Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study.

BACKGROUND: The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals. OBJECTIVE: The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers. METHODS: In this study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors. RESULTS: A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week study duration, the algorithm generated 665 positive alerts, of which 153 (23.0%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2%, and the positive-predictive value ranged from 4% to 10% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result. CONCLUSIONS: The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This study showed the potential of wearables with embedded alerting systems to provide information on wellness measures.

The Tomato Brown Rugose Fruit Virus Movement Protein Gene Is a Novel Microbial Source Tracking Marker.

Microbial source tracking (MST) identifies sources of fecal contamination in the environment using host-associated fecal markers. While there are numerous bacterial MST markers that can be used herein, there are few such viral markers. Here, we designed and tested novel viral MST markers based on tomato brown rugose fruit virus (ToBRFV) genomes. We assembled eight nearly complete genomes of ToBRFV from wastewater and stool samples from the San Francisco Bay Area in the United States. Next, we developed two novel probe-based reverse transcription-PCR (RT-PCR) assays based on conserved regions of the ToBRFV genome and tested the markers' sensitivities and specificities using human and non-human animal stool as well as wastewater. The ToBRFV markers are sensitive and specific; in human stool and wastewater, they are more prevalent and abundant than a commonly used viral marker, the pepper mild mottle virus (PMMoV) coat protein (CP) gene. We used the assays to detect fecal contamination in urban stormwater samples and found that the ToBRFV markers matched cross-assembly phage (crAssphage), an established viral MST marker, in prevalence across samples. Taken together, these results indicate that ToBRFV is a promising viral human-associated MST marker. IMPORTANCE Human exposure to fecal contamination in the environment can cause transmission of infectious diseases. Microbial source tracking (MST) can identify sources of fecal contamination so that contamination can be remediated and human exposures can be reduced. MST requires the use of host-associated MST markers. Here, we designed and tested novel MST markers from genomes of tomato brown rugose fruit virus (ToBRFV). The markers are sensitive and specific to human stool and highly abundant in human stool and wastewater samples.

Tomato brown rugose fruit virus Mo gene is a novel microbial source tracking marker.

Microbial source tracking (MST) identifies sources of fecal contamination in the environment using fecal host-associated markers. While there are numerous bacterial MST markers, there are few viral markers. Here we design and test novel viral MST markers based on tomato brown rugose fruit virus (ToBRFV) genomes. We assembled eight nearly complete genomes of ToBRFV from wastewater and stool samples from the San Francisco Bay Area in the United States of America. Next, we developed two novel probe-based RT-PCR assays based on conserved regions of the ToBRFV genome, and tested the markers’ sensitivities and specificities using human and non-human animal stool as well as wastewater. TheToBRFV markers are sensitive and specific; in human stool and wastewater, they are more prevalent and abundant than a currently used marker, the pepper mild mottle virus (PMMoV) coat protein (CP) gene. We applied the assays to detect fecal contamination in urban stormwater samples and found that the ToBRFV markers matched cross-assembly phage (crAssphage), an established viral MST marker, in prevalence across samples. Taken together, ToBRFV is a promising viral human-associated MST marker. Importance: Human exposure to fecal contamination in the environment can cause transmission of infectious diseases. Microbial source tracking (MST) can identify sources of fecal contamination so that contamination can be remediated and human exposures can be reduced. MST requires the use of fecal host-associated MST markers. Here we design and test novel MST markers from genomes of tomato brown rugose fruit virus (ToBRFV). The markers are sensitive and specific to human stool, and highly abundant in human stool and wastewater samples.

Mindful breathing as an effective technique in the management of hypertension.

Introduction: Hypertension is one of the most important, modifiable risk factors for cardiovascular disease. The popularity of wearable devices provides an opportunity to test whether device guided slow mindful breathing may serve as a non-pharmacological treatment in the management of hypertension. Methods: Fitbit Versa-3 and Sense devices were used for this study. In addition, participants were required to own an FDA or Health Canada approved blood pressure measuring device. Advertisements were shown to 655,910 Fitbit users, of which 7,365 individuals expressed interest and filled out the initial survey. A total of 1,918 participants entered their blood pressure readings on at least 1 day and were considered enrolled in the study. Participants were instructed to download a guided mindful breathing app on their smartwatch device, and to engage with the app once a day prior to sleep. Participants measured their systolic and diastolic blood pressure prior to starting each mindful breathing session, and again after completion. All measurements were self reported. Participants were located in the United States or Canada. Results: Values of systolic and diastolic blood pressure were reduced following mindful breathing. There was also a decrease in resting systolic and diastolic measurements when measured over several days. For participants with a systolic pressure ≥ 130 mmHg, there was a decrease of 9.7 mmHg following 15 min of mindful breathing at 6 breaths per minute. When measured over several days, the resting systolic pressure decreased by an average of 4.3 mmHg. Discussion: Mindful breathing for 15 min a day, at a rate of 6 breaths per minute is effective in lowering blood pressure, and has both an immediate, and a short term effect (over several days). This large scale study demonstrates that device guided mindful breathing with a consumer wearable for 15 min a day is effective in lowering blood pressure, and a helpful complement to the standard of care.

Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19.

The COVID-19 disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become one of the worst global pandemics of the century. Wearable devices are well suited for continuously measuring heart rate. Here we show that the Resting Heart Rate is modified for several weeks following a COVID-19 infection. The Resting Heart Rate shows 3 phases: 1) elevated during symptom onset, with average peak increases relative to the baseline of 1.8% (3.4%) for females (males), 2) decrease thereafter, reaching a minimum on average ≈13 days after symptom onset, and 3) subsequent increase, reaching a second peak on average ≈28 days from symptom onset, before falling back to the baseline ≈112 days from symptom onset. All estimates vary with disease severity.

Gastrointestinal symptoms and fecal shedding of SARS-CoV-2 RNA suggest prolonged gastrointestinal infection.

Background: COVID-19 manifests with respiratory, systemic, and gastrointestinal (GI) symptoms.1, SARS-CoV-2 RNA is detected in respiratory and fecal samples, and recent reports demonstrate viral replication in both the lung and intestinal tissue.2, 3, 4 Although much is known about early fecal RNA shedding, little is known about long-term shedding, especially in those with mild COVID-19. Furthermore, most reports of fecal RNA shedding do not correlate these findings with GI symptoms.5. Methods: We analyzed the dynamics of fecal RNA shedding up to 10 months after COVID-19 diagnosis in 113 individuals with mild to moderate disease. We also correlated shedding with disease symptoms. Findings: Fecal SARS-CoV-2 RNA is detected in 49.2% [95% confidence interval, 38.2%-60.3%] of participants within the first week after diagnosis. Whereas there was no ongoing oropharyngeal SARS-CoV-2 RNA shedding in subjects at 4 months, 12.7% [8.5%-18.4%] of participants continued to shed SARS-CoV-2 RNA in the feces at 4 months after diagnosis and 3.8% [2.0%-7.3%] shed at 7 months. Finally, we found that GI symptoms (abdominal pain, nausea, vomiting) are associated with fecal shedding of SARS-CoV-2 RNA. Conclusions: The extended presence of viral RNA in feces, but not in respiratory samples, along with the association of fecal viral RNA shedding with GI symptoms suggest that SARS-CoV-2 infects the GI tract and that this infection can be prolonged in a subset of individuals with COVID-19. Funding: This research was supported by a Stanford ChemH-IMA grant; fellowships from the AACR and NSF; and NIH R01-AI148623, R01-AI143757, and UL1TR003142.

Heart rate variability during mindful breathing meditation.

We discuss Heart Rate Variability (HRV) measured during mindful breathing meditation. We provide a pedagogical computation of two commonly used heart rate variability metrics, i.e. the root mean square of successive differences (RMSSD) and the standard deviation of RR intervals (SDRR), in terms of Fourier components. It is shown that the root mean square of successive differences preferentially weights higher frequency Fourier modes, making it unsuitable as a biosignal for mindful breathing meditation which encourages slow breathing. We propose a new metric called the autonomic balance index (ABI) which uses Respiratory Sinus Arrhythmia to quantify the fraction of heart rate variability contributed by the parasympathetic nervous system. We apply this metric to heart rate variability data collected during two different meditation techniques, and show that the autonomic balance index is significantly elevated during mindful breathing, making it a good signal for biofeedback during meditation sessions.

Standardized preservation, extraction and quantification techniques for detection of fecal SARS-CoV-2 RNA.

Patients with COVID-19 shed SARS-CoV-2 RNA in stool, sometimes well after their respiratory infection has cleared. This may be significant for patient health, epidemiology, and diagnosis. However, methods to preserve stool, and to extract and quantify viral RNA are not standardized. We test the performance of three preservative approaches at yielding detectable SARS-CoV-2 RNA: the OMNIgene-GUT kit, Zymo DNA/RNA shield kit, and the most commonly applied, storage without preservative. We test these in combination with three extraction kits: QIAamp Viral RNA Mini Kit, Zymo Quick-RNA Viral Kit, and MagMAX Viral/Pathogen Kit. We also test the utility of ddPCR and RT-qPCR for the reliable quantification of SARS-CoV-2 RNA from stool. We identify that the Zymo DNA/RNA preservative and the QiaAMP extraction kit yield more detectable RNA than the others, using both ddPCR and RT-qPCR. Taken together, we recommend a comprehensive methodology for preservation, extraction and detection of RNA from SARS-CoV-2 and other coronaviruses in stool.

