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Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the infectious disease COVID-19, which has rapidly become an international pandemic with significant impact on healthcare systems and the global economy. To assist antiviral therapy and vaccine development efforts, we performed a natural history/time course study of SARS-CoV-2 infection in ferrets to characterise and assess the suitability of this animal model. Ten ferrets of each sex were challenged intranasally with 4.64 × 104 TCID50 of SARS-CoV-2 isolate Australia/VIC01/2020 and monitored for clinical disease signs, viral shedding, and tissues collected post-mortem for histopathological and virological assessment at set intervals. We found that SARS-CoV-2 replicated in the upper respiratory tract of ferrets with consistent viral shedding in nasal wash samples and oral swab samples up until day 9. Infectious SARS-CoV-2 was recovered from nasal washes, oral swabs, nasal turbinates, pharynx, and olfactory bulb samples within 3–7 days post-challenge; however, only viral RNA was detected by qRT-PCR in samples collected from the trachea, lung, and parts of the gastrointestinal tract. Viral antigen was seen exclusively in nasal epithelium and associated sloughed cells and draining lymph nodes upon immunohistochemical staining. Due to the absence of clinical signs after viral challenge, our ferret model is appropriate for studying asymptomatic SARS-CoV-2 infections and most suitable for use in vaccine efficacy studies.

Plasma samples taken at different time points from donors who received either AstraZeneca (Vaxzevria) or Pfizer (Comirnaty) or Moderna (Spikevax) coronavirus disease-19 (COVID-19) vaccine were assessed in virus neutralization assays against Delta and Omicron variants of concern and a reference isolate (VIC31). With the Pfizer vaccine there was 6-8-fold reduction in 50% neutralizing antibody titres (NT50) against Delta and VIC31 at 6 months compared to 2 weeks after the second dose; followed by 25-fold increase at 2 weeks after the third dose. Neutralisation of Omicron was only consistently observed 2 weeks after the third dose, with most samples having titres below the limit of detection at earlier timepoints. Moderna results were similar to Pfizer at 2 weeks after the second dose, while the titres for AstraZeneca samples derived from older donors were 7-fold lower against VIC31 and below the limit of detection against Delta and Omicron. Age and gender were not found to significantly impact our results. These findings indicate that vaccine matching may be needed, and that at least a third dose of these vaccines is necessary to generate sufficient neutralising antibodies against emerging variants of concern, especially Omicron, amidst the challenges of ensuring vaccine equity worldwide.

We investigated the genetic overlap between Alzheimer's disease (AD) and Parkinson's disease (PD). Using summary statistics (P-values) from large recent genome-wide association studies (GWAS) (total n=89 904 individuals), we sought to identify single nucleotide polymorphisms (SNPs) associating with both AD and PD. We found and replicated association of both AD and PD with the A allele of rs393152 within the extended MAPT region on chromosome 17 (meta analysis P-value across five independent AD cohorts=1.65 × 10(-7)). In independent datasets, we found a dose-dependent effect of the A allele of rs393152 on intra-cerebral MAPT transcript levels and volume loss within the entorhinal cortex and hippocampus. Our findings identify the tau-associated MAPT locus as a site of genetic overlap between AD and PD, and extending prior work, we show that the MAPT region increases risk of Alzheimer's neurodegeneration.

The global response to Coronavirus Disease 2019 (COVID-19) is now facing new challenges such as vaccine inequity and the emergence of SARS-CoV-2 variants of concern (VOCs). Preclinical models of disease, in particular animal models, are essential to investigate VOC pathogenesis, vaccine correlates of protection and postexposure therapies. Here, we provide an update from the World Health Organization (WHO) COVID-19 modeling expert group (WHO-COM) assembled by WHO, regarding advances in preclinical models. In particular, we discuss how animal model research is playing a key role to evaluate VOC virulence, transmission and immune escape, and how animal models are being refined to recapitulate COVID-19 demographic variables such as comorbidities and age.

