RESUMEN
Serum antibodies IgM and IgG are elevated during COVID-19 to defend against viral attack. Atypical results such as negative and abnormally high antibody expression were frequently observed whereas the underlying molecular mechanisms are elusive. In our cohort of 144 COVID-19 patients, 3.5% were both IgM and IgG negative whereas 29.2% remained only IgM negative. The remaining patients exhibited positive IgM and IgG expression, with 9.3% of them exhibiting over 20-fold higher titers of IgM than the others at their plateau. IgG titers in all of them were significantly boosted after vaccination in the second year. To investigate the underlying molecular mechanisms, we classed the patients into four groups with diverse serological patterns and analyzed their two-year clinical indicators. Additionally, we collected 111 serum samples for TMTpro-based longitudinal proteomic profiling and characterized 1494 proteins in total. We found that the continuously negative IgM and IgG expression during COVID-19 were associated with mild inflammatory reactions and high T cell responses. Low levels of serum IgD, inferior complement 1 activation of complement cascades, and insufficient cellular immune responses might collectively lead to compensatory serological responses, causing overexpression of IgM. Serum CD163 was positively correlated with antibody titers during seroconversion. This study suggests that patients with negative serology still developed cellular immunity for viral defense, and that high titers of IgM might not be favorable to COVID-19 recovery.
Asunto(s)
COVID-19RESUMEN
More than 450 million individuals have recovered from COVID-19, but little is known about the host responses to long COVID. We performed proteomic and metabolomic analyses of 991 blood and urine specimens from 144 COVID-19 patients with comprehensive clinical data and up to 763 days of follow up. Our data showed that the lungs and kidneys are the most vulnerable organs in long COVID patients. Pulmonary and renal long COVID of one-year revisit can be predicted by a machine learning model based on clinical and multi-omics data collected during the first month from the disease onset with an ACC of 87.5%. Serum protein SFTPB and ATR were associated with pulmonary long COVID and might be potential therapeutic targets. Notably, our data show that all the patients with persistent pulmonary ground glass opacity or patchy opacity lesions developed into pulmonary fibrosis at two-year revisit. Together, this study depicts the longitudinal clinical and molecular landscape of COVID-19 with up to two-year follow-up and presents a method to predict pulmonary and renal long COVID.
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COVID-19RESUMEN
Purpose of the study: The first purpose of this study is to present the detailed requirements from employers;the second is to share a powerful data collecting and visualization method with translation scholars and practitioners. Method: We proposed a new method of data collection and data analyzation. The technology of web scraping, such as programs written in Python can retrieve the first-hand data in a faster and more convenient manner than traditional questionnaires or interviews;the technology of data visualization, such as Tableau Desktop, can show the results between graphic marks and data values in a graphic representation. With these technologies, we collected, cleaned, analyzed, mapped, and visualized 46144 listings from 51job, an online recruitment website, across China in terms of translators. Results: After analyzation, we find: (1) employers value applicants’ bilingual competences most;(2) employers require applicants should have some working experience;(3) employers require applicants should have relevant knowledge in specific fields;(4) office software and translation technologies are required. Conclusion: Job listing is an important source to analyze industry requirements. With the help of web scraping and visualization technology, a new world will be opened to scholars and practitioners in translation studies.