Search

Objective: To develop criteria for interpreting the high-dose dexamethasone suppression test using urine free cortisol as an end point for the differential diagnosis of the Cushing syndrome.Design: Retrospective review.Setting: Inpatient research ward.Patients: Patients (118) with surgically confirmed causes of the Cushing syndrome: 94 with pituitary disease, 14 with primary adrenal disease, and 10 with ectopic adrenocorticotropic hormone (ACTH) secretion.Main Outcome Measures: The sensitivity, specificity, and diagnostic accuracy were determined for the high-dose dexamethasone suppression test using urine free cortisol and using 17-hydroxysteroid excretion. For each analysis, patients with pituitary disease were considered to be "diseased" and patients with nonpituitary disease were considered to be "non-diseased". The level of suppression that gave 100% specificity was determined for each steroid.Results: The accuracy of urine free cortisol when used as an end point in the high-dose dexamethasone suppression test was equivalent to that of 17-hydroxysteroid excretion. At all levels of sensitivity and specificity, however, the degree of suppression of urine free cortisol used for the diagnosis of pituitary disease was greater than that of 17-hydroxysteroid excretion. The likelihood ratios for pituitary disease based on urine free cortisol suppression of greater than 50%, of greater than 80%, and of greater than 90% were 4.2, 10.1, and "infinite," respectively. Suppression of urine free cortisol greater than 90% or suppression of 17-hydroxysteroid excretion greater than 64% was associated with 100% specificity. When these criteria were combined, the percentage of correct predictions (102 of 118 [86%; 95% CI, 78% to 92%]) was higher than that obtained using either steroid alone (89 of 118 [75%; CI, 65% to 83%]) (P = 0.009) and higher than that obtained using the traditional criterion of 50% suppression for 17-hydroxysteroid excretion (95 of 118 [80%; CI, 71% to 87%]) (P = 0.016).Conclusions: In the high-dose dexamethasone suppression test, the degree of suppression of urine free cortisol used for the diagnosis of pituitary disease is greater than that traditionally used for 17-hydroxysteroid excretion. The diagnostic performance of the test is improved by measuring both urine free cortisol and 17-hydroxysteroid excretion and by requiring greater suppression of both steroids. [ABSTRACT FROM AUTHOR]

Background: Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach., Objective: The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being., Methods: To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV., Results: In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant., Conclusions: In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions., (©Robert P Hirten, Matteo Danieletto, Kyle Landell, Micol Zweig, Eddye Golden, Renata Pyzik, Sparshdeep Kaur, Helena Chang, Drew Helmus, Bruce E Sands, Dennis Charney, Girish Nadkarni, Emilia Bagiella, Laurie Keefer, Zahi A Fayad. Originally published in JMIR Mental Health (https://mental.jmir.org), 25.04.2024.)

Background: The increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables multimodal data collection from a mobile app and connected sources. They offer an opportunity to recruit at scale, acquire data longitudinally at a high frequency, and engage study participants at any time of the day in any place. Few published descriptions of centralized digital research platforms provide a framework for their development., Objective: This study aims to serve as a road map for those seeking to develop a centralized digital research platform. We describe the technical and functional aspects of the ehive app, the centralized digital research platform of the Hasso Plattner Institute for Digital Health at Mount Sinai Hospital, New York, New York. We then provide information about ongoing studies hosted on ehive, including usership statistics and data infrastructure. Finally, we discuss our experience with ehive in the broader context of the current landscape of digital health research platforms., Methods: The ehive app is a multifaceted and patient-facing central digital research platform that permits the collection of e-consent for digital health studies. An overview of its development, its e-consent process, and the tools it uses for participant recruitment and retention are provided. Data integration with the platform and the infrastructure supporting its operations are discussed; furthermore, a description of its participant- and researcher-facing dashboard interfaces and the e-consent architecture is provided., Results: The ehive platform was launched in 2020 and has successfully hosted 8 studies, namely 6 observational studies and 2 clinical trials. Approximately 1484 participants downloaded the app across 36 states in the United States. The use of recruitment methods such as bulk messaging through the EPIC electronic health records and standard email portals enables broad recruitment. Light-touch engagement methods, used in an automated fashion through the platform, maintain high degrees of engagement and retention. The ehive platform demonstrates the successful deployment of a central digital research platform that can be modified across study designs., Conclusions: Centralized digital research platforms such as ehive provide a novel tool that allows investigators to expand their research beyond their institution, engage in large-scale longitudinal studies, and combine multimodal data streams. The ehive platform serves as a model for groups seeking to develop similar digital health research programs., International Registered Report Identifier (irrid): DERR1-10.2196/49204., (©Robert P Hirten, Matteo Danieletto, Kyle Landell, Micol Zweig, Eddye Golden, Georgy Orlov, Jovita Rodrigues, Eugenia Alleva, Ipek Ensari, Erwin Bottinger, Girish N Nadkarni, Thomas J Fuchs, Zahi A Fayad. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 16.11.2023.)

