A diagnostic model for COVID-19 based on proteomics analysis.

BACKGROUND AND OBJECTIVE: Early diagnosis of Coronavirus Disease 2019 (COVID-19) can help save patients' lives before the disease turns severe. This can be achieved through an effective and correct treatment protocol. In this paper, a prediction model is proposed to detect infected cases and determine the severity level of the disease. METHODS: The proposed model is based on utilizing proteins and metabolites as features for each patient, which are then analyzed using feature selection methods such as Principal Component Analysis (PCA), Information Gain (IG), and analysis of Variance (ANOVA) to select the most significant features. The model employs three classifiers, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), to predict and classify the severity level of the COVID-19 infection. The proposed model is evaluated using four performance measures: accuracy, sensitivity, specificity, and precision. RESULTS: The experiment results show that the proposed model accuracy can reach 80% using RF classifier with PCA. The PCA selects 22 proteins and 10 metabolites. While ANOVA selects 9 proteins and 5 metabolites. The accuracy reaches 92% after applying RF classifier with the ANOVA. Finally, the accuracy reaches 93% using the RF classifier with only ten features. The selected features are 7 proteins and 3 metabolites. Moreover, it shows that the selected features have a relation to the immune system and respiratory systems. CONCLUSION: The proposed model uses three classifiers and shows promising results by selecting the important features and maximizing the prediction accuracy.

ATR-FTIR spectrum analysis of plasma samples for rapid identification of recovered COVID-19 individuals.

The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID-19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post-vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme-linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detect seroconversion against SARS-CoV-2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID-19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR-FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID-19 patients. We built classification models based on PCA associated with LDA and ANN. Our analysis led to 87% accuracy for PCA-LDA model and 91% accuracy for ANN, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective tool for detecting seroconversion against SARS-CoV-2. This approach could be used as an alternative to ELISA.

Exploration of the impact of political ideology disparity on COVID-19 transmission in the United States.

BACKGROUND: Based on individual-level studies, previous literature suggested that conservatives and liberals in the United States had different perceptions and behaviors when facing the COVID-19 threat. From a state-level perspective, this study further explored the impact of personal political ideology disparity on COVID-19 transmission before and after the emergence of Omicron. METHODS: A new index was established, which depended on the daily cumulative number of confirmed cases in each state and the corresponding population size. Then, by using the 2020 United States presidential election results, the values of the built index were further divided into two groups concerning the political party affiliation of the winner in each state. In addition, each group was further separated into two parts, corresponding to the time before and after Omicron predominated. Three methods, i.e., functional principal component analysis, functional analysis of variance, and function-on-scalar linear regression, were implemented to statistically analyze and quantify the impact. RESULTS: Findings reveal that the disparity of personal political ideology has caused a significant discrepancy in the COVID-19 crisis in the United States. Specifically, the findings show that at the very early stage before the emergence of Omicron, Democratic-leaning states suffered from a much greater severity of the COVID-19 threat but, after July 2020, the severity of COVID-19 transmission in Republican-leaning states was much higher than that in Democratic-leaning states. Situations were reversed when the Omicron predominated. Most of the time, states with Democrat preferences were more vulnerable to the threat of COVID-19 than those with Republican preferences, even though the differences decreased over time. CONCLUSIONS: The individual-level disparity of political ideology has impacted the nationwide COVID-19 transmission and such findings are meaningful for the government and policymakers when taking action against the COVID-19 crisis in the United States.

Projection in genomic analysis: A theoretical basis to rationalize tensor decomposition and principal component analysis as feature selection tools.

Identifying differentially expressed genes is difficult because of the small number of available samples compared with the large number of genes. Conventional gene selection methods employing statistical tests have the critical problem of heavy dependence of P-values on sample size. Although the recently proposed principal component analysis (PCA) and tensor decomposition (TD)-based unsupervised feature extraction (FE) has often outperformed these statistical test-based methods, the reason why they worked so well is unclear. In this study, we aim to understand this reason in the context of projection pursuit (PP) that was proposed a long time ago to solve the problem of dimensions; we can relate the space spanned by singular value vectors with that spanned by the optimal cluster centroids obtained from K-means. Thus, the success of PCA- and TD-based unsupervised FE can be understood by this equivalence. In addition to this, empirical threshold adjusted P-values of 0.01 assuming the null hypothesis that singular value vectors attributed to genes obey the Gaussian distribution empirically corresponds to threshold-adjusted P-values of 0.1 when the null distribution is generated by gene order shuffling. For this purpose, we newly applied PP to the three data sets to which PCA and TD based unsupervised FE were previously applied; these data sets treated two topics, biomarker identification for kidney cancers (the first two) and the drug discovery for COVID-19 (the thrid one). Then we found the coincidence between PP and PCA or TD based unsupervised FE is pretty well. Shuffling procedures described above are also successfully applied to these three data sets. These findings thus rationalize the success of PCA- and TD-based unsupervised FE for the first time.

