The severity and outcome of COVID-19 cases has been associated with the percentage of circulating lymphocytes (LYM%), levels of C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT), lactic acid (LA), and viral load (ORF1ab Ct). However, the predictive power of each of these indicators in disease classification and prognosis remains largely unclear.
We retrospectively collected information on the above parameters in 142 patients with COVID-19, stratifying them by survival or disease severity.
CRP, PCT, IL-6, LYM%, and ORF1ab Ct were significantly altered between survivors and non-survivors. LYM%, CRP, and IL-6 were the most sensitive and reliable factors in distinguishing between survivors and non-survivors. These indicators were significantly different between critically ill and severe/moderate patients. Only LYM% levels were significantly different between severe and moderate types. Among all the investigated indicators, LYM% was the most sensitive and reliable in discriminating between critically ill, severe, and moderate types and between survivors and non-survivors.
CRP, PCT, IL-6, LYM%, and ORF1ab Ct, but not LA, could predict prognosis and guide classification of COVID-19 patients. LYM% was the most sensitive and reliable predictor for disease typing and prognosis. We recommend that LYM% be further investigated in the management of COVID-19.
This study was supported in part by awards from the National Natural Science Foundation of China, the Foundation and Frontier Research Project of Chongqing, and the Chongqing Youth Top Talent Project.
This study was supported in part by awards from the National Natural Science Foundation of China, the Foundation and Frontier Research Project of Chongqing, and the Chongqing Youth Top Talent Project.
Significant delays in the rapid development and distribution of diagnostic testing for SARS-CoV-2 (COVID-19) infection have prevented adequate public health management of the disease, impacting the timely mapping of viral spread and the conservation of personal protective equipment. Furthermore, vulnerable populations, such as those served by the Boston Medical Center (BMC), the largest safety net hospital in New England, represent a high-risk group across multiple dimensions, including a higher prevalence of pre-existing conditions and substance use disorders, lower health maintenance, unstable housing, and a propensity for rapid community spread, highlighting the urgent need for expedient and reliable in-house testing.
We developed a SARS-CoV-2 diagnostic medium-throughput qRT-PCR assay with rapid turnaround time and utilized this Clinical Laboratory Improvement Amendments (CLIA)-certified assay for testing nasopharyngeal swab samples from BMC patients, with emergency authorization from the Food and Drug Administration (FDA) and the Massachusetts Department of Public Health.
The in-house testing platform displayed robust accuracy and reliability in validation studies and reduced institutional sample turnaround time from 5-7days to less than 24 h. Of over 1,000 unique patient samples tested, 44.1% were positive for SARS-CoV-2 infection.
This work provides a blueprint for academic centers and community hospitals lacking automated laboratory machinery to implement rapid in-house testing.
This study was supported by funding from the Boston University School of Medicine, the National Institutes of Health, Boston Medical Center, and the Massachusetts Consortium on Pathogen Readiness (MASS CPR).
This study was supported by funding from the Boston University School of Medicine, the National Institutes of Health, Boston Medical Center, and the Massachusetts Consortium on Pathogen Readiness (MASS CPR).
In December, 2019, a novel zoonotic severe acute respiratory syndrome-related coronavirus emerged in China. The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became pandemic within weeks and the number of human infections and severe cases is increasing. We aimed to investigate the susceptibilty of potential animal hosts and the risk of anthropozoonotic spill-over infections.
We intranasally inoculated nine fruit bats (
), ferrets (
), pigs (
), and 17 chickens (
) with 10
TCID
of a SARS-CoV-2 isolate per animal. Direct contact animals (n=3) were included 24 h after inoculation to test viral transmission. Animals were monitored for clinical signs and for virus shedding by nucleic acid extraction from nasal washes and rectal swabs (ferrets), oral swabs and pooled faeces samples (fruit bats), nasal and rectal swabs (pigs), or oropharyngeal and cloacal swabs (chickens) on days 2, 4, 8, 12, 16, and 21 after infection by quantitative RT-PCR (RT-qPCR). On days 4, 8, and 12, two inocuecame infected. U0126 molecular weight More efficient virus replication but no clinical signs were observed in ferrets, with transmission to all three direct contact animals. Mild rhinitis was associated with viral antigen detection in the respiratory and olfactory epithelium. Prominent viral RNA loads of 0-10
viral genome copies per mL were detected in the upper respiratory tract of fruit bats and ferrets, and both species developed SARS-CoV-2-reactive antibodies reaching neutralising titres of up to 1/1024 after 21 days.
Pigs and chickens could not be infected intranasally by SARS-CoV-2, whereas fruit bats showed characteristics of a reservoir host. Virus replication in ferrets resembled a subclinical human infection with efficient spread. Ferrets might serve as a useful model for further studies-eg, testing vaccines or antivirals.
German Federal Ministry of Food and Agriculture.
German Federal Ministry of Food and Agriculture.The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.U0126 molecular weight
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