Antibody titer levels and the effect on subsequent SARS-CoV-2 infection in a large US-based cohort.

Despite a growing amount of data around the kinetics and durability of the antibody response induced by vaccination and previous infection, there is little understanding of whether or not a given quantitative level of antibodies correlates to protection against SARS-CoV-2 infection or reinfection. In this study, we examine SARS-CoV-2 anti-spike receptor binding domain (RBD) antibody titers and subsequent SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) tests in a large cohort of US-based patients. We analyzed antibody test results in a cohort of 22,204 individuals, 6.8% (n = 1,509) of whom eventually tested positive for SARS-CoV-2 RNA, suggesting infection or reinfection. Kaplan-Meier curves were plotted to understand the effect of various levels of anti-spike RBD antibody titers (classified into discrete ranges) on subsequent RT-PCR positivity rates. Statistical analyses included fitting a Cox proportional hazards model to estimate the age-, sex- and exposure-adjusted hazard ratios for S antibody titer, using zip-code positivity rates by week as a proxy for COVID-19 exposure. It was found that the best models of the temporally associated infection risk were those based on log antibody titer level (HR = 0.836 (p < 0.05)). When titers were binned, the hazard ratio associated with antibody titer >250 Binding Antibody Units (BAU) was 0.27 (p < 0.05, 95% CI [0.18, 0.41]), while the hazard ratio associated with previous infection was 0.20 (p < 0.05, 95% CI [0.10, 0.39]). Fisher exact odds ratio (OR) for Ab titers <250 BAU showed OR = 2.84 (p < 0.05; 95% CI: [2.30, 3.53]) for predicting the outcome of a subsequent PCR test. Antibody titer levels correlate with protection against subsequent SARS-CoV-2 infection or reinfection when examining a cohort of real-world patients who had the spike RBD antibody assay performed.

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes.

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

Cross-immunity against SARS-COV-2 variants of concern in naturally infected critically ill COVID-19 patients.

Critically ill patients infected with SARS-CoV-2 display adaptive immunity, but it is unknown if they develop cross-reactivity to variants of concern (VOCs). We profiled cross-immunity against SARS-CoV-2 VOCs in naturally infected, non-vaccinated, critically ill COVID-19 patients. Wave-1 patients (wild-type infection) were similar in demographics to Wave-3 patients (wild-type/alpha infection), but Wave-3 patients had higher illness severity. Wave-1 patients developed increasing neutralizing antibodies to all variants, as did patients during Wave-3. Wave-3 patients, when compared to Wave-1, developed more robust antibody responses, particularly for wild-type, alpha, beta and delta variants. Within Wave-3, neutralizing antibodies were significantly less to beta and gamma VOCs, as compared to wild-type, alpha and delta. Patients previously diagnosed with cancer or chronic obstructive pulmonary disease had significantly fewer neutralizing antibodies. Naturally infected ICU patients developed adaptive responses to all VOCs, with greater responses in those patients more likely to be infected with the alpha variant, versus wild-type.

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention-based neural networks.

Viral infections represent a major health concern worldwide. The alarming rate at which SARS-CoV-2 spreads, for example, led to a worldwide pandemic. Viruses incorporate genetic material into the host genome to hijack host cell functions such as the cell cycle and apoptosis. In these viral processes, protein-protein interactions (PPIs) play critical roles. Therefore, the identification of PPIs between humans and viruses is crucial for understanding the infection mechanism and host immune responses to viral infections and for discovering effective drugs. Experimental methods including mass spectrometry-based proteomics and yeast two-hybrid assays are widely used to identify human-virus PPIs, but these experimental methods are time-consuming, expensive, and laborious. To overcome this problem, we developed a novel computational predictor, named cross-attention PHV, by implementing two key technologies of the cross-attention mechanism and a one-dimensional convolutional neural network (1D-CNN). The cross-attention mechanisms were very effective in enhancing prediction and generalization abilities. Application of 1D-CNN to the word2vec-generated feature matrices reduced computational costs, thus extending the allowable length of protein sequences to 9000 amino acid residues. Cross-attention PHV outperformed existing state-of-the-art models using a benchmark dataset and accurately predicted PPIs for unknown viruses. Cross-attention PHV also predicted human-SARS-CoV-2 PPIs with area under the curve values >0.95. The Cross-attention PHV web server and source codes are freely available at https://kurata35.bio.kyutech.ac.jp/Cross-attention_PHV/ and https://github.com/kuratahiroyuki/Cross-Attention_PHV, respectively.

