Search

Purpose: We examined the relation between serum polybrominated biphenyl (PBB) levels and the risk of benign breast disease in a cohort of Michigan women unintentionally exposed to PBBs in 1973 and interviewed in 1997.Methods: We used extended Cox models to generate adjusted hazard ratios; models included polychlorinated biphenyls (PCBs) and risk factors for benign breast disease reported in the literature.Results: Two hundred fourteen (23%) of 951 women reported benign breast disease diagnosed by a physician. Compared with women with low PBB exposure, benign breast disease was not reported more frequently among those with moderate (>1-12 parts per billion [ppb]), (odds ratio [OR] 1.08, 95% confidence interval [CI] = 0.80-1.45), or high (>12 ppb), (OR 0.79, 95% CI = 0.46-1.38) PBB exposure. PCB exposure was also not associated with self-reported physician-diagnosed benign breast disease. Age, smoking, and annual number of health-care provider visits were significantly associated with benign breast disease.Conclusions: Our analysis did not demonstrate an association between serum PBB level and self- reported physician-diagnosed benign breast disease. We did observe an increased risk of benign breast disease for women who smoked, an association that has not been consistently found in previous studies. [ABSTRACT FROM AUTHOR]

Pressure injury (PI) detection is challenging, especially in dark skin tones, due to the unreliability of visual inspection. Thermography may serve as a viable alternative as temperature differences in the skin can indicate impending tissue damage. Although deep learning models hold considerable promise toward reliably detecting PI, existing work fails to evaluate performance on diverse skin tones and varying data collection protocols. We collected a new dataset of 35 participants focused on darker skin tones where temperature differences are induced through cooling and cupping protocols. The dataset includes different cameras, lighting, patient pose, and camera distance. We compare the performance of three convolutional neural network (CNN) models trained on either the thermal or the optical images on all skin tones. Our results suggest thermography-based CNN is robust to data collection protocols. Moreover, the visual explanation often captures the region of interest without requiring explicit bounding box labels.

Incidence of hospital-acquired pressure injury, a key indicator of nursing quality, is directly proportional to adverse outcomes, increased hospital stays, and economic burdens on patients, caregivers, and society. Thus, predicting hospital-acquired pressure injury is important. Prediction models use structured data more often than unstructured notes, although the latter often contain useful patient information. We hypothesize that unstructured notes, such as nursing notes, can predict hospital-acquired pressure injury. We evaluate the impact of using various natural language processing packages to identify salient patient information from unstructured text. We use named entity recognition to identify keywords, which comprise the feature space of our classifier for hospital-acquired pressure injury prediction. We compare scispaCy and Stanza, two different named entity recognition models, using unstructured notes in Medical Information Mart for Intensive Care III, a publicly available ICU data set. To assess the impact of vocabulary size reduction, we compare the use of all clinical notes with only nursing notes. Our results suggest that named entity recognition extraction using nursing notes can yield accurate models. Moreover, the extracted keywords play a significant role in the prediction of hospital-acquired pressure injury., (Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc.)

Hospital-acquired pressure injury is one of the most harmful events in clinical settings. Patients who do not receive early prevention and treatment can experience a significant financial burden and physical trauma. Several hospital-acquired pressure injury prediction algorithms have been developed to tackle this problem, but these models assume a consensus, gold-standard label (i.e., presence of pressure injury or not) is present for all training data. Existing definitions for identifying hospital-acquired pressure injuries are inconsistent due to the lack of high-quality documentation surrounding pressure injuries. To address this issue, we propose in this paper an ensemble-based algorithm that leverages truth inference methods to resolve label inconsistencies between various case definitions and the level of disagreements in annotations. Application of our method to MIMIC-III, a publicly available intensive care unit dataset, gives empirical results that illustrate the promise of learning a prediction model using truth inference-based labels and observed conflict among annotators., Competing Interests: Declaration of competing interest None declared., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Personalized diagnosis prediction based on electronic health records (EHR) of patients is a promising yet challenging task for AI in healthcare. Existing studies typically ignore the heterogeneity of diseases across different patients. For example, diabetes can have different complications across different patients (e.g., hyperlipidemia and circulatory disorder), which requires personalized diagnoses and treatments. Specifically, existing models fail to consider 1) varying severity of the same diseases for different patients, 2) complex interactions among syndromic diseases, and 3) dynamic progression of chronic diseases. In this work, we propose to perform personalized diagnosis prediction based on EHR data via capturing disease severity, interaction, and progression. In particular, we enable personalized disease representations via severity-driven embeddings at the disease level. Then, at the visit level, we propose to capture higher-order interactions among diseases that can collectively affect patients' health status via hypergraph-based aggregation; at the patient level, we devise a personalized generative model based on neural ordinary differential equations to capture the continuous-time disease progressions underlying discrete and incomplete visits. Extensive experiments on two real-world EHR datasets show significant performance gains brought by our approach, yielding average improvements of 10.70% for diagnosis prediction over state-of-the-art competitors.

To determine the hepatitis C virus (HCV) care cascade among persons who were born during 1945 to 1965 and received outpatient care on or after January 2014 at a large academic healthcare system. Deidentified electronic health record data in an existing research database were analyzed for this study. Laboratory test results for HCV antibody and HCV ribonucleic acid (RNA) indicated seropositivity and confirmatory testing. HCV genotyping was used as a proxy for linkage to care. A direct-acting antiviral (DAA) prescription indicated treatment initiation, an undetectable HCV RNA at least 20 weeks after initiation of antiviral treatment indicated a sustained virologic response. Of the 121,807 patients in the 1945 to 1965 birth cohort who received outpatient care between January 1, 2014 and June 30, 2017, 3399 (3%) patients were screened for HCV; 540 (16%) were seropositive. Among the seropositive, 442 (82%) had detectable HCV RNA, 68 (13%) had undetectable HCV RNA, and 30 (6%) lacked HCV RNA testing. Of the 442 viremic patients, 237 (54%) were linked to care, 65 (15%) initiated DAA treatment, and 32 (7%) achieved sustained virologic response. While only 3% were screened for HCV, the seroprevalence was high in the screened sample. Despite the established safety and efficacy of DAAs, only 15% initiated treatment during the study period. To achieve HCV elimination, improved HCV screening and linkage to HCV care and DAA treatment are needed., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Patients develop pressure injuries (PIs) in the hospital owing to low mobility, exposure to localized pressure, circulatory conditions, and other predisposing factors. Over 2.5 million Americans develop PIs annually. The Center for Medicare and Medicaid considers hospital-acquired PIs (HAPIs) as the most frequent preventable event, and they are the second most common claim in lawsuits. With the growing use of electronic health records (EHRs) in hospitals, an opportunity exists to build machine learning models to identify and predict HAPI rather than relying on occasional manual assessments by human experts. However, accurate computational models rely on high-quality HAPI data labels. Unfortunately, the different data sources within EHRs can provide conflicting information on HAPI occurrence in the same patient. Furthermore, the existing definitions of HAPI disagree with each other, even within the same patient population. The inconsistent criteria make it impossible to benchmark machine learning methods to predict HAPI., Objective: The objective of this project was threefold. We aimed to identify discrepancies in HAPI sources within EHRs, to develop a comprehensive definition for HAPI classification using data from all EHR sources, and to illustrate the importance of an improved HAPI definition., Methods: We assessed the congruence among HAPI occurrences documented in clinical notes, diagnosis codes, procedure codes, and chart events from the Medical Information Mart for Intensive Care III database. We analyzed the criteria used for the 3 existing HAPI definitions and their adherence to the regulatory guidelines. We proposed the Emory HAPI (EHAPI), which is an improved and more comprehensive HAPI definition. We then evaluated the importance of the labels in training a HAPI classification model using tree-based and sequential neural network classifiers., Results: We illustrate the complexity of defining HAPI, with <13% of hospital stays having at least 3 PI indications documented across 4 data sources. Although chart events were the most common indicator, it was the only PI documentation for >49% of the stays. We demonstrate a lack of congruence across existing HAPI definitions and EHAPI, with only 219 stays having a consensus positive label. Our analysis highlights the importance of our improved HAPI definition, with classifiers trained using our labels outperforming others on a small manually labeled set from nurse annotators and a consensus set in which all definitions agreed on the label., Conclusions: Standardized HAPI definitions are important for accurately assessing HAPI nursing quality metric and determining HAPI incidence for preventive measures. We demonstrate the complexity of defining an occurrence of HAPI, given the conflicting and incomplete EHR data. Our EHAPI definition has favorable properties, making it a suitable candidate for HAPI classification tasks., (©Mani Sotoodeh, Wenhui Zhang, Roy L Simpson, Vicki Stover Hertzberg, Joyce C Ho. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 23.02.2023.)

