclusion Higher HPV vaccination coverage in urban areas and among racial/ethnic minorities in areas with high poverty may be attributable to factors such as vaccine acceptance, health-care practices, and their access to HPV vaccines through the Vaccines for Children Program, which provides free vaccines to uninsured and under-insured children. Given the low HPV vaccination rates among boys in the U.S., these results provide important evidence to inform public health interventions to increase HPV vaccination.

Background There is extensive interest in understanding how neighborhood socioeconomic status (nSES) may affect cancer incidence or survival. However, variability regarding items included and approaches used to form a composite nSES index presents challenges in summarizing overall associations with cancer. Given recent calls for standardized measures of neighborhood sociodemographic effects in cancer disparity research, the objective of this systematic review was to identify and compare existing nSES indices studied across the cancer continuum (incidence, screening, diagnosis, treatment, survival/mortality) and summarize associations by race/ethnicity and cancer site to inform future cancer disparity studies. Methods Using PRISMA guidelines, peer‐reviewed articles published between 2010 and 2019 containing keywords related to nSES and cancer were identified in PubMed. Results Twenty‐four nSES indices were identified from 75 studies. In general, findings indicated a significant association between nSES and cancer outcomes (n = 64/75 studies; 85.33%), with 42/64 (65.63%) adjusting for highly‐correlated individual SES factors (e.g., education). However, the direction of association differed by cancer site, race/ethnicity, and nSES index. Conclusions This review highlights several methodologic and conceptual issues surrounding nSES measurement and potential associations with cancer disparities. Recommendations pertaining to the selection of nSES measures are provided, which may help inform disparity‐related disease processes and improve the identification of vulnerable populations in need of intervention. After a comprehensive systematic review, 24 unique nSES indices were identified with significant associations only observed between nSES, cancer incidence among non‐Hispanic Whites, and cancer survival for select sites. Findings highlight the complex association between nSES and cancer control outcomes and a need for standardized approaches to nSES measurement and variable selection in future studies.

Staphylococcal protein A (SpA) and streptococcal protein G (SpG) affinity chromatography are the gold standards for purifying monoclonal antibodies (mAbs) in therapeutic applications. However, camelid VHH single-domain Abs (sdAbs or VHHs) are not bound by SpG and only sporadically bound by SpA. Currently, VHHs require affinity tag-based purification, which limits their therapeutic potential and adds considerable complexity and cost to their production. Here we describe a simple and rapid mutagenesis-based approach designed to confer SpA binding upon a priori non-SpA-binding VHHs. We show that SpA binding of VHHs is determined primarily by the same set of residues as in human mAbs, albeit with an unexpected degree of tolerance to substitutions at certain core and non-core positions and some limited dependence on at least one residue outside the SpA interface, and that SpA binding could be successfully introduced into five VHHs against three different targets with no adverse effects on expression yield or antigen binding. Next-generation sequencing of llama, alpaca and dromedary VHH repertoires suggested that species differences in SpA binding may result from frequency variation in specific deleterious polymorphisms, especially Ile57. Thus, the SpA binding phenotype of camelid VHHs can be easily modulated to take advantage of tag-less purification techniques, although the frequency with which this is required may depend on the source species.

Background In response to citizens’ concerns about elevated cancer incidence in their locales, US CDC proposed publishing cancer incidence at sub-county scales. At these scales, confidence in patients’ residential geolocation becomes a key constraint of geospatial analysis. To support monitoring cancer incidence in sub-county areas, we presented summary metrics to numerically delimit confidence in residential geolocation. Results We defined a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., using Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. Leveraging different productivity of probabilistic, deterministic, and interactive geocoding record linkage, we simultaneously detected CEL for 5,807 cancer cases reported to North Carolina Central Cancer Registry (NC CCR)- in January 2022. Batch-match probabilistic and deterministic algorithms matched 86.0% cases to their unique ED address point candidates or a CEL, 4.4% to parcel site address, and 1.4% to street centerline. Interactively geocoded cases were 8.2%. To demonstrate differences in residential geolocation confidence between enumeration areas, we calculated sRGDP for cancer cases by county and assessed the existing uncertainty within the ED data, i.e., identified duplicate addresses (as CEL) for each ED address point in the 2014 version of the NC ED data and calculated ED_sRGDP by county. Both summary RGDP (sRGDP) (0.62–1.00) and ED_sRGDP (0.36–1.00) varied across counties and were lower in rural counties (p < 0.05); sRGDP correlated with ED_sRGDP (r = 0.42, p < 0.001). The discussion covered multiple conceptual and economic issues attendant to quantifying confidence in residential geolocation and presented a set of organizing principles for future work. Conclusions Our methodology produces simple metrics – sRGDP – to capture confidence in residential geolocation via leveraging ED address points as CEL. Two facts demonstrate the usefulness of sRGDP as area-based summary metrics: sRGDP variability between counties and the overall lower quality of residential geolocation in rural vs. urban counties. Low sRGDP for the cancer cases within the area of interest helps manage expectations for the uncertainty in cancer incidence data. By supplementing cancer incidence data with sRGDP and ED_sRGDP, CCRs can demonstrate transparency in geocoding success, which may help win citizen trust. Highlights Confidence in patients’ residential geolocation becomes a key constraint of geospatial analysis for small areas. The presented novel methodology uses a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. RGDP can be summarized for cases across a specific area (sRGDP) and serve as a threshold of confidence in residential geolocation. The analysis of data from the NC Cancer Registry showed wide variability of sRGDP between NC counties. The proposed methods for sRGDP apply to countries besides the US, specifically, those with a system of ED address points.