Purpose/objectives: This study aimed to determine how the COVID-19 pandemic and extensive use of personal protective equipment (PPE) have affected the perception of the practice of dentistry in an urban dental school in the USA., Methods: In 2021, an electronic cross-sectional questionnaire was sent to school faculty via email, while predoctoral, clinical dental students were invited to participate during clinical group meetings. Data were analyzed using descriptive statistics., Results: Sixty-six faculty and 209 students completed the questionnaire (n = 275, response rate = 39%). The faculty self-identified into two groups: dentists who only teach (teaching faculty, n = 33) and dentists who teach and also work in outside clinics (practicing faculty, n = 33). Practicing faculty (45.5%) were significantly more pessimistic about the future of dentistry than the other two groups. Students were the least concerned with treating COVID-19 patients compared to other groups and only 15.8% of students reported a change in enthusiasm toward dentistry. Practicing faculty did not feel that extra PPE presented a physical challenge. Teaching faculty were least alarmed by extra PPE and least concerned about PPE increasing patient stress. More faculty were in favor of vaccination requirements (90.9%) than students (73.7%)., Conclusions: Neither COVID-19 nor the use of extensive PPE negatively affected the participants' perception of the future of dentistry. Most participants felt safe wearing extra PPE during the pandemic but were concerned about the negative environmental effects. Students did not feel strongly that vaccination should be a requirement for clinical dentists and staff., (© 2023 American Dental Education Association.)

Objective: To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device., Materials and Methods: Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline., Results: We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median Connor-Davidson Resilience Scale-2 score of 6 (interquartile range = 5-7), with an AUC of 0.60. When predicting resilience as a continuous variable, multivariate linear models had a correlation of 0.24 ( P  = .029) and RMSE of 1.37 in the testing data. A positive psychological construct, comprised of resilience, optimism, and emotional support was also evaluated. The oblique random forest method performed best in estimating high- versus low-composite scores stratified on a median of 32.5, with an AUC of 0.65, a sensitivity of 0.60, and a specificity of 0.70., Discussion: In a post hoc analysis, machine learning models applied to physiological metrics collected from wearable devices had some predictive ability in identifying resilience states and a positive psychological construct., Conclusions: These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies., Competing Interests: DC is a coinventor on patents filed by the Icahn School of Medicine at Mount Sinai (ISMMS) relating to the treatment for treatment-resistant depression, suicidal ideation, and other disorders. ISMMS has entered into a licensing agreement with Janssen Pharmaceuticals, Inc, and it has received and will receive payments from Janssen under the license agreement related to these patents for the treatment of treatment-resistant depression and suicidal ideation. Consistent with the ISMMS Faculty Handbook (the medical school policy), DC is entitled to a portion of the payments received by the ISMMS. Because SPRAVATO has received regulatory approval for treatment-resistant depression, through the ISMMS, DC will be entitled to additional payments beyond those already received under the license agreement. DC is a named coinventor on several patents filed by ISMMS for a cognitive training intervention to treat depression and related psychiatric disorders. The ISMMS has entered into a licensing agreement with Click Therapeutics, Inc and has received and will receive payments related to the use of this cognitive training intervention for the treatment of psychiatric disorders. In accordance with the ISMMS Faculty Handbook, DC has received a portion of these payments and is entitled to a portion of any additional payments that the medical school may receive from this license with Click Therapeutics. DC is a named coinventor on a patent application filed by the ISMMS for the use of intranasally administered Neuropeptide Y for the treatment of mood and anxiety disorders. This intellectual property has not been licensed. DC is a named coinventor on a patent application in the United States and several issued patents outside the United States filed by the ISMMS related to the use of ketamine for the treatment of posttraumatic stress disorder. This intellectual property has not been licensed. EPB reports consultancy agreements with Deloitte and Roland Berger; ownership interest in Digital Medicine E. Böttinger GmbH, EBCW GmbH, and Ontomics, Inc; receiving honoraria from Bayer, Bosch Health Campus, Sanofi, and Siemens; and serving as a scientific advisor or member of Bosch Health Campus and Seer Biosciences Inc. LK declares research funding from Abbvie and Pfizer, consulting for Abbvie and Pfizer, and equity ownership/stock options in MetaMe Health and Trellus Health. MS-F declares research support from Novartis and Allergenis. GNN reports employment with, consultancy agreements with, and ownership interest in Pensieve Health and Renalytix AI; receiving consulting fees from AstraZeneca, BioVie, GLG Consulting, and Reata; and serving as a scientific advisor or member of Pensieve Health and Renalytix AI. ZAF discloses consulting fees from Alexion, GlaxoSmithKline, and Trained Therapeutix Discovery and research funding from Daiichi Sankyo, Amgen, Bristol Myers Squibb, and Siemens Healthineers. ZAF receives financial compensation as a board member and advisor to Trained Therapeutix Discovery and owns equity in Trained Therapeutix Discovery as a cofounder. The remaining authors declare no conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice., (©Stefan Konigorski, Sarah Wernicke, Tamara Slosarek, Alexander M Zenner, Nils Strelow, Darius F Ruether, Florian Henschel, Manisha Manaswini, Fabian Pottbäcker, Jonathan A Edelman, Babajide Owoyele, Matteo Danieletto, Eddye Golden, Micol Zweig, Girish N Nadkarni, Erwin Böttinger. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.07.2022.)

