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Background This study aims to evaluate the prognostic value of a pulmonary involvement (PI) score in COVID-19 patients, both independently and in combination with clinical and laboratory parameters, following the adjustment of the dynamic zeroing policy in China.Methods A total of 288 confirmed COVID-19 pneumonia patients (mild/moderate group, 155; severe group, 133) from the Emergence Department, Beijing Chaoyang Hospital, were enrolled in this study and allocated to the training and validation cohort. The PI score of the initial chest CT was evaluated using a semi-quantitative scoring system, and clinical and laboratory parameters were collected. Radiomics and combination predictive models were developed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and multivariate logistic regression. The models' performance for predicting severe COVID-19 was assessed by receiver operating characteristics curve (ROC) analysis and calibration curve.Results Compared with the mild/moderate patients, the severe patients had higher levels of C-reactive protein (CRP), D-dimer, procalcitonin (PCT), and brain natriuretic peptide (BNP), but lower blood oxygen saturation and vaccination rate (P < 0.05). The severe group had a higher incidence of consolidation, multi-lobe involvement, interlobular septal thickening, air bronchogram sign, and pleural effusion compared to the mild/moderate group (P < 0.05). Moreover, the PI total score of severe patients was 16.4 ± 3.8, significantly higher than 8.5 ± 3.8 of milder patients (P < 0.001). The developed predictive nomogram, which includes four clinical characteristics and one CT feature, exhibited good performance in predicting severe COVID-19 with an area under the ROC (AUC) of 0.98 (95% CI, 0.97-1.00) in the training dataset, and 0.97 (95% CI, 0.94-1.00) in the validation dataset.Conclusions The combination predictive model, including CT score, clinical factors, and laboratory data, shows favorable predictive efficacy for severe COVID-19, which could potentially aid clinicians in triaging emergency patients.
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Derrame Pleural , Neumonía , COVID-19RESUMEN
COVID-19 has quickly become a worldwide pandemic, which has significantly impacted the economy, education, and social interactions. Understanding the humoral antibody response to SARS-CoV-2 proteins may help identify biomarkers that can be used to detect and treat COVID-19 infection. However, no immuno-proteomics platform exists that can perform such proteome-wide analysis. To address this need, we created a SARS-CoV-2 proteome microarray to analyze antibody interactions at amino acid resolution by spotting peptides 15 amino acids long with 5-amino acid offsets representing full-length SARS-CoV-2 proteins. Moreover, the array processing time is short (1.5 hours), the dynamic range is ~2 orders of magnitude, and the lowest limit of detection is 94 pg/mL. Here, the SARS-CoV-2 proteome array reveals that antibodies commercially available for SARS-CoV-1 proteins can also target SARS-CoV-2 proteins. These readily available reagents could be used immediately in COVID-19 research. Second, IgM and IgG immunogenic epitopes of SARS-CoV-2 proteins were profiled in the serum of ten COVID-19 patients. Such epitope biomarkers provide insight into the immune response to COVID-19 and are potential targets for COVID-19 diagnosis and vaccine development. Finally, serological antibodies that may neutralize viral entry into host cells via the ACE2 receptor were identified. Further investigation into whether these antibodies can inhibit the propagation of SARS-CoV-2 is warranted. Antibody and epitope profiling in response to COVID-19 is possible with our peptide-based SARS-COV-2 proteome microarray. The data gleaned from the array could provide invaluable information to the scientific community to understand, detect, and treat COVID-19.