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Background: Pneumonia due to typical bacterial, atypical bacterial and viral pathogens can be difficult to clinically differentiate. Host response-based diagnostics are emerging as a complementary diagnostic strategy to pathogen detection., Methods: We used murine models of typical bacterial, atypical bacterial and viral pneumonia to develop diagnostic signatures and understand the host's response to these types of infections. Mice were intranasally inoculated with Streptococcus pneumoniae , Mycoplasma pneumoniae , influenza or saline as a control. Peripheral blood gene expression analysis was performed at multiple time points. Differentially expressed genes were used to perform gene set enrichment analysis and generate diagnostic signatures. These murine-derived signatures were externally validated in silico using human gene expression data. The response to S. pneumoniae was the most rapid and robust., Results: Mice infected with M. pneumoniae had a delayed response more similar to influenza-infected animals. Diagnostic signatures for the three types of infection had 0.94-1.00 area under the receiver operator curve (auROC). Validation in five human gene expression datasets revealed auROC of 0.82-0.96., Discussion: This study identified discrete host responses to typical bacterial, atypical bacterial and viral aetiologies of pneumonia in mice. These signatures validated well in humans, highlighting the conserved nature of the host response to these pathogen classes., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Chronic pain is a prevalent condition with enormous economic burden. Opioids such as tramadol, codeine, and hydrocodone are commonly used to treat chronic pain; these drugs are activated to more potent opioid receptor agonists by the hepatic CYP2D6 enzyme. Results from clinical studies and mechanistic understandings suggest that CYP2D6-guided therapy will improve pain control and reduce adverse drug events. However, CYP2D6 is rarely used in clinical practice due in part to the demand for additional clinical trial evidence. Thus, we designed the ADOPT-PGx (A Depression and Opioid Pragmatic Trial in Pharmacogenetics) chronic pain study, a multicenter, pragmatic, randomized controlled clinical trial, to assess the effect of CYP2D6 testing on pain management. The study enrolled 1048 participants who are taking or being considered for treatment with CYP2D6-impacted opioids for their chronic pain. Participants were randomized to receive immediate or delayed (by 6 months) genotyping of CYP2D6 with clinical decision support (CDS). CDS encouraged the providers to follow the CYP2D6-guided trial recommendations. The primary study outcome is the 3-month absolute change in the composite pain intensity score assessed using Patient-Reported Outcomes Measurement Information System (PROMIS) measures. Follow-up will be completed in July 2024. Herein, we describe the design of this trial along with challenges encountered during enrollment., (© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Specific selective serotonin reuptake inhibitors (SSRIs) metabolism is strongly influenced by two pharmacogenes, CYP2D6 and CYP2C19. However, the effectiveness of prospectively using pharmacogenetic variants to select or dose SSRIs for depression is uncertain in routine clinical practice. The objective of this prospective, multicenter, pragmatic randomized controlled trial is to determine the effectiveness of genotype-guided selection and dosing of antidepressants on control of depression in participants who are 8 years or older with ≥3 months of depressive symptoms who require new or revised therapy. Those randomized to the intervention arm undergo pharmacogenetic testing at baseline and receive a pharmacy consult and/or automated clinical decision support intervention based on an actionable phenotype, while those randomized to the control arm have pharmacogenetic testing at the end of 6-months. In both groups, depression and drug tolerability outcomes are assessed at baseline, 1 month, 3 months (primary), and 6 months. The primary end point is defined by change in Patient-Reported Outcomes Measurement Information System (PROMIS) Depression score assessed at 3 months versus baseline. Secondary end points include change inpatient health questionnaire (PHQ-8) measure of depression severity, remission rates defined by PROMIS score < 16, medication adherence, and medication side effects. The primary analysis will compare the PROMIS score difference between trial arms among those with an actionable CYP2D6 or CYP2C19 genetic result or a CYP2D6 drug-drug interaction. The trial has completed accrual of 1461 participants, of which 562 were found to have an actionable phenotype to date, and follow-up will be complete in April of 2024., (© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Objectives: To determine the relationship between lipoprotein particle size/number with hepatic steatosis (HS), given its association with traditional lipoproteins and coronary atherosclerosis., Methods: Individuals with available CT data and blood samples enrolled in the PROMISE trial were studied. HS was defined based on CT attenuation. Lipoprotein particle size/number were measured by nuclear magnetic resonance spectroscopy. Principal components analysis (PCA) was used for dimensionality reduction. The association of PCA factors and individual lipoprotein particle size/number with HS were assessed in multivariable regression models. Associations were validated in an independent cohort of 59 individuals with histopathology defined HS., Results: Individuals with HS (n=410/1,509) vs those without (n=1,099/1,509), were younger (59±8 vs 61±8 years) and less often females (47.6 % vs 55.9 %). All PCA factors were associated with HS: factor 1 (OR:1.36, 95 %CI:1.21-1.53), factor 3 (OR:1.75, 95 %CI:1.53-2.02) and factor 4 (OR:1.49; 95 %CI:1.32-1.68) were weighted heavily with small low density lipoprotein (LDL) and triglyceride-rich (TRL) particles, while factor 2 (OR:0.86, 95 %CI:0.77-0.97) and factor 5 (OR:0.74, 95 %CI:0.65-0.84) were heavily loaded with high density lipoprotein (HDL) and larger LDL particles. These observations were confirmed with the analysis of individual lipoprotein particles in PROMISE. In the validation cohort, association between HS and large TRL (OR: 8.16, 95 %CI:1.82-61.98), and mean sizes of TRL- (OR: 2.82, 95 %CI:1.14-9.29) and HDL (OR:0.35, 95 %CI:0.13-0.72) were confirmed., Conclusions: Large TRL, mean sizes of TRL-, and HDL were associated with radiographic and histopathologic HS. The use of lipoprotein particle size/number could improve cardiovascular risk assessment in HS., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier B.V.)

