A pilot study to identify factors associated with frailty within the World Trade Center general responder cohort.

Background: Given the significant exposures experienced by the World Trade Center (WTC) general responders, there is increasing interest in understanding the effect of these exposures on aging in this population. We aim to identify factors that may be associated with frailty, a clinical syndrome characterized by a decrease in one's reserve that has been linked to poor health outcomes.

Methods: WTC general responders enrolled in the WTC Health Program aged 50 and older provided informed consent.

Machine Learning as a Tool for Early Detection: A Focus on Late-Stage Colorectal Cancer across Socioeconomic Spectrums.

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities.

Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy.

Development and Validation of a Clinical Frailty Index for the World Trade Center General Responder Cohort.

To develop and validate a clinical frailty index to characterize aging among responders to the 9/11 World Trade Center (WTC) attacks. This study was conducted on health monitoring data on a sample of 6197 responders. A clinical frailty index, WTC FI-Clinical, was developed according to the cumulative deficit model of frailty.

Early life metal dysregulation in amyotrophic lateral sclerosis.

Objective: Deficiencies and excess of essential elements and toxic metals are implicated in amyotrophic lateral sclerosis (ALS), but the age when metal dysregulation appears remains unknown. This study aims to determine whether metal uptake is dysregulated during childhood in individuals eventually diagnosed with ALS.

Methods: Laser ablation-inductively coupled plasma-mass spectrometry was used to obtain time series data of metal uptake using biomarkers in teeth from autopsies or dental extractions of ALS (n = 36) and control (n = 31) participants.

Cell-bound complement activation products associate with lupus severity in SLE.

Objectives: To evaluate the association between lupus severity and cell-bound complement activation products (CB-CAPs) or low complement proteins C3 and C4.

Methods: All subjects (n=495) fulfilled the American College of Rheumatology (ACR) classification criteria for SLE. Abnormal CB-CAPs (erythrocyte-bound C4d or B-lymphocyte-bound C4d levels >99th percentile of healthy) and complement proteins C3 and C4 were determined using flow cytometry and turbidimetry, respectively.

Lagged WQS regression for mixtures with many components.

The developmental timing of exposures to toxic chemicals or combinations of chemicals may be as important as the dosage itself. This concept is called "critical windows of exposure." The time boundaries of such windows can be detected if exposure data are collected repeatedly in short time intervals.

Blood manganese levels during pregnancy and postpartum depression: A cohort study among women in Mexico.

Background: Occupational studies have shown an association between elevated Mn exposure and depressive symptoms. Blood Mn (BMn) naturally rises during pregnancy due to mobilization from tissues, suggesting it could contribute to pregnancy and postpartum depressive symptoms.

Objectives: To assess the association between BMn levels during pregnancy and postpartum depression (PPD), creating opportunities for possible future interventions.

Deep learning cardiac motion analysis for human survival prediction.

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors.

Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach.

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images.

Development of a Physiological Frailty Index for the World Trade Center General Responder Cohort.

Responders to the 9/11/2001 WTC attacks were exposed to multiple toxic pollutants. Since 2002, the health of the responder cohort has been continuously tracked by the WTC Health Monitoring Program. However, no assessments have been made of frailty, an important health metric given the current average age of the WTC responder cohort (55 years).

The adoption of generic immunosuppressant medications in kidney, liver, and heart transplantation among recipients in Colorado or nationally with Medicare part D.

The transplant community is divided regarding whether substitution with generic immunosuppressants is appropriate for organ transplant recipients. We estimated the rate of uptake over time of generic immunosuppressants using US Medicare Part D Prescription Drug Event (PDE) and Colorado pharmacy claims (including both Part D and non-Part D) data from 2008 to 2013. Data from 26 070 kidney, 15 548 liver, and 6685 heart recipients from Part D, and 1138 kidney and 389 liver recipients from Colorado were analyzed.

Association of 2,4-dichlorophenol urinary concentrations and olfactory dysfunction in a national sample of middle-aged and older U.S. adults.

Olfaction is a key sensory mechanism in humans. Deficits in this chemosensory function have wide-ranging impacts on overall health and quality of life. This study examines the role of environmental phenols as risk factors for olfactory dysfunction among a random sample of 839 middle-aged and older U.

Assessment of cumulative health risk in the World Trade Center general responder cohort.

Background: Multiple comorbidities have been reported among rescue/recovery workers responding to the 9/11/2001 WTC disaster. In this study, we developed an index that quantifies the cumulative physiological burden of comorbidities and predicts life expectancy in this cohort.