Aims: Various cardiovascular risk prediction models have been developed for patients with type 2 diabetes mellitus. Yet few models have been validated externally. We perform a comprehensive validation of existing risk models on a heterogeneous population of patients with type 2 diabetes using secondary analysis of electronic health record data., Methods: Electronic health records of 47,988 patients with type 2 diabetes between 2013 and 2017 were used to validate 16 cardiovascular risk models, including 5 that had not been compared previously, to estimate the 1-year risk of various cardiovascular outcomes. Discrimination and calibration were assessed by the c-statistic and the Hosmer-Lemeshow goodness-of-fit statistic, respectively. Each model was also evaluated based on the missing measurement rate. Sub-analysis was performed to determine the impact of race on discrimination performance., Results: There was limited discrimination (c-statistics ranged from 0.51 to 0.67) across the cardiovascular risk models. Discrimination generally improved when the model was tailored towards the individual outcome. After recalibration of the models, the Hosmer-Lemeshow statistic yielded p-values above 0.05. However, several of the models with the best discrimination relied on measurements that were often imputed (up to 39% missing)., Conclusion: No single prediction model achieved the best performance on a full range of cardiovascular endpoints. Moreover, several of the highest-scoring models relied on variables with high missingness frequencies such as HbA1c and cholesterol that necessitated data imputation and may not be as useful in practice. An open-source version of our developed Python package, cvdm, is available for comparisons using other data sources.

Context: Depression is prevalent among Asian Americans (AsA) during the COVID-19 pandemic, and depression often leads to sleep disturbance in this population. The gut microbiota (GM) plays a critical role in mental health and sleep quality, and the composition of the GM is largely unknown among AsA., Objectives: Examine associations of the GM with depressive symptoms and sleep disturbance among Chinese and Korean American immigrants., Methods: Depressive symptoms (PROMIS Short Form-Depression) and sleep quality (Pittsburgh Sleep Quality Index [PSQI]) were collected via surveys. PROMIS measure T-score > 55 indicates positive depressive symptoms, and a total PSQI score > 5 indicates sleep disturbance. 16S rRNA V3-V4 gene regions were sequenced from fecal specimens to measure GM. Permutational multivariate analysis of variance and linear discriminant analysis effect size were applied to examine associations of the GM with symptoms., Results: Among 20 participants, 55% ( n = 11) reported depressive symptoms and 35% ( n = 7) reported sleep disturbance. A higher α-diversity was marginally associated with lower depressive symptoms: Chao1 (r = -0.39, p = 0.09) and Shannon index (r = -0.41, p = 0.08); β-diversity distinguished participants between categories of depressive symptoms (weighted UniFrac, p =0.04) or sleep disturbance (Jaccard, p =0.05). Those with depressive symptoms showed a higher abundance of Actinobacteria, while those without depressive symptoms had a higher abundance of Bacteroidetes . No significant taxa were identified for sleep disturbance., Conclusions: Gut microbial diversity showed promising associations with depressive symptoms and sleep disturbance among Chinese and Korean immigrants. Specific taxa were identified as associated with depressive symptoms. Future studies with a larger sample size are warranted to confirm our findings.

To examine the health status of Hispanic agricultural workers in Florida and Georgia. Health data from agricultural workers in the Farm Worker Family Health Program (June 2019) and research studies in Florida (May 2015 and May 2019) were examined. Data from 728 agricultural workers were collected through sociodemographic questionnaire and clinical data. In the Florida sample, 83% were overweight or obese, 70% elevated blood pressure, 60% met the definition of prediabetes. In Georgia, 64% were overweight or obese and 67% had elevated blood pressure. Weak correlations were observed between BMI and systolic blood pressure (unadjusted r = 0.20), diastolic blood pressure (unadjusted r = 0.19), and glucose (unadjusted r = 0.14). Adjusting for age and gender did not show statistically significant correlation between BMI and systolic and diastolic blood pressure or glucose. While BMI has been shown to be strongly associated with high blood pressure and impaired glucose, we found a weak correlation among agricultural workers. Given the common and high use of pesticides and elevated rates of hypertension, impaired glucose, and adiposity in agricultural workers, the public health impact of this relationship may require and lead to occupational reform that protects the health of agricultural workers. Future studies should assess occupational and environmental factors and lifestyle differences between agricultural workers and the general population to better understand these discrepancies in health status., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Hotter summers caused by global warming and increased workload and duration are endangering the health of farmworkers, a high-risk population for heat-related illness (HRI), and deaths. Although prior studies using wearable sensors show the feasibility of employing field-collected data for HRI monitoring, existing devices still have limitations, such as data loss from motion artifacts, device discomfort from rigid electronics, difficulties with administering ingestible sensors, and low temporal resolution. Here, this paper introduces a wireless, wearable bioelectronic system with functionalities for continuous monitoring of skin temperature, electrocardiograms (ECG), heart rates (HR), and activities, configured in a single integrated package. Advanced nanomanufacturing based on laser machining allows rapid device fabrication and direct incorporation of sensors with a highly breathable substrate, allowing for managing excessive sweating and multimodal stresses. To validate the device's performance in agricultural settings, the device is applied to multiple farmworkers at various operations, including fernery, nursery, and crop. The accurate data recording, including high-fidelity ECG (signal-to-noise ratio: >20 dB), accurate HR (r = 0.89, r 2 = 0.65 in linear correlation), and reliable temperature/activity, confirms the device's capability for multiparameter health monitoring of farmworkers., (© 2022 Wiley-VCH GmbH.)

