Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study).

Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection. A wearable medical device measured breathing rate, heart rate, heart rate variability, and wrist skin temperature in 1163 participants (mean age = 44.1 years, standard deviation [SD] = 5.6; 667 [57%] females). Participants reported daily symptoms and confounders in a complementary app. A machine learning algorithm retrospectively ingested daily biophysical parameters to detect COVID-19 infections. COVID-19 serology samples were collected from all participants at baseline and follow-up. We analysed potential sex-specific differences in physiology and antibody titres using multilevel modelling and t-tests. Over 1.5 million hours of physiological data were recorded. During the symptomatic period of infection, men demonstrated larger increases in skin temperature, breathing rate, and heart rate as well as larger decreases in heart rate variability than women. The COVID-19 infection detection algorithm performed similarly well for men and women. Our study belongs to the first research to provide evidence for differential physiological responses to COVID-19 between females and males, highlighting the potential of wearable technology to inform future precision medicine approaches.

Droplet-interfaced microchip and capillary electrophoretic separations.

Both capillary and chip-based electrophoresis are powerful separation methods widely used for the separation of complex analytical mixtures in the fields of genomics, proteomics, metabolomics, and cellular analysis. However their utility as basic tools in high-throughput analysis and multidimensional separations has been hampered by inefficient or biased sample injection methods. Herein, we address this problem through the development of a simple separation platform that incorporates droplet-based microfluidic module for the encapsulation of analytes prior to the analytical separation. This method allows for the precise and reproducible injection of pL to nL volume isolated plugs into an electrophoretic separation channel. The developed platform is free from inter sample contamination, allows for small sample size, high-throughput analysis, and can provide quantitative analytical information.

Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP).

OBJECTIVES: We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device. DESIGN: Interim analysis of a prospective cohort study. SETTING, PARTICIPANTS AND INTERVENTIONS: Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays. RESULTS: A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO. CONCLUSION: Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial. Trial registration number ISRCTN51255782; Pre-results.

High-resolution local imaging of temperature in dielectrophoretic platforms.

The use of dielectrophoretic forces is crucially tied to the knowledge of Joule heating within a fluid, since the use of planar microelectrodes creates a temperature gradient within which the particle of interest is manipulated. Mapping temperature with sufficient spatial resolution within a dielectrophoretic trap is recognized to be of high importance. Herein, we demonstrate local temperature measurements in the vicinity of a trapped micrometer-size particle using confocal fluorescence spectroscopy. Such measurements are shown to provide a novel calibration tool for screening temperature-mediated processes with high resolution.

CE-based sample quality assessment prior to 2-D gel electrophoresis: towards the standardization of gel-based proteomics.

2-DE remains one of the most commonly used separation techniques for complex protein mixtures. This article describes a new approach to 2-DE sample assessment using SDS capillary gel electrophoresis (in Beckman Coulter sieving medium) combined with multi-pixel detection. The performance of this platform was investigated using protein samples prepared for 2-DE. The capability to characterize 2-DE sample was tested and the results show that the repeatability of peak migration time and intensity are better than 2% RSD. The system gives good resolution, accurate molecular mass assignment, as well as absolute and relative quantification of proteins. Notably, this study also demonstrates the use of this platform to screen the quality of simple and complex 2-DE samples. Implementation of this technique in the proteomics workflow will not only improve the success rate of 2-DE, but will also enable sample verification before 2-DE and allow the relative quantification of proteins. The validation of differential protein expression is also demonstrated using the combined information of relative molecular mass and relative quantification. It is the first time that a rapid and visual evaluation method is reported for the quality assessment of 2-DE samples.

Antibody testing for COVID-19: A report from the National COVID Scientific Advisory Panel.