Respiratory diseases are a leading cause of morbidity and mortality globally. However, existing systems of care, built around scheduled appointments, are not well designed to support the needs of people with chronic and acute respiratory conditions that can change rapidly and unexpectedly. Home-based and personal digital health technologies (DHTs) allow implementation of new models of care catering to the unique needs of individuals. The high number of respiratory triggers and unique responses to them require a personalised solution for each patient. The real-world, repetitive monitoring capabilities of DHTs enable identification of the normal operating characteristics for each individual and, therefore, recognition of the earliest deviations from that state. However, despite this potential, the number of clinical efficacy studies of DHTs is quite small. Evaluation of clinical effectiveness of DHTs in improving health quality in real-world settings is urgently needed., Competing Interests: Declaration of interests JD reports personal fees as a Scientific Advisor to Veri and grant support from AstraZeneca. AC is the CEO, co-founder, and a shareholder of HumanFirst; and was a board member of the Digital Medical Society until 2023. MF is an advisor and shareholder of HumanFirst. SRS reports financial compensation as a consultant for physIQ; declares research grant support from Janssen Research & Development; and received reimbursement as an Executive Committee member for the Heartline Study, which is a trial on wearables to detect atrial fibrillation. GSG declared no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Index-cluster studies may help characterize the spread of communicable infections in the presymptomatic state. We describe a prospective index-cluster sampling strategy (ICSS) to detect presymptomatic respiratory viral illness and its implementation in a college population., Methods: We enrolled an annual cohort of first-year undergraduates who completed daily electronic symptom diaries to identify index cases (ICs) with respiratory illness. Investigators then selected 5-10 potentially exposed, asymptomatic close contacts (CCs) who were geographically co-located to follow for infections. Symptoms and nasopharyngeal samples were collected for 5 days. Logistic regression model-based predictions for proportions of self-reported illness were compared graphically for the whole cohort sampling group and the CC group., Results: We enrolled 1379 participants between 2009 and 2015, including 288 ICs and 882 CCs. The median number of CCs per IC was 6 (interquartile range, 3-8). Among the 882 CCs, 111 (13%) developed acute respiratory illnesses. Viral etiology testing in 246 ICs (85%) and 719 CCs (82%) identified a pathogen in 57% of ICs and 15% of CCs. Among those with detectable virus, rhinovirus was the most common (IC: 18%; CC: 6%) followed by coxsackievirus/echovirus (IC: 11%; CC: 4%). Among 106 CCs with a detected virus, only 18% had the same virus as their associated IC. Graphically, CCs did not have a higher frequency of self-reported illness relative to the whole cohort sampling group., Conclusions: Establishing clusters by geographic proximity did not enrich for cases of viral transmission, suggesting that ICSS may be a less effective strategy to detect spread of respiratory infection., Competing Interests: Potential conflicts of interest. M. T. M. reports grants from the Defense Advanced Research Projects Agency (DARPA) and the National Institutes of Health (NIH), and has a patent pending on “Methods to diagnose and treat acute respiratory infections.” T. W. B. reports grants from DARPA and NIH; reports owning equity in and serving as a consultant for Biomeme; and has a patent pending on Methods to diagnose and treat acute respiratory infections. E. L. T. reports consultancy fees and equity from Biomeme; has patents pending on Biomarkers for the molecular classification of bacterial infection and Methods to diagnose and treat acute respiratory infections; and is currently an employee of Danaher Diagnostics. C. W. W. and G. S. G. have patents pending on Molecular classification of bacterial infection and gene expression signatures useful to predict or diagnose sepsis and methods of using the same, and have patents issued on Methods to diagnose and treat acute respiratory disease and Methods of identifying infectious disease and assays for identifying infectious disease. C. W. W. reports owning equity in and consulting for Biomeme; reports grants from DARPA, NIH, Antibacterial Resistance Leadership Group, and Sanofi; and has received consultancy fees from bioMérieux, Roche, Biofire, Giner, and Biomeme. All other authors report no potential conflicts., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens., (© 2023. The Author(s).)

Background The interplay between branched-chain amino acid (BCAA) metabolism, an important pathway in adiposity and cardiometabolic disease, and visceral adipose depots such as hepatic steatosis (HS) and epicardial adipose tissue is unknown. We leveraged the PROMISE clinical trial with centrally adjudicated coronary computed tomography angiography imaging to determine relationships between adipose depots, BCAA dysregulation, and coronary artery disease (CAD). Methods and Results The PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial randomized 10 003 outpatients with stable chest pain to computed tomography angiography versus standard-of-care diagnostics. For this study, we included 1798 participants with available computed tomography angiography data and biospecimens. Linear and logistic regression were used to determine associations between a molar sum of BCAAs measured by nuclear magnetic resonance spectroscopy with body mass index, adipose traits, and obstructive CAD. Mendelian randomization was then used to determine if BCAAs are in the causal pathway for adipose depots or CAD. The study sample had a mean age of 60 years (SD, 8.0), body mass index of 30.6 (SD, 5.9), and epicardial adipose tissue volume of 57.3 (SD, 21.3) cm 3 /m 2 ; 27% had HS, and 14% had obstructive CAD. BCAAs were associated with body mass index (multivariable beta 0.12 per SD increase in BCAA [95% CI, 0.08-0.17]; P =4×10 -8 ). BCAAs were also associated with HS (multivariable odds ratio [OR], 1.46 per SD increase in BCAAs [95% CI, 1.28-1.67]; P =2×10 -8 ), but BCAAs were associated only with epicardial adipose tissue volume (odds ratio, 1.18 [95% CI, 1.07-1.32]; P =0.002) and obstructive CAD (OR, 1.18 [95% CI, 1.04-1.34]; P =0.009) in univariable models. Two-sample Mendelian randomization did not support the role of BCAAs as within the causal pathways for HS or CAD. Conclusions BCAAs have been implicated in the pathogenesis of cardiometabolic diseases, and adipose depots have been associated with the risk of CAD. Leveraging a large clinical trial, we further establish the role of dysregulated BCAA catabolism in HS and CAD, although BCAAs did not appear to be in the causal pathway of either disease. This suggests that BCAAs may serve as an independent circulating biomarker of HS and CAD but that their association with these cardiometabolic diseases is mediated through other pathways.

