rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography.

BACKGROUND: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values to facilitate meaningful recommendations for echocardiography. METHODS: Using 2 232 130 ECGs linked to electronic health records and echocardiography reports from 484 765 adults between 1984 to 2021, we trained machine learning models to predict the presence or absence of any of 7 echocardiography-confirmed diseases within 1 year. This composite label included the following: moderate or severe valvular disease (aortic/mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction <50%, or interventricular septal thickness >15 mm. We tested various combinations of input features (demographics, laboratory values, structured ECG data, ECG traces) and evaluated model performance using 5-fold cross-validation, multisite validation trained on 1 site and tested on 10 independent sites, and simulated retrospective deployment trained on pre-2010 data and deployed in 2010. RESULTS: Our composite rECHOmmend model used age, sex, and ECG traces and had a 0.91 area under the receiver operating characteristic curve and a 42% positive predictive value at 90% sensitivity, with a composite label prevalence of 17.9%. Individual disease models had area under the receiver operating characteristic curves from 0.86 to 0.93 and lower positive predictive values from 1% to 31%. Area under the receiver operating characteristic curves for models using different input features ranged from 0.80 to 0.93, increasing with additional features. Multisite validation showed similar results to cross-validation, with an aggregate area under the receiver operating characteristic curve of 0.91 across our independent test set of 10 clinical sites after training on a separate site. Our simulated retrospective deployment showed that for ECGs acquired in patients without preexisting structural heart disease in the year 2010, 11% were classified as high risk and 41% (4.5% of total patients) developed true echocardiography-confirmed disease within 1 year. CONCLUSIONS: An ECG-based machine learning model using a composite end point can identify a high-risk population for having undiagnosed, clinically significant structural heart disease while outperforming single-disease models and improving practical utility with higher positive predictive values. This approach can facilitate targeted screening with echocardiography to improve underdiagnosis of structural heart disease.

An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk.

BACKGROUND: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained to predict AF risk from 12­lead electrocardiogram (ECG) would be more efficient than criteria based on clinical variables in indicating a population for AF screening to potentially prevent AF-related stroke. METHODS: We retrospectively included all patients with clinical encounters in Geisinger without a prior history of AF. Incidence of AF within 1 year and AF-related strokes within 3 years of the encounter were identified. AF-related stroke was defined as a stroke where AF was diagnosed at the time of stroke or within a year after the stroke. The efficiency of five methods was evaluated for selecting a cohort for AF screening. The methods were selected from four clinical trials (mSToPS, GUARD-AF, SCREEN-AF and STROKESTOP) and the ECG-based ML model. We simulated patient selection for the five methods between the years 2011 and 2014 and evaluated outcomes for 1 year intervals between 2012 and 2015, resulting in a total of twenty 1-year periods. Patients were considered eligible if they met the criteria before the start of the given 1-year period or within that period. The primary outcomes were numbers needed to screen (NNS) for AF and AF-associated stroke. RESULTS: The clinical trial models indicated large proportions of the population with a prior ECG for AF screening (up to 31%), coinciding with NNS ranging from 14 to 18 for AF and 249-359 for AF-associated stroke. At comparable sensitivity, the ECG ML model indicated a modest number of patients for screening (14%) and had the highest efficiency in NNS for AF (7.3; up to 60% reduction) and AF-associated stroke (223; up to 38% reduction). CONCLUSIONS: An ECG-based ML risk prediction model is more efficient than contemporary AF-screening criteria based on age alone or age and clinical features at indicating a population for AF screening to potentially prevent AF-related strokes.

Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.

BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke. METHODS: We used 1.6 M resting 12-lead digital ECG traces from 430 000 patients collected from 1984 to 2019. Deep neural networks were trained to predict new-onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve and precision-recall curve. We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs before 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We identified the patients at risk for AF-related stroke among those predicted to be high risk for AF by the model at different prediction thresholds. RESULTS: The area under the receiver operating characteristic curve and area under the precision-recall curve were 0.85 and 0.22, respectively, for predicting new-onset AF within 1 year of an ECG. The hazard ratio for the predicted high- versus low-risk groups over a 30-year span was 7.2 (95% CI, 6.9-7.6). In a simulated deployment scenario, the model predicted new-onset AF at 1 year with a sensitivity of 69% and specificity of 81%. The number needed to screen to find 1 new case of AF was 9. This model predicted patients at high risk for new-onset AF in 62% of all patients who experienced an AF-related stroke within 3 years of the index ECG. CONCLUSIONS: Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.

Sleep in the Natural Environment: A Pilot Study.

Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, numerous commercial digital devices have been developed that record physiological data, such as movement, heart rate, and respiratory rate, which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. The sleep-related output metrics from these devices include sleep staging and total sleep duration and are derived via proprietary algorithms that utilize a variety of these physiological recordings. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether sleep metric outputs from self-reported sleep metrics (SRSMs) and four sensors, specifically Fitbit Surge (a smart watch), Withings Aura (a sensor pad that is placed under a mattress), Hexoskin (a smart shirt), and Oura Ring (a smart ring), were related to known cognitive and psychological metrics, including the n-back test and Pittsburgh Sleep Quality Index (PSQI). We analyzed correlation between multiple device-related sleep metrics. Furthermore, we investigated relationships between these sleep metrics and cognitive scores across different timepoints and SRSM through univariate linear regressions. We found that correlations for sleep metrics between the devices across the sleep cycle were almost uniformly low, but still significant (P < 0.05). For cognitive scores, we found the Withings latency was statistically significant for afternoon and evening timepoints at P = 0.016 and P = 0.013. We did not find any significant associations between SRSMs and PSQI or cognitive scores. Additionally, Oura Ring's total sleep duration and efficiency in relation to the PSQI measure was statistically significant at P = 0.004 and P = 0.033, respectively. These findings can hopefully be used to guide future sensor-based sleep research.

Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept.

BACKGROUND: The protection of private data is a key responsibility for research studies that collect identifiable information from study participants. Limiting the scope of data collection and preventing secondary use of the data are effective strategies for managing these risks. An ideal framework for data collection would incorporate feature engineering, a process where secondary features are derived from sensitive raw data in a secure environment without a trusted third party. OBJECTIVE: This study aimed to compare current approaches based on how they maintain data privacy and the practicality of their implementations. These approaches include traditional approaches that rely on trusted third parties, and cryptographic, secure hardware, and blockchain-based techniques. METHODS: A set of properties were defined for evaluating each approach. A qualitative comparison was presented based on these properties. The evaluation of each approach was framed with a use case of sharing geolocation data for biomedical research. RESULTS: We found that approaches that rely on a trusted third party for preserving participant privacy do not provide sufficiently strong guarantees that sensitive data will not be exposed in modern data ecosystems. Cryptographic techniques incorporate strong privacy-preserving paradigms but are appropriate only for select use cases or are currently limited because of computational complexity. Blockchain smart contracts alone are insufficient to provide data privacy because transactional data are public. Trusted execution environments (TEEs) may have hardware vulnerabilities and lack visibility into how data are processed. Hybrid approaches combining blockchain and cryptographic techniques or blockchain and TEEs provide promising frameworks for privacy preservation. For reference, we provide a software implementation where users can privately share features of their geolocation data using the hybrid approach combining blockchain with TEEs as a supplement. CONCLUSIONS: Blockchain technology and smart contracts enable the development of new privacy-preserving feature engineering methods by obviating dependence on trusted parties and providing immutable, auditable data processing workflows. The overlap between blockchain and cryptographic techniques or blockchain and secure hardware technologies are promising fields for addressing important data privacy needs. Hybrid blockchain and TEE frameworks currently provide practical tools for implementing experimental privacy-preserving applications.

Building a Secure Biomedical Data Sharing Decentralized App (DApp): Tutorial.

