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11. The Airplane Cabin Microbiome

Author

The FlyHealthy Research Team, Weiss, Howard, Hertzberg, Vicki Stover, Dupont, Chris, Espinoza, Josh L., Levy, Shawn, Nelson, Karen, and Norris, Sharon

Published
2019

15. Black-White Differences in Chronic Stress Exposures to Predict Preterm Birth: Interpretable, Race/Ethnicity-Specific Machine Learning Models.

Author

Sangmi KIM, HIGGINS, Melinda K., BRENNAN, Patricia, SLAVICH, George M., Li XIONG, HERTZBERG, Vicki, KELLY, Ursula, and DUNLOP, Anne L.

Abstract

We developed Multivariate Adaptive Regression Splines (MARS) machine learning models of chronic stressors using the Pregnancy Risk Assessment Monitoring System data (2012-2017) to predict preterm birth (PTB) more accurately and identify chronic stressors driving PTB among non-Hispanic (N-H) Black and N-H White pregnant women in the U.S. We trained the MARS models using 5-fold cross-validation, whose performance was evaluated with AUC. We computed variable importance for PTB prediction. Our models showed high accuracy (AUC: 0.754-0.765). The number of prenatal care visits, premature rupture of membrane, and medical conditions were the most important variables in predicting PTB across the populations. Chronic stressors (e.g., low maternal education and violence) and their correlates were pivotal for PTB prediction only for N-H Black women. Interpretable, race/ethnicity-specific MARS models can predict PTB accurately and explain the most impactful life stressors and their magnitude of effect on PTB risk among N-H Black and N-H White women. [ABSTRACT FROM AUTHOR]

Published
2024
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20. The Airplane Cabin Microbiome

Author

Weiss, Howard, Hertzberg, Vicki Stover, Dupont, Chris, Espinoza, Josh L., Levy, Shawn, Nelson, Karen, Norris, Sharon, and The FlyHealthy Research Team

Published
2019
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36. Girasoles 2.0: Mechanisms Relating the Gut Microbiome to Heat-Related Illness

Author

Hertzberg, Vicki

Abstract

With climate models projecting increasing frequency and severity of heat waves, heat-related illness (HRI) due to environmental exposure is of increasing public health concern. Agricultural workers are particularly vulnerable to HRI. HRI covers a spectrum of symptoms, ranging in severity from sweating to heat stroke or, perhaps, death. Despite recent scientific advances, the processes underlying HRI are not well understood, and health inequities persist among agricultural workers. Heat is the initiating factor in HRI, but septicemia becomes a driving factor after adaptive mechanisms fail. Evidence suggests that gut microbiota and inflammation play a role in escalating the response to heat stress into florid HRI. Gut microbiota, and the short chain fatty acids (SCFAs) they produce, help regulate levels and activity of antioxidant enzymes and modulate immune function. Gut permeability increases in HRI as demonstrated by biomarkers of inflammation and non-negligible plasma levels of lipopolysaccharide (LPS, also known as endotoxin), a constituent of bacterial cell walls that, when present in circulation, provides evidence of bacterial translocation from the gut. Recent evidence indicates that gut bile salts and interleukin (IL)-22, an inflammatory biomarker, may also play roles in shaping gut microbial community structure. We will assess the role of gut microbiota and their interplay with inflammation and metabolism to further explore HRI pathophysiology. The overarching hypothesis of this VICTeR proposal is gut microbial communities, systemic and gut inflammation, SCFAs, and bile salts influence the likelihood and severity of HRI. We propose a cross-sectional design to evaluate 100 agricultural workers. Our project translates from 1) our experience since 2009 collaborating with the Farmworker Association of Florida in prior and current investigations of occupational health issues among Hispanic agricultural workers and the resulting infrastructure; and 2) exciting preliminary data demonstrating elevations of inflammatory biomarkers in serum and stool and perturbations of metabolic pathways for SCFAs among heat-exposed agricultural workers in relation to HRI. To complement our expertise in HRI and inflammation, we are fostering new collaborations with Dr. Eric Ortlund (Emory University; lipidomics) and Dr. Kostas Konstantinidis (Georgia Institute of Technology or “Georgia Tech”; microbiome). We will collect blood and stool samples from participants and resolve gut microbes to the strain level using whole genome sequencing (WGS). We will carry out the following specific aims: 1) evaluate gut species or stain genomes recovered in the WGS data that correlate with symptoms and perform targeted isolation of the most promising strains to validate their functional role using human cell lines under high vs. normal body temperatures; 2) evaluate plasma and stool inflammatory biomarkers in relation to symptoms; and 3) evaluate SCFA and bile salt concentrations in plasma and stool. Our findings will allow us to identify microbial and bile salt biomarkers of HRI vulnerability and spur new preventive approaches or HRI treatments in the setting of extreme heat work environments.

Published
2022
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38. A Pilot Study of the Gut Microbiota Associated With Depressive Symptoms and Sleep Disturbance Among Chinese and Korean Immigrants in the United States.