Aims: Gene expression biosignatures may hold promise to individualize antiplatelet therapy in conjunction with current guidelines and risk scores. The Aspirin Response Signature (ARS) score is comprised of a weighted sum of correlated, pro-thrombotic gene transcripts measured in whole blood. In prior work where volunteers were exposed to aspirin 325 mg daily, higher ARS score was associated with lower platelet function; separately, in a clinical cohort of patients, higher ARS scores were associated with increased risk of adverse cardiovascular events. To better understand this apparent paradox, we measured ARS gene expression and score in volunteers to determine aspirin dose-response and ticagrelor relationships with ARS score and separately in patients to assess whether ARS is associated with incident bleeding., Methods and Results: Blood samples were collected from volunteers (N = 188) who were exposed to 4 weeks of daily aspirin 81 mg, daily aspirin 325 mg, and/or twice-daily ticagrelor 90 mg. ARS scores were calculated from whole blood RNA qPCR, and platelet function and protein expression were assessed in platelet-rich plasma. In mixed linear regression models, aspirin 81 mg exposure was not associated with changes in ARS gene expression or score. Aspirin 325 mg exposure resulted in a 6.0% increase in ARS gene expression (P = 7.5 × 10-9 vs. baseline, P = 2.1 × 10-4 vs. aspirin 81 mg) and an increase in expression of platelet proteins corresponding to ARS genes. Ticagrelor exposure resulted in a 30.7% increase in ARS gene expression (P < 1 × 10-10 vs. baseline and each aspirin dose) and ARS score (P = 7.0 × 10-7 vs. baseline, P = 3.6 × 10-6 and 5.59 × 10-4 vs. aspirin 81 and 325 mg, respectively). Increases in ARS gene expression or score were associated with the magnitude of platelet inhibition across agents. To assess the association between ARS scores and incident bleeding, ARS scores were calculated in patients undergoing cardiac catheterization (N = 1421), of whom 25.4% experienced bleeding events over a median 6.2 years of follow-up. In a Cox model adjusting for demographics and baseline antithrombotic medication use, patients with ARS scores above the median had a higher risk of incident bleeding [hazard ratio 1.26 (95% CI 1.01-1.56), P = 0.038]., Conclusions: The ARS is an Antiplatelet Response Signature that increases in response to greater platelet inhibition due to antiplatelet therapy and may represent a homeostatic mechanism to prevent bleeding. ARS scores could inform future strategies to prevent bleeding while maintaining antiplatelet therapy's benefit of ischaemic cardiovascular event protection., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

BACKGROUND More than half of major adverse cardiovascular events (MACE) occur in the absence of obstructive coronary artery disease and are often attributed to the rupture of high-risk coronary atherosclerotic plaque (HRP). Blood-based biomarkers that associate with imaging-defined HRP and predict MACE are lacking. METHODS AND RESULTS Nuclear magnetic resonance-based lipoprotein particle profiling was performed in the biomarker substudy of the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial (N=4019) in participants who had stable symptoms suspicious for coronary artery disease. Principal components analysis was used to reduce the number of correlated lipoproteins into uncorrelated lipoprotein factors. The association of lipoprotein factors and individual lipoproteins of significantly associated factors with core laboratory determined coronary computed tomographic angiography features of HRP was determined using logistic regression models. The association of HRP-associated lipoproteins with MACE was assessed in the PROMISE trial and validated in an independent coronary angiography biorepository (CATHGEN [Catheterization Genetics]) using Cox proportional hazards models. Lipoprotein factors composed of high-density lipoprotein (HDL) subclasses were associated with HRP. In these factors, large HDL (odds ratio [OR], 0.70 [95% CI, 0.56-0.85]; P <0.001) and medium HDL (OR, 0.84 [95% CI, 0.72-0.98]; P =0.028) and HDL size (OR, 0.82 [95% CI, 0.69-0.96]; P =0.018) were associated with HRP in multivariable models. Medium HDL was associated with MACE in PROMISE (hazard ratio [HR], 0.76 [95% CI, 0.63-0.92]; P =0.004), which was validated in the CATHGEN biorepository (HR, 0.91 [95% CI, 0.88-0.94]; P <0.001). CONCLUSIONS Large and medium HDL subclasses and HDL size inversely associate with HRP features, and medium HDL subclasses inversely associate with MACE in PROMISE trial participants. These findings may aid in the risk stratification of individuals with chest pain and provide insight into the pathobiology of HRP. REGISTRATION URL: https://clinicaltrials.gov; Unique identifier: NCT01174550.

The fields of genetics and genomics have greatly expanded across medicine through the development of new technologies that have revealed genetic contributions to a wide array of traits and diseases. Thus, the development of widely available educational resources for all healthcare providers is essential to ensure the timely and appropriate utilization of genetics and genomics patient care. In 2020, the National Human Genome Research Institute released a call for new proposals to develop accessible, sustainable online education for health providers. This paper describes the efforts of the six teams awarded to reach the goal of providing genetic and genomic training modules that are broadly available for busy clinicians.