Decentralized apps (DApps) are computer programs that run on a distributed computing system, such as a blockchain network. Unlike the client-server architecture that powers most internet apps, DApps that are integrated with a blockchain network can execute app logic that is guaranteed to be transparent, verifiable, and immutable. This new paradigm has a number of unique properties that are attractive to the biomedical and health care communities. However, instructional resources are scarcely available for biomedical software developers to begin building DApps on a blockchain. Such apps require new ways of thinking about how to build, maintain, and deploy software. This tutorial serves as a complete working prototype of a DApp, motivated by a real use case in biomedical research requiring data privacy. We describe the architecture of a DApp, the implementation details of a smart contract, a sample iPhone operating system (iOS) DApp that interacts with the smart contract, and the development tools and libraries necessary to get started. The code necessary to recreate the app is publicly available.

Designing Robust N-of-1 Studies for Precision Medicine: Simulation Study and Design Recommendations.

BACKGROUND: Recent advances in molecular biology, sensors, and digital medicine have led to an explosion of products and services for high-resolution monitoring of individual health. The N-of-1 study has emerged as an important methodological tool for harnessing these new data sources, enabling researchers to compare the effectiveness of health interventions at the level of a single individual. OBJECTIVE: N-of-1 studies are susceptible to several design flaws. We developed a model that generates realistic data for N-of-1 studies to enable researchers to optimize study designs in advance. METHODS: Our stochastic time-series model simulates an N-of-1 study, incorporating all study-relevant effects, such as carryover and wash-in effects, as well as various sources of noise. The model can be used to produce realistic simulated data for a near-infinite number of N-of-1 study designs, treatment profiles, and patient characteristics. RESULTS: Using simulation, we demonstrate how the number of treatment blocks, ordering of treatments within blocks, duration of each treatment, and sampling frequency affect our ability to detect true differences in treatment efficacy. We provide a set of recommendations for study designs on the basis of treatment, outcomes, and instrument parameters, and make our simulation software publicly available for use by the precision medicine community. CONCLUSIONS: Simulation can facilitate rapid optimization of N-of-1 study designs and increase the likelihood of study success while minimizing participant burden.

Cancer cell chemokines direct chemotaxis of activated stellate cells in pancreatic ductal adenocarcinoma.

The mechanisms by which the extreme desmoplasia observed in pancreatic tumors develops remain unknown and its role in pancreatic cancer progression is unsettled. Chemokines have a key role in the recruitment of a wide variety of cell types in health and disease. Transcript and protein profile analyses of human and murine cell lines and human tissue specimens revealed a consistent elevation in the receptors CCR10 and CXCR6, as well as their respective ligands CCL28 and CXCL16. Elevated ligand expression was restricted to tumor cells, whereas receptors were in both epithelial and stromal cells. Consistent with its regulation by inflammatory cytokines, CCL28 and CCR10, but not CXCL16 or CXCR6, were upregulated in human pancreatitis tissues. Cytokine stimulation of pancreatic cancer cells increased CCL28 secretion in epithelial tumor cells but not an immortalized activated human pancreatic stellate cell line (HPSC). Stellate cells exhibited dose- and receptor-dependent chemotaxis in response to CCL28. This functional response was not linked to changes in activation status as CCL28 had little impact on alpha smooth muscle actin levels or extracellular matrix deposition or alignment. Co-culture assays revealed CCL28-dependent chemotaxis of HPSC toward cancer but not normal pancreatic epithelial cells, consistent with stromal cells being a functional target for the epithelial-derived chemokine. These data together implicate the chemokine CCL28 in the inflammation-mediated recruitment of cancer-associated stellate cells into the pancreatic cancer parenchyma.

Cyclic AMP regulates the migration and invasion potential of human pancreatic cancer cells.