Objective: To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices., Materials and Methods: Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app., Results: We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±∼4%) and specificity of 77% (CI ±∼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age., Discussion: We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection., Conclusion: Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: The COVID-19 pandemic has resulted in a high degree of psychological distress among health care workers (HCWs). There is a need to characterize which HCWs are at an increased risk of developing psychological effects from the pandemic. Given the differences in the response of individuals to stress, an analysis of both the perceived and physiological consequences of stressors can provide a comprehensive evaluation of its impact., Objective: This study aimed to determine characteristics associated with longitudinal perceived stress in HCWs and to assess whether changes in heart rate variability (HRV), a marker of autonomic nervous system function, are associated with features protective against longitudinal stress., Methods: HCWs across 7 hospitals in New York City, NY, were prospectively followed in an ongoing observational digital study using the custom Warrior Watch Study app. Participants wore an Apple Watch for the duration of the study to measure HRV throughout the follow-up period. Surveys measuring perceived stress, resilience, emotional support, quality of life, and optimism were collected at baseline and longitudinally., Results: A total of 361 participants (mean age 36.8, SD 10.1 years; female: n=246, 69.3%) were enrolled. Multivariate analysis found New York City's COVID-19 case count to be associated with increased longitudinal stress (P=.008). Baseline emotional support, quality of life, and resilience were associated with decreased longitudinal stress (P<.001). A significant reduction in stress during the 4-week period after COVID-19 diagnosis was observed in the highest tertial of emotional support (P=.03) and resilience (P=.006). Participants in the highest tertial of baseline emotional support and resilience had a significantly different circadian pattern of longitudinally collected HRV compared to subjects in the low or medium tertial., Conclusions: High resilience, emotional support, and quality of life place HCWs at reduced risk of longitudinal perceived stress and have a distinct physiological stress profile. Our findings support the use of these characteristics to identify HCWs at risk of the psychological and physiological stress effects of the pandemic., (©Robert P Hirten, Matteo Danieletto, Lewis Tomalin, Katie Hyewon Choi, Micol Zweig, Eddye Golden, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Renata Pyzik, Claudia Calcagno, Robert Freeman, Bruce E Sands, Dennis Charney, Erwin P Bottinger, James W Murrough, Laurie Keefer, Mayte Suarez-Farinas, Girish N Nadkarni, Zahi A Fayad. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.09.2021.)

Background: The COVID-19 pandemic has resulted in increased strain on health care systems and negative psychological effects on health care workers (HCWs). This is anticipated to result in long-term negative mental health effects on the population, with HCWs representing a particularly vulnerable group. The scope of the COVID-19 pandemic necessitates the development of a scalable mental health platform to provide services to large numbers of at-risk or affected individuals. The Mount Sinai Health System in New York City was at the epicenter of the pandemic in the United States., Objective: The Center for Stress, Resilience, and Personal Growth (CSRPG) was created to address the current and anticipated psychological impact of the pandemic on the HCWs in the health system. The mission of the Center is to support the resilience and mental health of employees through educational offerings, outreach, and clinical care. Our aim was to build a mobile app to support the newly founded Center in its mission., Methods: We built the app as a standalone digital platform that hosts a suite of tools that users can interact with on a daily basis. With consideration for the Center's aims, we determined the overall vision, initiatives, and goals for the Wellness Hub app, followed by specific milestone tasks and deliverables for development. We defined the app's primary features based on the mental health assessment and needs of HCWs. Feature definition was informed by the results of a resilience survey widely distributed to Mount Sinai HCWs and by the resources offered at CSRPG, including workshop content., Results: We launched our app over the course of two phases, the first phase being a "soft" launch and the second being a broader launch to all of Mount Sinai. Of the 231 HCWs who downloaded the app, 173 (74.9%) completed our baseline assessment of all mental health screeners in the app. Results from the baseline assessment show that more than half of the users demonstrate a need for support in at least one psychological area. As of 3 months after the Phase 2 launch, approximately 55% of users re-entered the app after their first opening to explore additional features, with an average of 4 app openings per person., Conclusions: To address the mental health needs of HCWs during the COVID-19 pandemic, the Wellness Hub app was built and deployed throughout the Mount Sinai Health System. To our knowledge, this is the first resilience app of its kind. The Wellness Hub app is a promising proof of concept, with room to grow, for those who wish to build a secure mobile health app to support their employees, communities, or others in managing and improving mental and physical well-being. It is a novel tool offering mental health support broadly., (©Eddye A Golden, Micol Zweig, Matteo Danieletto, Kyle Landell, Girish Nadkarni, Erwin Bottinger, Lindsay Katz, Ricardo Somarriba, Vansh Sharma, Craig L Katz, Deborah B Marin, Jonathan DePierro, Dennis S Charney. Originally published in JMIR Formative Research (https://formative.jmir.org), 05.05.2021.)