Using data from a longitudinal viral challenge study, we find that the post-exposure viral shedding and symptom severity are associated with a novel measure of pre-exposure cognitive performance variability (CPV), defined before viral exposure occurs. Each individual's CPV score is computed from data collected from a repeated NeuroCognitive Performance Test (NCPT) over a 3 day pre-exposure period. Of the 18 NCPT measures reported by the tests, 6 contribute materially to the CPV score, prospectively differentiating the high from the low shedders. Among these 6 are the 4 clinical measures digSym-time, digSym-correct, trail-time, and reaction-time, commonly used for assessing cognitive executive functioning. CPV is found to be correlated with stress and also with several genes previously reported to be associated with cognitive development and dysfunction. A perturbation study over the number and timing of NCPT sessions indicates that as few as 5 sessions is sufficient to maintain high association between the CPV score and viral shedding, as long as the timing of these sessions is balanced over the three pre-exposure days. Our results suggest that variations in cognitive function are closely related to immunity and susceptibility to severe infection. Further studying these relationships may help us better understand the links between neurocognitive and neuroimmune systems which is timely in this COVID-19 pandemic era., (© 2022. The Author(s).)

Background: Systematically assessing disease risk can improve population health by identifying those eligible for enhanced prevention/screening strategies. This study aims to determine the clinical impact of a systematic risk assessment in diverse primary care populations., Methods: Hybrid implementation-effectiveness trial of a family health history-based health risk assessment (HRA) tied to risk-based guideline recommendations enrolling from 2014-2017 with 12 months of post-intervention survey data and 24 months of electronic medical record (EMR) data capture., Setting: 19 primary care clinics at four geographically and culturally diverse U.S. healthcare systems., Participants: any English or Spanish-speaking adult with an upcoming appointment at an enrolling clinic., Methods: A personal and family health history based HRA with integrated guideline-based clinical decision support (CDS) was completed by each participant prior to their appointment. Risk reports were provided to patients and providers to discuss at their clinical encounter., Outcomes: provider and patient discussion and provider uptake (i.e. ordering) and patient uptake (i.e. recommendation completion) of CDS recommendations., Measures: patient and provider surveys and EMR data., Results: One thousand eight hundred twenty nine participants (mean age 56.2 [SD13.9], 69.6% female) completed the HRA and had EMR data available for analysis. 762 (41.6%) received a recommendation (29.7% for genetic counseling (GC); 15.2% for enhanced breast/colon cancer screening). Those with recommendations frequently discussed disease risk with their provider (8.7%-38.2% varied by recommendation, p-values ≤ 0.004). In the GC subgroup, provider discussions increased referrals to counseling (44.4% with vs. 5.9% without, P < 0.001). Recommendation uptake was highest for colon cancer screening (provider = 67.9%; patient = 86.8%) and lowest for breast cancer chemoprevention (0%)., Conclusions: Systematic health risk assessment revealed that almost half the population were at increased disease risk based on guidelines. Risk identification resulted in shared discussions between participants and providers but variable clinical action uptake depending upon the recommendation. Understanding the barriers and facilitators to uptake by both patients and providers will be essential for optimizing HRA tools and achieving their promise of improving population health., Trial Registration: Clinicaltrials.gov number NCT01956773 , registered 10/8/2013., (© 2022. The Author(s).)

Cardiovascular disease (CVD) is the leading cause of mortality in adults with hepatic steatosis (HS). However, risk factors for CVD in HS are unknown. We aimed to identify factors associated with coronary artery disease (CAD) and incident major adverse cardiovascular events (MACE) in individuals with HS. We performed a nested cohort study of adults with HS detected on coronary computed tomography in the PROspective Multicenter Imaging Study for Evaluation of chest pain (PROMISE) trial. Obstructive CAD was defined as ≥50% coronary stenosis. MACE included hospitalization for unstable angina, nonfatal myocardial infarction, or all-cause death. Multivariate modeling, adjusted for age, sex, atherosclerotic CVD (ASCVD) risk score and body mass index, identified factors associated with obstructive CAD. Cox regression, adjusted for ASCVD risk score, determined the predictors of MACE. A total of 959 of 3,756 (mean age 59.4 years, 55.0% men) had HS. Obstructive CAD was present in 15.2% (145 of 959). Male sex (adjusted odds ratio [aOR] = 1.83, 95% confidence interval [CI] 1.18-1.2.84; p = 0.007), ASCVD risk score (aOR = 1.05, 95% CI 1.03-1.07; p < 0.001), and n-terminal pro-b-type natriuretic peptide (NT-proBNP; aOR = 1.90, 95% CI 1.38-2.62; p < 0.001) were independently associated with obstructive CAD. In the 25-months median follow-up, MACE occurred in 4.4% (42 of 959). Sedentary lifestyle (adjusted hazard ratio [aHR] = 2.53, 95% CI 1.27-5.03; p = 0.008) and NT-proBNP (aOR = 1.50, 95% CI 1.01-2.25; p = 0.046) independently predicted MACE. Furthermore, the risk of MACE increased by 3% for every 1% increase in ASCVD risk score (aHR = 1.03, 95% CI 1.01-1.05; p = 0.02). Conclusion: In individuals with HS, male sex, NT-pro-BNP, and ASCVD risk score are associated with obstructive CAD. Furthermore, ASCVD, NT-proBNP, and sedentary lifestyle are independent predictors of MACE. These factors, with further validation, may help risk-stratify adults with HS for incident CAD and MACE., (© 2022 The Authors. Hepatology Communications published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases.)

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages., (© 2022. The Author(s).)