Author

Hope, Chloe, Shen, Natalie, Zhang, Wenhui, Noh, Hye In, Hertzberg, Vicki S., Kim, Sangmi, and Bai, Jinbing

Subjects
MENTAL depression risk factors, IMMIGRANTS, PILOT projects, STATISTICS, KRUSKAL-Wallis Test, ANALYSIS of variance, SCIENTIFIC observation, GUT microbiome, MULTIVARIATE analysis, EFFECT sizes (Statistics), BACTEROIDES, CROSS-sectional method, HEALTH outcome assessment, DISCRIMINANT analysis, ACTINOBACTERIA, SLEEP disorders, KOREANS, RISK assessment, FECES, PSYCHOSOCIAL factors, DESCRIPTIVE statistics, CHI-squared test, RESEARCH funding, DATA analysis, DATA analysis software, COVID-19 pandemic, DISEASE risk factors
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2023
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43. Pressure Ulcer Injury in Unstructured Clinical Notes: Detection and Interpretation

Author

Sotoodeh, Mani, Gero, Zelalem H., Zhang, Wenhui, Hertzberg, Vicki Stover, and Ho, Joyce C.

Subjects
Pressure Ulcer, Logistic Models, Electronic Health Records, Humans, Articles, Neural Networks, Computer, Algorithms
Abstract

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.

Published
2021

45. Effect of laboratory practices on the incidence rate of congenital hypothyroidism

Author

Hertzberg, Vicki, Mei, Joanne, and Therrell, Bradford L.

Subjects
Company distribution practices, Congenital hypothyroidism -- Distribution, Congenital hypothyroidism -- Statistics, Congenital hypothyroidism -- Research, Infants (Newborn) -- Medical examination, Infants (Newborn) -- Methods, Infants (Newborn) -- Research
Published
2010

47. Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models

Author

Li, Changmao, Fisher, Elaine, Thomas, Rebecca, Pittard, Steve, Hertzberg, Vicki, and Choi, Jinho D.

Subjects
Computer Science - Computation and Language, I.2.7
Abstract

This paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable candidates. A total of 6,492 resumes are extracted from 24,933 job applications for 252 positions designated into four levels of experience for Clinical Research Coordinators (CRC). Each resume is manually annotated to its most appropriate CRC position by experts through several rounds of triple annotation to establish guidelines. As a result, a high Kappa score of 61% is achieved for inter-annotator agreement. Given this dataset, novel transformer-based classification models are developed for two tasks: the first task takes a resume and classifies it to a CRC level (T1), and the second task takes both a resume and a job description to apply and predicts if the application is suited to the job T2. Our best models using section encoding and multi-head attention decoding give results of 73.3% to T1 and 79.2% to T2. Our analysis shows that the prediction errors are mostly made among adjacent CRC levels, which are hard for even experts to distinguish, implying the practical value of our models in real HR platforms., Comment: Accepted by the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)

Published
2020

50. Adverse health consequences that co-occur with depression: a longitudinal study of black adolescent females

Author

DiClemente, Ralph J., Wingood, Gina M., Lang, Delia L., Crosby, Richard A., Salazar, Laura F., Harrington, Kathy, and Hertzberg, Vicki Stover

Subjects
Blacks -- Health aspects, Blacks -- Psychological aspects, Depression in adolescence -- Health aspects, Depression in adolescence -- Complications and side effects, Depression in adolescence -- Psychological aspects
Abstract

Objective. The purpose of this study was to identify adverse health consequences that may co-occur with depression among black female adolescents. Methods. Adolescents were recruited from high-risk neighborhoods in Birmingham, Alabama. The sample comprised 460 black female adolescents (aged 14-18 years) who completed assessments at baseline and at 6 and 12 months. Only adolescents who consistently scored above the threshold for depression at all 3 assessments (n = 76) or below the threshold at all 3 assessments (n = 174) were included (N = 250) in the data analysis. Within this sample, adolescents who were depressed were compared with those who were not depressed with respect to the following health consequences: low self-esteem, emotional abuse, physical abuse, verbal abuse, poor body image, and antisocial behavior. Results. Using generalized estimating equations and controlling for covariates, depressed adolescents were 5.3 times more likely to report low self-esteem, 4.3 times more likely to report emotional abuse, 3.7 times more likely to report being physically abused, and almost 3 times as likely to report being verbally abused. Furthermore, depressed adolescents were more than twice as likely to report poor body image and nearly twice as likely to report engaging in antisocial behaviors. Conclusions. The findings suggest that a broad range of adverse health consequences may accompany depression among black female adolescents. Physicians need to be alert to the co-occurrence of depression and low self-esteem; emotional, physical, and verbal abuse; poor body image; and antisocial behaviors among this population. Pediatrics 2005;116:78-81; adolescent depression, adolescent health, adolescent sexual behavior, adverse outcomes, black. ABBREVIATIONS. CES-D, Center for Epidemiological Studies Depression Scale; GEE, generalized estimating equations., One of the most consistent findings in the adolescent literature is the higher prevalence of depression among girls compared with boys. (1-5) Moreover, this gender difference in prevalence rates continues [...]

Published
2005

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