Pyroptotic Patterns in Blood Leukocytes Predict Disease Severity and Outcome in COVID-19 Patients

The global coronavirus disease 2019 (COVID-19) pandemic has lasted for over 2 years now and has already caused millions of deaths. In COVID-19, leukocyte pyroptosis has been previously associated with both beneficial and detrimental effects, so its role in the development of this disease remains controversial. Using transcriptomic data (GSE157103) of blood leukocytes from 126 acute respiratory distress syndrome patients (ARDS) with or without COVID-19, we found that COVID-19 patients present with enhanced leukocyte pyroptosis. Based on unsupervised clustering, we divided 100 COVID-19 patients into two clusters (PYRcluster1 and PYRcluster2) according to the expression of 35 pyroptosis-related genes. The results revealed distinct pyroptotic patterns associated with different leukocytes in these PYRclusters. PYRcluster1 patients were in a hyperinflammatory state and had a worse prognosis than PYRcluster2 patients. The hyperinflammation of PYRcluster1 was validated by the results of gene set enrichment analysis (GSEA) of proteomic data (MSV000085703). These differences in pyroptosis between the two PYRclusters were confirmed by the PYRscore. To improve the clinical treatment of COVID-19 patients, we used least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model based on differentially expressed genes between PYRclusters (PYRsafescore), which can be applied as an effective prognosis tool. Lastly, we explored the upstream transcription factors of different pyroptotic patterns, thereby identifying 112 compounds with potential therapeutic value in public databases.

TransMUSE: Transferable Traffic Prediction in MUlti-Service EdgeNetworks (preprint)

The Covid-19 pandemic has forced the workforce to switch to working from home, which has put significant burdens on the management of broadband networks and called for intelligent service-by-service resource optimization at the network edge. In this context, network traffic prediction is crucial for operators to provide reliable connectivity across large geographic regions. Although recent advances in neural network design have demonstrated potential to effectively tackle forecasting, in this work we reveal based on real-world measurements that network traffic across different regions differs widely. As a result, models trained on historical traffic data observed in one region can hardly serve in making accurate predictions in other areas. Training bespoke models for different regions is tempting, but that approach bears significant measurement overhead, is computationally expensive, and does not scale. Therefore, in this paper we propose TransMUSE, a novel deep learning framework that clusters similar services, groups edge-nodes into cohorts by traffic feature similarity, and employs a Transformer-based Multi-service Traffic Prediction Network (TMTPN), which can be directly transferred within a cohort without any customization. We demonstrate that TransMUSE exhibits imperceptible performance degradation in terms of mean absolute error (MAE) when forecasting traffic, compared with settings where a model is trained for each individual edge node. Moreover, our proposed TMTPN architecture outperforms the state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic prediction task. To the best of our knowledge, this is the first work that jointly employs model transfer and multi-service traffic prediction to reduce measurement overhead, while providing fine-grained accurate demand forecasts for edge services provisioning.

Effectiveness of Convalescent Plasma for Treatment of COVID-19 Patients (preprint)

Background and objective: The outbreak of COVID-19 has become a global health concern. In this study, we evaluate the effectiveness and safety of convalescent plasma therapy in patients with severe and critically ill COVID-19. Methods: Sixteen COVID-19 patients received transfusion of anti-COVID-19 antibody-positive convalescent plasma. The main outcome was time for viral nucleic acid amplification (NAA) test turning negative. Clinical laboratory parameters were measured at the baseline (d0) before plasma transfusion, and day 1 (d1), day 3 (d3) after transfusion as well. Results: Among the 16 patients, 10 of them had a consistently positive result of viral NAA test before convalescent plasma transfusion. Eight patients (8/10) became negative from day 2 to day 8 after transfusion. Severe patients showed a shorter time for NAA test turning negative after transfusion (mean rank 2.17 vs 5.90, P = 0.036). Two critically ill patients transfused plasma with lower antibody level remained a positive result of NAA test. CRP level demonstrated a decline 1 day after convalescent plasma treatment, compared with the baseline (P = 0.017). No adverse events were observed during convalescent plasma transfusion. Conclusions: Viral NAA test of most patients with COVID-19 who received convalescent plasma transfusion turned negative on the 2nd to 8th days after transfusion, and the negative time of severe patients was shorter than that of critically ill patients.