Background: Increased inflammation and myocardial injury can be observed in the absence of myocardial infarction or obstructive coronary artery disease (CAD)., Objectives: The authors determined whether biomarkers of inflammation and myocardial injury-interleukin (IL)-6 and high-sensitivity cardiac troponin (hs-cTn)-were associated with the presence and extent of CAD and were independent predictors of major adverse cardiovascular events (MACEs) in stable chest pain., Methods: Using participants from the PROMISE trial, the authors measured hs-cTn I and IL-6 concentrations and analyzed computed tomography angiography (CTA) images in the core laboratory for CAD characteristics: significant stenosis (≥70%), high-risk plaque (HRP), Coronary Artery Disease Reporting and Data System (CAD-RADS) categories, segment involvement score (SIS), and coronary artery calcium (CAC) score. The primary endpoint was a composite MACE (death, myocardial infarction, or unstable angina)., Results: The authors included 1,796 participants (age 60.2 ± 8.0 years; 47.5% men, median follow-up 25 months). In multivariable linear regression adjusted for atherosclerotic cardiovascular disease (ASCVD) risk, hs-cTn was associated with HRP, stenosis, CAD-RADS, and SIS. IL-6 was only associated with stenosis and CAD-RADS. hs-cTn above median (1.5 ng/L) was associated with MACEs in univariable analysis (HR: 2.1 [95% CI: 1.3-3.6]; P = 0.006), but not in multivariable analysis adjusted for ASCVD and CAD. IL-6 above median (1.8 ng/L) was associated with MACEs in multivariable analysis adjusted for ASCVD and HRP (HR: 1.9 [95% CI: 1.1-3.3]; P = 0.03), CAC (HR: 1.9 [95% CI: 1.0-3.4]; P = 0.04), and SIS (HR: 1.8 [95% CI: 1.0-3.2]; P = 0.04), but not for stenosis or CAD-RADS. In participants with nonobstructive CAD (stenosis 1%-69%), the presence of both hs-cTn and IL-6 above median was strongly associated with MACEs (HR: 2.5-2.7 after adjustment for CAD characteristics)., Conclusions: Concentrations of hs-cTn and IL-6 were associated with CAD characteristics and MACEs, indicating that myocardial injury and inflammation may each contribute to pathways in CAD pathophysiology. This association was most pronounced among participants with nonobstructive CAD representing an opportunity to tailor treatment in this at-risk group. (PROspective Multicenter Imaging Study for Evaluation of Chest Pain [PROMISE]; NCT01174550)., Competing Interests: Funding Support and Author Disclosures The PROMISE trial was funded by grants R01HL098237, R01HL098236, R01HL98305, and R01HL098235 from the National Heart, Lung, and Blood Institute (NHLBI). The funding source had no role in the design and conduct of this study, study analyses and interpretation of the data, the drafting and editing of the manuscript and its final contents, approval of the manuscript, and the decision to submit the manuscript for publication. The views expressed in this article do not necessarily represent the official views of the NHLBI. This article was prepared while Dr Ginsburg was employed at Duke University. The opinions expressed in this article are the author’s own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. Dr Ferencik has received a grant from the American Heart Association, National Institutes of Health; and has received consulting fees from Biograph, Inc. Dr Bittner was supported by a grant from the National Institutes of Health/NHLBI (5K24HL113128). Dr Lu has received grant support from the American Roentgen Ray Society Scholarship during the conduct of the study; and has received personal fees from PQBypass outside of the submitted work. Dr Meyersohn was supported by National Institutes of Health/NHLBI (T32 HL076136). Dr Douglas has received grant support from HeartFlow; and has served on a data and safety monitoring board for GE HealthCare outside of the submitted work. Dr Hoffmann has received grants from the American College of Radiology Imaging Network and HeartFlow during the conduct of the study, and from Siemens Healthcare outside of the submitted work. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings., Materials and Methods: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy., Results: Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address., Discussion: Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2022. This work is written by US Government employees and is in the public domain in the US.)

SARS-CoV-2 infection triggers profound and variable immune responses in human hosts. Chromatin remodeling has been observed in individuals severely ill or convalescing with COVID-19, but chromatin remodeling early in disease prior to anti-spike protein IgG seroconversion has not been defined. We performed the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and RNA-seq on peripheral blood mononuclear cells (PBMCs) from outpatients with mild or moderate symptom severity at different stages of clinical illness. Early in the disease course prior to IgG seroconversion, modifications in chromatin accessibility associated with mild or moderate symptoms were already robust and included severity-associated changes in accessibility of genes in interleukin signaling, regulation of cell differentiation and cell morphology. Furthermore, single-cell analyses revealed evolution of the chromatin accessibility landscape and transcription factor motif accessibility for individual PBMC cell types over time. The most extensive remodeling occurred in CD14+ monocytes, where sub-populations with distinct chromatin accessibility profiles were observed prior to seroconversion. Mild symptom severity was marked by upregulation of classical antiviral pathways, including those regulating IRF1 and IRF7, whereas in moderate disease, these classical antiviral signals diminished, suggesting dysregulated and less effective responses. Together, these observations offer novel insight into the epigenome of early mild SARS-CoV-2 infection and suggest that detection of chromatin remodeling in early disease may offer promise for a new class of diagnostic tools for COVID-19., (© 2022. The Author(s).)