Monocytosis in the acute phase of SARS-CoV-2 infection predicts the presence of anosognosia for cognitive deficits in the chronic phase.

Reduced awareness of neuropsychological disorders (i.e., anosognosia) is a striking symptom of post-COVID-19 condition. Some leukocyte markers in the acute phase may predict the presence of anosognosia in the chronic phase, but they have not yet been identified. This study aimed to determine whether patients with anosognosia for their memory deficits in the chronic phase presented specific leukocyte distribution in the acute phase, and if so, whether these leukocyte levels might be predictive of anosognosia. First, we compared the acute immunological data (i.e., white blood cell differentiation count) of 20 patients who displayed anosognosia 6-9 months after being infected with SARS-CoV-2 (230.25 ± 46.65 days) versus 41 patients infected with SARS-Cov-2 who did not develop anosognosia. Second, we performed an ROC analysis to evaluate the predictive value of the leukocyte markers that emerged from this comparison. Blood circulating monocytes (%) in the acute phase of SARS-CoV-2 infection were associated with long-term post-COVID-19 anosognosia. A monocyte percentage of 7.35% of the total number of leukocytes at admission seemed to predict the presence of chronic anosognosia 6-9 months after infection.

Development and validation of a paper spray mass spectrometry method for the rapid quantitation of remdesivir and its active metabolite, GS-441524, in human plasma.

Introduction: Remdesivir (GS-5734) is a nucleoside analog prodrug with antiviral activity against several single-stranded RNA viruses, including the novel severe respiratory distress syndrome virus 2 (SARS-CoV-2). It is currently the only FDA-approved antiviral agent for the treatment of individuals with COVID-19 caused by SARS-CoV-2. However, remdesivir pharmacokinetics/pharmacodynamics (PK/PD) and toxicity data in humans are extremely limited. It is imperative that precise analytical methods for the quantification of remdesivir and its active metabolite, GS-441524, are developed for use in further studies. We report, herein, the first validated anti-viral paper spray-mass spectrometry (PS-MS/MS) assay for the quantification of remdesivir and GS-441524 in human plasma. We seek to highlight the utility of PS-MS/MS technology and automation advancements for its potential future use in clinical research and the clinical laboratory setting. Methods: Calibration curves for remdesivir and GS-441524 were created utilizing seven plasma-based calibrants of varying concentrations and two isotopic internal standards of set concentrations. Four plasma-based quality controls were prepared in a similar fashion to the calibrants and utilized for validation. No sample preparation was needed. Briefly, plasma samples were spotted on a paper substrate contained within pre-manufactured plastic cassette plates, and the spots were dried for 1 h. The samples were then analyzed directly for 1.2 min utilizing PS-MS/MS. All experiments were performed on a Thermo Scientific Altis triple quadrupole mass spectrometer utilizing automated technology. Results: The calibration ranges were 20 - 5000 and 100 - 25000 ng/mL for remdesivir and GS-441524, respectively. The calibration curves for the two antiviral agents showed excellent linearity (average R2 = 0.99-1.00). The inter- and intra-day precision (%CV) across validation runs at four QC levels for both analytes was less than 11.2% and accuracy (%bias) was within ± 15%. Plasma calibrant stability was assessed and degradation for the 4 °C and room temperature samples were seen beginning at Day 7. The plasma calibrants were stable at -20 °C. No interference, matrix effects, or carryover was discovered during the validation process. Conclusions: PS-MS/MS represents a useful methodology for rapidly quantifying remdesivir and GS-441524, which may be useful for clinical PK/PD, therapeutic drug monitoring (TDM), and toxicity assessment, particularly during the current COVID-19 pandemic and future viral outbreaks.

A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in Coronavirus Disease 2019.