Integrated Prediction Framework for Clinical Scores of Cognitive Functions in ESRD Patients.

The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV to predict clinical scores of cognitive functions in ESRD patients. GPLWLSV incorporated three parts, graph theoretic algorithm (GTA) and principal component analysis (PCA), whale optimization algorithm with Levy flight (LWOA), and least squares support vector regression machine (LSSVRM). GTA was adopted to extract features from the brain functional networks in ESRD patients, while PCA was used to select features. LSSVRM was built to explore the relationship between the selected features and the clinical scores of ESRD patients. Whale optimization algorithm (WOA) was introduced to select better parameters of the kernel function in LSSVRM; it aims to improve the exploration competence of LSSVRM. Levy flight was used to optimize the ability to jump out of local optima in WOA and improve the convergence of coefficient vectors in WOA, which lead to an increase in the generalization ability and convergence speed of WOA. The results validated that the prediction accuracy of GPLWLSV was higher than that of several comparable frameworks, such as GPSV, GPLSV, and GPWLSV. In particular, the average of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of ESRD patients was 2.40, 2.06, and 9.83%, respectively. The proposed framework not only can predict the clinical scores more accurately but also can capture imaging markers associated with decline of cognitive function. It helps to understand the potential relationship between structural changes in the brain and cognitive function of ESRD patients.

Classification of Polish Natural Bee Honeys Based on Their Chemical Composition.

The targeted quantitative NMR (qNMR) approach is a powerful analytical tool, which can be applied to classify and/or determine the authenticity of honey samples. In our study, this technique was used to determine the chemical profiles of different types of Polish honey samples, featured by variable contents of main sugars, free amino acids, and 5-(hydroxymethyl)furfural. One-way analysis of variance (ANOVA) was performed on concentrations of selected compounds to determine significant differences in their levels between all types of honey. For pattern recognition, principal component analysis (PCA) was conducted and good separations between all honey samples were obtained. The results of present studies allow the differentiation of honey samples based on the content of sucrose, glucose, and fructose, as well as amino acids such as tyrosine, phenylalanine, proline, and alanine. Our results indicated that the combination of qNMR with chemometric analysis may serve as a supplementary tool in specifying honeys.

Tracking the national and regional COVID-19 epidemic status in the UK using weighted principal component analysis.

One of the difficulties in monitoring an ongoing pandemic is deciding on the metric that best describes its status when multiple intercorrelated measurements are available. Having a single measure, such as the effective reproduction number [Formula: see text], has been a simple and useful metric for tracking the epidemic and for imposing policy interventions to curb the increase when [Formula: see text]. While [Formula: see text] is easy to interpret in a fully susceptible population, it is more difficult to interpret for a population with heterogeneous prior immunity, e.g. from vaccination and prior infection. We propose an additional metric for tracking the UK epidemic that can capture the different spatial scales. These are the principal scores from a weighted principal component analysis. In this paper, we have used the methodology across the four UK nations and across the first two epidemic waves (January 2020-March 2021) to show that first principal score across nations and epidemic waves is a representative indicator of the state of the pandemic and is correlated with the trend in R. Hospitalizations are shown to be consistently representative; however, the precise dominant indicator, i.e. the principal loading(s) of the analysis, can vary geographically and across epidemic waves. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Plasticity of the 340-Loop in Influenza Neuraminidase Offers New Insight for Antiviral Drug Development.

The recently discovered 340-cavity in influenza neuraminidase (NA) N6 and N7 subtypes has introduced new possibilities for rational structure-based drug design. However, the plasticity of the 340-loop (residues 342-347) and the role of the 340-loop in NA activity and substrate binding have not been deeply exploited. Here, we investigate the mechanism of 340-cavity formation and demonstrate for the first time that seven of nine NA subtypes are able to adopt an open 340-cavity over 1.8 µs total molecular dynamics simulation time. The finding that the 340-loop plays a role in the sialic acid binding pathway suggests that the 340-cavity can function as a druggable pocket. Comparing the open and closed conformations of the 340-loop, the side chain orientation of residue 344 was found to govern the formation of the 340-cavity. Additionally, the conserved calcium ion was found to substantially influence the stability of the 340-loop. Our study provides dynamical evidence supporting the 340-cavity as a druggable hotspot at the atomic level and offers new structural insight in designing antiviral drugs.

Trend clustering from COVID-19 tweets using graphical lasso-guided iterative principal component analysis.