Background: Periodontal disease in pregnancy is considered a risk factor for adverse birth outcomes. Periodontal disease has a microbial etiology, however, the current state of knowledge about the subgingival microbiome in pregnancy is not well understood., Objective: To characterize the structure and diversity of the subgingival microbiome in early and late pregnancy and explore relationships between the subgingival microbiome and preterm birth among pregnant Black women., Methods: This longitudinal descriptive study used 16S rRNA sequencing to profile the subgingival microbiome of 59 Black women and describe microbial ecology using alpha and beta diversity metrics. We also compared microbiome features across early (8-14 weeks) and late (24-30 weeks) gestation overall and according to gestational age at birth outcomes (spontaneous preterm, spontaneous early term, full term)., Results: In this sample of Black pregnant women, the top twenty bacterial taxa represented in the subgingival microbiome included a spectrum representative of various stages of biofilm progression leading to periodontal disease, including known periopathogens Porphyromonas gingivalis and Tannerella forsythia. Other organisms associated with periodontal disease reflected in the subgingival microbiome included several Prevotella spp., and Campylobacter spp. Measures of alpha or beta diversity did not distinguish the subgingival microbiome of women according to early/late gestation or full term/spontaneous preterm birth; however, alpha diversity differences in late pregnancy between women who spontaneously delivered early term and women who delivered full term were identified. Several taxa were also identified as being differentially abundant according to early/late gestation, and full term/spontaneous early term births., Conclusions: Although the composition of the subgingival microbiome is shifted toward complexes associated with periodontal disease, the diversity of the microbiome remains stable throughout pregnancy. Several taxa were identified as being associated with spontaneous early term birth. Two, in particular, are promising targets of further investigation. Depletion of the oral commensal Lautropia mirabilis in early pregnancy and elevated levels of Prevotella melaninogenica in late pregnancy were both associated with spontaneous early term birth., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yang, Claussen, Arthur, Hertzberg, Geurs, Corwin and Dunlop.)

Background: Immigrants often work in jobs that are known as dirty, demanding, and dangerous. Globally, the agricultural occupations have been associated with the emergence of chronic kidney disease of unknown etiology (CKDu) primarily in outdoor worker populations. The disease has also been reported in immigrants in the United States who work in agricultural occupations, but little research has been done outside of agricultural workers to determine whether immigrants who work other occupations are at risk for developing CKDu., Methods: This study assessed the self-reported occupational histories of undocumented immigrants receiving frequent, emergent-only dialysis in Atlanta, GA. We assessed demographics, employment status, and work history, using the Grady Dialysis Questionnaire and the Occupational/Environmental Health History Form., Results: Fifty undocumented immigrants receiving frequent, emergent-only hemodialysis were recruited for this study. The average age was 49.5 years ( SD ± 11.5), and the majority (52%) were female and originated from Mexico (66%). A majority (74%) reported having worked in the past 5 years and 28% reported currently working. A total of 68 unique jobs were reported. In decreasing order of frequency, our sample worked in occupations with documented renal toxicant exposures, such as applying pesticides in landscaping, heat exposure in agriculture, construction, landscaping, and dry cleaning, and lead paint fumes in construction., Discussion: Occupational histories provide a greater understanding of the exposures and working conditions of those receiving frequent, emergent-only hemodialysis. This exploratory study suggests that further research is needed to investigate and assess whether renal toxicants are associated with occupations with high numbers of undocumented workers., Application to Practice: A detailed and thorough occupational history should be conducted from those receiving frequent, emergent-only hemodialysis. Developing continuing education for nursing and medical staff in dialysis centers on taking an occupational history, mandatory State reporting requirements, and hazard communication training for workers should be considered. Collaborations between occupational health professionals, public health authorities, employers, dialysis providers, and clinicians who see undocumented workers is required to understand and develop appropriate prevention measures for this population.

Chronic kidney disease of unknown etiology (CKDu) has been well described in farmworkers in Latin America. Agricultural workers in the United States (US) are exposed to similar hot and humid working conditions, but CKDu in the US is under-described. This review aims to better understand the current literature describing the connection between heat stress and kidney function in farmworkers in the United States. Utilizing a scoping review methodology, we searched CINAHL, Embase, PubMed, and Web of Science databases to better understand the current state of the heat stress and kidney function research in farmworkers within the United States. In this review, 229 pieces of literature were screened. Ultimately, 4 articles were chosen to be included in the scoping review. Common themes within the articles were variations in study protocol lengths and type of heat stress measurement. Additionally, the majority of the work completed was quantitative to date, with only one study providing a critical social lens for analysis of CKDu in the United States. We found evidence that more work is needed within the US to understand the relationship between working in the heat and kidney function in agricultural and other workers who experience high heat conditions at work and are susceptible to the deleterious effects of working in said conditions.

Objective: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that is incurable and ultimately fatal. Few therapeutic options are available to patients. In this study, we explored differences in microbiome composition associated with ALS. Methods: We compared the gut microbiome and inflammatory marker profiles of ALS patients (n = 10) to those of their spouses (n = 10). Gut microbiome profiles were determined by 16S rRNA gene sequencing. Results: The gut microbial communities of the ALS patients were more diverse and were deficient in Prevotella spp. compared with those of their spouses. In contrast, healthy couples (n = 10 couples of the opposite sex) recruited from the same geographic region as the patient population did not exhibit these differences. Stool and plasma inflammatory markers were similar between ALS patients and their spouses. Predictive analysis of microbial enzymes revealed that ALS patients had decreased activity in several metabolic pathways, including carbon metabolism, butyrate metabolism, and systems involving histidine kinase and response regulators. Conclusions: ALS patients exhibit differences in their gut microbial communities compared with spouse controls. Our findings suggest that modifying the gut microbiome, such as via amelioration of Prevotella spp. deficiency, and/or altering butyrate metabolism may have translational value for ALS treatment.

Globally, there is increasing recognition that agricultural workers are at risk for chronic kidney disease of unknown etiology (CKD u ). Recurrent heat exposure, physical exertion, dehydration, muscle damage, and inflammation are hypothesized to contribute to the development of CKD u , but the relative importance of these processes and the interactions among them remain unclear. Moreover, there is a need to identify biomarkers that could distinguish individuals who are at greatest risk for kidney damage to target preventative interventions for CKD u . In this study, we evaluated dehydration and markers of inflammation, muscle damage, and renal function in agricultural workers at a non-workday baseline assessment. Urine specific gravity and kidney function were measured before and after work shifts on three subsequent days, and heat index, core body temperature, and heart rate were monitored during the work shifts. A combination of direct comparisons and machine learning algorithms revealed that reduced levels of uromodulin and sodium in urine and increased levels of interleukin-6 and C-reactive protein in serum were indicative of dehydration at baseline, and that dehydration, high body mass index, reduced urine uromodulin, and increased serum interleukin-6, C-reactive protein, and lipopolysaccharide-binding protein at baseline were predictive of acute kidney injury on subsequent workdays. Our findings suggest a method for identifying agricultural workers at greatest risk for kidney injury and reveal potential mechanisms responsible for this process, including pathways overlapping in dehydration and kidney injury. These results will guide future studies confirming these mechanisms and introducing interventions to protect kidney health in this vulnerable population.