Background: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification., Objective: We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms., Methods: Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily., Results: Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19-related symptom compared to all other symptom-free days (P=.01)., Conclusions: Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19-related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection., (©Robert P Hirten, Matteo Danieletto, Lewis Tomalin, Katie Hyewon Choi, Micol Zweig, Eddye Golden, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Renata Pyzik, Alexander Charney, Riccardo Miotto, Benjamin S Glicksberg, Matthew Levin, Ismail Nabeel, Judith Aberg, David Reich, Dennis Charney, Erwin P Bottinger, Laurie Keefer, Mayte Suarez-Farinas, Girish N Nadkarni, Zahi A Fayad. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.02.2021.)

An investment bubble occurs when there is a surge in asset prices that is not warranted by asset fundamentals because of irrationally exuberant market behavior. When prices rise to a level where no additional investors are willing to buy at the elevated price, then a massive sell-off typically occurs. Digital health investments represent approximately 10% of venture capital-backed startup investments, and diabetes digital health startups represent 4% of digital health investments. Attributes of a bubble indicate evidence for and against the current time period being in an investment bubble for digital health startups. After analyzing these attributes as well as the overall economy and the demand for healthcare products, we conclude that digital health startups and particularly digital health startups for diabetes are not in a bubble.

Widespread adoption of smart mobile platforms coupled with a growing ecosystem of sensors including passive location tracking and the ability to leverage external data sources create an opportunity to generate an unprecedented depth of data on individuals. Mobile health technologies could be utilized for chronic disease management as well as research to advance our understanding of common diseases, such as asthma. We conducted a prospective observational asthma study to assess the feasibility of this type of approach, clinical characteristics of cohorts recruited via a mobile platform, the validity of data collected, user retention patterns, and user data sharing preferences. We describe data and descriptive statistics from the Asthma Mobile Health Study, whereby participants engaged with an iPhone application built using Apple's ResearchKit framework. Data from 6346 U.S. participants, who agreed to share their data broadly, have been made available for further research. These resources have the potential to enable the research community to work collaboratively towards improving our understanding of asthma as well as mobile health research best practices.

Background: To address the need for more effective genomics training, beginning in 2012 the Icahn School of Medicine at Mount Sinai has offered a unique laboratory-style graduate genomics course, "Practical Analysis of Your Personal Genome" (PAPG), in which students optionally sequence and analyze their own whole genome. We hypothesized that incorporating personal genome sequencing (PGS) into the course pedagogy could improve educational outcomes by increasing student motivation and engagement. Here we extend our initial study of the pilot PAPG cohort with a report on student attitudes towards genome sequencing, decision-making, psychological wellbeing, genomics knowledge and pedagogical engagement across three course years., Methods: Students enrolled in the 2013, 2014 and 2015 course years completed questionnaires before (T1) and after (T2) a prerequisite workshop (n = 110) and before (T3) and after (T4) PAPG (n = 66)., Results: Students' interest in PGS was high; 56 of 59 eligible students chose to sequence their own genome. Decisional conflict significantly decreased after the prerequisite workshop (T2 vs. T1 p < 0.001). Most, but not all students, reported low levels of decision regret and test-related distress post-course (T4). Each year baseline decisional conflict decreased (p < 0.001) suggesting, that as the course became more established, students increasingly made their decision prior to enrolling in the prerequisite workshop. Students perceived that analyzing their own genome enhanced the genomics pedagogy, with students self-reporting being more persistent and engaged as a result of analyzing their own genome. More than 90% of respondents reported spending additional time outside of course assignments analyzing their genome., Conclusions: Incorporating personal genome sequencing in graduate medical education may improve student motivation and engagement. However, more data will be needed to quantitatively evaluate whether incorporating PGS is more effective than other educational approaches.

Cushing's disease (CD) is caused by pituitary corticotroph adenomas that secrete excess adrenocorticotropic hormone (ACTH). In these tumors, somatic mutations in the gene USP8 have been identified as recurrent and pathogenic and are the sole known molecular driver for CD. Although other somatic mutations were reported in these studies, their contribution to the pathogenesis of CD remains unexplored. No molecular drivers have been established for a large proportion of CD cases and tumor heterogeneity has not yet been investigated using genomics methods. Also, even in USP8 -mutant tumors, a possibility may exist of additional contributing mutations, following a paradigm from other neoplasm types where multiple somatic alterations contribute to neoplastic transformation. The current study utilizes whole-exome discovery sequencing on the Illumina platform, followed by targeted amplicon-validation sequencing on the Pacific Biosciences platform, to interrogate the somatic mutation landscape in a corticotroph adenoma resected from a CD patient. In this USP8 -mutated tumor, we identified an interesting somatic mutation in the gene RASD1 , which is a component of the corticotropin-releasing hormone receptor signaling system. This finding may provide insight into a novel mechanism involving loss of feedback control to the corticotropin-releasing hormone receptor and subsequent deregulation of ACTH production in corticotroph tumors.