BACKGROUND AND AIMS: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the health care systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized (inpatients) or discharged (outpatients). Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration, and discharged patients often returned to the hospital for aggravation of their condition. Here, we have developed a new combined approach of omics to identify factors that could distinguish coronavirus disease 19 (COVID-19) inpatients from outpatients. METHODS: Saliva and blood samples were collected over the course of two observational cohort studies. By using machine learning approaches, we compared salivary metabolome of 50 COVID-19 patients with that of 270 healthy individuals having previously been exposed or not to SARS-CoV-2. We then correlated the salivary metabolites that allowed separating COVID-19 inpatients from outpatients with serum biomarkers and salivary microbiota taxa differentially represented in the two groups of patients. RESULTS: We identified nine salivary metabolites that allowed assessing the need of hospitalization. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrrolidineacetic acid) and one serum protein, chitinase 3-like-1 (CHI3L1), were sufficient to separate inpatients from outpatients completely and correlated with modulated microbiota taxa. In particular, we found Corynebacterium 1 to be overrepresented in inpatients, whereas Actinomycetaceae F0332, Candidatus Saccharimonas, and Haemophilus were all underrepresented in the hospitalized population. CONCLUSION: This is a proof of concept that a combined omic analysis can be used to stratify patients independently from COVID-19.

Predicting hospital readmission risk in patients with COVID-19: A machine learning approach.

Introduction: The Coronavirus 2019 (COVID-19) epidemic stunned the health systems with severe scarcities in hospital resources. In this critical situation, decreasing COVID-19 readmissions could potentially sustain hospital capacity. This study aimed to select the most affecting features of COVID-19 readmission and compare the capability of Machine Learning (ML) algorithms to predict COVID-19 readmission based on the selected features. Material and methods: The data of 5791 hospitalized patients with COVID-19 were retrospectively recruited from a hospital registry system. The LASSO feature selection algorithm was used to select the most important features related to COVID-19 readmission. HistGradientBoosting classifier (HGB), Bagging classifier, Multi-Layered Perceptron (MLP), Support Vector Machine ((SVM) kernel = linear), SVM (kernel = RBF), and Extreme Gradient Boosting (XGBoost) classifiers were used for prediction. We evaluated the performance of ML algorithms with a 10-fold cross-validation method using six performance evaluation metrics. Results: Out of the 42 features, 14 were identified as the most relevant predictors. The XGBoost classifier outperformed the other six ML models with an average accuracy of 91.7%, specificity of 91.3%, the sensitivity of 91.6%, F-measure of 91.8%, and AUC of 0.91%. Conclusion: The experimental results prove that ML models can satisfactorily predict COVID-19 readmission. Besides considering the risk factors prioritized in this work, categorizing cases with a high risk of reinfection can make the patient triaging procedure and hospital resource utilization more effective.

Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis.

Chest X-ray (CXR) imaging is a low-cost, easy-to-use imaging alternative that can be used to diagnose/screen pulmonary abnormalities due to infectious diseaseX: Covid-19, Pneumonia and Tuberculosis (TB). Not limited to binary decisions (with respect to healthy cases) that are reported in the state-of-the-art literature, we also consider non-healthy CXR screening using a lightweight deep neural network (DNN) with a reduced number of epochs and parameters. On three diverse publicly accessible and fully categorized datasets, for non-healthy versus healthy CXR screening, the proposed DNN produced the following accuracies: 99.87% on Covid-19 versus healthy, 99.55% on Pneumonia versus healthy, and 99.76% on TB versus healthy datasets. On the other hand, when considering non-healthy CXR screening, we received the following accuracies: 98.89% on Covid-19 versus Pneumonia, 98.99% on Covid-19 versus TB, and 100% on Pneumonia versus TB. To further precisely analyze how well the proposed DNN worked, we considered well-known DNNs such as ResNet50, ResNet152V2, MobileNetV2, and InceptionV3. Our results are comparable with the current state-of-the-art, and as the proposed CNN is light, it could potentially be used for mass screening in resource-constraint regions.

Prognostic value of biochemical parameters among severe COVID-19 patients admitted to an intensive care unit of a tertiary hospital in South Africa.

Background: Data on biochemical markers and their association with mortality rates in patients with severe coronavirus disease 2019 (COVID-19) admitted to intensive care units (ICUs) in sub-Saharan Africa are scarce. An evaluation of baseline routine biochemical parameters was performed in COVID-19 patients admitted to the ICU, in order to identify prognostic biomarkers. Methods: Demographic, clinical, and laboratory data were collected prospectively from patients with PCR-confirmed COVID-19 admitted to the adult ICU of a tertiary hospital in Cape Town, South Africa, between October 2020 and February 2021. Robust Poisson regression methods and the receiver operating characteristic (ROC) curve were used to explore the association of biochemical parameters with severity and mortality. Results: A total of 82 patients (median age 53.8 years, interquartile range 46.4-59.7 years) were enrolled, of whom 55 (67%) were female and 27 (33%) were male. The median duration of ICU stay was 10 days (interquartile range 5-14 days); 54/82 patients died (66% case fatality rate). Baseline lactate dehydrogenase (LDH) (adjusted relative risk 1.002, 95% confidence interval 1.0004-1.004; P = 0.016) and N-terminal pro B-type natriuretic peptide (NT-proBNP) (adjusted relative risk 1.0004, 95% confidence interval 1.0001-1.0007; P = 0.014) were both found to be independent risk factors of a poor prognosis, with optimal cut-off values of 449.5 U/l (sensitivity 100%, specificity 43%) and 551 pg/ml (sensitivity 49%, specificity 86%), respectively. Conclusions: LDH and NT-proBNP appear to be promising predictors of a poor prognosis in COVID-19 patients in the ICU. Studies with a larger sample size are required to confirm the validity of this combination of biomarkers.