Sequential pattern mining can be used to extract meaningful sequences from electronic health records. However, conventional sequential pattern mining algorithms that discover all frequent sequential patterns can incur a high computational and be susceptible to noise in the observations. Approximate sequential pattern mining techniques have been introduced to address these shortcomings yet, existing approximate methods fail to reflect the true frequent sequential patterns or only target single-item event sequences. Multi-item event sequences are prominent in healthcare as a patient can have multiple interventions for a single visit. To alleviate these issues, we propose GASP, a graph-based approximate sequential pattern mining, that discovers frequent patterns for multi-item event sequences. Our approach compresses the sequential information into a concise graph structure which has computational benefits. The empirical results on two healthcare datasets suggest that GASP outperforms existing approximate models by improving recoverability and extracts better predictive patterns.

Objectives: This study aimed to compare the concordance of pressure injury (PI) site, stage, and count documented in electronic health records (EHRs); explore if PI count during each patient hospitalization is consistent based on PI site or stage count in the diagnosis or chart event records; and examine if discrepancies in PI count were associated with patient characteristics., Methods: Hospitalization records with the International Classification of Diseases ninth edition (ICD-9) codes, chart events from two systems (CareVue, MetaVision), and clinical notes on PI were extracted from the Medical Information Mart for Intensive Care (MIMIC)-III database. PI site and stage counts from individual hospitalization were computed. Hospitalizations with the same or different counts of site and stage according to ICD-9 codes (site and stage), CareVue (site and stage), or MetaVision (stage) charts were defined as consistent or discrepant reporting. Chi-squared, independent t -, and Kruskal-Wallis tests were examined if the count discrepancy was associated with patient characteristics. ICD-9 codes and charts were also compared for people with one site or stage., Results: A total of 31,918 hospitalizations had PI data. Within hospitalizations with ICD-9-coded sites and stages, 55.9% reported different counts. Within hospitalizations with CareVue charts on PI, 99.3% reported the same count. For hospitalizations with stages based on ICD-9 codes or MetaVision chart data, only 42.9% reported the same count. Discrepancies in counts were consistently and significantly associated with variables including PI recording in clinical notes, dead/hospice at discharge, more caregivers, longer hospitalization or intensive care unit stays, and more days to first transfer. Discrepancies between ICD-9 code and chart values on the site and stage were also reported., Conclusion: Patient characteristics associated with PI count discrepancies identified patients at risk of having discrepant PI counts or worse outcomes. PI documentation quality could be improved with better communication, care continuity, and integrity. Clinical research using EHRs should adopt systematic data quality analysis to inform limitations., Competing Interests: None declared., (Thieme. All rights reserved.)

Introduction: Although a considerable proportion of Asians in the USA experience depression, anxiety and poor sleep, these health issues have been underestimated due to the model minority myth about Asians, the stigma associated with mental illness, lower rates of treatment seeking and a shortage of culturally tailored mental health services. Indeed, despite emerging evidence of links between psychosocial risk factors, the gut microbiome and depression, anxiety and sleep quality, very few studies have examined how these factors are related in Chinese and Korean immigrants in the USA. The purpose of this pilot study was to address this issue by (a) testing the usability and feasibility of the study's multilingual survey measures and biospecimen collection procedure among Chinese and Korean immigrants in the USA and (b) examining how stress, discrimination, acculturation and the gut microbiome are associated with depression, anxiety and sleep quality in this population., Method and Analysis: This is a cross-sectional pilot study among first and second generations of adult Chinese and Korean immigrants in the greater Atlanta area (Georgia, USA). We collected (a) gut microbiome samples and (b) data on psychosocial risk factors, depression, anxiety and sleep disturbance using validated, online surveys in English, Chinese and Korean. We aim to recruit 60 participants (30 Chinese, 30 Korean). We will profile participants' gut microbiome using 16S rRNA V3-V4 sequencing data, which will be analysed by QIIME 2. Associations of the gut microbiome and psychosocial factors with depression, anxiety and sleep disturbance will be analysed using descriptive and inferential statistics, including linear regression., Ethics and Dissemination: This study has been approved by the Institutional Review Board at Emory University (IRB ID: STUDY00000935). Results will be made available to Chinese and Korean community members, the funder and other researchers and the broader scientific community., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: This study uses machine learning techniques to identify sociodemographic and clinical predictors of progression through the hepatitis C (HCV) cascade of care for patients in the 1945-1965 birth cohort in the Southern United States., Methods: We compared sociodemographic and clinical variables between groups of patients for three care outcomes: linkage to care, initiation of antiviral treatment, and virologic cure. A decision tree model and random forest model were built for each outcome., Results: Patients were primarily male, African American/Black or Caucasian/White, non-Hispanic or Latino, and insured. The average age at first HCV screening was 60 years old, and common medical diagnoses included chronic kidney disease, fibrosis and/or cirrhosis, transplanted liver, diabetes mellitus, and liver cell carcinoma. Variables used in predicting linkage to care included age at first HCV screening, insurance at first HCV screening, race, fibrosis and/or cirrhosis, other liver disease, ascites, and transplanted liver. Variables used in predicting initiation of antiviral treatment included insurance at first HCV screening, gender, other liver cancer, steatosis, and liver cell carcinoma. Variables used in predicting virologic cure included insurance at first HCV screening, transplanted liver, and ethnicity., Conclusion: These patients have a high hepatic health burden, likely reflecting complications of untreated HCV and highlighting the urgency to cure HCV in this birth cohort. We found differences in HCV care outcomes based on sociodemographic and clinical variables. More work is needed to understand the mechanisms of these differences in care outcomes and to improve HCV care., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This project piloted an educational intervention focused on use and management of EHR data by Doctor of Nursing Practice students in quality improvement initiatives. Recommendations from academic and clinical nursing promote the integration of EHR data findings into practice. Nursing's general lack of understanding about how to use and manage data is a barrier to using EHR data to guide quality improvement initiatives. Doctor of Nursing Practice students at a hospital-affiliated university participated in a pre-test, training, and post-test through an online learning management system. Training content and assessments focused on data and planning for its use in quality improvement initiatives. Sixteen students experienced a median of 17.6% increase in scores after completing the post-test. There was a statistically significant increase in scores between the pre-test and post-test (P = .0006). These results suggest educational content included in the Doctor of Nursing Practice Quality Improvement Toolkit increases knowledge about use and management of EHR data. Future considerations include use for educating a variety of students and healthcare staff., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

From electronic health records (EHRs), the relationship between patients' conditions, treatments, and outcomes can be discovered and used in various healthcare research tasks such as risk prediction. In practice, EHRs can be stored in one or more data warehouses, and mining from distributed data sources becomes challenging. Another challenge arises from privacy laws because patient data cannot be used without some patient privacy guarantees. Thus, in this paper, we propose a privacy-preserving framework using sequential pattern mining in distributed data sources. Our framework extracts patterns from each source and shares patterns with other sources to discover discriminative and representative patterns that can be used for risk prediction while preserving privacy. We demonstrate our framework using a case study of predicting Cardiovascular Disease in patients with type 2 diabetes and show the effectiveness of our framework with several sources and by applying differential privacy mechanisms., (©2021 AMIA - All rights reserved.)