Aggressive dissemination and metastasis of pancreatic ductal adenocarcinoma (PDAC) results in poor prognosis and marked lethality. Rho monomeric G protein levels are increased in pancreatic cancer tissue. As the mechanisms underlying PDAC malignancy are little understood, we investigated the role for cAMP in regulating monomeric G protein regulated invasion and migration of pancreatic cancer cells. Treatment of PDAC cells with cAMP elevating agents that activate adenylyl cyclases, forskolin, protein kinase A (PKA), 6-Bnz-cAMP, or the cyclic nucleotide phosphodiesterase inhibitor cilostamide significantly decreased migration and Matrigel invasion of PDAC cell lines. Inhibition was dose-dependent and not significantly different between forskolin or cilostamide treatment. cAMP elevating drugs not only blocked basal migration, but similarly abrogated transforming-growth factor-ß-directed PDAC cell migration and invasion. The inhibitory effects of cAMP were prevented by the pharmacological blockade of PKA. Drugs that increase cellular cAMP levels decreased levels of active RhoA or RhoC, with a concomitant increase in phosphorylated RhoA. Diminished Rho signaling was correlated with the appearance of thickened cortical actin bands along the perimeter of non-motile forskolin or cilostamide-treated cells. Decreased migration did not reflect alterations in cell growth or programmed cell death. Collectively these data support the notion that increased levels of cAMP specifically hinder PDAC cell motility through F-actin remodeling.

E-cadherin is critical for collective sheet migration and is regulated by the chemokine CXCL12 protein during restitution.

Chemokines and other immune mediators enhance epithelial barrier repair. The intestinal barrier is established by highly regulated cell-cell contacts between epithelial cells. The goal of these studies was to define the role for the chemokine CXCL12 in regulating E-cadherin during collective sheet migration during epithelial restitution. Mechanisms regulating E-cadherin were investigated using Caco2(BBE) and IEC-6 model epithelia. Genetic knockdown confirmed a critical role for E-cadherin in in vitro restitution and in vivo wound repair. During restitution, both CXCL12 and TGF-ß1 tightened the monolayer by decreasing the paracellular space between migrating epithelial cells. However, CXCL12 differed from TGF-ß1 by stimulating the significant increase in E-cadherin membrane localization during restitution. Chemokine-stimulated relocalization of E-cadherin was paralleled by an increase in barrier integrity of polarized epithelium during restitution. CXCL12 activation of its cognate receptor CXCR4 stimulated E-cadherin localization and monolayer tightening through Rho-associated protein kinase activation and F-actin reorganization. These data demonstrate a key role for E-cadherin in intestinal epithelial restitution.

Gut Microbiota-Derived Indole-3-Carboxylate Influences Mucosal Integrity and Immunity Through the Activation of the Aryl Hydrocarbon Receptors and Nutrient Transporters in Broiler Chickens Challenged With Eimeria maxima.

Two studies were conducted to evaluate the effects of indole-3-carboxylate (ICOOH) as a postbiotic on maintaining intestinal homeostasis against avian coccidiosis. In the first study, an in vitro culture system was used to investigate the effects of ICOOH on the proinflammatory cytokine response of chicken macrophage cells (CMCs), gut integrity of chicken intestinal epithelial cells (IECs), differentiation of quail muscle cells (QMCs), and primary chicken embryonic muscle cells (PMCs) and anti-parasitic effect against Eimeria maxima. Cells to be tested were seeded in the 24-well plates and treated with ICOOH at concentrations of 0.1, 1.0, and 10.0 µg. CMCs were first stimulated by lipopolysaccharide (LPS) to induce an innate immune response, and QMCs and PMCs were treated with 0.5% and 2% fetal bovine serum, respectively, before they were treated with ICOOH. After 18 h of incubation, cells were harvested, and RT-PCR was performed to measure gene expression of proinflammatory cytokines of CMCs, tight junction (TJ) proteins of IECs, and muscle cell growth markers of QMCs and PMCs. In the second study, in vivo trials were carried out to study the effect of dietary ICOOH on disease parameters in broiler chickens infected with E. maxima. One hundred twenty male broiler chickens (0-day-old) were allocated into the following four treatment groups: 1) basal diet without infection (CON), 2) basal diet with E. maxima (NC), 3) ICOOH at 10.0 mg/kg feed with E. maxima (HI), and 4) ICOOH at 1.0 mg/kg feed with E. maxima (LO). Body weights (BWs) were measured on 0, 7, 14, 20, and 22 days. All groups except the CON chickens were orally infected with E. maxima on day 14. Jejunal samples were collected for lesion score and the transcriptomic analysis of cytokines and TJ proteins. In vitro, ICOOH increased the expression of TJ proteins in IECs and decreased IL-1ß and IL-8 transcripts in the LPS-stimulated CMCs. In vivo, chickens on the HI diet showed reduced jejunal IL-1ß, IFN-γ, and IL-10 expression and increased expression of genes activated by aryl hydrocarbon receptors and nutrient transporters in E. maxima-infected chickens. In conclusion, these results demonstrate the beneficial effects of dietary ICOOH on intestinal immune responses and barrier integrity in broiler chickens challenged with E. maxima. Furthermore, the present finding supports the notion to use microbial metabolites as novel feed additives to enhance resilience in animal agriculture.

