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1.
Transboundary and Emerging Diseases ; 2023, 2023.
Artículo en Alemán | ProQuest Central | ID: covidwho-2296316

RESUMEN

Porcine deltacoronavirus (PDCoV) is an emerging swine coronavirus that causes severe diarrhea to pigs of all ages, especially the suckling piglets under one-week-old. We previously isolated a highly pathogenic PDCoV strain, CZ2020, from a diarrheal piglet and have passaged it for over 100 passages. The adaptability of the CZ2020 increased gradually in vitro as the passage increased. Amino acid mutations were observed in pp1a, pp1ab, spike, envelop, and membrane proteins, and the spike protein accounts for 66.7% of all amino acid mutations. Then, the high passage strains, CZ2020-F80 and CZ2020-F100, were selected for evaluation of the pathogenicity in three-day-old piglets to examine whether these amino acid changes affected their virulence. At 2 days postchallenge (DPC), 2/5 piglets started to show typical diarrhea, and at 4 DPC, severe diarrhea was observed in the CZ2020-challenged piglets. Viral RNA could be detected at 1 DPC in rectal swabs and reached its highest at 4 DPC in the CZ2020-challenged group. CZ2020-F80- and CZ2020-F100-challenged groups have one piglet exhibiting mild diarrhea at 4 and 6 DPC, respectively. Compared with the CZ2020-challenged group, the piglets in CZ2020-F80- and F100-challenged groups had lower viral loads in rectal swabs, intestines, and other organs. No obvious histopathological lesions were observed in the intestines of CZ2020-F80- and F100-challenged piglets. Virulent PDCoV infection could also induce strong interferons and proinflammatory cytokines in vitro and in vivo. These data indicate that the strains, CZ2020-F80 and CZ2020-F100, were significantly attenuated via serial passaging in vitro and have the potential for developing attenuated vaccine candidates.

2.
Eur J Radiol ; 126: 108961, 2020 May.
Artículo en Inglés | MEDLINE | ID: covidwho-15135

RESUMEN

PURPOSE: To evaluate the diagnostic value of computed tomography (CT) and real-time reverse-transcriptase-polymerase chain reaction (rRT-PCR) for COVID-19 pneumonia. METHODS: This retrospective study included all patients with COVID-19 pneumonia suspicion, who were examined by both CT and rRT-PCR at initial presentation. The sensitivities of both tests were then compared. For patients with a final confirmed diagnosis, clinical and laboratory data, in addition to CT imaging findings were evaluated. RESULTS: A total of 36 patients were finally diagnosed with COVID-19 pneumonia. Thirty-five patients had abnormal CT findings at presentation, whereas one patient had a normal CT. Using rRT-PCR, 30 patients were tested positive, with 6 cases initially missed. Amongst these 6 patients, 3 became positive in the second rRT-PCR assay(after 2 days, 2 days and 3 days respectively), and the other 3 became positive only in the third round of rRT-PCR tests(after 5 days, 6 days and 8 days respectively). At presentation, CT sensitivity was therefore 97.2%, whereas the sensitivity of initial rRT-PCR was only 83.3%. CONCLUSION: rRT-PCR may produce initial false negative results. We suggest that patients with typical CT findings but negative rRT-PCR results should be isolated, and rRT-PCR should be repeated to avoid misdiagnosis.


Asunto(s)
Betacoronavirus/genética , Infecciones por Coronavirus/diagnóstico , Neumonía Viral/diagnóstico , Adulto , Anciano , COVID-19 , Prueba de COVID-19 , Vacunas contra la COVID-19 , Técnicas de Laboratorio Clínico/métodos , Femenino , Humanos , Masculino , Persona de Mediana Edad , Pandemias , Reacción en Cadena en Tiempo Real de la Polimerasa , Estudios Retrospectivos , SARS-CoV-2 , Tomografía Computarizada por Rayos X
3.
Acad Radiol ; 27(5): 603-608, 2020 05.
Artículo en Inglés | MEDLINE | ID: covidwho-14220

RESUMEN

Coronavirus disease is an emerging infection caused by a novel coronavirus that is moving rapidly. High resolution computed tomography (CT) allows objective evaluation of the lung lesions, thus enabling us to better understand the pathogenesis of the disease. With serial CT examinations, the occurrence, development, and prognosis of the disease can be better understood. The imaging can be sorted into four phases: early phase, progressive phase, severe phase, and dissipative phase. The CT appearance of each phase and temporal progression of the imaging findings are demonstrated.


Asunto(s)
Infecciones por Coronavirus/diagnóstico por imagen , Pulmón/diagnóstico por imagen , Neumonía Viral/diagnóstico por imagen , Tomografía Computarizada por Rayos X , Inteligencia Artificial , COVID-19 , Infecciones por Coronavirus/diagnóstico , Progresión de la Enfermedad , Diagnóstico Precoz , Humanos , Pulmón/patología , Pandemias , Aislamiento de Pacientes , Neumonía Viral/diagnóstico
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