HIGH-RESOLUTION MAPPING OF T-CELL HYBRID IMMUNITY TO THE ENTIRE SARS-CoV-2 PROTEOME

Background: To understand T-cell responses to SARS-CoV-2, it is essential to define the contribution of infection versus immunization to virus-specific hybrid immunity. Here, we characterized the breadth and magnitude of T-cell responses to the entire SARS-CoV2 proteome over a 2-year follow-up period in infected and vaccinated (CoV2+Vac+) and vaccinated and infected (Vac+CoV2+) individuals. Method(s): We selected samples from 38 (19 CoV2+ and 19 CoV2-, time1, T1) ProHEpiC-19 cohort participants, a prospective, longitudinal study starting in March 2020 involving 7,776 healthcare workers in Spain. Longitudinal samples were available from 10 of them after a 3-dose mRNA vaccination, including 5 CoV2+Vac+ and 5 Vac+CoV2+, at 824.5 and 250.5 days from symptoms onset (DfSO, time 2, T2). We measured the breadth and magnitude of IFN-y T-cell responses by ELISpot assay in cryopreserved PBMCs, using a 15-mer overlapping peptide (OLP) library of 2,790 SARS-CoV-2 peptides in 100 pools. Result(s): We identified immunodominant T-cell responses in S1, S2, nsp3, Env, NC, and M proteins across the SARS-CoV2 proteome. We observed an increased breadth of T-cell responses (responding pools over the entire region) to S1 (44 - 30%) and S2 (31 - 40%) in CoV2+Vac+ and Vac+CoV2+, respectively. In addition, CoV2+Vac+ had an exclusive and sustained response to M. We found significantly stronger responses in CoV2+Vac+ (P=0.0313). Particularly the total magnitude was greater in CoV2+Vac+ vs. Vac+CoV2+ in S1 (4476.88 vs. 1498.53), Env (457.34 vs. 250.50), and M (455.13 vs. 0.00) but not in S2 and nsp3. The total number of peptides for deconvolution was higher in CoV2+Vac+ (32 peptides) than in Vac+CoV2+ (3 peptides) during the follow-up. Seventy-five percent of the responses targeted S, and 25% M, ORF1a, and Env. Conclusion(s): These results profile immunodominant T-cell responses in S1, S2, nsp3, Env, NC, and M proteins across the entire SARS-CoV2 proteome. The data delineate differences in the number of T-cell responses primed hybrid immunity by infection previous to vaccination (CoV2+Vac+), being broader and of higher magnitude and underlining an exclusive T-cell response to the M region. Overall, these findings identify differences in long-term T-cell hybrid immunity primed by infection or vaccination, which may have implications in protection from re-infection and vaccine design.

Dynamic and spatial transcriptional responses in COVID-19-related chilblains

"COVID-toes" are chilblains that occurred in patients who may have been exposed to SARS-CoV-2, but without COVID-19 symptoms and/or with negative PCR or serology. The literature suggests that chilblains are an unexpected consequence of a strong interferon-mediated antiviral response, but the underlying molecular mechanisms remain poorly understood. We thus sought to explore the physiopathology of COVID-related chilblains by using spatially and temporally resolved transcriptomics. We included 19 patients with COVID-toes, and performed a complete virological assessment to exclude SARS-CoV-2 infection including skin viral metagenomics. Some patients had clinical symptoms evoking viral infection, but none had COVID-19. Apart from low levels of non-conventional antiphospholipid antibodies, biological tests were unremarkable. We performed spatially resolved transcriptomics (Visium, 10X Genomics) in 3 patients at different timepoints and compared them with 1 vaccination-related chilblain. We observed a different transcriptional profile in COVID-toes compared with COVID-19 vaccine-related chilblains. IRF1, CXCL10, ISG15 and STAT1 were highly expressed in COVID-toes and their expression decreased over time, confirming an activation of interferon and JAK/STAT pathways that was absent in vaccine-related chilblains. The proportion of inflammatory cell types obtained by spatial deconvolution varied over time in COVID-toes. Migratory dendritic cells were present at early stages, while T lymphocytes populations increased later. Overall, this work explores the mechanisms of COVID-19-related chilblains using spatially and temporally resolved transcriptomics.Copyright © 2023