Saturation suppressor mutagenesis was used to generate thermostable mutants of the SARS-CoV-2 spike receptor-binding domain (RBD). A triple mutant with an increase in thermal melting temperature of ~7°C with respect to the wild-type B.1 RBD and was expressed in high yield in both mammalian cells and the microbial host, Pichia pastoris, was downselected for immunogenicity studies. An additional derivative with three additional mutations from the B.1.351 (beta) isolate was also introduced into this background. Lyophilized proteins were resistant to high-temperature exposure and could be stored for over a month at 37°C. In mice and hamsters, squalene-in-water emulsion (SWE) adjuvanted formulations of the B.1-stabilized RBD were considerably more immunogenic than RBD lacking the stabilizing mutations and elicited antibodies that neutralized all four current variants of concern with similar neutralization titers. However, sera from mice immunized with the stabilized B.1.351 derivative showed significantly decreased neutralization titers exclusively against the B.1.617.2 (delta) VOC. A cocktail comprising stabilized B.1 and B.1.351 RBDs elicited antibodies with qualitatively improved neutralization titers and breadth relative to those immunized solely with either immunogen. Immunized hamsters were protected from high-dose viral challenge. Such vaccine formulations can be rapidly and cheaply produced, lack extraneous tags or additional components, and can be stored at room temperature. They are a useful modality to combat COVID-19, especially in remote and low-resource settings.

Although various vaccines are now commercially available, they have not been able to stop the spread of COVID-19 infection completely. An excellent strategy to get safe, effective, and affordable COVID-19 treatments quickly is to repurpose drugs that are already approved for other diseases. The process of developing an accurate and standardized drug repurposing dataset requires considerable resources and expertise due to numerous commercially available drugs that could be potentially used to address the SARS-CoV-2 infection. To address this bottleneck, we created the CoviRx.org platform. CoviRx is a user-friendly interface that allows analysis and filtering of large quantities of data, which is onerous to curate manually for COVID-19 drug repurposing. Through CoviRx, the curated data have been made open source to help combat the ongoing pandemic and encourage users to submit their findings on the drugs they have evaluated, in a uniform format that can be validated and checked for integrity by authenticated volunteers. This article discusses the various features of CoviRx, its design principles, and how its functionality is independent of the data it displays. Thus, in the future, this platform can be extended to include any other disease beyond COVID-19.

The global threat of COVID-19 has led to an increased use of metabolomics to study SARS-CoV-2 infections in animals and humans. In spite of these efforts, however, understanding the metabolome of SARS-CoV-2 during an infection remains difficult and incomplete. In this study, metabolic responses to a SAS-CoV-2 challenge experiment were studied in nasal washes collected from an asymptomatic ferret model (n = 20) at different time points before and after infection using an LC-MS-based metabolomics approach. A multivariate analysis of the nasal wash metabolome data revealed several statistically significant features. Despite no effects of sex or interaction between sex and time on the time course of SARS-CoV-2 infection, 16 metabolites were significantly different at all time points post-infection. Among these altered metabolites, the relative abundance of taurine was elevated post-infection, which could be an indication of hepatotoxicity, while the accumulation of sialic acids could indicate SARS-CoV-2 invasion. Enrichment analysis identified several pathways influenced by SARS-CoV-2 infection. Of these, sugar, glycan, and amino acid metabolisms were the key altered pathways in the upper respiratory channel during infection. These findings provide some new insights into the progression of SARS-CoV-2 infection in ferrets at the metabolic level, which could be useful for the development of early clinical diagnosis tools and new or repurposed drug therapies.

The repurposing of licenced drugs for use against COVID-19 is one of the most rapid ways to develop new and alternative therapeutic options to manage the ongoing pandemic. Given circa 7817 licenced compounds available from Compounds Australia that can be screened, this paper demonstrates the utility of commercially available ex vivo/3D airway and alveolar tissue models. These models are a closer representation of in vivo studies than in vitro models, but retain the benefits of rapid in vitro screening for drug efficacy. We demonstrate that several existing drugs appear to show anti-SARS-CoV-2 activity against both SARS-CoV-2 Delta and Omicron Variants of Concern in the airway model. In particular, fluvoxamine, as well as aprepitant, everolimus, and sirolimus, has virus reduction efficacy comparable to the current standard of care (remdesivir, molnupiravir, nirmatrelvir). Whilst these results are encouraging, further testing and efficacy studies are required before clinical use can be considered.

