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1.
Front Microbiol ; 13: 884034, 2022.
Article in English | MEDLINE | ID: covidwho-1847188

ABSTRACT

Since the outbreak of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), public health worldwide has been greatly threatened. The development of an effective treatment for this infection is crucial and urgent but is hampered by the incomplete understanding of the viral infection mechanisms and the lack of specific antiviral agents. We previously reported that teicoplanin, a glycopeptide antibiotic that has been commonly used in the clinic to treat bacterial infection, significantly restrained the cell entry of Ebola virus, SARS-CoV, and MERS-CoV by specifically inhibiting the activity of cathepsin L (CTSL). Here, we found that the cleavage sites of CTSL on the spike proteins of SARS-CoV-2 were highly conserved among all the variants. The treatment with teicoplanin suppressed the proteolytic activity of CTSL on spike and prevented the cellular infection of different pseudotyped SARS-CoV-2 viruses. Teicoplanin potently prevented the entry of SARS-CoV-2 into the cellular cytoplasm with an IC50 of 2.038 µM for the Wuhan-Hu-1 reference strain and an IC50 of 2.116 µM for the SARS-CoV-2 (D614G) variant. The pre-treatment of teicoplanin also prevented SARS-CoV-2 infection in hACE2 mice. In summary, our data reveal that CTSL is required for both SARS-CoV-2 and SARS-CoV infection and demonstrate the therapeutic potential of teicoplanin for universal anti-CoVs intervention.

2.
NPJ Prim Care Respir Med ; 31(1): 33, 2021 06 03.
Article in English | MEDLINE | ID: covidwho-1258582

ABSTRACT

Accurate prediction of the risk of progression of coronavirus disease (COVID-19) is needed at the time of hospitalization. Logistic regression analyses are used to interrogate clinical and laboratory co-variates from every hospital admission from an area of 2 million people with sporadic cases. From a total of 98 subjects, 3 were severe COVID-19 on admission. From the remaining subjects, 24 developed severe/critical symptoms. The predictive model includes four co-variates: age (>60 years; odds ratio [OR] = 12 [2.3, 62]); blood oxygen saturation (<97%; OR = 10.4 [2.04, 53]); C-reactive protein (>5.75 mg/L; OR = 9.3 [1.5, 58]); and prothrombin time (>12.3 s; OR = 6.7 [1.1, 41]). Cutoff value is two factors, and the sensitivity and specificity are 96% and 78% respectively. The area under the receiver-operator characteristic curve is 0.937. This model is suitable in predicting which unselected newly hospitalized persons are at-risk to develop severe/critical COVID-19.


Subject(s)
COVID-19/diagnosis , Hospitalization/statistics & numerical data , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , C-Reactive Protein/analysis , COVID-19/pathology , Child , Child, Preschool , Disease Progression , Female , Humans , Infant , Logistic Models , Male , Middle Aged , Oxygen/blood , Prognosis , Prothrombin Time , ROC Curve , Risk Assessment , Sensitivity and Specificity , Young Adult
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