Targeted intestinal epithelial deletion of the chemokine receptor CXCR4 reveals important roles for extracellular-regulated kinase-1/2 in restitution.

Barrier defects and/or alterations in the ability of the gut epithelium to repair itself are critical etiological mechanisms of gastrointestinal disease. Our ongoing studies indicate that the chemokine receptor CXCR4 and its cognate ligand CXCL12 regulate intestinal-epithelial barrier maturation and restitution in cell culture models. Gene-deficient mice lacking CXCR4 expression specifically by the cells of the intestinal epithelium were used to test the hypothesis that CXCR4 regulates mucosal barrier integrity in vivo. Epithelial expression of CXCR4 was assessed by RT-PCR, Southern blot, immunoblot and immunohistochemistry. In vivo wounding assays were performed by addition of 3% dextran sodium sulfate (DSS) in drinking water for 5 days. Intestinal damage and DAI scores were assessed by histological examination. Extracellular-regulated kinase (ERK) phosphorylation was assessed in vivo by immunoblot and immunofluorescence. CXCR4 knockdown cells were established using a lentiviral approach and ERK phosphorylation was assessed. Consistent with targeted roles in restitution, epithelium from patients with inflammatory bowel disease indicated that CXCR4 and CXCL12 expression was stable throughout the human colonic epithelium. Conditional CXCR4-deficient mice developed normally, with little phenotypic differences in epithelial morphology, proliferation or migration. Re-epithelialization was absent in CXCR4 conditional knockout mice following acute DSS-induced inflammation. In contrast, heterozygous CXCR4-depleted mice displayed significant improvement in epithelial ulcer healing in acute and chronic inflammation. Mucosal injury repair was correlated with ERK1/2 activity and localization along the crypt-villus axis, with heterozygous mice characterized by increased ERK1/2 activation. Lentiviral depletion of CXCR4 in IEC-6 cells similarly altered ERK1/2 activity and prevented chemokine-stimulated migration. Taken together, these data indicate that chemokine receptors participate in epithelial barrier responses through coordination of the ERK1/2 signaling pathway.

Effects of Dietary Maltol on Innate Immunity, Gut Health, and Growth Performance of Broiler Chickens Challenged With Eimeria maxima.