SARS-CoV-2 is the cause of the COVID-19 pandemic which has claimed more than 6.5 million lives worldwide, devastating the economy and overwhelming healthcare systems globally. The development of new drug molecules and vaccines has played a critical role in managing the pandemic; however, new variants of concern still pose a significant threat as the current vaccines cannot prevent all infections. This situation calls for the collaboration of biomedical scientists and healthcare workers across the world. Repurposing approved drugs is an effective way of fast-tracking new treatments for recently emerged diseases. To this end, we have assembled and curated a database consisting of 7817 compounds from the Compounds Australia Open Drug collection. We developed a set of eight filters based on indicators of efficacy and safety that were applied sequentially to down-select drugs that showed promise for drug repurposing efforts against SARS-CoV-2. Considerable effort was made to evaluate approximately 14,000 assay data points for SARS-CoV-2 FDA/TGA-approved drugs and provide an average activity score for 3539 compounds. The filtering process identified 12 FDA-approved molecules with established safety profiles that have plausible mechanisms for treating COVID-19 disease. The methodology developed in our study provides a template for prioritising drug candidates that can be repurposed for the safe, efficacious, and cost-effective treatment of COVID-19, long COVID, or any other future disease. We present our database in an easy-to-use interactive interface (CoviRx that was also developed to enable the scientific community to access to the data of over 7000 potential drugs and to implement alternative prioritisation and down-selection strategies.

The host response to SARS-CoV-2 infection provide insights into both viral pathogenesis and patient management. The host-encoded microRNA (miRNA) response to SARS-CoV-2 infection, however, remains poorly defined. Here we profiled circulating miRNAs from ten COVID-19 patients sampled longitudinally and ten age and gender matched healthy donors. We observed 55 miRNAs that were altered in COVID-19 patients during early-stage disease, with the inflammatory miR-31-5p the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-423-5p, miR-23a-3p and miR-195-5p) independently classified COVID-19 cases with an accuracy of 99.9%. In a ferret COVID-19 model, the three-miRNA signature again detected SARS-CoV-2 infection with 99.7% accuracy, and distinguished SARS-CoV-2 infection from influenza A (H1N1) infection and healthy controls with 95% accuracy. Distinct miRNA profiles were also observed in COVID-19 patients requiring oxygenation. This study demonstrates that SARS-CoV-2 infection induces a robust host miRNA response that could improve COVID-19 detection and patient management.

The ongoing COVID-19 pandemic has resulted in significant global morbidity and mortality on a scale similar to the influenza pandemic of 1918. Over the course of the last few months, a number of SARS-CoV-2 variants have been identified against which vaccine-induced immune responses may be less effective. These “variants-of-concern” have garnered significant attention in the media, with discussion around their impact on the future of the pandemic and the ability of leading COVID-19 vaccines to protect against them effectively. To address concerns about emerging SARS-CoV-2 variants affecting vaccine-induced immunity, we investigated the neutralisation of representative ‘G614’, ‘501Y.V1’ and ‘501Y.V2’ virus isolates using sera from ferrets that had received prime-boost doses of the DNA vaccine, INO-4800. Neutralisation titres against G614 and 501Y.V1 were comparable, but titres against the 501Y.V2 variant were approximately 4-fold lower, similar to results reported with other nucleic acid vaccines and supported by in silico biomolecular modelling. The results confirm that the vaccine-induced neutralising antibodies generated by INO-4800 remain effective against current variants-of-concern, albeit with lower neutralisation titres against 501Y.V2 similar to other leading nucleic acid-based vaccines.

Tuberculosis (TB) remains a major global threat and diagnosis of active TB ((ATB) both extra-pulmonary (EPTB), pulmonary (PTB)) and latent TB (LTBI) infection remains challenging, particularly in high-burden countries which still rely heavily on conventional methods. Although molecular diagnostic methods are available, e.g., Cepheid GeneXpert, they are not universally available in all high TB burden countries. There is intense focus on immune biomarkers for use in TB diagnosis, which could provide alternative low-cost, rapid diagnostic solutions. In our previous gene expression studies, we identified peripheral blood leukocyte (PBL) mRNA biomarkers in a non-human primate TB aerosol-challenge model. Here, we describe a study to further validate select mRNA biomarkers from this prior study in new cohorts of patients and controls, as a prerequisite for further development. Whole blood mRNA was purified from ATB patients recruited in the UK and India, LTBI and two groups of controls from the UK (i) a low TB incidence region (CNTRLA) and (ii) individuals variably-domiciled in the UK and Asia ((CNTRLB), the latter TB high incidence regions). Seventy-two mRNA biomarker gene targets were analyzed by qPCR using the Roche Lightcycler 480 qPCR platform and data analyzed using GeneSpring™ 14.9 bioinformatics software. Differential expression of fifty-three biomarkers was confirmed between MTB infected, LTBI groups and controls, seventeen of which were significant using analysis of variance (ANOVA): CALCOCO2, CD52, GBP1, GBP2, GBP5, HLA-B, IFIT3, IFITM3, IRF1, LOC400759 (GBP1P1), NCF1C, PF4V1, SAMD9L, S100A11, TAF10, TAPBP, and TRIM25. These were analyzed using receiver operating characteristic (ROC) curve analysis. Single biomarkers and biomarker combinations were further assessed using simple arithmetic algorithms. Minimal combination biomarker panels were delineated for primary diagnosis of ATB (both PTB and EPTB), LTBI and identifying LTBI individuals at high risk of progression which showed good performance characteristics. These were assessed for suitability for progression against the standards for new TB diagnostic tests delineated in the published World Health Organization (WHO) technology product profiles (TPPs).