Aim: To provide an example of a tweet analysis for nurse researchers using Twitter in their research., Design: A content analysis using tweets about "heat illness + health.", Methods: Tweets were pulled from Twitter's application programming interface with premium access using Postman and the key words "heat illness + health." All data cleaning and analysis was performed in R Version 3.5.2, and the tweet set was analyzed for term frequency, sentiment, and topic modeling. Principal R packages included LDAvis, tidytext, tm, and zyuzhet., Results: 6,317 tweets were analyzed with a date range of April 6, 2009, to December 30, 2019. The most common terms in the tweets were heat (n = 4,532), illness (n = 4,085), and health (n = 2,257). Sentiment analysis showed that the majority of tweets (55%) had a negative sentiment. Topic modeling showed that there were three topics within the tweet set: increasing impact, prevention and safety, and symptoms., Conclusions: Twitter can be a useful tool for nursing researchers, serving as a viable adjunct to current research methodologies. This practical example has facilitated a deeper understanding of the social media representation of heat illness and health that can be applied to other research., Clinical Relevance: Twitter serves as a tool for collecting health information for multiple groups, ranging from clinicians and researchers to patients. By utilizing the plethora of data that comes from the platform, we can work towards developing theories and interventions related to numerous health conditions and phenomena., (© 2021 Sigma Theta Tau International.)

(1) Polybrominated diphenyl ethers (PBDEs) were widely produced in the United States until 2004 but remain highly persistent in the environment. The potential for PBDEs to disrupt normal neuroendocrine pathways resulting in depression and other neurological symptoms is largely understudied. This study examined whether PBDE exposure in pregnant women was associated with antenatal depressive symptomatology. (2) Data were collected from 193 African American pregnant women at 8-14 weeks gestation. Serum PBDEs and depressive symptoms were analyzed and a mixture effect was calculated. (3) Urban pregnant African American women in the Southeastern United States had a high risk of depression (27%) compared to the National average. Increased levels of PBDEs were found. BDE-47 and -99 exposures are significantly associated with depressive symptomatology in the pregnant cohort. The weighted body burden estimate of the PBDE mixture was associated with a higher risk of mild to moderate depression using an Edinburgh Depression Scale cutoff score of ≥10 (OR = 2.93; CI 1.18, 7.82). (4) Since antenatal depression may worsen in postpartum, reducing PBDE exposure may have significant clinical implications.

Hospital-acquired pressure ulcer injury (PUI) is a primary nursing quality metric, reflecting the caliber of nursing care within a hospital. Prior studies have used the Braden scale and structured data from the electronic health records to detect/predict PUI while the informative unstructured clinical notes have not been used. We propose automated PUI detection using a novel negation-detection algorithm applied to unstructured clinical notes. Our detection framework is on-demand, requiring minimal cost. In application to the MIMIC-III dataset, the text features produced using our algorithm resulted in improved PUI detection when evaluated using logistic regression, random forests, and neural networks compared to text features without negation detection. Exploratory analysis reveals substantial overlap between key classifier features and leading clinical attributes of PUI, adding interpretability to our solution. Our method could also considerably reduce nurses' evaluations by automatic detection of most cases, leaving only the most uncertain cases for nursing assessment., (©2020 AMIA - All rights reserved.)

Background: Outdoor workers are exposed to hot work environments and are at risk of heat-related morbidity and mortality. The purpose of this study was to evaluate the knowledge of migrant farmworkers about first aid for heat-related illness (HRI) symptoms. Methods: The authors recruited 60 migrant farmworkers out of 66 who were approached from vegetable farms in Georgia. They were workers who participated in the 2018 Farmworker Family Health Program (FWFHP). The authors surveyed the workers to assess demographics, prevalence of HRI symptoms, hydration practices, and knowledge of HRI first aid. Descriptive statistics for worker demographics, HRI symptoms, and hydration data were calculated, as were the percentages of correctly answered pilot questions. Findings: Of the 60 workers who chose to participate in this study, more than 50% incorrectly answered pilot questions related to their knowledge of HRI first aid. The two most common HRI symptoms reported were heavy sweating and muscle cramps. More than two thirds reported experiencing at least one HRI symptom during the workday. Mean liquid consumption within this sample was 72.95 oz per day, which is much less than the recommended 32 oz per hour. Conclusion/Application to Practice: Until larger structural change can occur to protect farmworkers, farm owners can prevent morbidity and mortality from inadequate hydration practices and working in high-heat conditions by providing migrant farmworkers with training in heat-related first aid. Appropriate heat-illness interventions should focus on first aid measures to reduce morbidity and mortality related to heat illness in farmworkers.

Objectives: To analyze the predictors of health care utilization among respondents to the National Agricultural Worker Survey. Specifically, we hypothesized that English proficiency would predict utilization of health care services within the last 2 years., Methods: Using the 2015-2016 National Agricultural Worker Survey, we performed a secondary data analysis to analyze the predictors of health care utilization within the last 2 years in the United States' agricultural worker population. Data were cleaned and analyzed using decision tree analysis, which produced a classification tree model that was trained on 90% of the data and validated on 10%., Results: Exposure to English was not a predictor of health care utilization in our classification tree. The first major partition that predicted utilization was insurance status. Additional partitions were on age, gender, hypertension diagnosis, and public aid., Conclusions: By partitioning on insurance status and use of public aid, the decision tree provided evidence that systemic factors are key determinants of health care utilization in the agricultural worker community. This highlights the importance of agencies that connect agricultural workers with resources that provide insurance and improve access to health care. This is especially important given that agricultural workers are one of the highest risk groups for occupational injury or death in the United States., (© 2020 Wiley Periodicals, Inc.)

Background: The purpose of this systematic review is to examine cooling intervention research in outdoor occupations, evaluate the effectiveness of such interventions, and offer recommendations for future studies. This review focuses on outdoor occupational studies conducted at worksites or simulated occupational tasks in climatic chambers., Methods: This systematic review was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. PubMed, Embase, and Web of Science were searched to identify original research on intervention studies published in peer-reviewed journals that aimed at reducing heat stress or heat-related illness from January 2000 to August 2020., Results: A systematic search yielded a total of 1042 articles, of which 21 met the inclusion criteria. Occupations with cooling intervention studies included agriculture (n = 5), construction (n = 5), industrial workers (n = 4), and firefighters (n = 7). The studies focused on multiple types of cooling interventions cooling gear (vest, bandanas, cooling shirts, or head-cooling gel pack), enhanced heat dissipation clothing, forearm or lower body immersion in cold water, water dousing, ingestion of a crushed ice slush drink, electrolyte liquid hydration, and modified Occupational Safety and Health Administration recommendations of drinking water and resting in the shade., Conclusion: Current evidence indicates that using multiple cooling gears along with rest cycles may be the most effective method to reduce heat-related illness. Occupational heat-related illnesses and death may be mitigated by targeted cooling intervention and workplace controls among workers of vulnerable occupational groups and industries., (© 2020 Wiley Periodicals LLC.)