Diagnosis and detection of COVID-19 infection on X-Ray and CT scans using deep learning based generative adversarial network

COVID-19 is presently one of the world's most serious health threats. However, PCR test kits are in poor supply, and the false-negative rate is significant in many countries. Patient triage is critical, and machine learning may be used to classify COVID-19 instances in chest X-ray or CT. X-rays scans will be utilised to extract and assess the pneumonia infection in the lungs caused by COVID-19. On the basis of GAN and FCN models, an image deep learning method is given that utilises these two models: GAN and FCN. First and foremost, the generator's network structure has been upgraded. With residual modules, convolutional learning can be more flexible in terms of how it responds to changes in the output. After reducing the sum of channels in the input feature by half, a larger convolution kernel is applied. Convolution and deconvolution layers are connected via a U-shaped network to prevent low-level info exchange. The GAN-FCN model achieved a CT scan accuracy of 94.32 percent and an X-ray picture accuracy of 95.62 percent, while existing deep learning models achieved a CT scan accuracy of almost 92 percent and an X-ray image accuracy of nearly 94 percent.Copyright © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Comparison of DDA, scanning window DIA, and HDMSE for assigning and quantifying glycopeptides

Amino acid substitutions to viral proteins can create or remove glycosites. While research groups have published assignment of viral protein glycosylation, there remains little consensus regarding how to quantify the glycosylation changes that occur among viral variants. This is because glycosylation is inherently micro-and macro-heterogeneous, making rigorous comparison of the complete glycosylated structures of viral proteins a statistical problem. In response, we have compared glycoproteomics data acquisition and bioinformatics methods for producing confident measurements of glycosylation similarity. We compared glycoproteomics assignments and quantification from data acquired with data-dependent acquisition (DDA), scanning window data-independent acquisition (swDIA), and broad mass range data-independent acquisition coupled with ion mobility spectrometry (HDMSE), respectively. We compared DDA, swDIA, and HDMSE mass spectral data to assign and quantify (i) the five N-linked glycosylation sites of the glycoprotein standard alpha-1-acid glycoprotein (AGP), (ii) the 12 sites of an influenza A virus hemagglutinin (HA) and (iii) the 22 sites of SARS-CoV-2 spike protein. For all three proteins, we observed that swDIA provided greater depth of coverage for glycopeptide precursor ions compared with DDA. The performance improvement of swDIA was mitigated to a degree by the difficulty of assigning low abundance precursor ions confidently. For this reason, we compared the performance of HDMSE data acquired using the Waters Cyclic IMS instrument, for which there is no precursor isolation step and no need for scanned quadrupole windows. The Cyclic IMS instrument alternated scans corresponding to low and high collision energy in a collision cell located after the mobility chamber. The resulting collision energy aligned retention time curves contained no missing data.Wedeveloped a glycopeptide-aware deconvolution approach to assign the HDMSE data accurately. For this, we connected precursors and product ions according to the combined retention time (RT) and ion mobility (IM) profiles. Using this approach, we demonstrated that HDMSE improved the coverage of glycopeptides over swDIA and DDA.

Blood transcriptome responses in patients correlate with severity of COVID-19 disease.

Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals display a wide spectrum of disease severity, as defined by the World Health Organization (WHO). One of the main factors underlying this heterogeneity is the host immune response, with severe COVID-19 often associated with a hyperinflammatory state. Aim: Our current study aimed to pinpoint the specific genes and pathways underlying differences in the disease spectrum and outcomes observed, through in-depth analyses of whole blood transcriptomics in a large cohort of COVID-19 participants. Results: All WHO severity levels were well represented and mild and severe disease displaying distinct gene expression profiles. WHO severity levels 1-4 were grouped as mild disease, and signatures from these participants were different from those with WHO severity levels 6-9 classified as severe disease. Severity level 5 (moderate cases) presented a unique transitional gene signature between severity levels 2-4 (mild/moderate) and 6-9 (severe) and hence might represent the turning point for better or worse disease outcome. Gene expression changes are very distinct when comparing mild/moderate or severe cases to healthy controls. In particular, we demonstrated the hallmark down-regulation of adaptive immune response pathways and activation of neutrophil pathways in severe compared to mild/moderate cases, as well as activation of blood coagulation pathways. Conclusions: Our data revealed discrete gene signatures associated with mild, moderate, and severe COVID-19 identifying valuable candidates for future biomarker discovery.

Molecular mechanisms underlying regulatory T cell deficiency and potential drugs in COVID-19

Background/Purpose: The 2019 outbreak of coronavirus disease COVID-19 causes immune system disruption. Recent studies reported that the decrease or depletion of regulatory T cell (Treg) may be responsible for overstimulation of the immune system and lung damage in patients with severe COVID-19. This study aims to find the molecular mechanisms and genetic biomarkers associated with Tregs in COVID-19, providing new ideas for the treatment of COVID-19. Method(s): RNA sequencing data of peripheral blood mononuclear cells (PBMC) from 252 COVID-19 infected patients and 69 healthy controls (HC) were obtained from the GEO database. The Tregs composition of COVID-19 samples was quantified using the CIBERSORT deconvolution method. The differential genes (DEGs) were identified by the limma R package. Gene co-expression network analysis (WGCNA) was used to identify the gene. Differentially expressed Tregs-related genes (DETregRGs) were obtained by intersecting DEGs with the highly related modular genes obtained in the previous step. The potential biological functions and pathways of DETregRGs were then explored. Protein-protein interaction (PPI) networks were subsequently constructed to identify hub genes. In addition, the prediction of small molecule drugs for the potential treatment of COVID-19 was made using the CMap database. Result(s): After the weighted gene co-expression network analysis (WGCNA), the turquoise module was highly correlated with Treg expression and a total of 134 DEGs was identified as DETregRGs. These genes were mainly involved in GO biological processes, such as the inflammatory response, and T cell differentiation of thymus. Then, 11 hub genes (including RPS12, RPL21, RPS3A, CD8B, CD3D, TRAT1, RPS6, CD3E, CD28, RPL3, and CD4) were ranked based on Molecular Complex Detection (MCODE) analysis. The TregRG score of COVID-19 patients showed significantly lower than HC, calculated by the 'singscore' algorithms. After the signature query of the CMap database, the KU-0063794, an mTOR inhibitor ranked second in the negative enrichment score, may restore immune system dysregulation caused by increased Th17 differentiation and decreased Treg differentiation during SARS-CoV- 2 infection. Conclusion(s): Our study examined in detail the molecular mechanisms underlying the inadequacy of Tregs in patients with COVID-19 infection. mTOR inhibitors may improve COVID-19 symptoms by expanding Tregs which may be one of the potential therapeutic methods that need further investigation. (Figure Presented).

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples.

Background: The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja). Methods: We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators. Results: Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants). Conclusion: This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

AKI in Patients With SARS-CoV-2 Is Significantly Associated With Mitochondrial Dysfunction and ER Stress

