Phylogenies of the 16S rRNA gene and its hypervariable regions lack concordance with core genome phylogenies.

BACKGROUND: The 16S rRNA gene is used extensively in bacterial phylogenetics, in species delineation, and now widely in microbiome studies. However, the gene suffers from intragenomic heterogeneity, and reports of recombination and an unreliable phylogenetic signal are accumulating. Here, we compare core gene phylogenies to phylogenies constructed using core gene concatenations to estimate the strength of signal for the 16S rRNA gene, its hypervariable regions, and all core genes at the intra- and inter-genus levels. Specifically, we perform four intra-genus analyses (Clostridium, n = 65; Legionella, n = 47; Staphylococcus, n = 36; and Campylobacter, n = 17) and one inter-genus analysis [41 core genera of the human gut microbiome (31 families, 17 orders, and 12 classes), n = 82]. RESULTS: At both taxonomic levels, the 16S rRNA gene was recombinant and subject to horizontal gene transfer. At the intra-genus level, the gene showed one of the lowest levels of concordance with the core genome phylogeny (50.7% average). Concordance for hypervariable regions was lower still, with entropy masking providing little to no benefit. A major factor influencing concordance was SNP count, which showed a positive logarithmic association. Using this relationship, we determined that 690 ± 110 SNPs were required for 80% concordance (average 16S rRNA gene SNP count was 254). We also found a wide range in 16S-23S-5S rRNA operon copy number among genomes (1-27). At the inter-genus level, concordance for the whole 16S rRNA gene was markedly higher (73.8% - 10th out of 49 loci); however, the most concordant hypervariable regions (V4, V3-V4, and V1-V2) ranked in the third quartile (62.5 to 60.0%). CONCLUSIONS: Ramifications of a poor phylogenetic performance for the 16S rRNA gene are far reaching. For example, in addition to incorrect species/strain delineation and phylogenetic inference, it has the potential to confound community diversity metrics if phylogenetic information is incorporated - for example, with popular approaches such as Faith's phylogenetic diversity and UniFrac. Our results highlight the problematic nature of these approaches and their use (along with entropy masking) is discouraged. Lastly, the wide range in 16S rRNA gene copy number among genomes also has a strong potential to confound diversity metrics. Video Abstract.

Characterization and utility of immobilized metal affinity-functionalized cellulose membranes for point-of-care malaria diagnostics.

Immobilized metal affinity chromatography (IMAC) is a well-established technique for protein separation and purification. IMAC has been previously utilized to capture the malaria biomarker histidine-rich protein 2 (HRP2) from blood, enhancing the sensitivity of field-appropriate diagnostic tools such as lateral flow assays. However, little work has been done to translate this technique to a truly field-usable design. In this study, IMAC-functionalized cellulose membranes are created and characterized fully for future use in applied malaria diagnostics. IMAC-functionalized cellulose membranes were investigated across a range of cellulose substrates, IMAC ligands, and divalent transition metals before use in a capture and elution flowthrough workflow. Following characterization and optimization, it was found that iminodiacetic acid bound to Zn(II) was the most promising ligand-metal pair, with three available coordination sites and a molar loading capacity of 57.7 µmol of metal/cm3 of cellulose. Using these parameters, more than 99% of HRP2 was captured from a large-volume lysed blood sample in a simple flow-through assay and 89% of the captured protein was eluted from the membrane using the chelating compound ethylenediaminetetraacetic acid. Use of this enhancement protocol on an in-house HRP2 lateral flow assay (LFA) yielded a limit of detection of 7 parasites/µL, a 15.8x enhancement factor compared to traditional LFA methods.

mHAT app for automated malaria rapid test result analysis and aggregation: a pilot study.