This article presents a method for trend clustering from tweets about coronavirus disease (COVID-19) to help us objectively review the past and make decisions about future countermeasures. We aim to avoid detecting usual trends based on seasonal events while detecting essential trends caused by the influence of COVID-19. To this aim, we regard daily changes in the frequencies of each word in tweets as time series signals and define time series signals with single peaks as target trends. To successfully cluster the target trends, we propose graphical lasso-guided iterative principal component analysis (GLIPCA). GLIPCA enables us to remove trends with indirect correlations generated by other essential trends. Moreover, GLIPCA overcomes the difficulty in the quantitative evaluation of the accuracy of trend clustering. Thus, GLIPCA's parameters are easier to determine than those of other clustering methods. We conducted experiments using Japanese tweets about COVID-19 from March 8, 2020, to May 7, 2020. The results show that GLIPCA successfully distinguished trends before and after the declaration of a state of emergency on April 7, 2020. In addition, the results reveal the international argument about whether the Tokyo 2020 Summer Olympics should be held. The results suggest the tremendous social impact of the words and actions of Japanese celebrities. Furthermore, the results suggest that people's attention moved from worry and fear of an unknown novel pneumonia to the need for medical care and a new lifestyle as well as the scientific characteristics of COVID-19.

Reduced levels of pulmonary surfactant in COVID-19 ARDS.

To provide novel data on surfactant levels in adult COVID-19 patients, we collected bronchoalveolar lavage fluid less than 72 h after intubation and used Fourier Transform Infrared Spectroscopy to measure levels of dipalmitoylphosphatidylcholine (DPPC). A total of eleven COVID-19 patients with moderate-to-severe ARDS (CARDS) and 15 healthy controls were included. CARDS patients had lower DPPC levels than healthy controls. Moreover, a principal component analysis was able to separate patient groups into distinguishable subgroups. Our findings indicate markedly impaired pulmonary surfactant levels in COVID-19 patients, justifying further studies and clinical trials of exogenous surfactant.

A new strategy based on PCA for inter-batches quality consistency evaluation.

Due to cultivation position, climate, harvest times, storage conditions and processing method, the evaluation of intra- and inter- batches quality consistency of botanical drugs has always been a thorny problem since it concerns safety and efficacy. The combination of fingerprint based on instrumental analysis and chemometrics is a common evaluation method in recent years. The differences between groups can be judged intuitively and superficially through principal component analysis (PCA) multi-dimensional score plots, but there is a lack of scientific and quantitative index to quantify the differences between groups. How to quantify the difference between groups is basically a blank area of research. Based on traditional F-statistic, we proposed a new F*-statistic to quantify the difference between groups in PCA score plots from the perspective of statistics. As the results revealed, the calculated F*-statistic was 2.58, smaller than the critical value 3.17 (α = 0.05), which indicated that there was no significant difference between groups. Our study add another dimension for PCA application, which offers a new strategy to quantify differences between groups by a new perspective, namely, a combination of fingerprint, chemometrics and statistics to evaluate inter-batches quality consistency quantitatively and objectively. Therefore, this manuscript could provide new ideas and technical references for the quality consistency evaluation of natural drugs, thus better guarantee their clinical efficacy and safety, and better promote industrial development.

Infection with the SARS-CoV-2 B.1.351 variant is lethal in aged BALB/c mice.

Models of animals that are susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can usefully evaluate the efficacy of vaccines and therapeutics. In this study, we demonstrate that infection with the SARS-CoV-2 B.1.351 variant (TY8-612 strain) induces bodyweight loss and inflammatory cytokine/chemokine production in wild-type laboratory mice (BALB/c and C57BL/6 J mice). Furthermore, compared to their counterparts, BALB/c mice had a higher viral load in their lungs and worse symptoms. Importantly, infecting aged BALB/c mice (older than 6 months) with the TY8-612 strain elicited a massive and sustained production of multiple pro-inflammatory cytokines/chemokines and led to universal mortality. These results indicated that the SARS-CoV-2 B.1.351 variant-infected mice exhibited symptoms ranging from mild to fatal depending on their strain and age. Our data provide insights into the pathogenesis of SARS-CoV-2 and may be useful in developing prophylactics and therapeutics.

Conserved MicroRNAs in Human Nasopharynx Tissue Samples from Swabs Are Differentially Expressed in Response to SARS-CoV-2.

The use of high-throughput small RNA sequencing is well established as a technique to unveil the miRNAs in various tissues. The miRNA profiles are different between infected and non-infected tissues. We compare the SARS-CoV-2 positive and SARS-CoV-2 negative RNA samples extracted from human nasopharynx tissue samples to show different miRNA profiles. We explored differentially expressed miRNAs in response to SARS-CoV-2 in the RNA extracted from nasopharynx tissues of 10 SARS-CoV-2-positive and 10 SARS-CoV-2-negative patients. miRNAs were identified by small RNA sequencing, and the expression levels of selected miRNAs were validated by real-time RT-PCR. We identified 943 conserved miRNAs, likely generated through posttranscriptional modifications. The identified miRNAs were expressed in both RNA groups, NegS and PosS: miR-148a, miR-21, miR-34c, miR-34b, and miR-342. The most differentially expressed miRNA was miR-21, which is likely closely linked to the presence of SARS-CoV-2 in nasopharynx tissues. Our results contribute to further understanding the role of miRNAs in SARS-CoV-2 pathogenesis, which may be crucial for understanding disease symptom development in humans.

