Blood Transcriptomes of Kidney Recipients with Covid-19 Demonstrate Deficient Adaptive Immune Activation

Purpose: Kidney transplant recipients (KTRs) have poor outcomes compared to non-KTRs with acute COVID-19. To provide insight into management of immunosuppression (IS) during COVID-19, we studied immune signatures from the peripheral blood during and after COVID-19 infection from a multicenter KTR cohort. Method(s): Clinical data were collected by chart review. Paxgene blood RNA was polyA-selected and sequenced at enrollment Results: A total of 64 KTRs affected with COVID-19 were enrolled (31 Early cases (<4weeks from a positive SARS-CoV-2 PCR test) and 33 late cases). Out of the 64 patients, eight died and three encountered graft losses during follow-up. Among 31 early cases, we detected differentially expressed genes (nominal p-value < 0.01) in the blood transcriptome that were positively or negatively associated with the COVID-19 severity score (scale of 1 to 7 with increasing severity;Fig 1A). Enrichment analyses showed upregulation of neutrophil and innate immune pathways and downregulation of adaptive immune activation pathways with increasing severity score (Fig 1B). This observation was independent of lymphocyte count, despite reduction in immunosuppression (IS) in 75% of KTRs. Interestingly, compared with early cases, the blood transcriptome in late cases showed "normalization" of these enriched pathways after 4 weeks, suggesting return of adaptive immune system activation despite re-initiation of immunosuppression (Fig 1C). The latter analyses were adjusted for the severity score. Interestingly, similar pathway enrichment with worsening severity of COVID-19 was identifiable from a public dataset of non-KTRs (GSE152418), showing overlapped signatures for acute COVID-19 between KTRs and non-KTRs (overlap P<0.05) (Fig 1D). Conclusion(s): Blood transcriptome of COVID-KTRs shows marked decrease in adaptive immune system activation during acute COVID-19, even during IS reduction, which show recovery after acute illness. (Figure Presented).

Blood Transcriptomes of Covid-19 Infected Kidney Recipients Demonstrate Frequent and Persistent SARS-CoV-2 Virome Gene Expression

Purpose: Kidney transplant recipients (KTRs) have poor outcomes vs non-KTRs with acute COVID-19. To provide insight into management of immunosuppression during acute COVID-19, we studied peripheral blood transcriptomes during and after COVID-19 from a multicenter KTR cohort. Method(s): Clinical data were collected by chart review. Paxgene blood RNA was polyA-selected and sequenced at enrollment. Result(s): A total of 64 KTRs with COVID-19 were enrolled (31 Early cases (<4weeks from a positive SARS-CoV-2 PCR test) and 33 late cases). Out of the 64 patients, eight died and three encountered graft losses during follow-up. Due to presence of mRNA reads in the blood transcriptome unmapped to the human genome, we aligned the mRNA short reads to the SARS-CoV-2 genome. Surprisingly, our strategy detected the SARS-Cov2 mRNA, especially Spike mRNA in 27 (87%) early cases, and 18 (54%) of late cases (Fig 1A and B). We then analyzed the raw reads from a public dataset of non-KTRs with Paxgene RNA (GSE172114). The SARS-CoV-2 Spike mRNA was detected in 2/47 (4.2%) critically ill COVID-19 cases and 0/25 noncritically ill cases in this non-KTR dataset (compared to KTRs, Chi-square P<0.001;Fig 1B). Among our KTRs, the amount of Spike mRNA was associated positively with the COVID-19 severity score (scale of 1 to 7 of increasing severity;Fig 1C) and inversely with time from initial positive PCR (Fig 1D). More interestingly, 7/64 patients had detectable Spike RNA-emia beyond 60 days after COVID-19 diagnosis. Of the 3 graft losses in our cohort, 2 occurred among these 7 patients. Conclusion(s): Blood transcriptome of KTRs with COVID-19 demonstrated a risk for persistent viremia with implications for pathogenesis of COVID-19 disease. This finding also supports using passive immune strategies in COVID-KTRs. (Figure Presented).

