Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

BACKGROUND: A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. METHODS: Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. RESULTS: About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95–100) to 100% and specificity from 99% (95% CI 97–100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76–87) to 94% (95% CI 89–98) and specificity ranging from 76% (95% CI 70–82) to 92% (95% CI 88–96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. CONCLUSIONS: People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people. Trial Registration NCT04509713 (clinicaltrials.gov)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Genetic mechanisms of critical illness in Covid-19.

Host-mediated lung inflammation is present,1 and drives mortality,2 in critical illness caused by Covid-19. Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development.3 Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study(GWAS) in 2244 critically ill Covid-19 patients from 208 UK intensive care units (ICUs). We identify and replicate novel genome-wide significant associations, on chr12q24.13 (rs10735079, p=1.65 [Formula: see text] 10-8) in a gene cluster encoding antiviral restriction enzyme activators (OAS1, OAS2, OAS3), on chr19p13.2 (rs2109069, p=2.3 [Formula: see text] 10-12) near the gene encoding tyrosine kinase 2 (TYK2), on chr19p13.3 (rs2109069, p=3.98 [Formula: see text] 10-12) within the gene encoding dipeptidyl peptidase 9 (DPP9), and on chr21q22.1 (rs2236757, p=4.99 [Formula: see text] 10-8) in the interferon receptor gene IFNAR2. We identify potential targets for repurposing of licensed medications: using Mendelian randomisation we found evidence in support of a causal link from low expression of IFNAR2, and high expression of TYK2, to life-threatening disease; transcriptome-wide association in lung tissue revealed that high expression of the monocyte/macrophage chemotactic receptor CCR2 is associated with severe Covid-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms, and mediators of inflammatory organ damage in Covid-19. Both mechanisms may be amenable to targeted treatment with existing drugs. Large-scale randomised clinical trials will be essential before any change to clinical practice

A multicentre, randomised controlled trial to compare the clinical and cost-effectiveness of Lee Silverman Voice Treatment versus standard NHS Speech and Language Therapy versus control in Parkinson’s disease: a study protocol for a randomised controlled trial

Abstract: Background: Parkinson’s disease (PD) affects approximately 145,519 people in the UK. Speech impairments are common with a reported prevalence of 68%, which increase physical and mental demands during conversation, reliance on family and/or carers, and the likelihood of social withdrawal reducing quality of life. In the UK, two approaches to Speech and Language Therapy (SLT) intervention are commonly available: National Health Service (NHS) SLT or Lee Silverman Voice Treatment (LSVT LOUD®). NHS SLT is tailored to the individuals’ needs per local practice typically consisting of six to eight weekly sessions; LSVT LOUD® comprises 16 sessions of individual treatment with home-based practice over 4 weeks. The evidence-base for their effectiveness is inconclusive. Methods/design: PD COMM is a phase III, multicentre, three-arm, unblinded, randomised controlled trial. Five hundred and forty-six people with idiopathic PD, reporting speech or voice problems will be enrolled. We will exclude those with a diagnosis of dementia, laryngeal pathology or those who have received SLT for speech problems in the previous 2 years. Following informed consent and completion of baseline assessments, participants will be randomised in a 1:1:1 ratio to no-intervention control, NHS SLT or LSVT LOUD® via a central computer-generated programme, using a minimisation procedure with a random element, to ensure allocation concealment. Participants randomised to the intervention groups will start treatment within 4 (NHS SLT) or 7 (LSVT LOUD®) weeks of randomisation. Primary outcome: Voice Handicap Index (VHI) total score at 3 months. Secondary outcomes include: VHI subscales, Parkinson’s Disease Questionnaire-39; Questionnaire on Acquired Speech Disorders; EuroQol-5D-5 L; ICECAP-O; resource utilisation; adverse events and carer quality of life. Mixed-methods process and health economic evaluations will take place alongside the trial. Assessments will be completed before randomisation and at 3, 6 and 12 months after randomisation. The trial started in December 2015 and will run for 77 months. Recruitment will take place in approximately 42 sites around the UK. Discussion: The trial will test the hypothesis that SLT is effective for the treatment of speech or voice problems in people with PD compared to no SLT. It will further test whether NHS SLT or LSVT LOUD® provide greater benefit and determine the cost-effectiveness of both interventions. Trial registration: International Standard Randomised Controlled Trials Number (ISRCTN) Registry, ID: 12421382. Registered on 18 April 2016

A multicentre, randomised controlled trial to compare the clinical and cost-effectiveness of Lee Silverman Voice Treatment versus standard NHS Speech and Language Therapy versus control in Parkinson’s disease: a study protocol for a randomised controlled trial

