Track D Social Science, Human Rights and Political Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Histological evaluation of effects produced in alveolar bone following gingival incision with an electrosurgical scalpel

Electrosurgery is defined as the application of electrically generated heat energy to living tissue to alter or destroy it for therapeutic purposes. The tissue damage that results varies with the type of electrosurgery unit used. There are 3 principal types: Electrosection: an undamped fully rectified high frequency alternating current with biterminal application is used, and individual cell dehydration and volatilization result, i.e. tissue damage involves merely the line of incision. Electrocoagulation: a highly or moderately damped unrectified alternating current is applied biterminally, causing tissue necrosis over a fairly well localized area. Electrodesiccation (electrocautery): in which a highly damped monoterminal alternating current is used; it produces coagulation necrosis over a wide area, i.e extends readily to deeper tissues. Gingival incisions were performed distal to each of the 2 lower incisors on 25 adult male guinea pigs. For every animal, electrosection with an electrosurgical scalpel was used on one side, and a conventional scalpel was used on the other. The surgical instruments in all cases were brought into direct contact with periosteum. Five animals were killed at each postoperative period (12, 24, 48, 72, and 96 hr), and sections of the areas of surgery were prepared by standard laboratory procedures. At 12 hr postoperatively there were far more soft tissue necrosis, a more extensive inflammatory reaction, and greater destruction of periosteum after electrosurgery. No significant changes in osteocyte viability were seen after either technique. However by 24 hr, many empty lacunae were observed in the bone associated with electrosurgery, such necrosis being even more extensive by 48 hr. In contrast, only very minor, localized areas devoid of some osteocytes were seen after use of the conventional scalpel. By 96 hr the electrosurgical connective tissue wounds were still lined by coagulum, but repair of the scalpel wounds had begun. The periosteum and bone had the same features that were seen at 48 hr. Throughout the study, no increase in osteoclasts was seen in any section, nor were significant changes in adjacent bone marrow observed

Statistical Geometrical Texture Description

Texture plays an important role in image analysis and understanding. Its potential applications include remote sensing, quality control, and medical diagnosis etc. As a front end in a typical classification system, texture feature extraction is of key significance to the overall system performance. There have been many papers, proposing various approaches to this challenging problem. Structural approaches are based on the theory of formal languages: a texture image is regarded as generated from a set of texture primitives using a set of placement rules. These approaches work well on "deterministic" textures but most natural textures, unfortunately, are not of this type. From a statistical point of view, texture images are complicated pictorial patterns on which sets of statistics can be defined to characterise these patterns. Aside from the most popularly used Spatial Grey Level Dependence Matrix (SGLDM), there are also other statistics such as the recently proposed Statistical Feature Matrix (SFM). These statistics, however, are largely heuristic, resulting in limited discrimination ability. Fourier transform based methods usually perform well on textures showing strong periodicity. Their performance significantly deteriorates, though, when the periodicity weakens. Stochastic models such as two-dimensional ARMA, Markov random fields etc. can also be used for texture feature extraction via parameter estimation. These approaches consider textures as realisations of a random process. We have developed a novel set of texture features - Statistical Geometrical Features (SGF) - based on the statistics of geometrical properties of connected regions in a stack of binary images obtained from a texture image