Digital health solutions, with apps, virtual care, and electronic medical records, are gaining momentum across all medical disciplines, and their adoption has been accelerated, in part, by the COVID-19 pandemic. Personal wearables, sensors, and mobile technologies are increasingly being used to identify health risks and assist in diagnosis, treatment, and monitoring of health and disease. Genomics is a vanguard of digital healthcare as we witness a convergence of the fields of genomic and digital medicine. Spurred by the acute need to increase health literacy, empower patients' preference-sensitive decisions, or integrate vast amounts of complex genomic data into the clinical workflow, there has been an emergence of digital support tools in genomics-enabled care. We present three use cases that demonstrate the application of these converging technologies: digital genomics decision support tools, conversational chatbots to scale the genetic counseling process, and the digital delivery of comprehensive genetic services. These digital solutions are important to facilitate patient-centered care delivery, improve patient outcomes, and increase healthcare efficiencies in genomic medicine. Yet the development of these innovative digital genomic technologies also reveals strategic challenges that need to be addressed before genomic digital health can be broadly adopted. Alongside key evidentiary gaps in clinical and cost-effectiveness, there is a paucity of clinical guidelines, policy, and regulatory frameworks that incorporate digital health. We propose a research agenda, guided by learning healthcare systems, to realize the vision of digital health-enabled genomics to ensure its sustainable and equitable deployment in clinical care., Competing Interests: Declaration of interests G.S.G. is an employee of the National Institutes of Health, the Department of Health and Human Services, and the United States government. The opinions expressed in this article are the author’s own and do not reflect the view of these organizations. This article was prepared while G.S.G. was employed at Duke University. At that time, G.S.G. was a consultant for KonicaMinolta and Fabric Genomics. G.S.G. was an owner of Peer Medical, Origin Commercial Advisors, Predigen, MeTree&You, and Coprata. G.S.G. received royalties from Elsevier. A.Y.Z. is a full-time employee and shareholder of Color Health, Inc. A.C.S. is an employee of 23andMe. The article was prepared while A.C.S. was employed at Geisinger. At that time, A.C.S. was a consultant for Invitae and 23andMe., (Copyright © 2022 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Both transcriptomics and metabolomics hold promise for identifying acute coronary syndrome (ACS) but they have not been used in combination, nor have dynamic changes in levels been assessed as a diagnostic tool. We assessed integrated analysis of peripheral blood miRNA and metabolite analytes to distinguish patients with myocardial ischemia on cardiac stress testing. We isolated and quantified miRNA and metabolites before and after stress testing from seven patients with myocardial ischemia and 1:1 matched controls. The combined miRNA and metabolomic data were analyzed jointly in a supervised, dimension-reducing discriminant analysis. We implemented a baseline model (T0) and a stress-delta model. This novel integrative analysis of the baseline levels of metabolites and miRNA expression showed modest performance for distinguishing cases from controls. The stress-delta model showed worse performance. This pilot study shows potential for an integrated precision medicine approach to cardiac stress testing.

Analysis of stool offers simple, non-invasive monitoring for many gastrointestinal (GI) diseases and access to the gut microbiome, however adherence to stool sampling protocols remains a major challenge because of the prevalent dislike of handling one's feces. We present a technology that enables individual stool specimen collection from toilet wastewater for fecal protein and molecular assay. Human stool specimens and a benchtop test platform integrated with a commercial toilet were used to demonstrate reliable specimen collection over a wide range of stool consistencies by solid/liquid separation followed by spray-erosion. The obtained fecal suspensions were used to perform occult blood tests for GI cancer screening and for microbiome 16S rRNA analysis. Using occult blood home test kits, we found overall 90% agreement with standard sampling, 96% sensitivity and 86% specificity. Microbiome analysis revealed no significant difference in within-sample species diversity compared to standard sampling and specimen cross-contamination was below the detection limit of the assay. Furthermore, we report on the use of an analogue turbidity sensor to assess in real time loose stools for tracking of diarrhea. Implementation of this technology in residential settings will improve the quality of GI healthcare by facilitating increased adherence to routine stool monitoring., (© 2022. The Author(s).)

Use of artificial intelligence (AI) is a burgeoning field in otolaryngology and the communication sciences. A virtual symposium on the topic was convened from Duke University on October 26, 2020, and was attended by more than 170 participants worldwide. This review presents summaries of all but one of the talks presented during the symposium; recordings of all the talks, along with the discussions for the talks, are available at https://www.youtube.com/watch?v=ktfewrXvEFg and https://www.youtube.com/watch?v=-gQ5qX2v3rg . Each of the summaries is about 2500 words in length and each summary includes two figures. This level of detail far exceeds the brief summaries presented in traditional reviews and thus provides a more-informed glimpse into the power and diversity of current AI applications in otolaryngology and the communication sciences and how to harness that power for future applications., (© 2022. The Author(s) under exclusive licence to Association for Research in Otolaryngology.)

Aspirin has known effects beyond inhibiting platelet cyclooxygenase-1 (COX-1) that have been incompletely characterized. Transcriptomics can comprehensively characterize the on- and off-target effects of medications. We used a systems pharmacogenomics approach of aspirin exposure in volunteers coupled with serial platelet function and purified platelet mRNA sequencing to test the hypothesis that aspirin's effects on the platelet transcriptome are associated with platelet function. We prospectively recruited 74 adult volunteers for a randomized crossover study of 81- vs. 325 mg/day, each for 4 weeks. Using mRNA sequencing of purified platelets collected before and after each 4-week exposure, we identified 208 aspirin-responsive genes with no evidence for dosage effects. In independent cohorts of healthy volunteers and patients with diabetes, we validated aspirin's effects on five genes: EIF2S3, CHRNB1, EPAS1, SLC9A3R2 and HLA-DRA. Functional characterization of the effects of aspirin on mRNA as well as platelet ribosomal RNA demonstrated that aspirin may act as an inhibitor of protein synthesis. Database searches for small molecules that mimicked the effects of aspirin on platelet gene expression in vitro identified aspirin but no other molecules that share aspirin's known mechanisms of action. The effects of aspirin on platelet mRNA were correlated with higher levels of platelet function both at baseline and after aspirin exposure-an effect that counteracts aspirin's known antiplatelet effect. In summary, this work collectively demonstrates a dose-independent effect of aspirin on the platelet transcriptome that counteracts the well-known antiplatelet effects of aspirin., (© 2021 British Pharmacological Society.)