Measurement of respiratory rate using wearable devices and applications to COVID-19 detection.

We show that heart rate enabled wearable devices can be used to measure respiratory rate. Respiration modulates the heart rate creating excess power in the heart rate variability at a frequency equal to the respiratory rate, a phenomenon known as respiratory sinus arrhythmia. We isolate this component from the power spectral density of the heart beat interval time series, and show that the respiratory rate thus estimated is in good agreement with a validation dataset acquired from sleep studies (root mean squared error = 0.648 min-1, mean absolute error = 0.46 min-1, mean absolute percentage error = 3%). We use this respiratory rate algorithm to illuminate two potential applications (a) understanding the distribution of nocturnal respiratory rate as a function of age and sex, and (b) examining changes in longitudinal nocturnal respiratory rate due to a respiratory infection such as COVID-19. 90% of respiratory rate values for healthy adults fall within the range 11.8-19.2 min-1 with a mean value of 15.4 min-1. Respiratory rate is shown to increase with nocturnal heart rate. It also varies with BMI, reaching a minimum at 25 kg/m2, and increasing for lower and higher BMI. The respiratory rate decreases slightly with age and is higher in females compared to males for age <50 years, with no difference between females and males thereafter. The 90% range for the coefficient of variation in a 14 day period for females (males) varies from 2.3-9.2% (2.3-9.5%) for ages 20-24 yr, to 2.5-16.8% (2.7-21.7%) for ages 65-69 yr. We show that respiratory rate is often elevated in subjects diagnosed with COVID-19. In a 7 day window from D-1 to D+5 (where D0 is the date when symptoms first present, for symptomatic individuals, and the test date for asymptomatic cases), we find that 36.4% (23.7%) of symptomatic (asymptomatic) individuals had at least one measurement of respiratory rate 3 min-1 higher than the regular rate.

Standardized and optimized preservation, extraction and quantification techniques for detection of fecal SARS-CoV-2 RNA.

COVID-19 patients shed SARS-CoV-2 viral RNA in their stool, sometimes well after they have cleared their respiratory infection. This feature of the disease may be significant for patient health, epidemiology, and diagnosis. However, to date, methods to preserve stool samples from COVID patients, and to extract and quantify viral RNA concentration have yet to be optimized. We sought to meet this urgent need by developing and benchmarking a standardized protocol for the fecal detection of SARS-CoV-2 RNA. We test three preservative conditions for their ability to yield detectable SARS-CoV-2 RNA: OMNIgene-GUT, Zymo DNA/RNA shield kit, and the most common condition, storage without any preservative. We test these in combination with three extraction kits: the QIAamp Viral RNA Mini Kit, Zymo Quick-RNA Viral Kit, and MagMAX Viral/Pathogen Kit. Finally, we also test the utility of two detection methods, ddPCR and RT-qPCR, for the robust quantification of SARS-CoV-2 viral RNA from stool. We identify that the Zymo DNA/RNA shield collection kit and the QiaAMP viral RNA mini kit yield more detectable RNA than the others, using both ddPCR and RT-qPCR assays. We also demonstrate key features of experimental design including the incorporation of appropriate controls and data analysis, and apply these techniques to effectively extract viral RNA from fecal samples acquired from COVID-19 outpatients enrolled in a clinical trial. Finally, we recommend a comprehensive methodology for future preservation, extraction and detection of RNA from SARS-CoV-2 and other coronaviruses in stool.

Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study.