As existing vaccines fail to completely prevent COVID-19 infections or community transmission, there is an unmet need for vaccines that can better combat SARS-CoV-2 variants of concern (VOC). We previously developed highly thermo-tolerant monomeric and trimeric receptor-binding domain derivatives that can withstand 100 °C for 90 min and 37 °C for four weeks and help eliminate cold-chain requirements. We show that mice immunised with these vaccine formulations elicit high titres of antibodies that neutralise SARS-CoV-2 variants VIC31 (with Spike: D614G mutation), Delta and Omicron (BA.1.1) VOC. Compared to VIC31, there was an average 14.4-fold reduction in neutralisation against BA.1.1 for the three monomeric antigen-adjuvant combinations and a 16.5-fold reduction for the three trimeric antigen-adjuvant combinations; the corresponding values against Delta were 2.5 and 3.0. Our findings suggest that monomeric formulations are suitable for upcoming Phase I human clinical trials and that there is potential for increasing the efficacy with vaccine matching to improve the responses against emerging variants. These findings are consistent with in silico modelling and AlphaFold predictions, which show that, while oligomeric presentation can be generally beneficial, it can make important epitopes inaccessible and also carries the risk of eliciting unwanted antibodies against the oligomerisation domain.

Precision near infrared (NIR) measurements are essential for the next generation of ground and space based instruments. The SuperNova Acceleration Probe (SNAP) will measure thousands of type Ia supernovae up to a redshift of 1.7. The highest redshift supernovae provide the most leverage for determining cosmological parameters, in particular the dark energy equation of state and its possible time evolution. Accurate NIR observations are needed to utilize the full potential of the highest redshift supernovae. Technological improvements in NIR detector fabrication have lead to high quantum efficiency, low noise detectors using a HgCdTe diode with a band-gap that is tuned to cutoff at 1:7 1m. The effects of detector quantum efficiency, read noise, and dark current on lightcurve signal to noise, lightcurve parameter errors, and distance modulus ?ts are simulated in the SNAP sim framework. Results show that improving quantum efficiency leads to the largest gains in photometric accuracy for type Ia supernovae. High quantum efficiency in the NIR reduces statistical errors and helps control systematic uncertainties at the levels necessary to achieve the primary SNAP science goals.

We present the results of a study of the performance of InGaAs detectors conducted for the SuperNova Acceleration Probe (SNAP) dark energy mission concept. Low temperature data from a nominal 1.7um cut-off wavelength 1kx1k InGaAs photodiode array, hybridized to a Rockwell H1RG multiplexer suggest that InGaAs detector performance is comparable to those of existing 1.7um cut-off HgCdTe arrays. Advances in 1.7um HgCdTe dark current and noise initiated by the SNAP detector research and development program makes it the baseline detector technology for SNAP. However, the results presented herein suggest that existing InGaAs technology is a suitable alternative for other future astronomy applications.

Large format (1k x 1k and 2k x 2k) near infrared detectors manufactured by Rockwell Scientific Center and Raytheon Vision Systems are characterized as part of the near infrared R&D effort for SNAP (the Super-Nova/Acceleration Probe). These are hybridized HgCdTe focal plane arrays with a sharp high wavelength cut-off at 1.7 um. This cut-off provides a sufficiently deep reach in redshift while it allows at the same time low dark current operation of the passively cooled detectors at 140 K. Here the baseline SNAP near infrared system is briefly described and the science driven requirements for the near infrared detectors are summarized. A few results obtained during the testing of engineering grade near infrared devices procured for the SNAP project are highlighted. In particular some recent measurements that target correlated noise between adjacent detector pixels due to capacitive coupling and the response uniformity within individual detector pixels are discussed.

