Brief Research Report: Serum clara cell 16 kDa protein levels are increased in patients hospitalized for severe SARS-CoV-2 or sepsis infection.

Background: Clara cell 16 kDa protein (CC16) is a secretory protein primarily expressed in epithelial cells in the lungs. Previous studies show that CC16 exerts anti-inflammatory and immune-modulatory properties in both acute and chronic pulmonary diseases. However, despite the evidence of CC16's high biomarker potential, evaluation of its role in infectious diseases is yet very limited. Methods: Serum CC16 concentrations were measured by ELISA and assessed in two different types of severe infections. Using a case-control study design, patients treated for either severe SARS-CoV-2 or severe non-pulmonary sepsis infection were compared to age- and sex-matched healthy human subjects. Results: Serum CC16 was significantly increased in both types of infection (SARS-CoV-2: 96.22 ± 129.01 ng/ml vs. healthy controls: 14.05 ± 7.48 ng/ml, p = 0.022; sepsis: 35.37 ± 28.10 ng/ml vs. healthy controls: 15.25 ± 7.51 ng/ml, p = 0.032) but there were no distinct differences between infections with and without pulmonary focus (p = 0.089). Furthermore, CC16 serum levels were positively correlated to disease duration and inversely to the platelet count in severe SARS-CoV-2 infection. Conclusions: Increased CC16 serum levels in both SARS-CoV-2 and sepsis reinforce the high potential as a biomarker for epithelial cell damage and bronchoalveolar-blood barrier leakage in pulmonary as well as non-pulmonary infectious diseases.

Increased protease-activated receptor 1 autoantibodies are associated with severe COVID-19.

In patients with severe #COVID19, increased levels of autoantibodies against PAR1 were found. These might serve as allosteric agonists of PAR1 on endothelial cells and platelets, and thus might contribute to the pathogenesis of microthrombosis in COVID-19. https://bit.ly/3pqM9Vv.

Brief Research Report: Serum clara cell 16 kDa protein levels are increased in patients hospitalized for severe SARS-CoV-2 or sepsis infection

Background Clara cell 16 kDa protein (CC16) is a secretory protein primarily expressed in epithelial cells in the lungs. Previous studies show that CC16 exerts anti-inflammatory and immune-modulatory properties in both acute and chronic pulmonary diseases. However, despite the evidence of CC16’s high biomarker potential, evaluation of its role in infectious diseases is yet very limited. Methods Serum CC16 concentrations were measured by ELISA and assessed in two different types of severe infections. Using a case-control study design, patients treated for either severe SARS-CoV-2 or severe non-pulmonary sepsis infection were compared to age- and sex-matched healthy human subjects. Results Serum CC16 was significantly increased in both types of infection (SARS-CoV-2: 96.22 ± 129.01 ng/ml vs. healthy controls: 14.05 ± 7.48 ng/ml, p = 0.022;sepsis: 35.37 ± 28.10 ng/ml vs. healthy controls: 15.25 ± 7.51 ng/ml, p = 0.032) but there were no distinct differences between infections with and without pulmonary focus (p = 0.089). Furthermore, CC16 serum levels were positively correlated to disease duration and inversely to the platelet count in severe SARS-CoV-2 infection. Conclusions Increased CC16 serum levels in both SARS-CoV-2 and sepsis reinforce the high potential as a biomarker for epithelial cell damage and bronchoalveolar−blood barrier leakage in pulmonary as well as non-pulmonary infectious diseases.

Increased protease-activated receptor 1 autoantibodies are associated with severe COVID-19

Immune perturbation is a hallmark of Coronavirus Disease 2019 (COVID-19) with ambiguous roles of various immune cell compartments. Plasma cells, responsible for antibody production, have a two-pronged response while mounting an immune defence with 1) physiological immune response producing neutralizing antibodies against protein structures of SARS-CoV-2 and 2) potentially deleterious autoantibody generation. Growing evidence hints towards broad activation of plasma cells and the presence of pathologic autoantibodies (abs) that mediate immune perturbation in acute COVID-19 [1]. Recently, a systematic screening for abs confirmed induction of diverse functional abs in SARS-CoV-2 infection, targeting several immunomodulatory proteins, including cytokines/chemokines and their respective G-protein coupled receptors (GPCR) [1]. Abs against GPCR act as agonistic and allosteric receptor modulators and are linked to chronic inflammatory diseases [2] and, as we recently demonstrated, disease severity in acute COVID-19 [3].

Swarm Learning for decentralized and confidential clinical machine learning.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.