Epidermal growth factor family receptor (EGFR) is commonly overexpressed in many solid tumors and an attractive target for chimeric antigen receptor (CAR)-T therapy, but as EGFR is also expressed at lower levels in healthy tissues a therapeutic strategy must balance antigenic responsiveness against the risk of on-target off-tumor toxicity. Herein, we identify several camelid single-domain antibodies (also known as nanobodies) that are effective EGFR targeting moieties for CARs (EGFR-sdCARs) with very strong reactivity to EGFR-high and EGFR-low target cells. As a strategy to attenuate their potent antigenic sensitivity, we performed progressive truncation of the human CD8 hinge commonly used as a spacer domain in many CAR constructs. Single amino acid hinge-domain truncation progressively decreased both EGFR-sdCAR-Jurkat cell binding to EGFR-expressing targets and expression of the CD69 activation marker. Attenuated signaling in hinge-truncated EGFR-sdCAR constructs increased selectivity for antigen-dense EGFR-overexpressing cells over an EGFR-low tumor cell line or healthy donor derived EGFR-positive fibroblasts. We also provide evidence that epitope location is critical for determining hinge-domain requirement for CARs, as hinge truncation similarly decreased antigenic sensitivity of a membrane-proximal epitope targeting HER2-CAR but not a membrane-distal EGFRvIII-specific CAR. Hinge-modified EGFR-sdCAR cells showed clear functional attenuation in Jurkat-CAR-T cells and primary human CAR-T cells from multiple donors in vitro and in vivo. Overall, these results indicate that hinge length tuning provides a programmable strategy for throttling antigenic sensitivity in CARs targeting membrane-proximal epitopes, and could be employed for CAR-optimization and improved tumor selectivity.

Background Spatial analysis can identify communities where men are at risk for aggressive prostate cancer (PCan) and need intervention. However, there are several definitions for aggressive PCan. In this study, we evaluate geospatial patterns of 3 different aggressive PCan definitions in relation to PCan-specific mortality and provide methodologic and practical insights into how each definition may affect intervention targets. Methods Using the Pennsylvania State Cancer Registry data (2005–2015), we used 3 definitions to assign “aggressive” status to patients diagnosed with PCan. Definition one (D1, recently recommended as the primary definition, given high correlation with PCan death) was based on staging criteria T4/N1/M1 or Gleason score  ≥  8. Definition two (D2, most frequently-used definition in geospatial studies) included distant SEER summary stage. Definition three (D3) included Gleason score  ≥  7 only. Using Bayesian spatial models, we identified geographic clusters of elevated odds ratios for aggressive PCan (binomial model) for each definition and compared overlap between those clusters to clusters of elevated hazard ratios for PCan-specific mortality (Cox regression). Results The number of “aggressive” PCan cases varied by definition, and influenced quantity, location, and extent/size of geographic clusters in binomial models. While spatial patterns overlapped across all three definitions, using D2 in binomial models provided results most akin to PCan-specific mortality clusters as identified through Cox regression. This approach resulted in fewer clusters for targeted intervention and less sensitive to missing data compared to definitions that rely on clinical TNM staging. Conclusions Using D2, based on distant SEER summary stage, in future research may facilitate consistency and allow for standardized comparison across geospatial studies.

Enterohemorrhagic Escherichia coli (EHEC) has consistently been one of the foremost foodborne pathogen threats worldwide based on the past 30 years of surveillance. EHEC primarily colonizes the bovine gastrointestinal (GI) tract from which it can be transmitted to nearby farm environments and remain viable for months. There is an urgent need for effective and easily implemented pre-harvest interventions to curtail EHEC contamination of the food and water supply. In an effort to address this problem, we isolated single-domain antibodies (VHHs) specific for intimin, an EHEC adhesin required for colonization, and designed chimeric VHH fusions with secretory IgA functionality intended for passive immunotherapy at the mucosal GI surface. The antibodies were produced in leaves of Nicotiana benthamiana with production levels ranging between 1 and 3% of total soluble protein. in vivo assembly of all subunits into a hetero-multimeric complex was verified by co-immunoprecipitation. Analysis of multivalent protection across the most prevalent EHEC strains identified one candidate antibody, VHH10-IgA, that binds O145:Hnm, O111:Hnm, O26:H11, and O157:H7. Fluorometric and microscopic analysis also indicated that VHH10-IgA completely neutralizes the capacity of the latter three strains to adhere to epithelial cells in vitro. This study provides proof of concept that a plant-produced chimeric secretory IgA can confer cross-serotype inhibition of bacterial adhesion to epithelial cells.