The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma. Our platform enabled prospective collection of longitudinal, multidimensional data (e.g., surveys, devices, geolocation, and air quality) in a subset of users over the 6-month study period. Consistent trending and correlation of interrelated variables support the quality of data obtained via this method. We detected increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires. Potential challenges with this technology include selection bias, low retention rates, reporting bias, and data security. These issues require attention to realize the full potential of mobile platforms in research and patient care.

In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices. The ability to identify patient groups or patterns of symptoms that might predict adverse outcomes such as asthma exacerbations or hospitalizations from these types of large, prospectively collected data sets, would be of significant general interest. However, conventional clustering methods cannot be applied to these types of longitudinally collected data, especially survey data actively collected from app users, given heterogeneous patterns of missing values due to: 1) varying survey response rates among different users, 2) varying survey response rates over time of each user, and 3) non-overlapping periods of enrollment among different users. To handle such complicated missing data structure, we proposed a probability imputation model to infer missing data. We also employed a consensus clustering strategy in tandem with the multiple imputation procedure. Through simulation studies under a range of scenarios reflecting real data conditions, we identified favorable performance of the proposed method over other strategies that impute the missing value through low-rank matrix completion. When applying the proposed new method to study asthma triggers and symptoms collected as part of the AMHS, we identified several patient groups with distinct phenotype patterns. Further validation of the methods described in this paper might be used to identify clinically important patterns in large data sets with complicated missing data structure, improving the ability to use such data sets to identify at-risk populations for potential intervention.

Background: Personalized therapy provides the best outcome of cancer care and its implementation in the clinic has been greatly facilitated by recent convergence of enormous progress in basic cancer research, rapid advancement of new tumor profiling technologies, and an expanding compendium of targeted cancer therapeutics., Methods: We developed a personalized cancer therapy (PCT) program in a clinical setting, using an integrative genomics approach to fully characterize the complexity of each tumor. We carried out whole exome sequencing (WES) and single-nucleotide polymorphism (SNP) microarray genotyping on DNA from tumor and patient-matched normal specimens, as well as RNA sequencing (RNA-Seq) on available frozen specimens, to identify somatic (tumor-specific) mutations, copy number alterations (CNAs), gene expression changes, gene fusions, and also germline variants. To provide high sensitivity in known cancer mutation hotspots, Ion AmpliSeq Cancer Hotspot Panel v2 (CHPv2) was also employed. We integrated the resulting data with cancer knowledge bases and developed a specific workflow for each cancer type to improve interpretation of genomic data., Results: We returned genomics findings to 46 patients and their physicians describing somatic alterations and predicting drug response, toxicity, and prognosis. Mean 17.3 cancer-relevant somatic mutations per patient were identified, 13.3-fold, 6.9-fold, and 4.7-fold more than could have been detected using CHPv2, Oncomine Cancer Panel (OCP), and FoundationOne, respectively. Our approach delineated the underlying genetic drivers at the pathway level and provided meaningful predictions of therapeutic efficacy and toxicity. Actionable alterations were found in 91 % of patients (mean 4.9 per patient, including somatic mutations, copy number alterations, gene expression alterations, and germline variants), a 7.5-fold, 2.0-fold, and 1.9-fold increase over what could have been uncovered by CHPv2, OCP, and FoundationOne, respectively. The findings altered the course of treatment in four cases., Conclusions: These results show that a comprehensive, integrative genomic approach as outlined above significantly enhanced genomics-based PCT strategies.

Background: As whole-genome sequencing (WGS) increases in availability, WGS educational aids are needed for research participants, patients, and the general public. Our aim was therefore to develop an accessible and scalable WGS educational aid., Methods: We engaged multiple stakeholders in an iterative process over a 1-year period culminating in the production of a novel 10-minute WGS educational animated video, "Whole Genome Sequencing and You" (https://goo.gl/HV8ezJ). We then presented the animated video to 281 online-survey respondents (the video-information group). There were also two comparison groups: a written-information group (n = 281) and a no-information group (n = 300)., Results: In the video-information group, 79% reported the video was easy to understand, satisfaction scores were high (mean 4.00 on 1-5 scale, where 5 = high satisfaction), and knowledge increased significantly. There were significant differences in knowledge compared with the no-information group but few differences compared with the written-information group. Intention to receive personal results from WGS and decisional conflict in response to a hypothetical scenario did not differ between the three groups., Conclusions: The educational animated video, "Whole Genome Sequencing and You," was well received by this sample of online-survey respondents. Further work is needed to evaluate its utility as an aid to informed decision making about WGS in other populations.Genet Med 18 5, 501-512.