The intertidal zone often has varying levels of environmental stresses (desiccation, temperature, light) that result in highly stress-tolerant macrobiota occupying the upper zone while less tolerant species occupy the lower zone, but little comparative information is available for intertidal bacteria. Here we describe natural (unmanipulated) bacterial communities of three Fucus congeners ( F. spiralis , high zone; F. vesiculosus , mid zone; F. distichus , low zone) as well as those of F. vesiculosus transplanted to the high zone (Dry and Watered treatments) and to the mid zone (Procedural Control) during summer in Maine (United States). We predicted that bacterial communities would be different among the differently zoned natural congeners, and that higher levels of desiccation stress in the high zone would cause bacterial communities of Dry transplants to become similar to F. spiralis , whereas relieving desiccation stress on Watered transplants would maintain the mid-zone F. vesiculosus bacterial community. Bacteria were identified as amplicon sequence variants (ASVs) after sequencing the V4 hypervariable region of the 16S rRNA gene. Microbiome composition and structure were significantly different between the differently zoned congeners at each tissue type (holdfasts, receptacles, vegetative tips). ASVs significantly associated with the mid-zone congener were frequently also present on the high-zone or low-zone congener, whereas overlap in ASVs between the high-zone and low-zone congeners was rare. Only 7 of 6,320 total ASVs were shared among tissues over all congeners and transplant treatments. Holdfast bacterial community composition of Dry transplants was not significantly different from that of F. spiralis , but Watered holdfast communities were significantly different from those of F. spiralis and not significantly different from those of procedural controls. Additional stressor(s) appeared important, because bacterial communities of Dry and Watered transplants were only marginally different from each other ( p = 0.059). The relative abundance of Rhodobacteraceae associated with holdfasts generally correlated with environmental stress with highest abundance associated with F. spiralis and the two high-zone transplant treatments. These findings suggest that the abiotic stressors that shape distributional patterns of host species also affect their bacterial communities., (Copyright © 2020 Quigley, Capistrant-Fossa, Morrison, Johnson, Morozov, Hertzberg and Brawley.)

Background: Laboring in hot and humid conditions is a risk factor for heat-related illnesses. Little is known about the amount of physical activity performed in the field setting by agricultural workers, a population that is among those at highest risk for heat-related mortality in the United States., Methods: We measured accelerometer-based physical activity and work activities performed in 244 Florida agricultural workers, 18 to 54 years of age, employed in the fernery, nursery, and crop operations during the summer work seasons of 2015-2017. Environmental temperature data during the participant's workdays were collected from the Florida Automated Weather Network and used to calculate wet bulb globe temperature (WBGT). Generalized linear mixed model regression was used to examine the association between WBGT on physical activity, stratified by the agricultural sector., Results: Fernery workers had the highest overall volume of physical activity, spending nearly 4 hours in moderate to vigorous activity per workday. Activity over the course of the workday also differed by the agricultural sector. A reduction on average physical activity with increasing environmental temperature was observed only among crop workers., Conclusions: The quantity and patterns of physical activity varied by the agricultural sector, sex, and age, indicating that interventions that aim to reduce heat-related morbidity and mortality should be tailored to specific subpopulations. Some workers did not reduce overall physical activity under dangerously hot environmental conditions, which has implications for policies protecting worker health. Future research is needed to determine how physical activity and climatic conditions impact the development of heat-related disorders in this population., (© 2019 Wiley Periodicals, Inc.)

Objective: A growing body of research has investigated the human microbiota and pregnancy outcomes, especially preterm birth. Most studies of the prenatal microbiota have focused on the vagina, with fewer investigating other body sites during pregnancy. Although pregnancy involves profound hormonal, immunological and metabolic changes, few studies have investigated either shifts in microbiota composition across pregnancy at different body sites or variation in composition at any site that may be explained by maternal characteristics. The purpose of this study was to investigate: (1) the stability of the vaginal, oral, and gut microbiota from early (8-14 weeks) through later (24-30 weeks) pregnancy among African American women according to measures of socioeconomic status, accounting for prenatal antibiotic use; (2) whether measures of socioeconomic status are associated with changes in microbiota composition over pregnancy; and (3) whether exposure to prenatal antibiotics mediate any observed associations between measures of socioeconomic status and stability of the vaginal, oral, and gut microbiota across pregnancy., Methods: We used paired vaginal, oral, or gut samples available for 16S rRNA gene sequencing from two time points in pregnancy (8-14 and 24-30 weeks) to compare within-woman changes in measures of alpha diversity (Shannon and Chao1) and beta-diversity (Bray-Curtis dissimilarity) among pregnant African American women ( n  = 110). Multivariable linear regression was used to examine the effect of level of education and prenatal health insurance as explanatory variables for changes in diversity, considering antibiotic exposure as a mediator, adjusting for age, obstetrical history, and weeks between sampling., Results: For the oral and gut microbiota, there were no significant associations between measures of socioeconomic status or prenatal antibiotic use and change in Shannon or Chao1 diversity. For the vaginal microbiota, low level of education (high school or less) was associated with an increase in Shannon and Chao1 diversity over pregnancy, with minimal attenuation when controlling for prenatal antibiotic use. Conversely, for within-woman Bray-Curtis dissimilarity for early compared to later pregnancy, low level of education and prenatal antibiotics were associated with greater dissimilarity for the oral and gut sites, with minimal attenuation when controlling for prenatal antibiotics, and no difference in dissimilarity for the vaginal site., Conclusions: Measures of maternal socioeconomic status are variably associated with changes in diversity across pregnancy for the vaginal, oral, and gut microbiota, with minimal attenuation by prenatal antibiotic exposure. Studies that evaluate stability of the microbiota across pregnancy in association with health outcomes themselves associated with socioeconomic status (such as preterm birth) should incorporate measures of socioeconomic status to avoid finding spurious relationships., Competing Interests: Timothy Read is an Academic Editor for PeerJ. The other authors declare that they have no competing interests., (©2019 Dunlop et al.)

Nurse scientists are adept at translating findings from basic science into useful clinical- and community-based interventions to improve health. Over time, the focus of some nursing research has grown to include the assessment and evaluation of genomic and other output from high-throughput, or "omic," technologies as indicators related to health and disease. To date, the growth in the application of omics technologies in nursing research has included calls to increase attention to omics in nursing school curricula and educational training opportunities, such as the Summer Genetics Institute offered by the National Institute of Nursing Research. However, there has been scant attention paid in the nursing literature to the complexity of data analysis or issues of reproducibility related to omics studies. The goals of this article are to (1) familiarize nurse scientists with tools that encourage reproducibility in omics studies, with a focus on the free and open-source data processing and analysis pipeline, and (2) provide a baseline understanding of how these tools can be used to improve collaboration and cohesion among interdisciplinary research team members. Knowledge of these tools and skill in applying them will be important for communication across disciplines and imperative for the advancement of omics research in nursing.

