7 research outputs found

    Finishing the euchromatic sequence of the human genome

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    The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

    Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

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    SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

    Contrast enhancement of eye fundus images

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    International audienceA significant number of digital eye fundus images have strong contrast variations which can be a limiting factor for the diagnosis of the diabetic retinopathy lesions. Currently, to address this problem, graders have to manually adjust the image contrast which is person dependent and therefore not easily reproducible. Images may still be considered un-gradable because they are too bright or too dark.We have developed a fully automatic method, which achieves contrast uniformity across the entire image.The method is based on a colour model consistent with the physical principles of image formation. The contrast of the dark or the bright elements are adjusted in a way that provides a similar colour aspect to lesions such as micro-aneurysms or to anatomical structures such as veins. This method is much more powerful than the previous existing grey level methods using polynomial adjustment, mathematical morphology or histogram equalisations.Our method has been tested on more than 2000 images acquired from different screening services ranging from a high resource country with quality controlled process while others were obtained from low resource countries under harsher conditions. Some images were bright while others were dark making diagnosis difficult. However for all images, the lighting variations have been corrected and the contrast has been enhanced for lesions such as micro-aneurysms and the vascular structures. They are now easier to be detected by graders.This new colour contrast method is a very promising tool to assist graders in diagnosing the presence of diabetic retinopathy and other lesions present in digital eye fundus images since the lesions appear to be much more evident in comparison of the original image. Importantly our method is fully automatic and can be easily integrated in a screening system

    Enhancing Eye Fundus Images for Diabetic Retinopathy Screening

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    International audienceMany eye fundus images present strong variations of contrast which can be a limitation to the diagnosis of the retinopathy. Either some lesions are not taken into account or only a limited part of the domain of the image can be read. Graders have to manually adjust the contrast, which is tedious and not easily reproducible. We have developed an automatic system, which standardises the colour contrast across the whole domain of the image. The method is consistent with the physical principles or image formation and ensures that the colour aspect of lesions such as micro-aneurysms or anatomical structures such as veins are similar. It is more powerful than the existing grey level methods. We have tested our approach on several thousand images acquired in good or in harsher conditions. Some were bright while others were dark. Expert graders have checked the enhanced images. Diagnosis becomes more obvious and the grading more comfortable. Another limitation for the diagnosis is that images of the same patient acquired for different examinations cannot be directly superimposed. Indeed, the eye of the patient is never in the exact same position, the image is a projection of a 3D scene into the plane of the sensor, the optics of the camera creates a radial deformation and the colour of the image may have changed. We have developed an automatic method to superimpose eye fundus images acquired in the same position (nasal or macular). It is based on contrast standardisation, matching of salient points and a deformation model taking into account two radial distortions. We have performed tests for 69 patients with pairs of retinal examinations acquired in good conditions at an interval of one year with and without the same camera. A similar test has been performed on 5 patients with 20 pairs acquired in harsher conditions. A minimum of 96% of pairs were correctly superimposed. This is an important step towards the longitudinal analysis of large public health databases
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