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Int J Mol Sci ; 23(7)2022 Apr 02.
Article in English | MEDLINE | ID: covidwho-1785741


The understanding of how genetic information may be inherited through generations was established by Gregor Mendel in the 1860s when he developed the fundamental principles of inheritance. The science of genetics, however, began to flourish only during the mid-1940s when DNA was identified as the carrier of genetic information. The world has since then witnessed rapid development of genetic technologies, with the latest being genome-editing tools, which have revolutionized fields from medicine to agriculture. This review walks through the historical timeline of genetics research and deliberates how this discipline might furnish a sustainable future for humanity.

Heredity , Databases, Genetic , Inheritance Patterns
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325503


Background: Malaria, caused by Plasmodium infection, is a global life-threatening infection disease especially during the COVID-19 pandemic. However, it is still unclear about the dynamic change and the interactions of the intestinal microbiota and immunity during the whole parasite infection. Here, we investigated the change of intestinal microbiome and transcriptome during the whole Plasmodium infection process in mice to analyze the dynamic landscape of parasitaemia dependent intestinal microbiota shifting and related to host immunity.ResultsThere were significant parasitaemia dependent changes of intestinal microbiota and transcriptome, and the microbiota was significantly correlated to the intestinal immunity. We found that (i) the diversity and composition of the intestinal microbiota represented a significant correlation along with the Plasmodium infection in family, genus and species level;(ii) particularly, Erysipelotrichaceae bacterium canine oral taxon 255, Sutterella*, Ruminococcus 1* and Eubacterium plexicaudatum ASF492 were specific during the parasitaemia rising state, while Eubacterium nodatum group* was specific in the recovering phase at species level;(iii) the up-regulated genes from the intestinal transcriptome were mainly enriched in immune cell differentiation pathways along with the malaria development, particularly, naive CD4+ T cells differentiated into Th1, Th2 and Tfh cells in the early immune response and into Th17 cells in the later response, while B cells were activated during the whole Plasmodium infection process;(iv) the abundance of the parasitaemia phase-specific microbiota represented a high correlation with the phase-specific immune cells development, particularly, Th1 cell with family Bacteroidales BS11 gut group, genera Prevotella 9, Ruminococcaceae UCG 008, Moryella and specie Sutterella* , Th2 cell with specie Sutterella* , Th17 cell with family Peptococcaceae , genus Lachnospiraceae FCS020 group and spices Ruminococcus 1*, Ruminococcus UGG 014* and Eubacterium plexicaudatum ASF492, Tfh and B cell with genera Moryella and species Erysipelotrichaceae bacterium canine oral taxon 255.ConclusionThere were a remarkable dynamic landscape of the parasitaemia dependent shifting of intestinal microbiota and immunity, and a notable correlation between the abundance of intestinal microbiota, especially at species level, and immune cells.

EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324822


Background: Optimal fluid management in patients with COVID-19 has not been reported. This retrospective, multicenter study investigated the impact of intravenous infusion volume in the early stage of COVID-19 on clinical outcomes. Methods 127 patients from two tertiary hospitals were separated into the “conservative” and “liberal” groups based on average daily intravenous infusion volume within the first seven days after admission. Basic information, demographic and epidemiological characteristics, laboratory findings, treatments, and outcome measures were retrieved from medical records. The disease progression and prognosis were analyzed and compared. Results The average daily intravenous infusion volume within 7 days was 500 (150–700) ml/day in the conservative-strategy group (n = 87), and 1100 (1000–1288) ml/day in the liberal-strategy group (n = 40) ( p  < 0.001). There were no statistical differences in median age, male-to-female ratio, epidemiology, laboratory findings on admission, comorbidities, and average daily urine output within the seven days ( p  > 0.05). The final K + in the liberal group was slightly higher than that at admission, and the final hematocrit level in the conservative group had a significant difference than that at admission ( p  < 0.05). The mean (± SD) duration of hospitalization was 22.41 ± 11.99 days in the conservative group and 25.28 ± 12.08 days in the liberal group ( p  = 0.120). However, compared to the liberal group, conservative group had statistically lower rates of disease progression (9.3% vs 37.5%, p  < 0.001), mechanical ventilation (2.3% vs 27.5%, p  < 0.001) and in-hospital mortality (2.3% vs 15.0%, p  = 0.012). Conclusions Although there appeared to be no significant difference in the duration of hospitalization between using conservative and liberal fluid management strategies, the former was associated with lower rates of disease progression, mechanical ventilation and in-hospital mortality without increased nonpulmonary-organ dysfunction. These results support the importance of implementing conservative intravenous fluid infusion in the early stage of COVID-19.

EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-323878


Background: Many of severe COVID-19 patients are admitted to the hospital or even to the Intensive Care Unit(ICU). The present study was aimed to investigated the risk factors in death from COVID-19. Methods: In this retrospective study, all inpatients confirmed severe or critical COVID-19 from two tertiary hospital in Huangshi were included, who had been discharged or died by March19,2020. Demographic,clinical,treatment,laboratory data and information were extracted from electronic medical records and compared between survivors group and non-survivors group. The univariable and multivariable logistic regression analysis was used to analyze the risk factors associated with in-hospital death. Results: 81 patients were included in this study, of whom 55 were discharged and 26 died in hospital. In all patients, 36(44.4%) patients had comorbidity, including hypertension(27[33.3%]), diabetes(11[13.6%]) and coronary heart disease (CHD)(11[13.6%]), and 16(19.8%) patients accompanied with more than 2 kinds of underlying diseases. The proportion of CHD in non-survivors group was significantly higher than that in survivors group(26.9% vs 7.3%, P=0.032), but there were no differences in hypertension, diabetes and COPD between the non-survivors group and the survivors group. Multivariable logistic regression analysis showed increasing odds of in-hospital death associated with aspartate aminotransferase(AST) and invasive mechanical ventilation (IMV) (P<0.001)(P=0.017). Conclusions: : Invasive Mechanical Ventilation may contribute to mortality of severe/critical COVID-19 pneumonia, and with higher AST at admission was one of the indicators of poor prognosis. Trial registration: Chinese Clinical Trial Registration;ChiCTR2000031494;Registered 02 April 2020;http://

Curr Opin Pharmacol ; 54: 72-81, 2020 10.
Article in English | MEDLINE | ID: covidwho-778682


Kawasaki disease is an acute childhood self-limited vasculitis, causing the swelling or inflammation of medium-sized arteries, eventually leading to cardiovascular problems such as coronary artery aneurysms. Acetylsalicylic acid combined with intravenous immunoglobulin (IVIG) is the standard treatment of Kawasaki disease (KD). However, a rising number of IVIG resistant cases were reported with severe disease complications such as the KD Shock Syndrome or KD-Macrophage activation syndrome. Recent reports have depicted the overlapped number of children with SARS-CoV-2 and KD, which was called multisystem inflammatory syndrome. Simultaneously, the incidence rate of KD-like diseases are increased after the outbreak of COVID-19, suggesting the virus may be associated with KD. New intervention is important to overcome the problem of IVIG treatment resistance. This review aims to introduce the current pharmacological intervention and possible resistance genes for the discovery of new drug for IVIG resistant KD.

Drug Resistance/genetics , Immunoglobulins, Intravenous/therapeutic use , Mucocutaneous Lymph Node Syndrome/drug therapy , Mucocutaneous Lymph Node Syndrome/genetics , COVID-19/epidemiology , COVID-19/genetics , COVID-19/virology , Comorbidity , Humans , Mucocutaneous Lymph Node Syndrome/epidemiology , Mucocutaneous Lymph Node Syndrome/virology , SARS-CoV-2/pathogenicity
Elife ; 92020 05 12.
Article in English | MEDLINE | ID: covidwho-245716


Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

Platelets are small cells in the blood that primarily help stop bleeding after an injury by sticking together with other blood cells to form a clot that seals the broken blood vessel. Blood clots, however, can sometimes cause harm. For example, if a clot blocks the blood flow to the heart or the brain, it can result in a heart attack or stroke, respectively. Blood clots have also been linked to harmful inflammation and the spread of cancer, and there are now preliminary reports of remarkably high rates of clotting in COVID-19 patients in intensive care units. A variety of chemicals can cause platelets to stick together. It has long been assumed that it would be impossible to tell apart the clots formed by different chemicals (which are also known as agonists). This is largely because these aggregates all look very similar under a microscope, making it incredibly time consuming for someone to look at enough microscopy images to reliably identify the subtle differences between them. However, finding a way to distinguish the different types of platelet aggregates could lead to better ways to diagnose or treat blood vessel-clogging diseases. To make this possible, Zhou, Yasumoto et al. have developed a method called the "intelligent platelet aggregate classifier" or iPAC for short. First, numerous clot-causing chemicals were added to separate samples of platelets taken from healthy human blood. The method then involved using high-throughput techniques to take thousands of images of these samples. Then, a sophisticated computer algorithm called a deep learning model analyzed the resulting image dataset and "learned" to distinguish the chemical causes of the platelet aggregates based on subtle differences in their shapes. Finally, Zhou, Yasumoto et al. verified iPAC method's accuracy using a new set of human platelet samples. The iPAC method may help scientists studying the steps that lead to clot formation. It may also help clinicians distinguish which clot-causing chemical led to a patient's heart attack or stroke. This could help them choose whether aspirin or another anti-platelet drug would be the best treatment. But first more studies are needed to confirm whether this method is a useful tool for drug selection or diagnosis.

Neural Networks, Computer , Platelet Aggregation , Flow Cytometry , Humans , Lab-On-A-Chip Devices , Microfluidic Analytical Techniques , Platelet Activation , Thrombosis/classification