Serving over three billion passengers annually, air travel serves as a conduit for infectious disease spread, including emerging infections and pandemics. Over two dozen cases of in-flight transmissions have been documented. To understand these risks, a characterization of the airplane cabin microbiome is necessary. Our study team collected 229 environmental samples on ten transcontinental US flights with subsequent 16S rRNA sequencing. We found that bacterial communities were largely derived from human skin and oral commensals, as well as environmental generalist bacteria. We identified clear signatures for air versus touch surface microbiome, but not for individual types of touch surfaces. We also found large flight-to-flight beta diversity variations with no distinguishing signatures of individual flights, rather a high between-flight diversity for all touch surfaces and particularly for air samples. There was no systematic pattern of microbial community change from pre- to post-flight. Our findings are similar to those of other recent studies of the microbiome of built environments. In summary, the airplane cabin microbiome has immense airplane to airplane variability. The vast majority of airplane-associated microbes are human commensals or non-pathogenic, and the results provide a baseline for non-crisis-level airplane microbiome conditions.

Amyotrophic lateral sclerosis (ALS) etiology and pathophysiology are not well understood. Recent data suggest that dysbiosis of gut microbiota may contribute to ALS etiology and progression. This review aims to explore evidence of associations between gut microbiota and ALS etiology and pathophysiology. Databases were searched for publications relevant to the gut microbiome in ALS. Three publications provided primary evidence of changes in microbiome profiles in ALS. An ALS mouse model revealed damaged tight junction structure and increased permeability in the intestine versus controls along with a shifted microbiome profile, including decreased levels of butyrate-producing bacteria. In a subsequent publication, again using an ALS mouse model, researchers showed that dietary supplementation with butyrate relieved symptoms and lengthened both time to onset of weight loss and survival time. In a small study of ALS patients and healthy controls, investigators also found decreased levels of butyrate-producing bacteria. Essential for maintaining gut barrier integrity, butyrate is the preferred energy source of intestinal epithelial cells. Ten other articles were reviews and commentaries providing indirect support for a role of gut microbiota in ALS pathophysiology. Thus, these studies provide a modicum of evidence implicating gut microbiota in ALS disease, although more research is needed to confirm the connection and determine pathophysiologic mechanisms. Nurses caring for these patients need to understand the gut microbiome and its potential role in ALS in order to effectively counsel patients and their families about emerging therapies (e.g., prebiotics, probiotics, and fecal microbial transplant) and their off-label uses.

OBJECTIVESThe risk of cross infection in a busy emergency department (ED) is a serious public health concern, especially in times of pandemic threats. We simulated cross infections due to respiratory diseases spread by large droplets using empirical data on contacts (ie, close-proximity interactions of ≤1m) in an ED to quantify risks due to contact and to examine factors with differential risks associated with them.DESIGNProspective study.PARTICIPANTSHealth workers (HCWs) and patients.SETTINGA busy ED.METHODSData on contacts between participants were collected over 6 months by observing two 12-hour shifts per week using a radiofrequency identification proximity detection system. We simulated cross infection due to a novel agent across these contacts to determine risks associated with HCW role, chief complaint category, arrival mode, and ED disposition status.RESULTSCross-infection risk between HCWs was substantially greater than between patients or between patients and HCWs. Providers had the least risk, followed by nurses, and nonpatient care staff had the most risk. There were no differences by patient chief complaint category. We detected differential risk patterns by arrival mode and by HCW role. Although no differential risk was associated with ED disposition status, 0.1 infections were expected per shift among patients admitted to hospital.CONCLUSIONThese simulations demonstrate that, on average, 11 patients who were infected in the ED will be admitted to the hospital over the course of an 8-week local influenza outbreak. These patients are a source of further cross-infection risk once in the hospital.Infect Control Hosp Epidemiol 2018;39:688-693.

Background: Gastrointestinal symptoms are common in Parkinson's disease and frequently precede the development of motor impairments. Intestinal inflammation has been proposed as a driver of disease pathology, and evaluation of inflammatory mediators in stool could possibly identify valuable early-stage biomarkers. We measured immune- and angiogenesis-related proteins in human stool to examine inflammatory profiles associated with Parkinson's disease., Methods: Stool samples and subjects' self-reported metadata were obtained from 156 individuals with Parkinson's disease and 110 without, including spouse and nonhousehold controls. Metadata were probed for disease-associated differences, and levels of 37 immune and angiogenesis factors in stool homogenates were measured by multiplexed immunoassay and compared across experimental groups., Results: Parkinson's disease patients reported greater incidence of intestinal disease and digestive problems than controls. Direct comparison of levels of stool analytes in patients and controls revealed elevated vascular endothelial growth factor receptor 1, interleukin-1α, and CXCL8 in patients' stool. Paired comparison of patients and spouses suggested higher levels of multiple factors in patients, but this was complicated by sex differences. Sex, body mass index, a history of smoking, and use of probiotics were found to strongly influence levels of stool analytes. Multivariate analysis accounting for these and other potential confounders confirmed elevated levels of interleukin-1α and CXCL8 and also revealed increased interleukin-1β and C-reactive protein in stool in Parkinson's disease. These differences were not dependent on subject age or disease duration., Conclusions: Levels of stool immune factors indicate that intestinal inflammation is present in patients with Parkinson's disease. © 2018 International Parkinson and Movement Disorder Society., (© 2018 International Parkinson and Movement Disorder Society.)

With over 3 billion airline passengers annually, the inflight transmission of infectious diseases is an important global health concern. Over a dozen cases of inflight transmission of serious infections have been documented, and air travel can serve as a conduit for the rapid spread of newly emerging infections and pandemics. Despite sensational media stories and anecdotes, the risks of transmission of respiratory viruses in an airplane cabin are unknown. Movements of passengers and crew may facilitate disease transmission. On 10 transcontinental US flights, we chronicled behaviors and movements of individuals in the economy cabin on single-aisle aircraft. We simulated transmission during flight based on these data. Our results indicate there is low probability of direct transmission to passengers not seated in close proximity to an infectious passenger. This data-driven, dynamic network transmission model of droplet-mediated respiratory disease is unique. To measure the true pathogen burden, our team collected 229 environmental samples during the flights. Although eight flights were during Influenza season, all qPCR assays for 18 common respiratory viruses were negative., Competing Interests: Conflict of interest statement: The authors received support from The Boeing Company (H.W.) by way of a subcontract to the Georgia Institute of Technology (V.S.H.). S.L.N. is an employee of Boeing., (Copyright © 2018 the Author(s). Published by PNAS.)

