Mineralogy and petrology of comet 81P/wild 2 nucleus samples

The bulk of the comet 81P/Wild 2 (hereafter Wild 2) samples returned to Earth by the Stardust spacecraft appear to be weakly constructed mixtures of nanometer-scale grains, with occasional much larger (over 1 micrometer) ferromagnesian silicates, Fe-Ni sulfides, Fe-Ni metal, and accessory phases. The very wide range of olivine and low-Ca pyroxene compositions in comet Wild 2 requires a wide range of formation conditions, probably reflecting very different formation locations in the protoplanetary disk. The restricted compositional ranges of Fe-Ni sulfides, the wide range for silicates, and the absence of hydrous phases indicate that comet Wild 2 experienced little or no aqueous alteration. Less abundant Wild 2 materials include a refractory particle, whose presence appears to require radial transport in the early protoplanetary disk

Physiological responses during ascent to high altitude and the incidence of acute mountain sickness.

Acute mountain sickness (AMS) occurs when there is failure of acclimatisation to high altitude. The aim of this study was to describe the relationship between physiological variables and the incidence of AMS during ascent to 5300 m. A total of 332 lowland-dwelling volunteers followed an identical ascent profile on staggered treks. Self-reported symptoms of AMS were recorded daily using the Lake Louise score (mild 3-4; moderate-severe ≥5), alongside measurements of physiological variables (heart rate, respiratory rate (RR), peripheral oxygen saturation (SpO2 ) and blood pressure) before and after a standardised Xtreme Everest Step-Test (XEST). The overall occurrence of AMS among participants was 73.5% (23.2% mild, 50.3% moderate-severe). There was no difference in gender, age, previous AMS, weight or body mass index between participants who developed AMS and those who did not. Participants who had not previously ascended >5000 m were more likely to get moderate-to-severe AMS. Participants who suffered moderate-to-severe AMS had a lower resting SpO2 at 3500 m (88.5 vs. 89.6%, p = 0.02), while participants who suffered mild or moderate-to-severe AMS had a lower end-exercise SpO2 at 3500 m (82.2 vs. 83.8%, p = 0.027; 81.5 vs. 83.8%, p 5000 m (OR 2.740, p-value 0.003) predicted the development of moderate-to-severe AMS. The Xtreme Everest Step-Test offers a simple, reproducible field test to help predict AMS, albeit with relatively limited predictive precision

Performance evaluation of a non-invasive one-step multiplex RT-qPCR assay for detection of SARS-CoV-2 direct from saliva

Polymerase chain reaction (PCR) has proven to be the gold-standard for SARS-CoV-2 detection in clinical settings. The most common approaches rely on nasopharyngeal specimens obtained from swabs, followed by RNA extraction, reverse transcription and quantitative PCR. Although swab-based PCR is sensitive, swabbing is invasive and unpleasant to administer, reducing patient compliance for regular testing and resulting in an increased risk of improper sampling. To overcome these obstacles, we developed a non-invasive one-step RT-qPCR assay performed directly on saliva specimens. The University of Nottingham Asymptomatic Testing Service protocol simplifies sample collection and bypasses the need for RNA extraction, or additives, thus helping to encourage more regular testing and reducing processing time and costs. We have evaluated the assay against the performance criteria specified by the UK regulatory bodies and attained accreditation (BS EN ISO/IEC 17,025:2017) for SARS-CoV-2 diagnostic testing by the United Kingdom Accreditation Service. We observed a sensitivity of 1 viral copy per microlitre of saliva, and demonstrated a concordance of > 99.4% between our results and those of other accredited testing facilities. We concluded that saliva is a stable medium that allows for a highly precise, repeatable, and robust testing method

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19

An exploration of the adverse effects of psilocybin in controlled studies and autistic adults’ experiences of using psychedelic drugs

Research into the therapeutic potential of psychedelic drugs has increased greatly in recent years. Various studies have provided preliminary evidence that psychedelics show promise in the treatment of a range of mental health conditions. These studies have mainly focussed on the beneficial effects of psychedelics and less research has focussed on their adverse effects. Moreover, to our knowledge, there have been no modern studies investigating the effects of psychedelics in autistic adults – who have been found to experience high levels of mental health conditions and social isolation. The aim of this thesis was to take preliminary steps to explore the above under-researched areas. There are three parts to this volume. Part One presents a systematic review investigating adverse effects of one type of psychedelic (psilocybin) in modern controlled studies. Importantly, the quality of the ascertainment and reporting of adverse events data is also considered. Part Two presents an exploratory online cross-sectional survey investigating the experiences of autistic adults who have taken at least one psychedelic. This was part of a larger joint project conducted with my fellow trainee, Charlotte Rice (see Appendix 1). This study presents an exploration of the association between drug-use context, subjective drug experience and perceived changes in mental health, social engagement, and the characteristics of autism. Lastly, Part Three presents a critical appraisal of the above research, including reflections on the choice of research topic and the process of design, implementation and analysis of the literature review and survey project

Structure and fabric : mitchells building construction

Part 12264 p.; 25 cm

AIDS and optic neuritis in a rhesus monkey infected with the R5 clade C SHIV-1157ipd3N4

A Chinese rhesus macaque infected with the pathogenic CCR5-tropic clade C simian-human immunodeficiency virus, SHIV-1157ipd3N4, had persistent viremia, depletion of CD4+ T cells to \u3c200 cells/μl, opportunistic infections, coagulopathy, and gradual development of bilateral blindness. MRI revealed marked thickening of both optic nerves. Histopathological evaluation showed diffuse cellular infiltration at necropsy and a focus of SHIV-infected cells. This is the first report of CNS pathology following chronic infection with an obligate R5 SHIV

Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression

Background: Natural speech analytics has seen some improvements over recent years, and this has opened a window for objective and quantitative diagnosis in psychiatry. Here, we used a machine learning algorithm applied to natural speech to ask whether language properties measured before psilocybin for treatment-resistant can predict for which patients it will be effective and for which it will not. Methods: A baseline autobiographical memory interview was conducted and transcribed. Patients with treatment-resistant depression received 2 doses of psilocybin, 10 mg and 25 mg, 7 days apart. Psychological support was provided before, during and after all dosing sessions. Quantitative speech measures were applied to the interview data from 17 patients and 18 untreated age-matched healthy control subjects. A machine learning algorithm was used to classify between controls and patients and predict treatment response. Results: Speech analytics and machine learning successfully differentiated depressed patients from healthy controls and identified treatment responders from non-responders with a significant level of 85% of accuracy (75% precision). Conclusions: Automatic natural language analysis was used to predict effective response to treatment with psilocybin, suggesting that these tools offer a highly cost-effective facility for screening individuals for treatment suitability and sensitivity. Limitations: The sample size was small and replication is required to strengthen inferences on these results.Fil: Carrillo, Facundo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Laboratorio de Inteligencia Artificial Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Sigman, Mariano. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernandez Slezak, Diego. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Laboratorio de Inteligencia Artificial Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Ashton, Philip. Imperial College London; Reino UnidoFil: Fitzgerald, Lily. Imperial College London; Reino UnidoFil: Stroud, Jack. Imperial College London; Reino UnidoFil: Nutt, David J.. Imperial College London; Reino UnidoFil: Carhart Harris, Robin L.. Imperial College London; Reino Unid