Background: AKI is a common complication of COVID-19. The peripheral blood molecular signatures are unknown and could unveil potential therapeutic targets. Method(s): We enrolled a prospective patient cohort of 283 patients with COVID-19 (Mar 24-Aug 26, 2020), with blood samples from Mount Sinai Biobank. We determined AKI severity using KDIGO criteria on admission parameters. 31 patients with severe AKI (AKI 2-3) were defined as cases. We then performed bulk peripheral RNA sequencing and fit a multivariate linear regression model adjusting for key covariates. We also performed cell-type deconvolution following to adjust for neutrophils, and whole blood cells. We considered a significant p-value (0.05) after Bonferroni correction and then used ingenuity pathway analysis (IPA) to analyze differentially expressed genes. Result(s): Patients who developed AKI were significantly older (67 vs. 60 yrs.) and had a greater prevalence of type 2 diabetes (37% vs 20%), and chronic kidney disease (20% vs 4%) vs. controls. Of the 18539 genes in the analysis, 1597 were upregulated and 1267 were downregulated after Bonferroni correction. Top canonical pathways (Fig 1) showed significantly downregulated genes including EIF2, eIF4, and p70S6K via activation of ATF6, a marker of ER stress. Potential mechanisms displayed by our analyses include upregulation of the NF-KB inhibitor and IL6 pathways. Genes involved in oxidative Phosphorylation and mitochondrial dysfunction were heavily downregulated and there was upregulation of markers of kidney cell necrosis. In contrast, upregulated genes CRK and TIMP2 have been previously implicated in kidney injury and progression. Downregulated mTOR pathway is responsible for the activation of the ER stress response via the eIF2/4 complex which is also supported by our finding of upregulated NRF2- transcriptional pathway. Conclusion(s): Transcriptomic analysis of AKI in COVID-19 revealed evidence of mitochondrial dysfunction driven by ER stress and immune-mediated pathways. Addressing these pathways could aide development of targeted therapies. (Figure Presented).

Simultaneous analysis of two drugs used as supportive treatment for COVID-19: comparative statistical studies and analytical ecological appraisal.

Pharmaceutical quality control products (QC) demand quick, sensitive, and cost-effective methods to ensure high production at a low cost. Green analytical methods are also becoming more common in pharmaceutical research to cut down on the amount of waste that goes into the environment. Meclizine hydrochloride (MZH) and pyridoxine hydrochloride (PYH) are reported to be excellent for calming down COVID-19. As a result, the amount of MZH and PYH manufactured by multinational pharmaceutical organizations has increased considerably during the last several months. The present work proposes three environmentally friendly, straightforward, and sensitive spectrophotometric procedures for quantification of MZH in the presence of PYH in a pure and marketable formulations. The approaches under examination include ratio subtraction (RSM), induced dual wavelength (IDW), and Fourier self-deconvolution (FSD). PYH, on the other hand, was directly quantified at 290 nm. For both drugs, the procedures follow Beer's law in the range of (5-50 µg/mL). The RSM, IDW, and FSD methods, as well as the zero-order approach for PYH, have all been verified in accordance with ICH standards. The ecological value of established methodologies was determined using four distinct ways: the national environmental methods index (NEMI), the analytical Eco-scale, the Analytical Greenness Metric (AGREE), and the green analytical process index (GAPI). Comparing the findings to those of the previously described spectrophotometric technique, no major changes were identified.

People with cystic fibrosis do not show an increased interferonresponse transcriptomic signature in nasal epithelial cells