Background: Efforts to identify fluid biomarkers of Parkinson's disease (PD) have intensified in the last decade. As the role of inflammation in PD pathophysiology becomes increasingly recognized, investigators aim to define inflammatory signatures to help elucidate underlying mechanisms of disease pathogenesis and aid in identification of patients with inflammatory endophenotypes that could benefit from immunomodulatory interventions. However, discordant results in the literature and a lack of information regarding the stability of inflammatory factors over a 24-h period have hampered progress., Methods: Here, we measured inflammatory proteins in serum and CSF of a small cohort of PD (n = 12) and age-matched healthy control (HC) subjects (n = 6) at 11 time points across 24 h to (1) identify potential diurnal variation, (2) reveal differences in PD vs HC, and (3) to correlate with CSF levels of amyloid β (Aβ) and α-synuclein in an effort to generate data-driven hypotheses regarding candidate biomarkers of PD., Results: Despite significant variability in other factors, a repeated measures two-way analysis of variance by time and disease state for each analyte revealed that serum IFNγ, TNF, and neutrophil gelatinase-associated lipocalin (NGAL) were stable across 24 h and different between HC and PD. Regression analysis revealed that C-reactive protein (CRP) was the only factor with a strong linear relationship between CSF and serum. PD and HC subjects showed significantly different relationships between CSF Aβ proteins and α-synuclein and specific inflammatory factors, and CSF IFNγ and serum IL-8 positively correlated with clinical measures of PD. Finally, linear discriminant analysis revealed that serum TNF and CSF α-synuclein discriminated between PD and HC with a minimum of 82% sensitivity and 83% specificity., Conclusions: Our findings identify a panel of inflammatory factors in serum and CSF that can be reliably measured, distinguish between PD and HC, and monitor inflammation as disease progresses or in response to interventional therapies. This panel may aid in generating hypotheses and feasible experimental designs towards identifying biomarkers of neurodegenerative disease by focusing on analytes that remain stable regardless of time of sample collection.

A Phase III, double-blind, placebo-controlled trial (ProTECT III) found that administration of progesterone did not reduce mortality or improve functional outcome as measured by the Glasgow Outcome Scale Extended (GOSE) in subjects with moderate to severe traumatic brain injury. We conducted a secondary analysis of neuropsychological outcomes to evaluate whether progesterone is associated with improved recovery of cognitive and motor functioning. ProTECT III was conducted at 49 level I trauma centers in the United States. Adults with moderate to severe TBI were randomized to receive intravenous progesterone or placebo within 4 h of injury for a total of 4 days. At 6 months, subjects underwent evaluation of memory, attention, executive functioning, language, and fine motor coordination/dexterity. Chi-square analysis revealed no significant difference in the proportion of subjects (263/280 progesterone, 283/295 placebo) with Galveston Orientation and Amnesia Test scores ≥75. Analyses of covariance did not reveal significant treatment effects for memory (Buschke immediate recall, p = 0.53; delayed recall, p = 0.94), attention (Trails A speed, p = 0.81 and errors, p = 0.22; Digit Span Forward length, p = 0.66), executive functioning (Trails B speed, p = 0.97 and errors, p = 0.93; Digit Span Backward length, p = 0.60), language (timed phonemic fluency, p = 0.05), and fine motor coordination/dexterity (Grooved Pegboard dominant hand time, p = 0.75 and peg drops, p = 0.59; nondominant hand time, p = 0.74 and peg drops, p = 0.61). Pearson Product Moment Correlations demonstrated significant (p < 0.001) associations between better neuropsychological performance and higher GOSE scores. Similar to the ProTECT III trial's results of the primary outcome, the secondary outcomes do not provide evidence of a neuroprotective effect of progesterone., Competing Interests: Author Disclosure Statement Dr. Wright reports receiving royalties from a patent related to progesterone for the treatment of patients with traumatic brain injury (U.S. patents 7,473,687, 7,915,244, and 8,455,468), which was licensed to BHR Pharma. For the remaining authors, no competing financial interests exist.

Objective: The objective of this study was to determine the feasibility of field-based biomonitoring of heat-related illness (HRI) phenomena in Florida farmworkers. The authors determined feasibility through participant interviews regarding acceptability, data capture, recruitment and retention, and observed barriers and challenges to implementation., Methods: Study participants were employed in fernery operations in northeast Central Florida where ornamental ferns are grown and harvested in a seasonally high-heat environment. In this pilot, a total of 43 farmworkers participated during summers 2012 and 2013 and measurements included body core temperature, heart rate, energy expenditure, urine and blood osmolality, and self-reported HRI symptoms., Results: Data capture was approximately 90%. Participants reported that the study methods were nonobtrusive to their work, and that they were comfortable with study measures., Conclusions: These results open possibilities for characterizing HRI utilizing physiologic biomonitoring in vulnerable occupational groups.

Emergency departments play a critical role in the public health system, particularly in times of pandemic. Infectious patients presenting to emergency departments bring a risk of cross-infection to other patients and staff through close proximity interactions or contacts. To understand factors associated with cross-infection risk, we measured close proximity interactions of emergency department staff and patients by radiofrequency identification in a working emergency department. The number of contacts (degree) is not related to patient demographic characteristics. However, the amount of time in close proximity (weighted degree) of patients with ED personnel did differ, with black patients having approximately 15 min more contact with staff than non-white patients. Patients arriving by EMS had fewer contacts with other patients than patients arriving by other means. There are differences in the number of contacts based on staff role and arrival mode. When crowding is low, providers have the most contact time with patients, while administrative staff have the least. However, when crowding is high, this differential is reversed. The effect of arrival mode is modified by the extent of crowding. When crowding is low, patients arriving by EMS had longer contact with administrative staff, compared to patients arriving by other means. However, when crowding is high, patients arriving by EMS had less contact with administrative staff compared to patients arriving by other means. Our findings should help designers of emergency care focus on higher risk situations for transmission of dangerous pathogens in an emergency department. For instance, the effects of arrival and crowding should be considered as targets for engineering or architectural interventions that could artificially increase social distances., (© 2016 Published by Elsevier B.V.)

Background: With over two billion airline passengers annually, in-flight transmission of infectious diseases is an important global health concern. Many instances of in-flight transmission have been documented, but the relative influence of the many factors (see below) affecting in-flight transmission has not been quantified. Long-standing guidance by public health agencies is that the primary transmission risk associated with air travel for most respiratory infectious diseases is associated with sitting within two rows of an infectious passenger. The effect of proximity may be one of these factors., Objective: The aim of this study was to determine the risk of infection within and beyond the 2-row rule given by public health guidance., Methods: We searched the literature for reports of in-flight transmission of infection which included seat maps indicating where the infectious and infected passengers were seated., Findings: There is a ∼ 6% risk to passengers seated within the 2-rows of infected individual(s) and there is ∼ 2% risk to passengers seated beyond 2-rows from the infectious individual., Discussion: Contact tracing limited to passengers within 2-rows of the infectious individual(s) could fail to detect other cases of infections. This has important consequences for assessing the spread of infectious diseases., Conclusions: Infection at a distance from the index case indicates other factors, such as airflow, movement of passenger/crew members, fomites and contacts between passengers in the departure gate before boarding, or after deplaning, are involved., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)