Two studies were conducted to evaluate the effects of maltol as a postbiotic on innate immunity, gut health, and enteric infection. In the first study, an in vitro culture system was used to evaluate the effects of maltol on the innate immune response of chicken macrophage cells (CMC), gut integrity of chicken intestinal epithelial cells (IEC), anti-parasitic activity against Eimeria maxima, and differentiation of quail muscle cells (QMC) and primary chicken embryonic muscle cells (PMC). All cells seeded in the 24-well plates were treated with maltol at concentrations of 0.1, 1.0, and 10.0 µg. CMC and IEC were stimulated by lipopolysaccharide to induce an innate immune response, and QMC and PMC were treated with 0.5 and 2% fetal bovine serum, respectively. After 18 h of incubation, pro-inflammatory cytokines, tight junction proteins (TJPs), and muscle cell growth markers were measured. In the second study, the dietary effect of maltol was evaluated on disease parameters in broiler chickens infected with E. maxima. Eighty male 1-day-old broiler chickens were allocated into the following four treatment groups: (1) Control group without infection, (2) Basal diet with E. maxima, (3) High maltol (HI; 10.0 mg /kg feed) with E. maxima, and (4) Low maltol (LO; 1.0 mg/kg feed) with E. maxima. Body weights (BW) were measured on days 0, 7, 14, 20, and 22. All chickens except the CON group were orally infected with 104 E. maxima per chicken on day 14. Jejunum samples were collected for gut lesion scoring, and the gene expression of cytokines and TJPs. Data was analyzed using PROC MIXED in SAS. In vitro, maltol not only increased TJPs in IEC and cytokines in the LPS-stimulated CMC but also showed direct cytotoxicity against sporozoites of E. maxima. In vivo, the HI group improved the BW, reduced the gut lesion scores and fecal oocyst shedding, and decreased jejunal TNFSF15 and IL-1ß expression in E. maxima-infected chickens. In conclusion, these results demonstrate the beneficial effects of dietary maltol in the enhancement of growth performance, gut health, and coccidiosis resistance and the applicability of maltol as a postbiotic for the replacement of antibiotic growth promoters in commercial poultry production.

Forming 4-Methylcatechol as the Dominant Bioavailable Metabolite of Intraruminal Rutin Inhibits p-Cresol Production in Dairy Cows.

Rutin, a natural flavonol glycoside, elicits its diverse health-promoting effects from the bioactivities of quercetin, its aglycone. While widely distributed in the vegetables and fruits of human diet, rutin is either absent or inadequate in common animal feed ingredients. Rutin has been supplemented to dairy cows for performance enhancement, but its metabolic fate in vivo has not been determined. In this study, plasma, urine, and rumen fluid samples were collected before and after the intraruminal dosing of 100 mg/kg rutin to 4 Holsteins, and then characterized by both targeted and untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomic analysis. In plasma and urine, 4-methylcatechol sulfate was identified as the most abundant metabolite of rutin, instead of quercetin and its flavonol metabolites, and its concentration was inversely correlated with the concentration of p-cresol sulfate. In rumen fluid, the formation of 3,4-dihydroxyphenylacetic acid (DHPAA) and 4-methylcatechol after rapid degradation of rutin and quercetin concurred with the decrease of p-cresol and the increase of its precursor, 4-hydroxyphenylacetic acid. Overall, the formation of 4-methylcatechol, a bioactive microbial metabolite, as the dominant bioavailable metabolite of rutin and quercetin, could contribute to their beneficial bioactivities in dairy cows, while the decrease of p-cresol, a microbial metabolite with negative biological and sensory properties, from the competitive inhibition between microbial metabolism of rutin and tyrosine, has the potential to reduce environmental impact of dairy operations and improve the health of dairy cattle.

Dietary Supplementation With Bacillus subtilis Direct-Fed Microbials Alters Chicken Intestinal Metabolite Levels.