BACKGROUND: Heart rate variability, or the variation in the time interval between consecutive heart beats, is a non-invasive dynamic metric of the autonomic nervous system and an independent risk factor for cardiovascular death. Consumer wrist-worn tracking devices using photoplethysmography, such as Fitbit, now provide the unique potential of continuously measuring surrogates of sympathetic and parasympathetic nervous system activity through the analysis of interbeat intervals. We aimed to leverage wrist-worn trackers to derive and describe diverse measures of cardiac autonomic function among Fitbit device users. METHODS: In this cross-sectional study, we collected interbeat interval data that are sent to a central database from Fitbit devices during a randomly selected 24 h period. Age, sex, body-mass index, and steps per day in the 90 days preceding the measurement were extracted. Interbeat interval data were cleaned and heart rate variability features were computed. We analysed heart rate variability metrics across the time (measured via the root mean square of successive RR interval differences [RMSSD] and SD of the RR interval [SDRR]), frequency (measured by high-frequency and low-frequency power), and graphical (measured by Poincare plots) domains. We considered 5 min windows for the time and frequency domain metrics and 60 min measurements for graphical domain metrics. Data from participants were analysed to establish the correlation between heart rate variability metrics and age, sex, time of day, and physical activity. We also determined benchmarks for heart rate variability (HRV) metrics among the users. FINDINGS: We included data from 8 203 261 Fitbit users, collected on Sept 1, 2018. HRV metrics decrease with age, and parasympathetic function declines faster than sympathetic function. We observe a strong diurnal variation in the heart rate variability. SDRR, low-frequency power, and Poincare S2 show a significant variation with sex, whereas such a difference is not seen with RMSSD, high-frequency power, and Poincare S1. For males, when measured from 0600 h to 0700 h, the mean low-frequency power decreased by a factor of 66·5% and high-frequency power decreased by a factor of 82·0% from the age of 20 years to 60 years. For females, the equivalent factors were 69·3% and 80·9%, respectively. Comparing low-frequency power between males and females at the ages of 40-41 years, measured from 0600 h to 0700 h, we found excess power in males, with a Cohen's d effect size of 0·33. For high-frequency power, the equivalent effect size was -0·04. Increased daily physical activity, across age and sex, was highly correlated with improvement in diverse measures of heart rate variability in a dose-dependent manner. We provide benchmark tables for RMSSD, SDRR, high and low frequency powers, and Poincare S1 and S2, separately for different ages and sex and computed at two times of the day. INTERPRETATION: Diverse metrics of cardiac autonomic health can be derived from wrist-worn trackers. Empirical distributions of heart rate variability can potentially be used as a framework for individual-level interpretation. Increased physical activity might yield improvement in heart rate variability and requires prospective trials for confirmation. FUNDING: Fitbit.

Assessment of physiological signs associated with COVID-19 measured using wearable devices.

Respiration rate, heart rate, and heart rate variability (HRV) are some health metrics that are easily measured by consumer devices, which can potentially provide early signs of illness. Furthermore, mobile applications that accompany wearable devices can be used to collect relevant self-reported symptoms and demographic data. This makes consumer devices a valuable tool in the fight against the COVID-19 pandemic. Data on 2745 subjects diagnosed with COVID-19 (active infection, PCR test) were collected from May 21 to September 11, 2020, consisting of PCR positive tests conducted between February 16 and September 9. Considering male (female) participants, 11.9% (11.2%) of the participants were asymptomatic, 48.3% (47.8%) recovered at home by themselves, 29.7% (33.7%) recovered at home with the help of someone else, 9.3% (6.6%) required hospitalization without ventilation, and 0.5% (0.4%) required ventilation. There were a total of 21 symptoms reported, and the prevalence of symptoms varies by sex. Fever was present in 59.4% of male subjects and in 52% of female subjects. Based on self-reported symptoms alone, we obtained an AUC of 0.82 ± 0.017 for the prediction of the need for hospitalization. Based on physiological signs, we obtained an AUC of 0.77 ± 0.018 for the prediction of illness on a specific day. Respiration rate and heart rate are typically elevated by illness, while HRV is decreased. Measuring these metrics, taken in conjunction with molecular-based diagnostics, may lead to better early detection and monitoring of COVID-19.

Engineering orthogonal human O-linked glycoprotein biosynthesis in bacteria.

A major objective of synthetic glycobiology is to re-engineer existing cellular glycosylation pathways from the top down or construct non-natural ones from the bottom up for new and useful purposes. Here, we have developed a set of orthogonal pathways for eukaryotic O-linked protein glycosylation in Escherichia coli that installed the cancer-associated mucin-type glycans Tn, T, sialyl-Tn and sialyl-T onto serine residues in acceptor motifs derived from different human O-glycoproteins. These same glycoengineered bacteria were used to supply crude cell extracts enriched with glycosylation machinery that permitted cell-free construction of O-glycoproteins in a one-pot reaction. In addition, O-glycosylation-competent bacteria were able to generate an antigenically authentic Tn-MUC1 glycoform that exhibited reactivity with antibody 5E5, which specifically recognizes cancer-associated glycoforms of MUC1. We anticipate that the orthogonal glycoprotein biosynthesis pathways developed here will provide facile access to structurally diverse O-glycoforms for a range of important scientific and therapeutic applications.

A cell-free biosynthesis platform for modular construction of protein glycosylation pathways.