Disentangling post-vaccination symptoms from early COVID-19.

BACKGROUND: Identifying and testing individuals likely to have SARS-CoV-2 is critical for infection control, including post-vaccination. Vaccination is a major public health strategy to reduce SARS-CoV-2 infection globally. Some individuals experience systemic symptoms post-vaccination, which overlap with COVID-19 symptoms. This study compared early post-vaccination symptoms in individuals who subsequently tested positive or negative for SARS-CoV-2, using data from the COVID Symptom Study (CSS) app. METHODS: We conducted a prospective observational study in 1,072,313 UK CSS participants who were asymptomatic when vaccinated with Pfizer-BioNTech mRNA vaccine (BNT162b2) or Oxford-AstraZeneca adenovirus-vectored vaccine (ChAdOx1 nCoV-19) between 8 December 2020 and 17 May 2021, who subsequently reported symptoms within seven days (N=362,770) (other than local symptoms at injection site) and were tested for SARS-CoV-2 (N=14,842), aiming to differentiate vaccination side-effects per se from superimposed SARS-CoV-2 infection. The post-vaccination symptoms and SARS-CoV-2 test results were contemporaneously logged by participants. Demographic and clinical information (including comorbidities) were recorded. Symptom profiles in individuals testing positive were compared with a 1:1 matched population testing negative, including using machine learning and multiple models considering UK testing criteria. FINDINGS: Differentiating post-vaccination side-effects alone from early COVID-19 was challenging, with a sensitivity in identification of individuals testing positive of 0.6 at best. Most of these individuals did not have fever, persistent cough, or anosmia/dysosmia, requisite symptoms for accessing UK testing; and many only had systemic symptoms commonly seen post-vaccination in individuals negative for SARS-CoV-2 (headache, myalgia, and fatigue). INTERPRETATION: Post-vaccination symptoms per se cannot be differentiated from COVID-19 with clinical robustness, either using symptom profiles or machine-derived models. Individuals presenting with systemic symptoms post-vaccination should be tested for SARS-CoV-2 or quarantining, to prevent community spread. FUNDING: UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Chronic Disease Research Foundation, Zoe Limited.

MALDI-ToF protein profiling as a potential rapid diagnostic platform for COVID-19.

More than a year after the COVID-19 pandemic was declared, the need still exists for accurate, rapid, inexpensive and non-invasive diagnostic methods that yield high specificity and sensitivity towards the current and newly emerging SARS-CoV-2 strains. Compared to the nasopharyngeal swabs, several studies have established saliva as a more amenable specimen type for early detection of SARS-CoV-2. Considering the limitations and high demand for COVID-19 testing, we employed MALDI-ToF mass spectrometry in the analysis of 60 gargle samples from human donors and compared the resultant spectra against COVID-19 status. Several standards, including isolated human serum immunoglobulins, and controls, such as pre-COVID-19 saliva and heat inactivated SARS-CoV-2 virus, were simultaneously analyzed to provide a relative view of the saliva and viral proteome as they would appear in this workflow. Five potential biomarker peaks were established that demonstrated high concordance with COVID-19 positive individuals. Overall, the agreement of these results with RT-qPCR testing on nasopharyngeal swabs was ≥90% for the studied cohort, which consisted of young and largely asymptomatic student athletes. From a clinical standpoint, the results from this pilot study suggest that MALDI-ToF could be used to develop a relatively rapid and inexpensive COVID-19 assay.

Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements.