Objectives: People with CF (PwCF) are at increased risk of respiratory infections and chronic inflammation.We sought to determine whether the inflammatory response is different in nasal epithelium of PwCF compared to healthy volunteers (HV). Since Interferons can increase ACE2 expression, a protein required for SARS-CoV-2 entry,we focused our analysis on the the focusing on the interferon-response signature. Methods: We reanalysed nasal curettage sample bulk RNA-seq signatures of pilot and validation datasets for which the study methods and demographics of the recruited cohort have already been reported. For this analysis, we performed in-silico deconvolution of bulk RNA-seq data using publicly available single-cell RNA-seq data from nasal epitheliumas a reference to determine the abundance of the specific cell types in each sample. Results: Hierarchical clustering of the pilot and validation cohorts revealed 3 clusters. Analysis of the larger validation cohort revealed that Cluster A included HV (11 out of 11 subjects) and both homozygous (7 out of 13 subjects) and heterozygous (3 out of 10 subjects) PwCF. Subject cluster A was characterised by increased expression of genes related to secretory and ciliated epithelial cells, whereas Clusters B and C contained both homozygous and heterozygous PwCF only and were characterised by genes restricted to neutrophils and involved in immune responses. We then compared samples from cluster A that contained samples from HV (n = 11), PwCF homozygous (n = 7) and heterozygous (n = 3) for F508del. This analysis identified 379 genes upregulated in HV and 146 genes upregulated in PwCF homozygous for F508del and only 44 and 6 genes upregulated in HV and PwCF heterozygous for F508del, respectively (FDR q < 0.05). ACE2, TMPRS2 or other interferon-response genes were not deferentially expressed in either comparison of cluster A. Conclusion: PwCF do not have higher expression of interferon-response genes in nasal epithelial cells

Estimation of Reproduction Number for Covid-19 with Statistical Correction of Notifications Delay

The pandemic of Covid-19 began in Brazil in February 2020. To evaluate the evolution of pandemics some metrics can be estimated, such as the reproduction number, Rt, and the basic reproduction number, R0. Due to the delay in the notifications, these estimates may present a bias. Taking the reported data, besides a sample of individuals who reported the day of symptoms onset, it is possible to estimate delay probabilities and to perform a deconvolution to correct the notifications' delay. In this work, it was performed a corrected estimate of Rt. This estimate is done based on the curve of notifications corrected through deconvolution. The approach is applied in three country cities and in the capital of Minas Gerais state. The behavior of Rt concerning the Minas Consciente program was evaluated. It was observed that the corrected Rt was more suitable to measure the effect of the program when compared to the raw Rt. When it was determined a more rigid mobility and activities regime by the program, it was observed a decrease in the median of the variation of the Rt of the cities studied. © 2003-2012 IEEE.

Mature neutrophils and a NF-κB-to-IFN transition determine the unifying disease recovery dynamics in COVID-19.

Disease recovery dynamics are often difficult to assess, as patients display heterogeneous recovery courses. To model recovery dynamics, exemplified by severe COVID-19, we apply a computational scheme on longitudinally sampled blood transcriptomes, generating recovery states, which we then link to cellular and molecular mechanisms, presenting a framework for studying the kinetics of recovery compared with non-recovery over time and long-term effects of the disease. Specifically, a decrease in mature neutrophils is the strongest cellular effect during recovery, with direct implications on disease outcome. Furthermore, we present strong indications for global regulatory changes in gene programs, decoupled from cell compositional changes, including an early rise in T cell activation and differentiation, resulting in immune rebalancing between interferon and NF-κB activity and restoration of cell homeostasis. Overall, we present a clinically relevant computational framework for modeling disease recovery, paving the way for future studies of the recovery dynamics in other diseases and tissues.

Real-Time Estimation of COVID-19 Infections: Deconvolution and Sensor Fusion

We propose, implement, and evaluate a method to estimate the daily number of new symptomatic COVID-19 infections, at the level of individual U.S. counties, by deconvolving daily reported COVID-19 case counts using an estimated symptom-onset-to-case-report delay distribution. Importantly, we focus on estimating infections in real-time (rather than retrospectively), which poses numerous challenges. To address these, we develop new methodology for both the distribution estimation and deconvolution steps, and we employ a sensor fusion layer (which fuses together predictions from models that are trained to track infections based on auxiliary surveillance streams) in order to improve accuracy and stability.

DNA methylation profiles in pneumonia patients reflect changes in cell types and pneumonia severity.