Direct-fed microbials (DFMs) are dietary supplements containing live microorganisms which confer a performance and health benefit to the host, but the mechanisms are unclear. Here, a metabolomics approach was used to identify changes in intestinal metabolite levels in chickens fed an unsupplemented diet or a diet supplemented with B. subtilis strain 1781 or strain 747. Body weight gains of chickens fed the B. subtilis-supplemented diets were increased up to 5.6% in the B. subtilis 1781 group and 7.6% in the B. subtilis 747 group compared with chickens fed the unsupplemented diet. Compared with unsupplemented controls, the levels of 83 metabolites were altered (p < 0.05) (25 increased, 58 decreased) in chickens given the B. subtilis 1781-supplemented diet, while 50 were altered (p < 0.05) (12 increased, 38 decreased) with the B. subtilis 747-supplemented diet. Twenty-two metabolites were altered (p < 0.05) (18 increased, 4 decreased) in the B. subtilis 1781 vs. B. subtilis 747 groups. A random forest analysis of the B. subtilis 1781 vs. control groups gave a predictive accuracy of 87.5%, while that of the B. subtilis 747 vs. control groups was 62.5%. A random forest analysis of the B. subtilis 1781 vs. B. subtilis 747 groups gave a predictive accuracy of 75.0%. Changes in the levels of these intestinal biochemicals provided a distinctive biochemical signature unique to each B. subtilis-supplemented group, and were characterized by alterations in the levels of dipeptides (alanylleucine, glutaminylleucine, phenylalanylalanine, valylglutamine), nucleosides (N1-methyladenosine, N6-methyladenosine, guanine, 2-deoxyguanosine), fatty acids (sebacate, valerylglycine, linoleoylcholine), and carbohydrates (fructose). These results provide the foundation for future studies to identify biochemicals that might be used to improve poultry growth performance in the absence of antibiotic growth promoters.

Factors Associated With Trial Completion and Adherence in App-Based N-of-1 Trials: Protocol for a Randomized Trial Evaluating Study Duration, Notification Level, and Meaningful Engagement in the Brain Boost Study.

BACKGROUND: N-of-1 trials promise to help individuals make more informed decisions about treatment selection through structured experiments that compare treatment effectiveness by alternating treatments and measuring their impacts in a single individual. We created a digital platform that automates the design, administration, and analysis of N-of-1 trials. Our first N-of-1 trial, the app-based Brain Boost Study, invited individuals to compare the impacts of two commonly consumed substances (caffeine and L-theanine) on their cognitive performance. OBJECTIVE: The purpose of this study is to evaluate critical factors that may impact the completion of N-of-1 trials to inform the design of future app-based N-of-1 trials. We will measure study completion rates for participants that begin the Brain Boost Study and assess their associations with study duration (5, 15, or 27 days) and notification level (light or moderate). METHODS: Participants will be randomized into three study durations and two notification levels. To sufficiently power the study, a minimum of 640 individuals must begin the study, and 97 individuals must complete the study. We will use a multiple logistic regression model to discern whether the study length and notification level are associated with the rate of study completion. For each group, we will also compare participant adherence and the proportion of trials that yield statistically meaningful results. RESULTS: We completed the beta testing of the N1 app on a convenience sample of users. The Brain Boost Study on the N1 app opened enrollment to the public in October 2019. More than 30 participants enrolled in the first month. CONCLUSIONS: To our knowledge, this will be the first study to rigorously evaluate critical factors associated with study completion in the context of app-based N-of-1 trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT04056650; https://clinicaltrials.gov/ct2/show/NCT04056650. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/16362.

Digital Medicine: A Primer on Measurement.

Technology is changing how we practice medicine. Sensors and wearables are getting smaller and cheaper, and algorithms are becoming powerful enough to predict medical outcomes. Yet despite rapid advances, healthcare lags behind other industries in truly putting these technologies to use. A major barrier to entry is the cross-disciplinary approach required to create such tools, requiring knowledge from many people across many fields. We aim to drive the field forward by unpacking that barrier, providing a brief introduction to core concepts and terms that define digital medicine. Specifically, we contrast "clinical research" versus routine "clinical care," outlining the security, ethical, regulatory, and legal issues developers must consider as digital medicine products go to market. We classify types of digital measurements and how to use and validate these measures in different settings. To make this resource engaging and accessible, we have included illustrations and figures throughout that we hope readers will borrow from liberally. This primer is the first in a series that will accelerate the safe and effective advancement of the field of digital medicine.

A systematic review of feasibility studies promoting the use of mobile technologies in clinical research.