Methods: A machine learning approach (gradient boosting) was used to model the relationship between mortality and several clinical parameters (laboratory test results, blood pressure, pulmonary function measures).

Optimal and lead-in adaptive allocation for binary outcomes: a comparison of Bayesian methodologies.

We compare posterior and predictive estimators and probabilities in response-adaptive randomization designs for two- and three-group clinical trials with binary outcomes. Adaptation based upon posterior estimates are discussed, as are two predictive probability algorithms: one using the traditional definition, the other using a skeptical distribution. Optimal and natural lead-in designs are covered.

New Research Strategy for Measuring Pre- and Postnatal Metal Dysregulation in Psychotic Disorders.

While previous studies have found evidence for detrimental effects of metals on neurodevelopment, the long-term effects on mental health remain unclear. The objective was to explore the effect of early metal exposure on risk of psychotic disorder and on symptom severity following illness onset. Through the use of validated tooth-biomarkers, we estimated pre- and postnatal exposure levels of essential elements (copper, magnesium, manganese, and zinc) and elements associated with neurotoxicity (lead, arsenic, lithium, and tin).

Current Developments in Machine Learning Techniques in Biological Data Mining.

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques.

Extending the Distributed Lag Model framework to handle chemical mixtures.

Distributed Lag Models (DLMs) are used in environmental health studies to analyze the time-delayed effect of an exposure on an outcome of interest. Given the increasing need for analytical tools for evaluation of the effects of exposure to multi-pollutant mixtures, this study attempts to extend the classical DLM framework to accommodate and evaluate multiple longitudinally observed exposures. We introduce 2 techniques for quantifying the time-varying mixture effect of multiple exposures on an outcome of interest.

A Random Forest-Induced Distance-Based Measure of Physiologic al Dysregulation.

Background: Aging involves gradual, multisystemic Physiological Dysregulation (PD) which increases risk of age-related comorbidities. Ability to quantify age-related PD could provide insights into biological mechanisms underlying the aging process. One approach to measuring PD exploits the fact that increasing PD manifests as a gradual deviation of physiological parameters away from normal levels.

Development and validation of a simple lupus severity index using ACR criteria for classification of SLE.

Objective: To develop a simple systemic lupus erythematosus (SLE) severity index that requires knowledge of only American College of Rheumatology (ACR) criteria and subcriteria.

Methods: This study used demographic, mortality and medical records data of 1915 patients with lupus from the Lupus Family Registry and Repository. The data were randomly split (2:1 ratio) into independent training and validation sets.

Development and Application of a Genetic Algorithm for Variable Optimization and Predictive Modeling of Five-Year Mortality Using Questionnaire Data.

The problem of selecting important variables for predictive modeling of a specific outcome of interest using questionnaire data has rarely been addressed in clinical settings. In this study, we implemented a genetic algorithm (GA) technique to select optimal variables from questionnaire data for predicting a five-year mortality. We examined 123 questions (variables) answered by 5,444 individuals in the National Health and Nutrition Examination Survey.

Development and Validation of a Clinical Risk-Assessment Tool Predictive of All-Cause Mortality.

In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient's health and overall function of multiple organ systems. We have developed a statistical procedure that condenses the information from a variety of health biomarkers into a composite index, which could be used as a risk score for predicting all-cause mortality.

Frequency and prioritization of patient health risks from a structured health risk assessment.

Purpose: To describe the frequency and patient-reported readiness to change, desire to discuss, and perceived importance of 13 health risk factors in a diverse range of primary care practices.

Methods: Patients (n = 1,707) in 9 primary care practices in the My Own Health Report (MOHR) trial reported general, behavioral, and psychosocial risk factors (body mass index [BMI], health status, diet, physical activity, sleep, drug use, stress, anxiety or worry, and depression). We classified responses as "at risk" or "healthy" for each factor, and patients indicated their readiness to change and/or desire to discuss identified risk factors with providers.

Engaging primary care patients to use a patient-centered personal health record.

Purpose: Health care leaders encourage clinicians to offer portals that enable patients to access personal health records, but implementation has been a challenge. Although large integrated health systems have promoted use through costly advertising campaigns, other implementation methods are needed for small to medium-sized practices where most patients receive their care.

Methods: We conducted a mixed methods assessment of a proactive implementation strategy for a patient portal (an interactive preventive health record [IPHR]) offered by 8 primary care practices.