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort, resulting in a data matrix containing 3,065 readings for 124 types of measurements over 52 days. A machine learning model was established to predict the disease progression based on the cohort consisting of training, validation, and internal test sets. A panel of eleven routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 98% in the discovery set. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.70, 0.99, 0.93, and 0.93, respectively. Our model captured predictive dynamics of lactate dehydrogenase (LDH) and creatine kinase (CK) while their levels were in the normal range. This model is accessible at https://www.guomics.com/covidAI/ for research purpose.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Comparison of virus neutralization activity and results of 10 different anti-SARS-CoV-2 serological tests in COVID-19 recovered plasma donors.

Serological testing is a tool to predict protection against later infection. This potential heavily relies on antibody levels showing acceptable agreement with gold standard virus neutralization tests. The aim of our study was to investigate diagnostic value of the available serological tests in terms of predicting virus neutralizing activity of serum samples drawn 5-7 weeks after onset of symptoms from 101 donors with a history of COVID-19. Immune responses against Receptor Binding Domain (RBD), Spike1 and 2 proteins and Nucleocapsid antigens were measured by various ELISA tests. Neutralizing antibody activity in serum samples was assessed by a cell-based virus neutralization test. Spearman correlation coefficients between serological and neutralization results ranged from 0.41 to 0.91 indicating moderate to strong correlation between ELISA test results and virus neutralization. The sensitivity and specificity of ELISA tests in the prediction of neutralization were 35-100% and 35-90% respectively. No clear cut off levels can be established that would reliably indicate neutralization activity. For some tests, however, a value below which the sample is not expected to neutralize can be established. Our data suggests that several of the ELISA kits tested may be suitable for epidemiological surveys 1-2 months after the infection, estimating whether a person may have recently exposed to the virus. Sensitivities considerably superseding specificity at the cut-off values proposed by the manufacturers suggest greater potential in the identification of insufficient antibody responses than in confirming protection. Nevertheless, the former might be important in assessing response to vaccination and characterizing therapeutic plasma preparations.

Development of in-house ELISAs for the detection of anti-SARS­CoV­2 RBD and N IgG and IgM antibodies in biological samples.

By the end of year 2019, the new virus SARS-CoV-2 appeared, causing the Coronavirus Disease 2019 (COVID-19), and spread very fast globally. A continuing need for diagnostic tools is a must to contain its spread. Till now, the gold standard method, the reverse transcription polymerase chain reaction (RT-PCR), is the precise procedure to detect the virus. However, SARS-CoV-2 may escape RT-PCR detection for several reasons. The development of well-designed, specific and sensitive serological test like enzyme immunoassay (EIA) is needed. This EIA can stand alone or work side by side with RT-PCR. In this study, we developed several EIAs including plates that are coated with either specially designed SARS-CoV-2 nucleocapsid or surface recombinant proteins. Each protein type can separately detect anti-SARS-CoV-2 IgM or IgG antibodies. For each EIAs, the cut-off value, specificity and sensitivity were determined utilizing RT-PCR confirmed Covid-19 and pre-pandemic healthy and other viruses-infected sera. Also, the receiver operator characteristic (ROC) analysis was performed to define the specificities and sensitivities of the optimized assay. The in-house EIAs were validated by comparing against commercial EIA kits. All in-house EIAs showed high specificity (98-99%) and sensitivity (97.8-98.9%) for the detection of IgG/IgM against RBD and N proteins of SARS-CoV-2. From these results, the developed Anti-RBD and anti-N IgG and IgM antibodies EIAs can be used as a specific and sensitive tool to detect SARS-CoV-2 infection, calculate the burden of disease and case fatality rates.

Increased levels of ferritin on admission predicts intensive care unit mortality in patients with COVID-19.

BACKGROUND: The aim of this study was to evaluate hyperferritinemia could be a predicting factor of mortality in hospitalized patients with coronavirus disease-2019 (COVID-19). METHODS: A total of 100 hospitalized patients with COVID-19 in intensive care unit (ICU) were enrolled and classified into moderate (n = 17), severe (n = 40) and critical groups (n = 43). Clinical information and laboratory results were collected and the concentrations of ferritin were compared among different groups. The association between ferritin and mortality was evaluated by logistic regression analysis. Moreover, the efficiency of the predicting value was assessed using receiver operating characteristic (ROC) curve. RESULTS: The amount of ferritin was significantly higher in critical group compared with moderate and severe groups. The median of ferritin concentration was about three times higher in death group than survival group (1722.25 µg/L vs. 501.90 µg/L, p < 0.01). The concentration of ferritin was positively correlated with other inflammatory cytokines, such as interleukin (IL)-8, IL-10, C-reactive protein (CRP) and tumor necrosis factor (TNF)-α. Logistic regression analysis demonstrated that ferritin was an independent predictor of in-hospital mortality. Especially, high-ferritin group was associated with higher incidence of mortality, with adjusted odds ratio of 104.97 [95% confidence interval (CI) 2.63-4185.89; p = 0.013]. Moreover, ferritin had an advantage of discriminative capacity with the area under ROC (AUC) of 0.822 (95% CI 0.737-0.907) higher than procalcitonin and CRP. CONCLUSION: The ferritin measured at admission may serve as an independent factor for predicting in-hospital mortality in patients with COVID-19 in ICU.