Immune cell-type composition changes with age, potentially weakening the response to infectious diseases. Profiling epigenetics marks of immune cells can help us understand the relationship with disease severity. We therefore leveraged a targeted DNA methylation method to study the differences in a cohort of pneumonia patients (both COVID-19 positive and negative) and unaffected individuals from peripheral blood.This approach allowed us to predict the pneumonia diagnosis with high accuracy (AUC = 0.92), and the PCR positivity to the SARS-CoV-2 viral genome with moderate, albeit lower, accuracy (AUC = 0.77). We were also able to predict the severity of pneumonia (PORT score) with an R2 = 0.69. By estimating immune cellular frequency from DNA methylation data, patients under the age of 65 positive to the SARS-CoV-2 genome (as revealed by PCR) showed an increase in T cells, and specifically in CD8+ cells, compared to the negative control group. Conversely, we observed a decreased frequency of neutrophils in the positive compared to the negative group. No significant difference was found in patients over the age of 65. The results suggest that this DNA methylation-based approach can be used as a cost-effective and clinically useful biomarker platform for predicting pneumonias and their severity.

Unexposed populations and potential COVID-19 hospitalisations and deaths in European countries as per data up to 21 November 2021.

We estimate the potential remaining COVID-19 hospitalisation and death burdens in 19 European countries by estimating the proportion of each country's population that has acquired immunity to severe disease through infection or vaccination. Our results suggest many European countries could still face high burdens of hospitalisations and deaths, particularly those with lower vaccination coverage, less historical transmission and/or older populations. Continued non-pharmaceutical interventions and efforts to achieve high vaccination coverage are required in these countries to limit severe COVID-19 outcomes.

Thermal Image Restoration Based on LWIR Sensor Statistics.

An imaging system has natural statistics that reflect its intrinsic characteristics. For example, the gradient histogram of a visible light image generally obeys a heavy-tailed distribution, and its restoration considers natural statistics. Thermal imaging cameras detect infrared radiation, and their signal processors are specialized according to the optical and sensor systems. Thermal images, also known as long wavelength infrared (LWIR) images, suffer from distinct degradations of LWIR sensors and residual nonuniformity (RNU). However, despite the existence of various studies on the statistics of thermal images, thermal image processing has seldom attempted to incorporate natural statistics. In this study, natural statistics of thermal imaging sensors are derived, and an optimization method for restoring thermal images is proposed. To verify our hypothesis about the thermal images, high-frequency components of thermal images from various datasets are analyzed with various measures (correlation coefficient, histogram intersection, chi-squared test, Bhattacharyya distance, and Kullback-Leibler divergence), and generalized properties are derived. Furthermore, cost functions accommodating the validated natural statistics are designed and minimized by a pixel-wise optimization method. The proposed algorithm has a specialized structure for thermal images and outperforms the conventional methods. Several image quality assessments are employed for quantitatively demonstrating the performance of the proposed method. Experiments with synthesized images and real-world images are conducted, and the results are quantified by reference image assessments (peak signal-to-noise ratio and structural similarity index measure) and no-reference image assessments (Roughness (Ro) and Effective Roughness (ERo) indices).

High-Density Blood Transcriptomics Reveals Precision Immune Signatures of SARS-CoV-2 Infection in Hospitalized Individuals.

The immune response to COVID-19 infection is variable. How COVID-19 influences clinical outcomes in hospitalized patients needs to be understood through readily obtainable biological materials, such as blood. We hypothesized that a high-density analysis of host (and pathogen) blood RNA in hospitalized patients with SARS-CoV-2 would provide mechanistic insights into the heterogeneity of response amongst COVID-19 patients when combined with advanced multidimensional bioinformatics for RNA. We enrolled 36 hospitalized COVID-19 patients (11 died) and 15 controls, collecting 74 blood PAXgene RNA tubes at multiple timepoints, one early and in 23 patients after treatment with various therapies. Total RNAseq was performed at high-density, with >160 million paired-end, 150 base pair reads per sample, representing the most sequenced bases per sample for any publicly deposited blood PAXgene tube study. There are 770 genes significantly altered in the blood of COVID-19 patients associated with antiviral defense, mitotic cell cycle, type I interferon signaling, and severe viral infections. Immune genes activated include those associated with neutrophil mechanisms, secretory granules, and neutrophil extracellular traps (NETs), along with decreased gene expression in lymphocytes and clonal expansion of the acquired immune response. Therapies such as convalescent serum and dexamethasone reduced many of the blood expression signatures of COVID-19. Severely ill or deceased patients are marked by various secondary infections, unique gene patterns, dysregulated innate response, and peripheral organ damage not otherwise found in the cohort. High-density transcriptomic data offers shared gene expression signatures, providing unique insights into the immune system and individualized signatures of patients that could be used to understand the patient's clinical condition. Whole blood transcriptomics provides patient-level insights for immune activation, immune repertoire, and secondary infections that can further guide precision treatment.

Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data.

BACKGROUND: Mortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution. METHODS: Age-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions. RESULTS: It was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 - 3.5) to 40.0% at age 95 years (CI: 36.6 - 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 - 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 - 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns. CONCLUSIONS: Deconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age.

COVID-19 deep classification network based on convolution and deconvolution local enhancement.

Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection rate and prevalence rate. The novel coronavirus pneumonia is a significant difference between the positive and negative patients with asymptomatic infections. To effectively improve the accuracy of doctors' manual judgment of positive and negative COVID-19, this paper proposes a deep classification network model of the novel coronavirus pneumonia based on convolution and deconvolution local enhancement. Through convolution and deconvolution operation, the contrast between the local lesion region and the abdominal cavity of COVID-19 is enhanced. Besides, the middle-level features that can effectively distinguish the image types are obtained. By transforming the novel coronavirus detection problem into the region of interest (ROI) feature classification problem, it can effectively determine whether the feature vector in each feature channel contains the image features of COVID-19. This paper uses an open-source COVID-CT dataset provided by Petuum researchers from the University of California, San Diego, which is collected from 143 novel coronavirus pneumonia patients and the corresponding features are preserved. The complete dataset (including original image and enhanced image) contains 1460 images. Among them, 1022 (70%) and 438 (30%) are used to train and test the performance of the proposed model, respectively. The proposed model verifies the classification precision in different convolution layers and learning rates. Besides, it is compared with most state-of-the-art models. It is found that the proposed algorithm has good classification performance. The corresponding sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and precision are 0.98, 0.96, 0.98, and 0.97, respectively.

Age-associated Ligand-receptor Interactions Imputed from Nasopharyngeal Transcriptomes of COVID-19 Patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has exhibited different clinical manifestations amongst various age cohorts. As the immune microenvironment may play a role in clinical progression, it is crucial to examine molecular interactions to gain insight into host response. Therefore, to elucidate any differences in host response related to age, the present study imputed ligand-receptor interactions within the nasopharyngeal immune microenvironment in patients affected with SARS-COV-2. Tissue purities, the proportion of non-immune cells in the tissue sample, of 467 nasopharyngeal transcriptome profiles were estimated using known mRNA expression signatures of stromal/immune cells. Using the purity estimates and bulk tissue expression values, non-negative linear regression was used to estimate average expression of each gene in the tissue/stroma compartments. The inferred expression profiles were annotated with a curated database of ligand-receptor interactions and assumed as reasonable proxies for the law of mass action, allowing for quantification of directional ligand-receptor complex concentrations under equilibrium. It was found that older patients (>60 years) exhibited decreased interactions with receptors selectin L receptor SELL and increased interactions with pro-inflammatory chemokine receptors CXCR2 and CCR1. Younger patients showed increased interactions with various members of the TNF receptor super family (TNFRSF). The interactions were further related to immune cell subtypes, with older patients predicted to have less CD8+ and CD4+ resting T cells but increased neutrophil proportions. Collectively, the results suggest certain ligand-receptor interactions of the nasopharyngeal immune microenvironment are age-associated in response to SARS-CoV-2.