Importance: Bacterial and viral causes of acute respiratory illness (ARI) are difficult to clinically distinguish, resulting in the inappropriate use of antibacterial therapy. The use of a host gene expression-based test that is able to discriminate bacterial from viral infection in less than 1 hour may improve care and antimicrobial stewardship., Objective: To validate the host response bacterial/viral (HR-B/V) test and assess its ability to accurately differentiate bacterial from viral infection among patients with ARI., Design, Setting, and Participants: This prospective multicenter diagnostic study enrolled 755 children and adults with febrile ARI of 7 or fewer days' duration from 10 US emergency departments. Participants were enrolled from October 3, 2014, to September 1, 2019, followed by additional enrollment of patients with COVID-19 from March 20 to December 3, 2020. Clinical adjudication of enrolled participants identified 616 individuals as having bacterial or viral infection. The primary analysis cohort included 334 participants with high-confidence reference adjudications (based on adjudicator concordance and the presence of an identified pathogen confirmed by microbiological testing). A secondary analysis of the entire cohort of 616 participants included cases with low-confidence reference adjudications (based on adjudicator discordance or the absence of an identified pathogen in microbiological testing). Thirty-three participants with COVID-19 were included post hoc., Interventions: The HR-B/V test quantified the expression of 45 host messenger RNAs in approximately 45 minutes to derive a probability of bacterial infection., Main Outcomes and Measures: Performance characteristics for the HR-B/V test compared with clinical adjudication were reported as either bacterial or viral infection or categorized into 4 likelihood groups (viral very likely [probability score <0.19], viral likely [probability score of 0.19-0.40], bacterial likely [probability score of 0.41-0.73], and bacterial very likely [probability score >0.73]) and compared with procalcitonin measurement., Results: Among 755 enrolled participants, the median age was 26 years (IQR, 16-52 years); 360 participants (47.7%) were female, and 395 (52.3%) were male. A total of 13 participants (1.7%) were American Indian, 13 (1.7%) were Asian, 368 (48.7%) were Black, 131 (17.4%) were Hispanic, 3 (0.4%) were Native Hawaiian or Pacific Islander, 297 (39.3%) were White, and 60 (7.9%) were of unspecified race and/or ethnicity. In the primary analysis involving 334 participants, the HR-B/V test had sensitivity of 89.8% (95% CI, 77.8%-96.2%), specificity of 82.1% (95% CI, 77.4%-86.6%), and a negative predictive value (NPV) of 97.9% (95% CI, 95.3%-99.1%) for bacterial infection. In comparison, the sensitivity of procalcitonin measurement was 28.6% (95% CI, 16.2%-40.9%; P < .001), the specificity was 87.0% (95% CI, 82.7%-90.7%; P = .006), and the NPV was 87.6% (95% CI, 85.5%-89.5%; P < .001). When stratified into likelihood groups, the HR-B/V test had an NPV of 98.9% (95% CI, 96.1%-100%) for bacterial infection in the viral very likely group and a positive predictive value of 63.4% (95% CI, 47.2%-77.9%) for bacterial infection in the bacterial very likely group. The HR-B/V test correctly identified 30 of 33 participants (90.9%) with acute COVID-19 as having a viral infection., Conclusions and Relevance: In this study, the HR-B/V test accurately discriminated bacterial from viral infection among patients with febrile ARI and was superior to procalcitonin measurement. The findings suggest that an accurate point-of-need host response test with high NPV may offer an opportunity to improve antibiotic stewardship and patient outcomes.

Background: Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another., Methods: This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies., Results: Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69-0.97 for viral classification. Signature size varied (1-398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months-1 year and 2-11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets., Conclusions: In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature's size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation., (© 2022. The Author(s).)

Objectives: Compare three host response strategies to distinguish bacterial and viral etiologies of acute respiratory illness (ARI)., Methods: In this observational cohort study, procalcitonin, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in 286 subjects with ARI from four emergency departments. Multinomial logistic regression and leave-one-out cross validation were used to evaluate the protein and mRNA tests., Results: The mRNA panel performed better than alternative strategies to identify bacterial infection: AUC 0.93 vs. 0.83 for the protein panel and 0.84 for procalcitonin (P<0.02 for each comparison). This corresponded to a sensitivity and specificity of 92% and 83% for the mRNA panel, 81% and 73% for the protein panel, and 68% and 87% for procalcitonin, respectively. A model utilizing all three strategies was the same as mRNA alone. For the diagnosis of viral infection, the AUC was 0.93 for mRNA and 0.84 for the protein panel (p<0.05). This corresponded to a sensitivity and specificity of 89% and 82% for the mRNA panel, and 85% and 62% for the protein panel, respectively., Conclusions: A gene expression signature was the most accurate host response strategy for classifying subjects with bacterial, viral, or non-infectious ARI., Competing Interests: ELT, RH, MTM, GSG, TWB, and CWW have filed for a patent for Methods to Diagnose and Treat Acute Respiratory Infections (WO 2017/004390 A1).

Background: Difficulty discriminating bacterial from viral infections drives antibacterial misuse. Host gene expression tests discriminate bacterial and viral etiologies, but their clinical utility has not been evaluated., Methods: Host gene expression and procalcitonin levels were measured in 582 emergency department participants with suspected infection. We also recorded clinician diagnosis and clinician-recommended treatment. These 4 diagnostic strategies were compared with clinical adjudication as the reference. To estimate the clinical impact of host gene expression, we calculated the change in overall Net Benefit (∆NB; the difference in Net Benefit comparing 1 diagnostic strategy with a reference) across a range of prevalence estimates while factoring in the clinical significance of false-positive and -negative errors., Results: Gene expression correctly classified bacterial, viral, or noninfectious illness in 74.1% of subjects, similar to the other strategies. Clinical diagnosis and clinician-recommended treatment revealed a bias toward overdiagnosis of bacterial infection resulting in high sensitivity (92.6% and 94.5%, respectively) but poor specificity (67.2% and 58.8%, respectively), resulting in a 33.3% rate of inappropriate antibacterial use. Gene expression offered a more balanced sensitivity (79.0%) and specificity (80.7%), which corresponded to a statistically significant improvement in average weighted accuracy (79.9% vs 71.5% for procalcitonin and 76.3% for clinician-recommended treatment; P <.0001 for both). Consequently, host gene expression had greater Net Benefit in diagnosing bacterial infection than clinician-recommended treatment (∆NB=6.4%) and procalcitonin (∆NB=17.4%)., Conclusions: Host gene expression-based tests to distinguish bacterial and viral infection can facilitate appropriate treatment, improving patient outcomes and mitigating the antibacterial resistance crisis., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2021.)