Coronavirus disease (COVID-19) is a contagious respiratory disease that is causing significant global morbidity and mortality. Understanding the impact of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection on the host metabolism is still in its infancy but of great importance. Herein, we investigated the metabolic response during viral shedding and post-shedding in an asymptomatic SARS-CoV-2 ferret model (n = 6) challenged with two SARS-CoV-2 isolates. Virological and metabolic analyses were performed on (minimally invasive) collected oral swabs, rectal swabs, and nasal washes. Fragments of SARS-CoV-2 RNA were only found in the nasal wash samples in four of the six ferrets, and in the samples collected 3 to 9 days post-infection (referred to as viral shedding). Central carbon metabolism metabolites were analyzed during viral shedding and post-shedding periods using a dynamic Multiple Reaction Monitoring (dMRM) database and method. Subsequent untargeted metabolomics and lipidomics of the same samples were performed using a Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry (LC-QToF-MS) methodology, building upon the identified differentiated central carbon metabolism metabolites. Multivariate analysis of the acquired data identified 29 significant metabolites and three lipids that were subjected to pathway enrichment and impact analysis. The presence of viral shedding coincided with the challenge dose administered and significant changes in the citric acid cycle, purine metabolism, and pentose phosphate pathways, amongst others, in the host nasal wash samples. An elevated immune response in the host was also observed between the two isolates studied. These results support other metabolomic-based findings in clinical observational studies and indicate the utility of metabolomics applied to ferrets for further COVID-19 research that advances early diagnosis of asymptomatic and mild clinical COVID-19 infections, in addition to assessing the effectiveness of new or repurposed drug therapies.

A study was conducted for three seasons in northwest Kansas, USA to evaluate acetolactate synthase (ALS)-inhibiting herbicides for downy brome (Bromus tectorum L.) and winter annual broadleaf weed control in winter wheat. Herbicides included pyroxsulam at 18.4 g ai ha−1, propoxycarbazone-Na at 44 g ai ha−1, premixed propoxycarbazone-Na & mesosulfuron-methyl at 27 g ai ha−1, and sulfosulfuron at 35 g ai ha−1. The herbicides were applied postemergence in fall and spring seasons. Averaged over time of application, no herbicide controlled downy brome more than 78% in any year. When downy brome densities were high, control was less than 60%. Pyroxsulam controlled downy brome greater than or similar to other herbicides tested. Flixweed (Descurainia sophia L.), blue mustard [Chorispora tenella (Pallas) DC.], and henbit (Lamium amplexicaule L.) control did not differ among herbicide treatments. All herbicides tested controlled flixweed and blue mustard at least 87% and 94%, respectively. However, none of the herbicides controlled henbit more than 73%. Fall herbicide applications improved weed control compared to early spring applications; improvement ranged from 3% to 31% depending on the weed species. Henbit control was greatly decreased by delaying herbicide applications until spring compared to fall applications (49% vs. 80% control). Herbicide injury was observed in only two instances. The injury was ≤13% with no difference between herbicides and the injury did not impact final plant height or grain yield.

Cotton offers next-to-skin comfort and hence is a preferred fiber for undergarments. There have been a number of studies on the effect of different fiber types, fabric structure, fabric finishes, etc. on the moisture vapor transport properties. However, there has been no report in the public domain on the relationship between cotton varieties on the moisture vapor transport characteristics of fabrics produced from them. The study reported in this paper focuses on the moisture vapor transport properties (MVTR) of cotton fabrics knitted from 31 different cotton fibers with different pedigrees grown over a three-year period in three major cotton growing regions of US, Southwest (Texas), Mid-South (Mississippi) and Southeast (Georgia). Results indicate that cotton varieties influence the MVTR of knitted fabrics produced from them. This study, for the first time has attempted to link varietal effects on the most important property of cotton—breathability quantified using MVTR. Preliminary analysis indicates a relationship between the basic sugar content such as verbascose of cotton and its moisture vapor transport. However this result has to be followed up with a thorough study.