Abstract: Background: Parkinson’s disease (PD) affects approximately 145,519 people in the UK. Speech impairments are common with a reported prevalence of 68%, which increase physical and mental demands during conversation, reliance on family and/or carers, and the likelihood of social withdrawal reducing quality of life. In the UK, two approaches to Speech and Language Therapy (SLT) intervention are commonly available: National Health Service (NHS) SLT or Lee Silverman Voice Treatment (LSVT LOUD®). NHS SLT is tailored to the individuals’ needs per local practice typically consisting of six to eight weekly sessions; LSVT LOUD® comprises 16 sessions of individual treatment with home-based practice over 4 weeks. The evidence-base for their effectiveness is inconclusive. Methods/design: PD COMM is a phase III, multicentre, three-arm, unblinded, randomised controlled trial. Five hundred and forty-six people with idiopathic PD, reporting speech or voice problems will be enrolled. We will exclude those with a diagnosis of dementia, laryngeal pathology or those who have received SLT for speech problems in the previous 2 years. Following informed consent and completion of baseline assessments, participants will be randomised in a 1:1:1 ratio to no-intervention control, NHS SLT or LSVT LOUD® via a central computer-generated programme, using a minimisation procedure with a random element, to ensure allocation concealment. Participants randomised to the intervention groups will start treatment within 4 (NHS SLT) or 7 (LSVT LOUD®) weeks of randomisation. Primary outcome: Voice Handicap Index (VHI) total score at 3 months. Secondary outcomes include: VHI subscales, Parkinson’s Disease Questionnaire-39; Questionnaire on Acquired Speech Disorders; EuroQol-5D-5 L; ICECAP-O; resource utilisation; adverse events and carer quality of life. Mixed-methods process and health economic evaluations will take place alongside the trial. Assessments will be completed before randomisation and at 3, 6 and 12 months after randomisation. The trial started in December 2015 and will run for 77 months. Recruitment will take place in approximately 42 sites around the UK. Discussion: The trial will test the hypothesis that SLT is effective for the treatment of speech or voice problems in people with PD compared to no SLT. It will further test whether NHS SLT or LSVT LOUD® provide greater benefit and determine the cost-effectiveness of both interventions. Trial registration: International Standard Randomised Controlled Trials Number (ISRCTN) Registry, ID: 12421382. Registered on 18 April 2016

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

An evaluation of analytical stream to groundwater exchange models: a comparison of gross exchanges based on different spatial flow distribution assumptions

In this paper, a new method for estimating gross gains and losses between streams and groundwater is developed and evaluated against two existing approaches. These three stream to groundwater exchange (SGE) estimation methods are distinct in their assumptions on the spatial distribution of the inflowing and outflowing fluxes along the stream. The two existing methods assume that the fluxes are independent and in a specific sequence, while the third and newly derived method assumes that both fluxes occur simultaneously and uniformly throughout the stream. The analytic expressions in connection to the underlying assumptions are investigated through numerical stream simulations to evaluate the individual and mutual dynamics of the SGE estimation methods and to understand the causes for the differences in performance. The results show that the three methods produce significantly different results and that the mean absolute normalized error can have up to an order of magnitude difference between the methods. These differences between the SGE methods are entirely due to the assumptions of the SGE spatial dynamics of the methods, and the performance for a particular approach strongly decreases if its assumptions are not fulfilled. The assessment of the three methods through numerical simulations, representing a variety of SGE dynamics, shows that the method introduced, considering simultaneous stream gains and losses, presents overall the highest performance according to the simulations. As the existing methods provide the minimum and maximum realistic values of SGE within a stream reach, all three methods could be used in conjunction for a full range of estimates. These SGE methods can also be used in conjunction with other end-member mixing models to acquire even more hydrologic information as both require the same type of input data

An evaluation of analytical stream to groundwater exchange models: a comparison of gross exchanges based on different spatial flow distribution assumptions

In this paper, a new method for estimating gross gains and losses between streams and groundwater is developed and evaluated against two existing approaches. These three stream to groundwater exchange (SGE) estimation methods are distinct in their assumptions on the spatial Distribution of the inflowing and outflowing fluxes along the stream. The two existing methods assume that the fluxes are independent and in a specific sequence, while the third and newly derived method assumes that both fluxes occur simultaneously and uniformly throughout the stream. The analytic expressions in connection to the underlying assumptions are investigated through numerical stream simulations to evaluate the individual and mutual dynamics of the SGE estimation methods and to understand the causes for the differences in performance. The results show that the three methods produce significantly different results and that the mean absolute normalized error can have up to an order of magnitude difference between the methods. These differences between the SGE methods are entirely due to the assumptions of the SGE spatial dynamics of the methods, and the performance for a particular approach strongly decreases if its assumptions are not fulfilled. The assessment of the three methods through numerical simulations, representing a variety of SGE dynamics, shows that the method introduced, considering simultaneous stream gains and losses, presents overall the highest performance according to the simulations. As the existing methods provide the minimum and maximum realistic values of SGE within a stream reach, all three methods could be used in conjunction for a full range of estimates. These SGE methods can also be used in conjunction with other endmember mixing models to acquire even more hydrologic Information as both require the same type of input data