Purpose: Health-care professionals need to be trained to work with whole-genome sequencing (WGS) in their practice. Our aim was to explore how students responded to a novel genome analysis course that included the option to analyze their own genomes., Methods: This was an observational cohort study. Questionnaires were administered before (T3) and after the genome analysis course (T4), as well as 6 months later (T5). In-depth interviews were conducted at T5., Results: All students (n = 19) opted to analyze their own genomes. At T5, 12 of 15 students stated that analyzing their own genomes had been useful. Ten reported they had applied their knowledge in the workplace. Technical WGS knowledge increased (mean of 63.8% at T3, mean of 72.5% at T4; P = 0.005). In-depth interviews suggested that analyzing their own genomes may increase students' motivation to learn and their understanding of the patient experience. Most (but not all) of the students reported low levels of WGS results-related distress and low levels of regret about their decision to analyze their own genomes., Conclusion: Giving students the option of analyzing their own genomes may increase motivation to learn, but some students may experience personal WGS results-related distress and regret. Additional evidence is required before considering incorporating optional personal genome analysis into medical education on a large scale.

The growing gap between the demand for genome sequencing and the supply of trained genomics professionals is creating an acute need to develop more effective genomics education. In response we developed "Practical Analysis of Your Personal Genome", a novel laboratory-style medical genomics course in which students have the opportunity to obtain and analyze their own whole genome. This report describes our motivations for and the content of a "practical" genomics course that incorporates personal genome sequencing and the lessons we learned during the first three iterations of this course.

Neisseria gonorrhoeae is an obligate human pathogen that colonizes the genital tract and causes gonorrhoea. Neisseria gonorrhoeae can form biofilms during natural cervical infections, on glass and in continuous flow-chamber systems. These biofilms contain large amounts of extracellular DNA, which plays an important role in biofilm formation. Many clinical isolates contain a gonococcal genetic island that encodes a type IV secretion system (T4SS). The T4SS of N. gonorrhoeae strain MS11 secretes ssDNA directly into the medium. Biofilm formation, studied in continuous flow-chamber systems by confocal laser scanning microscopy (CLSM), was strongly reduced, especially in the initial phases of biofilm formation, in the presence of Exonuclease I, which specifically degrades ssDNA or in a ΔtraB strain that does not secrete ssDNA. To specifically detect ssDNA in biofilms using CLSM, a novel method was developed in which thermostable fluorescently labelled ssDNA- and ss/dsDNA-binding proteins were used to visualize ssDNA and total DNA in biofilms and planktonic cultures. Remarkably, mainly dsDNA was detected in biofilms of the ssDNA secreting strain. We conclude that the secreted ssDNA facilitates initial biofilm formation, but that the secreted ssDNA is not retained in mature biofilms., (© 2013 Society for Applied Microbiology and John Wiley & Sons Ltd.)

Background: Multiple laboratories now offer clinical whole genome sequencing (WGS). We anticipate WGS becoming routinely used in research and clinical practice. Many institutions are exploring how best to educate geneticists and other professionals about WGS. Providing students in WGS courses with the option to analyze their own genome sequence is one strategy that might enhance students' engagement and motivation to learn about personal genomics. However, if this option is presented to students, it is vital they make informed decisions, do not feel pressured into analyzing their own genomes by their course directors or peers, and feel free to analyze a third-party genome if they prefer. We therefore developed a 26-hour introductory genomics course in part to help students make informed decisions about whether to receive personal WGS data in a subsequent advanced genomics course. In the advanced course, they had the option to receive their own personal genome data, or an anonymous genome, at no financial cost to them. Our primary aims were to examine whether students made informed decisions regarding analyzing their personal genomes, and whether there was evidence that the introductory course enabled the students to make a more informed decision., Methods: This was a longitudinal cohort study in which students (N = 19) completed questionnaires assessing their intentions, informed decision-making, attitudes and knowledge before (T1) and after (T2) the introductory course, and before the advanced course (T3). Informed decision-making was assessed using the Decisional Conflict Scale., Results: At the start of the introductory course (T1), most (17/19) students intended to receive their personal WGS data in the subsequent course, but many expressed conflict around this decision. Decisional conflict decreased after the introductory course (T2) indicating there was an increase in informed decision-making, and did not change before the advanced course (T3). This suggests that it was the introductory course content rather than simply time passing that had the effect. In the advanced course, all (19/19) students opted to receive their personal WGS data. No changes in technical knowledge of genomics were observed. Overall attitudes towards WGS were broadly positive., Conclusions: Providing students with intensive introductory education about WGS may help them make informed decisions about whether or not to work with their personal WGS data in an educational setting.