Glycosylation plays important roles in cellular function and endows protein therapeutics with beneficial properties. However, constructing biosynthetic pathways to study and engineer precise glycan structures on proteins remains a bottleneck. Here, we report a modular, versatile cell-free platform for glycosylation pathway assembly by rapid in vitro mixing and expression (GlycoPRIME). In GlycoPRIME, glycosylation pathways are assembled by mixing-and-matching cell-free synthesized glycosyltransferases that can elaborate a glucose primer installed onto protein targets by an N-glycosyltransferase. We demonstrate GlycoPRIME by constructing 37 putative protein glycosylation pathways, creating 23 unique glycan motifs, 18 of which have not yet been synthesized on proteins. We use selected pathways to synthesize a protein vaccine candidate with an α-galactose adjuvant motif in a one-pot cell-free system and human antibody constant regions with minimal sialic acid motifs in glycoengineered Escherichia coli. We anticipate that these methods and pathways will facilitate glycoscience and make possible new glycoengineering applications.

Glyco-recoded Escherichia coli: Recombineering-based genome editing of native polysaccharide biosynthesis gene clusters.

Recombineering-based redesign of bacterial genomes by adding, removing or editing large segments of genomic DNA is emerging as a powerful technique for expanding the range of functions that an organism can perform. Here, we describe a glyco-recoding strategy whereby major non-essential polysaccharide gene clusters in K-12 Escherichia coli are replaced with orthogonal glycosylation components for both biosynthesis of heterologous glycan structures and site-specific glycan conjugation to target proteins. Specifically, the native enterobacterial common antigen (ECA) and O-polysaccharide (O-PS) antigen loci were systematically replaced with ∼9-10 kbp of synthetic DNA encoding Campylobacter jejuni enzymes required for asparagine-linked (N-linked) protein glycosylation. Compared to E. coli cells carrying the same glycosylation machinery on extrachromosomal plasmids, glyco-recoded strains attached glycans to acceptor protein targets with equal or greater efficiency while exhibiting markedly better growth phenotypes and higher glycoprotein titers. Overall, our results define a convenient and reliable framework for bacterial glycome editing that provides a more stable route for chemical diversification of proteins in vivo and effectively expands the bacterial glycoengineering toolkit.

Author Correction: Single-pot glycoprotein biosynthesis using a cell-free transcription-translation system enriched with glycosylation machinery.

The original version of this Article contained an error in Figure 2, wherein the bottom right western blot panel in Figure 2a was blank. This has now been corrected in both the PDF and HTML versions of the Article.

Single-pot glycoprotein biosynthesis using a cell-free transcription-translation system enriched with glycosylation machinery.

The emerging discipline of bacterial glycoengineering has made it possible to produce designer glycans and glycoconjugates for use as vaccines and therapeutics. Unfortunately, cell-based production of homogeneous glycoproteins remains a significant challenge due to cell viability constraints and the inability to control glycosylation components at precise ratios in vivo. To address these challenges, we describe a novel cell-free glycoprotein synthesis (CFGpS) technology that seamlessly integrates protein biosynthesis with asparagine-linked protein glycosylation. This technology leverages a glyco-optimized Escherichia coli strain to source cell extracts that are selectively enriched with glycosylation components, including oligosaccharyltransferases (OSTs) and lipid-linked oligosaccharides (LLOs). The resulting extracts enable a one-pot reaction scheme for efficient and site-specific glycosylation of target proteins. The CFGpS platform is highly modular, allowing the use of multiple distinct OSTs and structurally diverse LLOs. As such, we anticipate CFGpS will facilitate fundamental understanding in glycoscience and make possible applications in on demand biomanufacturing of glycoproteins.

Metabolic engineering of glycoprotein biosynthesis in bacteria.

The demonstration more than a decade ago that glycoproteins could be produced in Escherichia coli cells equipped with the N-linked protein glycosylation machinery from Campylobacter jejuni opened the door to using simple bacteria for the expression and engineering of complex glycoproteins. Since that time, metabolic engineering has played an increasingly important role in developing and optimizing microbial cell glyco-factories for the production of diverse glycoproteins and other glycoconjugates. It is becoming clear that future progress in creating efficient glycoprotein expression platforms in bacteria will depend on the adoption of advanced strain engineering strategies such as rational design and assembly of orthogonal glycosylation pathways, genome-wide identification of metabolic engineering targets, and evolutionary engineering of pathway performance. Here, we highlight recent advances in the deployment of metabolic engineering tools and strategies to develop microbial cell glyco-factories for the production of high-value glycoprotein targets with applications in research and medicine.