BACKGROUND: There are a variety of approaches being used for malaria surveillance. While active and reactive case detection have been successful in localized areas of low transmission, concerns over scalability and sustainability keep the approaches from being widely accepted. Mobile health interventions are poised to address these shortcomings by automating and standardizing portions of the surveillance process. In this study, common challenges associated with current data aggregation methods have been quantified, and a web-based mobile phone application is presented to reduce the burden of reporting rapid diagnostic test (RDT) results in low-resource settings. METHODS: De-identified completed RDTs were collected at 14 rural health clinics as part of a malaria epidemiology study at Macha Research Trust, Macha, Zambia. Tests were imaged using the mHAT web application. Signal intensity was measured and a binary result was provided. App performance was validated by: (1) comparative limits of detection, investigated against currently used laboratory lateral flow assay readers; and, (2) receiver operating characteristic analysis comparing the application against visual inspection of RDTs by an expert. Secondary investigations included analysis of time-to-aggregation and data consistency within the existing surveillance structures established by Macha Research Trust. RESULTS: When compared to visual analysis, the mHAT app performed with 91.9% sensitivity (CI 78.7, 97.2) and specificity was 91.4% (CI 77.6, 97.0) regardless of device operating system. Additionally, an analysis of surveillance data from January 2017 through mid-February 2019 showed that while the majority of the data packets from satellite clinics contained correct data, 36% of data points required correction by verification teams. Between November 2018 and mid-February 2019, it was also found that 44.8% of data was received after the expected submission date, although most (65.1%) reports were received within 2 days. CONCLUSIONS: Overall, the mHAT mobile app was observed to be sensitive and specific when compared to both currently available benchtop lateral flow readers and visual inspection. The additional benefit of automating and standardizing LFA data collection and aggregation poses a vital improvement for low-resource health facilities and could increase the accuracy and speed of data reporting in surveillance campaigns.

Understanding On-Campus Interactions With a Semiautomated, Barcode-Based Platform to Augment COVID-19 Contact Tracing: App Development and Usage.

BACKGROUND: The COVID-19 pandemic has forced drastic changes to daily life, from the implementation of stay-at-home orders to mandating facial coverings and limiting in-person gatherings. While the relaxation of these control measures has varied geographically, it is widely agreed that contact tracing efforts will play a major role in the successful reopening of businesses and schools. As the volume of positive cases has increased in the United States, it has become clear that there is room for digital health interventions to assist in contact tracing. OBJECTIVE: The goal of this study was to evaluate the use of a mobile-friendly app designed to supplement manual COVID-19 contact tracing efforts on a university campus. Here, we present the results of a development and validation study centered around the use of the MyCOVIDKey app on the Vanderbilt University campus during the summer of 2020. METHODS: We performed a 6-week pilot study in the Stevenson Center Science and Engineering Complex on Vanderbilt University's campus in Nashville, TN. Graduate students, postdoctoral fellows, faculty, and staff >18 years who worked in Stevenson Center and had access to a mobile phone were eligible to register for a MyCOVIDKey account. All users were encouraged to complete regular self-assessments of COVID-19 risk and to key in to sites by scanning a location-specific barcode. RESULTS: Between June 17, 2020, and July 29, 2020, 45 unique participants created MyCOVIDKey accounts. These users performed 227 self-assessments and 1410 key-ins. Self-assessments were performed by 89% (n=40) of users, 71% (n=32) of users keyed in, and 48 unique locations (of 71 possible locations) were visited. Overall, 89% (202/227) of assessments were determined to be low risk (ie, asymptomatic with no known exposures), and these assessments yielded a CLEAR status. The remaining self-assessments received a status of NOT CLEAR, indicating either risk of exposure or symptoms suggestive of COVID-19 (7.5% [n=17] and 3.5% [n=8] of self-assessments indicated moderate and high risk, respectively). These 25 instances came from 8 unique users, and in 19 of these instances, the at-risk user keyed in to a location on campus. CONCLUSIONS: Digital contact tracing tools may be useful in assisting organizations to identify persons at risk of COVID-19 through contact tracing, or in locating places that may need to be cleaned or disinfected after being visited by an index case. Incentives to continue the use of such tools can improve uptake, and their continued usage increases utility to both organizational and public health efforts. Parameters of digital tools, including MyCOVIDKey, should ideally be optimized to supplement existing contact tracing efforts. These tools represent a critical addition to manual contact tracing efforts during reopening and sustained regular activity.

App Use and Usability of a Barcode-Based Digital Platform to Augment COVID-19 Contact Tracing: Postpilot Survey and Paradata Analysis.