Patients from traditionally underrepresented communities need to be involved in discussions around genomics research including attitudes towards participation and receiving personal results. Structured interviews, including open-ended and closed-ended questions, were conducted with 205 patients in an inner-city hospital outpatient clinic: 48 % of participants self-identified as Black or African American, 29 % Hispanic, 10 % White; 49 % had an annual household income of <$20,000. When the potential for personal results to be returned was not mentioned, 82 % of participants were willing to participate in genomics research. Reasons for willingness fell into four themes: altruism; benefit to family members; personal health benefit; personal curiosity and improving understanding. Reasons for being unwilling fell into five themes: negative perception of research; not personally relevant; negative feelings about procedures (e.g., blood draws); practical barriers; and fear of results. Participants were more likely to report that they would participate in genomics research if personal results were offered than if they were not offered (89 vs. 62 % respectively, p < 0.001). Participants were more interested in receiving personal genomic risk results for cancer, heart disease and type 2 diabetes than obesity (89, 89, 91, 80 % respectively, all p < 0.001). The only characteristic consistently associated with interest in receiving personal results was disease-specific worry. There was considerable willingness to participate in and desire for personal results from genomics research in this sample of predominantly low-income, Hispanic and African American patients. When returning results is not practical, or even when it is, alternatively or additionally providing generic information about genomics and health may also be a valuable commodity to underrepresented minority and other populations considering participating in genomics research.

Background: Most strains of Neisseria gonorrhoeae carry a Gonococcal Genetic Island which encodes a type IV secretion system involved in the secretion of ssDNA. We characterize the GGI-encoded ssDNA binding protein, SsbB. Close homologs of SsbB are located within a conserved genetic cluster found in genetic islands of different proteobacteria. This cluster encodes DNA-processing enzymes such as the ParA and ParB partitioning proteins, the TopB topoisomerase, and four conserved hypothetical proteins. The SsbB homologs found in these clusters form a family separated from other ssDNA binding proteins., Methodology/principal Findings: In contrast to most other SSBs, SsbB did not complement the Escherichia coli ssb deletion mutant. Purified SsbB forms a stable tetramer. Electrophoretic mobility shift assays and fluorescence titration assays, as well as atomic force microscopy demonstrate that SsbB binds ssDNA specifically with high affinity. SsbB binds single-stranded DNA with minimal binding frames for one or two SsbB tetramers of 15 and 70 nucleotides. The binding mode was independent of increasing Mg(2+) or NaCl concentrations. No role of SsbB in ssDNA secretion or DNA uptake could be identified, but SsbB strongly stimulated Topoisomerase I activity., Conclusions/significance: We propose that these novel SsbBs play an unknown role in the maintenance of genetic islands.

The clinical accuracy of diagnostic tests commonly is assessed by ROC analysis. ROC plots, however, do not directly incorporate the effect of prevalence or the value of the possible test outcomes on test performance, which are two important factors in the practical utility of a diagnostic test. We describe a new graphical method, referred to as a prevalence-value-accuracy (PVA) plot analysis, which includes, in addition to accuracy, the effect of prevalence and the cost of misclassifications (false positives and false negatives) in the comparison of diagnostic test performance. PVA plots are contour plots that display the minimum cost attributable to misclassifications (z-axis) at various optimum decision thresholds over a range of possible values for prevalence (x-axis) and the unit cost ratio (UCR; y-axis), which is an index of the cost of a false-positive vs a false-negative test result. Another index based on the cost of misclassifications can be derived from PVA plots for the quantitative comparison of test performance. Depending on the region of the PVA plot that is used to calculate the misclassification cost index, it can potentially lead to a different interpretation than the ROC area index on the relative value of different tests. A PVA-threshold plot, which is a variation of a PVA plot, is also described for readily identifying the optimum decision threshold at any given prevalence and UCR. In summary, the advantages of PVA plot analysis are the following: (a) it directly incorporates the effect of prevalence and misclassification costs in the analysis of test performance; (b) it yields a quantitative index based on the costs of misclassifications for comparing diagnostic tests; (c) it provides a way to restrict the comparison of diagnostic test performance to a clinically relevant range of prevalence and UCR; and (d) it can be used to directly identify an optimum decision threshold based on prevalence and misclassification costs.

Background: Minimal detectable concentration is an important analytic feature of certain clinical immunoassays. We believe that accuracy is an important component of the minimal detectable concentration; for a given observed concentration to be meaningful, it should reflect a consistent linear relationship with the amount of analyte actually present., Methods: To evaluate the minimal detectable concentration, we developed a linearity regression protocol based on accuracy and also accounting for between-run variability. Using serial twofold dilutions of serum samples, we regressed the log of concentration (x) and of dilution (y) with linear, second-, and third-order polynomials. Initially, we evaluated two elements to find the linear region of the dataset, establishing the statistical significance of the beta coefficients with a t test and the reduction of the sum of square of the residuals between the linear regression and the higher-order regressions by means of an F test. As needed, we successively eliminated the lowest point until the linear regression was the best fit. Once we found the best fit, we added the most recently removed point back and calculated the difference between the value predicted by the first-order regression and the observed value. If the difference was not analytically significant, then we considered the point to be part of the linear set; otherwise, it was not included. In either case, the lowest included point was considered to be the minimal detectable concentration., Results: We applied the technique in evaluating two automated systems for serum thyrotropin. One system appeared linear and accurate down to 0.02 mU/L, or better, approximately 77% of the time, and to 0.01 mU/L 68% of the time. The second system was linear infrequently and appeared to be useful down to 0.02 mU/L, or better, only about 20% of the time., Conclusions: This accuracy-based approach to determining the minimal detectable concentration is an attractive alternative to current empiric approaches, which are based only on interassay variability.