Viruses cause a wide spectrum of clinical disease, the majority being acute respiratory infections (ARI). In most cases, ARI symptoms are similar for different viruses although severity can be variable. The objective of this study was to understand the shared and unique elements of the host transcriptional response to different viral pathogens. We identified 162 subjects in the US and Sri Lanka with infections due to influenza, enterovirus/rhinovirus, human metapneumovirus, dengue virus, cytomegalovirus, Epstein Barr Virus, or adenovirus. Our dataset allowed us to identify common pathways at the molecular level as well as virus-specific differences in the host immune response. Conserved elements of the host response to these viral infections highlighted the importance of interferon pathway activation. However, the magnitude of the responses varied between pathogens. We also identified virus-specific responses to influenza, enterovirus/rhinovirus, and dengue infections. Influenza-specific differentially expressed genes (DEG) revealed up-regulation of pathways related to viral defense and down-regulation of pathways related to T cell and neutrophil responses. Functional analysis of entero/rhinovirus-specific DEGs revealed up-regulation of pathways for neutrophil activation, negative regulation of immune response, and p38MAPK cascade and down-regulation of virus defenses and complement activation. Functional analysis of dengue-specific up-regulated DEGs showed enrichment of pathways for DNA replication and cell division whereas down-regulated DEGs were mainly associated with erythrocyte and myeloid cell homeostasis, reactive oxygen and peroxide metabolic processes. In conclusion, our study will contribute to a better understanding of molecular mechanisms to viral infections in humans and the identification of biomarkers to distinguish different types of viral infections., Competing Interests: ELT, GSG, CWW, and TWB consult for and hold equity in Biomeme, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Tsalik, Fiorino, Aqeel, Liu, Henao, Ko, Burke, Reller, Bodinayake, Nagahawatte, Arachchi, Devasiri, Kurukulasooriya, McClain, Woods, Ginsburg, Tillekeratne and Schughart.)

A family health history-based risk assessment is particularly valuable for guiding cancer screening and treatment strategies, yet an optimal implementation depends upon end-users' values and needs. This is not only true prior to disease development, but also for those already affected. The aim of this study is to explore perceptions of the value of knowing one's family health history (FHH)-based risk, experience using a patient-facing FHH tool and the potential of the tool for wider implementation. Twenty multi-ethnic Asian patients undergoing breast cancer treatment in Singapore completed an FHH-based risk assessment. Semi-structured one-on-one interviews were conducted and data were thematically analyzed. All participants were female and slightly more than half were Chinese. The acceptance and usage of an FHH risk assessment tool for cancers and its broader implementation was affected by a perceived importance of personal control over early detection, patient concerns of anxiety for themselves and their families due to risk results, concerns for genetic discrimination, adequacy of follow-up care plans and Asian cultural beliefs toward disease and dying. This study uniquely sheds light on the factors affecting Asian breast cancer patients' perceptions about undergoing an FHH-based risk assessment, which should inform steps for a broader implementation in Asian healthcare systems.

Importance: Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation., Objective: To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus., Design, Setting, and Participants: The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated., Exposures: Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay., Main Outcomes and Measures: The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC)., Results: A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC)., Conclusions and Relevance: This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

Bacterial pneumonia is a major cause of morbidity and mortality worldwide despite the use of antibiotics, and novel therapies are urgently needed. Building on previous work, we aimed to 1 ) develop a baboon model of severe pneumococcal pneumonia and sepsis with organ dysfunction and 2 ) test the safety and efficacy of a novel extracorporeal blood filter to remove proinflammatory molecules and improve organ function. After a dose-finding pilot study, 12 animals were inoculated with Streptococcus pneumoniae [5 × 10 9 colony-forming units (CFU)], given ceftriaxone at 24 h after inoculation, and randomized to extracorporeal blood purification using a filter coated with surface-immobilized heparin sulfate ( n = 6) or sham treatment ( n = 6) for 4 h at 30 h after inoculation. For safety analysis, four uninfected animals also underwent purification. At 48 h, necropsy was performed. Inoculated animals developed severe pneumonia and septic shock. Compared with sham-treated animals, septic animals treated with purification displayed significantly less kidney injury, metabolic acidosis, hypoglycemia, and shock ( P < 0.05). Purification blocked the rise in peripheral blood S. pneumoniae DNA, attenuated bronchoalveolar lavage (BAL) CCL4, CCL2, and IL-18 levels, and reduced renal oxidative injury and classical NLRP3 inflammasome activation. Purification was safe in both uninfected and infected animals and produced no adverse effects. We demonstrate that heparin-based blood purification significantly attenuates levels of circulating S. pneumoniae DNA and BAL cytokines and is renal protective in baboons with severe pneumococcal pneumonia and septic shock. Purification was associated with less severe acute kidney injury, metabolic derangements, and shock. These results support future clinical studies in critically ill septic patients.

Objective: To develop a multi-channel device event segmentation and feature extraction algorithm that is robust to changes in data distribution., Methods: We introduce an adaptive transfer learning algorithm to classify and segment events from non-stationary multi-channel temporal data. Using a multivariate hidden Markov model (HMM) and Fisher's linear discriminant analysis (FLDA) the algorithm adaptively adjusts to shifts in distribution over time. The proposed algorithm is unsupervised and learns to label events without requiring a priori information about true event states. The procedure is illustrated on experimental data collected from a cohort in a human viral challenge (HVC) study, where certain subjects have disrupted wake and sleep patterns after exposure to an H1N1 influenza pathogen., Results: Simulations establish that the proposed adaptive algorithm significantly outperforms other event classification methods. When applied to early time points in the HVC data, the algorithm extracts sleep/wake features that are predictive of both infection and infection onset time., Conclusion: The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring., Significance: Our integrated multisensor signal processing and transfer learning method is applicable to many ambulatory monitoring applications.