BACKGROUND: The COVID-19 pandemic has drastically changed life in the United States, as the country has recorded over 23 million cases and 383,000 deaths to date. In the leadup to widespread vaccine deployment, testing and surveillance are critical for detecting and stopping possible routes of transmission. Contact tracing has become an important surveillance measure to control COVID-19 in the United States, and mobile health interventions have found increased prominence in this space. OBJECTIVE: The aim of this study was to investigate the use and usability of MyCOVIDKey, a mobile-based web app to assist COVID-19 contact tracing efforts, during the 6-week pilot period. METHODS: A 6-week study was conducted on the Vanderbilt University campus in Nashville, Tennessee. The study participants, consisting primarily of graduate students, postdoctoral researchers, and faculty in the Chemistry Department at Vanderbilt University, were asked to use the MyCOVIDKey web app during the course of the study period. Paradata were collected as users engaged with the MyCOVIDKey web app. At the end of the study, all participants were asked to report on their user experience in a survey, and the results were analyzed in the context of the user paradata. RESULTS: During the pilot period, 45 users enrolled in MyCOVIDKey. An analysis of their enrollment suggests that initial recruiting efforts were effective; however, participant recruitment and engagement efforts at the midpoint of the study were less effective. App use paralleled the number of users, indicating that incentives were useful for recruiting new users to sign up but did not result in users attempting to artificially inflate their use as a result of prize offers. Times to completion of key tasks were low, indicating that the main features of the app could be used quickly. Of the 45 users, 30 provided feedback through a postpilot survey, with 26 (58%) completing it in its entirety. The MyCOVIDKey app as a whole was rated 70.0 on the System Usability Scale, indicating that it performed above the accepted threshold for usability. When the key-in and self-assessment features were examined on their own, it was found that they individually crossed the same thresholds for acceptable usability but that the key-in feature had a higher margin for improvement. CONCLUSIONS: The MyCOVIDKey app was found overall to be a useful tool for COVID-19 contact tracing in a university setting. Most users suggested simple-to-implement improvements, such as replacing the web app framework with a native app format or changing the placement of the scanner within the app workflow. After these updates, this tool could be readily deployed and easily adapted to other settings across the country. The need for digital contact tracing tools is becoming increasingly apparent, particularly as COVID-19 case numbers continue to increase while more businesses begin to reopen.

Evaluating Network Readiness for mHealth Interventions Using the Beacon Mobile Phone App: Application Development and Validation Study.

BACKGROUND: Mobile health (mHealth) interventions have the potential to transform the global health care landscape. The processing power of mobile devices continues to increase, and growth of mobile phone use has been observed worldwide. Uncertainty remains among key stakeholders and decision makers as to whether global health interventions can successfully tap into this trend. However, when correctly implemented, mHealth can reduce geographic, financial, and social barriers to quality health care. OBJECTIVE: The aim of this study was to design and test Beacon, a mobile phone-based tool for evaluating mHealth readiness in global health interventions. Here, we present the results of an application validation study designed to understand the mobile network landscape in and around Macha, Zambia, in 2019. METHODS: Beacon was developed as an automated mobile phone app that continually collects spatiotemporal data and measures indicators of network performance. Beacon was used in and around Macha, Zambia, in 2019. Results were collected, even in the absence of network connectivity, and asynchronously uploaded to a database for further analysis. RESULTS: Beacon was used to evaluate three mobile phone networks around Macha. Carriers A and B completed 6820/7034 (97.0%) and 6701/7034 (95.3%) downloads and 1349/1608 (83.9%) and 1431/1608 (89.0%) uploads, respectively, while Carrier C completed only 62/1373 (4.5%) file downloads and 0/1373 (0.0%) file uploads. File downloads generally occurred within 4 to 12 seconds, and their maximum download speeds occurred between 2 AM and 5 AM. A decrease in network performance, demonstrated by increases in upload and download durations, was observed beginning at 5 PM and continued throughout the evening. CONCLUSIONS: Beacon was able to compare the performance of different cellular networks, show times of day when cellular networks experience heavy loads and slow down, and identify geographic "dead zones" with limited or no cellular service. Beacon is a ready-to-use tool that could be used by organizations that are considering implementing mHealth interventions in low- and middle-income countries but are questioning the feasibility of the interventions, including infrastructure and cost. It could also be used by organizations that are looking to optimize the delivery of an existing mHealth intervention with improved logistics management.

Inorganic Complexes and Metal-Based Nanomaterials for Infectious Disease Diagnostics.

Infectious diseases claim millions of lives each year. Robust and accurate diagnostics are essential tools for identifying those who are at risk and in need of treatment in low-resource settings. Inorganic complexes and metal-based nanomaterials continue to drive the development of diagnostic platforms and strategies that enable infectious disease detection in low-resource settings. In this review, we highlight works from the past 20 years in which inorganic chemistry and nanotechnology were implemented in each of the core components that make up a diagnostic test. First, we present how inorganic biomarkers and their properties are leveraged for infectious disease detection. In the following section, we detail metal-based technologies that have been employed for sample preparation and biomarker isolation from sample matrices. We then describe how inorganic- and nanomaterial-based probes have been utilized in point-of-care diagnostics for signal generation. The following section discusses instrumentation for signal readout in resource-limited settings. Next, we highlight the detection of nucleic acids at the point of care as an emerging application of inorganic chemistry. Lastly, we consider the challenges that remain for translation of the aforementioned diagnostic platforms to low-resource settings.