Background: The COVID-19 pandemic caused >1 million infections during January-March 2020. There is an urgent need for reliable antibody detection approaches to support diagnosis, vaccine development, safe release of individuals from quarantine, and population lock-down exit strategies. We set out to evaluate the performance of ELISA and lateral flow immunoassay (LFIA) devices. Methods: We tested plasma for COVID (severe acute respiratory syndrome coronavirus 2; SARS-CoV-2) IgM and IgG antibodies by ELISA and using nine different LFIA devices. We used a panel of plasma samples from individuals who have had confirmed COVID infection based on a PCR result (n=40), and pre-pandemic negative control samples banked in the UK prior to December-2019 (n=142). Results: ELISA detected IgM or IgG in 34/40 individuals with a confirmed history of COVID infection (sensitivity 85%, 95%CI 70-94%), vs. 0/50 pre-pandemic controls (specificity 100% [95%CI 93-100%]). IgG levels were detected in 31/31 COVID-positive individuals tested ≥10 days after symptom onset (sensitivity 100%, 95%CI 89-100%). IgG titres rose during the 3 weeks post symptom onset and began to fall by 8 weeks, but remained above the detection threshold. Point estimates for the sensitivity of LFIA devices ranged from 55-70% versus RT-PCR and 65-85% versus ELISA, with specificity 95-100% and 93-100% respectively. Within the limits of the study size, the performance of most LFIA devices was similar. Conclusions: Currently available commercial LFIA devices do not perform sufficiently well for individual patient applications. However, ELISA can be calibrated to be specific for detecting and quantifying SARS-CoV-2 IgM and IgG and is highly sensitive for IgG from 10 days following first symptoms.

CE of dsDNA in low-molecular-weight polyethylene oxide solutions.

To realise effective size separations of nucleic acid fragments using CE, gel-based matrices are commonly employed. The separation of label-free dsDNA ladders and plasmid fragments in an uncross-linked semi-dilute poly (ethylene) oxide solution using multi-pixel UV detection at 254 nm is reported. Improvements in the sensitivity of UV detection of dsDNA using signal averaging over multiple pixels is demonstrated. Separations performed using a diode array detector also allow the progress of the separation to be monitored as a function of time. Several polymers were examined including methyl cellulose, linear polyacrylamide, hydroxy (propyl) methylcellulose and polyethylene oxide. Operations parameters investigated included UV transparency, self-coating capacity and separation efficiency. The results show complete resolution of all fragments under a range of conditions, including short separation lengths.

Droplet-based in situ compartmentalization of chemically separated components after isoelectric focusing in a Slipchip.

Isoelectric focusing (IEF) is a powerful and widely used technique for protein separation and purification. There are many embodiments of microscale IEF that use capillary or microfluidic chips for the analysis of small sample volumes. Nevertheless, collecting the separated sample volumes without causing remixing remains a challenge. Herein, we describe a microfluidic Slipchip device that is able to efficiently compartmentalize focused analyte bands in situ into microdroplets. The device contains a microfluidic "zig-zag" separation channel that is composed of a sequence of wells formed in the two halves of the Slipchip. The analytes are focused in the channel and then compartmentalised into droplets by slipping the chip. Importantly, sample droplets can be analysed on chip or collected for subsequent analysis using electrophoresis or mass spectrometry for example. To demonstrate this approach, we perform IEF separation using standard markers and protein samples, with on-chip post-processing. Compared to alternative approaches for sample collection, the method avoids remixing, is scalable and is easily hyphenated with the other analytical methods.

A nano LC-MALDI mass spectrometry droplet interface for the analysis of complex protein samples.

The integration of matrix-assisted laser desorption ionization (MALDI) mass spectrometry with an upstream analytical separations (such as liquid chromatography and electrophoresis) has opened up new opportunities for the automated investigation of complex protein and peptide mixtures. The ability to efficiently analyze complex proteomic mixtures in this manner is primarily determined by the ability to preserve spatial discrimination of sample components as they leave the separation column. Current interfacing methods are problematic in this respect since minimum fraction volumes are limited to several microliters. Herein we show for the first time an LC-MALDI interface based on the formation, processing and destruction of a segmented flow. The interface consists of a droplet-generator to fractionate LC effluent into nL-volume droplets and a deposition probe that transfers the sample (and MALDI matrix) onto a conventional MALDI-MS target. The efficacy of the method is demonstrated through the analysis of Trypsin digests of both BSA and Cytochrome C, with a 50% enhancement in analytical performance when compared to conventional interface technology.