Numerical approach to design the graphene-based multilayered surface plasmon resonance biosensor for the rapid detection of the novel coronavirus

In this article, a graphene-based multilayered surface plasmon resonance (SPR) biosensor of (BK7/WS2/Au/BaTiO3/Graphene) is proposed for the rapid detection of the novel coronavirus (COVID-19). The proposed SPR biosensor is designed based on the angular interrogation attenuated total reflection (ATR) method for rapid detection of the COVID-19 virus. The sensor's surface plasmon polaritons (SPPs) and the sensing region refractive index (RI) are changed, owing to the interaction of various concentrated ligand-analytes. The specific ligand is mechanized with the proposed sensor surface and the target analyte that has flowed onto the sensing surface. The proposed sensor is capable of detecting the COVID-19 virus rapidly in two different ligand-analytes environments, such as: (i) the virus spike receptor-binding domain (RBD) as an analyte and monoclonal antibodies (mAbs) as a probe ligand, and (ii) the monoclonal antibodies (IgG or IgM) as an analyte and the virus spike RBD as a probe ligand. Due to the binding of the target ligand-analytes, the concentration level of the sensing region is incremented. As the increment in the concentration level, the RI of the sensing medium increases, therefore the change in RI causes the shift in the SPR angle resulting in the output reflectance intensity. The performance of the multilayered SPR sensor is analyzed numerically using the finite element method (FEM) method. Numerically, the proposed sensor provides the maximum angular shift sensitivity at 230.77 deg/refractive index unit (RIU), detection accuracy (DA) at 0.161 deg(-1), and the figure of merits (FOM) is at 37.22 RIU-1. In addition, with each additional graphene layer number (L), the proposed sensor exhibits the angular shift sensitivity increment (1 + 0.7L) times. The novelty of the proposed multilayer (BK7/WS2/Au/BaTiO3/Graphene) sensor is highly angular sensitivity, and capable of detecting the COVID-19 virus rapidly without a false-positive report. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

SVM Based COVID-19 Detection from CT Scan Image using Local Feature

During the outbreak time of pandemic COVID-19, we are so much worried and scared about our life. Going to the hospital is a very risky job to test COVID-19 because ordinary people can be easily infected by the COVID-19 patient. and also it is very time-consuming to get the test result of the COVID-19. In order to focus on these issues, a machine learning model is designed to detect COVID-19 using the local feature of CT-scan images and Support Vector Machine (SVM). A publicly available SARS-CoV-2 (severe Acute Respiratory Syndrome Coronavirus 2) dataset is used in the proposed system which contains 2481 CT scan images in total where 1252 samples are COVID and the rest of the samples are Non-COVID. Local Binary Pattern (LBP) algorithm is applied to extract local features from images. We also compare our proposed model with some state-of-the-art algorithms such as Decision Tree (DT), K-Nearest Neighbor (KNN), and AdaBoost based on some evaluation matrices i.e. precision, recall, and F1 score. According to the result, the proposed model provides 97.09% F1 score which is better to detect COVID-19 for clinical usage. © 2021 IEEE.

Secure IoT communication systems for prediction of COVID-19 Outbreak: An optimal signal processing algorithm

This paper proposes an innovative COVID-19 stochastic model to secure communication systems for transferring and storing epidemic data. Considering the observable and non-observable COVID-19 states such as infection, confirmed/unconfirmed cases, recovery, and reported/unreported deaths, the dynamic COVID-19 framework is developed and verified. For transmitting local observation information to the central control center like CDC, an innovative internet of things (IoT) based secure communication system is proposed. For predicting COVID-19 model, an optimal signal processing algorithm is developed and implemented. Based on the designed gain, the dynamic system forecasting error is reduced to develop an accurate COVID-19 prediction method. Extensive results show that the proposed technique can properly forecast COVID-19 states within a short period of time. Consequently, the developed simulator and analysis is observed as a valuable resource for COVID-19 state prediction, proactive action and also for information security and privacy. © 2020 IEEE.