A series of protocols were tested to examine the adjuvant effects of IL-12 on humoral and type 1 cytokine responses elicited in mice by recombinant gp120 envelope protein from HIV-1. This Ag fails to induce detectable Ab responses when administered s.c. alone, but stimulates low Ab levels when combined with aluminum hydroxide (alum). Moreover, when i.p. injected rIL-12 was included in the immunization, no increase in Ab production was observed. Importantly, optimal gp120 Ab responses were achieved by immunizing mice s.c. with gp120 and rIL-12 simultaneously coadsorbed to alum. These animals displayed a highly polarized, type 1 cytokine profile, with the emergence of anti-gp120 Ig belonging to the IgG2 and IgG3 isotypes. In addition, a major increase occurred in Ab of the IgG1 subclass. The superior adjuvant activity of alum-adsorbed IL-12 compared with that of the free cytokine correlated with the prolonged detection of IFN-gamma in the sera of animals immunized using the former procedure. In related experiments, in vitro neutralization of IL-12 was shown to inhibit IFN-gamma production by spleen cells from mice immunized with gp120 plus alum, but not by splenocytes from mice primed in the presence of IL-12, suggesting that the latter protocol induces a stable type 1 phenotype. These studies demonstrate that presentation of IL-12 on alum enhances its immunomodulatory effects and establish a protocol for the use of the cytokine as an adjuvant for simultaneously promoting both humoral Ab and type 1 cytokine responses.

Identification of clinically important coronary artery disease is a current goal in patient treatment. Serum lipid and apolipoprotein concentrations are commonly used to identify individuals who may have significant disease. With data published earlier from 304 men classified by coronary angiography, this study used receiver operating characteristic plots and analysis to examine the ability of 10 lipid or lipoprotein measures to discriminate between subjects with and without coronary artery disease. The analysis illustrated the simple elegance of receiver operating characteristic plots and demonstrated that all 10 measures are, at best, only moderately accurate.

The entire nef gene sequence of HIV-2, NIH-Z strain, has been cloned into the pJL6 expression vector and used for the synthesis of a 23-kDa protein in E. coli. The expressed protein is a fusion between the N-terminal 13 amino acids of the cII gene, 8 amino acids resulting from the ligation procedure, and the 180 amino acids that comprise the HIV-2 Nef sequence from the NIH-Z strain. The bacterially expressed Nef protein has been purified to apparent homogeneity on analytical scale (10-20 micrograms) by a combination of sequential detergent extraction, gel filtration, and reversed-phase high-performance chromatography. The expressed Nef protein is highly susceptible to proteolysis (chymotryptic-like activity) and this property accounts for the low yield obtained by gel filtration and RP-HPLC. Larger amounts (> 100 micrograms) of the purified Nef protein have been produced by a purification procedure that employs sequential detergent extraction, chromatography on Q-Sepharose in the presence of 7 M urea, and chromatography on hydroxylapatite, also in 7 M urea. The purified HIV-2 Nef protein has been used for the production of polyclonal and monoclonal antibodies. The milder method of purification should facilitate structure-function studies of the Nef protein and its role in the life cycle of HIV.

The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.

Recently, nonisotopic (often automated) immunoassays for measuring serum free thyroxin (FT4) have become available. Though more costly than radioimmunoassays, they are considerably more convenient. We studied the influence of endogenous albumin and thyroxin-binding globulin concentration on five automated, nonisotopic methods of measuring FT4 [Enzymun on ES300 (one-step), Stratus I and II (essentially two-step), Delfia (two-step), and IMx (two-step)] in a mixed patient population. We observed that they (a) are influenced very little by endogenous serum binding proteins and (b) seem to have sufficient within-run precision to justify performing single measurements on patients' specimens.

Clinical accuracy, defined as the ability to discriminate between states of health, is the fundamental property of any diagnostic test or system. It is readily expressed as clinical sensitivity and specificity, and elegantly represented by the receiver operating characteristic (ROC) curve. To demonstrate the use of ROC curves, we reexamine a study of the ability of serum lipid and apolipoprotein measures to discriminate among degrees of coronary artery disease in patients undergoing coronary angiography. ROC curve analysis reveals that none of these indexes is highly accurate, but demonstrates a modest increase in the accuracy of apolipoprotein over lipid indexes.