ANTECEDENTES: El objetivo de este estudio fue evaluar si la hiperferritinemia podría ser un factor predictivo de la mortalidad en pacientes hospitalizados con enfermedad por coronavirus de 2019 (COVID-19). MÉTODOS: Se incluyó un total de 100 pacientes hospitalizados con COVID-19 en la unidad de cuidados intensivos (UCI), clasificándose como grupos moderado (n = 17), grave (n = 40) y crítico (n = 43). Se recopiló la información clínica y de laboratorio, comparándose los niveles de ferritina entre los diferentes grupos. Se evaluó la asociación entre ferritina y mortalidad mediante un análisis de regresión logística. Además, se evaluó la eficacia del valor predictivo utilizando la curva ROC (receiver operating characteristic). RESULTADOS: La cantidad de ferritina fue significativamente superior en el grupo de pacientes críticos en comparación con el grupo de pacientes graves. La media de concentración de ferritina fue cerca de 3 veces superior en el grupo de muerte que en el grupo de supervivientes (1.722,25 µg/L vs. 501,90 µg/L, p < 0,01). La concentración de ferritina guardó una correlación positiva con otras citoquinas inflamatorias tales como interleucina (IL)-8, IL-10, proteína C reactiva (PRC) y factor de necrosis tumoral (TNF)-α. El análisis de regresión logística demostró que la ferritina era un factor predictivo independiente de la mortalidad intrahospitalaria. En especial, el grupo de ferritina alta estuvo asociado a una mayor incidencia de la mortalidad, con un valor de odds ratio ajustado de 104,97 [intervalo de confianza (IC) del 95% 2,63-4.185,89; p = 0,013]. Además, el valor de ferritina tuvo una ventaja de capacidad discriminativa en el área bajo la curva ROC (AUC) de 0,822 (IC 95% 0,737-0,907] superior al de procalcitonina y PRC. CONCLUSIÓN: El valor de ferritina medido durante el ingreso puede servir de factor independiente para prevenir la mortalidad intrahospitalaria en los pacientes de COVID-19 en la UCI.

Diagnosing Covid-19 chest x-rays with a lightweight truncated DenseNet with partial layer freezing and feature fusion.

Due to the unforeseen turn of events, our world has undergone another global pandemic from a highly contagious novel coronavirus named COVID-19. The novel virus inflames the lungs similarly to Pneumonia, making it challenging to diagnose. Currently, the common standard to diagnose the virus's presence from an individual is using a molecular real-time Reverse-Transcription Polymerase Chain Reaction (rRT-PCR) test from fluids acquired through nasal swabs. Such a test is difficult to acquire in most underdeveloped countries with a few experts that can perform the test. As a substitute, the widely available Chest X-Ray (CXR) became an alternative to rule out the virus. However, such a method does not come easy as the virus still possesses unknown characteristics that even experienced radiologists and other medical experts find difficult to diagnose through CXRs. Several studies have recently used computer-aided methods to automate and improve such diagnosis of CXRs through Artificial Intelligence (AI) based on computer vision and Deep Convolutional Neural Networks (DCNN), which some require heavy processing costs and other tedious methods to produce. Therefore, this work proposed the Fused-DenseNet-Tiny, a lightweight DCNN model based on a densely connected neural network (DenseNet) truncated and concatenated. The model trained to learn CXR features based on transfer learning, partial layer freezing, and feature fusion. Upon evaluation, the proposed model achieved a remarkable 97.99 % accuracy, with only 1.2 million parameters and a shorter end-to-end structure. It has also shown better performance than some existing studies and other massive state-of-the-art models that diagnosed COVID-19 from CXRs.