Mobile technologies, such as smart phone applications, wearables, ingestibles, and implantables, are increasingly used in clinical research to capture study endpoints. On behalf of the Clinical Trials Transformation Initiative, we aimed to conduct a systematic scoping review and compile a database summarizing pilot studies addressing mobile technology sensor performance, algorithm development, software performance, and/or operational feasibility, in order to provide a resource for guiding decisions about which technology is most suitable for a particular trial. Our systematic search identified 275 publications meeting inclusion criteria. From these papers, we extracted data including the medical condition, concept of interest captured by the mobile technology, outcomes captured by the digital measurement, and details regarding the sensors, algorithms, and study sample. Sixty-seven percent of the technologies identified were wearable sensors, with the remainder including tablets, smartphones, implanted sensors, and cameras. We noted substantial variability in terms of reporting completeness and terminology used. The data have been compiled into an online database maintained by the Clinical Trials Transformation Initiative that can be filtered and searched electronically, enabling a user to find information most relevant to their work. Our long-term goal is to maintain and update the online database, in order to promote standardization of methods and reporting, encourage collaboration, and avoid redundant studies, thereby contributing to the design and implementation of efficient, high-quality trials.

cGMP secreted from the tapeworm Hymenolepis diminuta is a signal molecule to the host intestine.

3',5'-Cyclic guanosine monophosphate (cGMP), a well-known intracellular second messenger, is released to the intestinal lumen by the tapeworm, Hymenolepis diminuta. Enzyme-linked immunosorbent assay analysis of tapeworm conditioned media shows that cGMP is released at a constant rate. Multidrug resistant (MDR) proteins are efflux transporters for cyclic nucleotides. Two MDR inhibitors, niflumic acid and zaprinast, inhibit cGMP secretion by tapeworms and change the cGMP localization within the tapeworm tegument, as assessed by immunochemistry. cGMP, normally present throughout the tapeworm tegumental cytoplasm, is absent from the outer cytoplasmic band upon treatment with inhibitors. Inhibition of cGMP secretion by colchicine indicates that cGMP secretion is cytoskeleton dependent. Binding studies of [3H]cGMP to ileal segments of intestine demonstrate 2 saturable, reversible, and high-affinity binding sites. These studies demonstrate that cGMP is secreted from the cestode via a cytoskeleton-dependent mechanism and MDR efflux transporters. In addition, cGMP reaching the intestinal lumen can bind to the mucosa via receptors for cGMP. These data, combined with earlier observations of cGMP altering intestinal motility and slowing lumenal transit, indicate that tapeworms alter the physiology of the host digestive process via the secretion and binding of extracellular cGMP to lumenal receptors in the host intestine.

Dietary Bacillus subtilis-based direct-fed microbials alleviate LPS-induced intestinal immunological stress and improve intestinal barrier gene expression in commercial broiler chickens.

This study investigated the effects of Bacillus subtilis-based probiotics on the performance, modulation of host inflammatory responses and intestinal barrier gene expression of broilers subjected to LPS challenge. Chickens were randomly allocated to one of the 3 dietary treatment groups - control, antibiotic, or probiotic. At 14days, half of the chickens in each treatment were injected with LPS (1mg/kg body weight), and the other half injected with sterile PBS. Chickens fed probiotics weighed significantly more than controls at 15days of age, irrespective of immune challenge. LPS challenge significantly reduced weight gain at 24h post-injection, and the probiotics did not alleviate the LPS-induced reduction of weight gain. Serum α-1-AGP levels were significantly higher in LPS-injected chickens, and probiotic supplementation significantly reduced their levels. The percentages of CD4+ lymphocytes were significantly increased in probiotic groups in the absence of immunological challenge but were reduced during LPS challenge compared to controls. CD8+ lymphocytes were significantly reduced in probiotic-fed birds. The LPS-induced increase in the expression of cytokines IL8 and TNFSF15 was reduced by probiotic supplementation, and IL17F, iNOS expression was found to be significantly elevated in probiotic-fed birds subjected to LPS challenge. The reduced gene expression of tight junction proteins (JAM2, occludin and ZO1) and MUC2 induced by LPS challenge was reversed by probiotic supplementation. The results indicate that B. subtilis-based probiotics differentially regulate intestinal immune and tight junction protein mRNA expression during states of LPS-mediated immunological challenge.