Sustainable development and health assessment model of higher education in India: A mathematical modeling approach.

The Coronavirus Disease 2019 has resulted in a transition from physical education to online learning, leading to a collapse of the established educational order and a wisdom test for the education governance system. As a country seriously affected by the pandemic, the health of the Indian higher education system urgently requires assessment to achieve sustainable development and maximize educational externalities. This research systematically proposes a health assessment model from four perspectives, including educational volume, efficiency, equality, and sustainability, by employing the Technique for Order Preference by Similarity to an Ideal Solution Model, Principal Component Analysis, DEA-Tobit Model, and Augmented Solow Model. Empirical results demonstrate that India has high efficiency and an absolute health score in the higher education system through multiple comparisons between India and the other selected countries while having certain deficiencies in equality and sustainability. Additionally, single-target and multiple-target path are simultaneously proposed to enhance the Indian current education system. The multiple-target approach of the India-China-Japan-Europe-USA process is more feasible to achieve sustainable development, which would improve the overall health score from .351 to .716. This finding also reveals that the changes are relatively complex and would take 91.5 years considering the relationship between economic growth rates and crucial indicators. Four targeted policies are suggested for each catching-up period, including expanding and increasing the social funding sources, striving for government expenditure support to improve infrastructures, imposing gender equality in education, and accelerating the construction of high-quality teachers.

Diagnosing COVID-19 in human serum using Raman spectroscopy.

This study proposed the diagnosis of COVID-19 by means of Raman spectroscopy. Samples of blood serum from 10 patients positive and 10 patients negative for COVID-19 by RT-PCR RNA and ELISA tests were analyzed. Raman spectra were obtained with a dispersive Raman spectrometer (830 nm, 350 mW) in triplicate, being submitted to exploratory analysis with principal component analysis (PCA) to identify the spectral differences and discriminant analysis with PCA (PCA-DA) and partial least squares (PLS-DA) for classification of the blood serum spectra into Control and COVID-19. The spectra of both groups positive and negative for COVID-19 showed peaks referred to the basal constitution of the serum (mainly albumin). The difference spectra showed decrease in the peaks referred to proteins and amino acids for the group positive. PCA variables showed more detailed spectral differences related to the biochemical alterations due to the COVID-19 such as increase in lipids, nitrogen compounds (urea and amines/amides) and nucleic acids, and decrease of proteins and amino acids (tryptophan) in the COVID-19 group. The discriminant analysis applied to the principal component loadings (PC2, PC4, PC5, and PC6) could classify spectra with 87% sensitivity and 100% specificity compared to 95% sensitivity and 100% specificity indicated in the RT-PCR kit leaflet, demonstrating the possibilities of a rapid, label-free, and costless technique for diagnosing COVID-19 infection.

Binding mechanism of inhibitors to SARS-CoV-2 main protease deciphered by multiple replica molecular dynamics simulations.

The outbreak caused by SARS-CoV-2 has received extensive worldwide attention. As the main protease (Mpro) in SARS-CoV-2 has no human homologues, it is feasible to reduce the possibility of targeting the host protein by accidental drugs. Thus, Mpro has been an attractive target of efficient drug design for anti-SARS-CoV-2 treatment. In this work, multiple replica molecular dynamics (MRMD) simulations, principal component analysis (PCA), free energy landscapes (FELs), and the molecular mechanics-generalized Born surface area (MM-GBSA) method were integrated together to decipher the binding mechanism of four inhibitors masitinib, O6K, FJC and GQU to Mpro. The results indicate that the binding of four inhibitors clearly affects the structural flexibility and internal dynamics of Mpro along with dihedral angle changes of key residues. The analysis of FELs unveils that the stability in the relative orientation and geometric position of inhibitors to Mpro is favorable for inhibitor binding. Residue-based free energy decomposition reveals that the inhibitor-Mpro interaction networks involving hydrogen bonding interactions and hydrophobic interactions provide significant information for the design of potent inhibitors against Mpro. The hot spot residues including H41, M49, F140, N142, G143, C145, H163, H164, M165, E166 and Q189 identified by computational alanine scanning are considered as reliable targets of clinically available inhibitors inhibiting the activities of Mpro.

Application of principal component analysis on temporal evolution of COVID-19.

The COVID-19 is one of the worst pandemics in modern history. We applied principal component analysis (PCA) to the daily time series of the COVID-19 death cases and confirmed cases for the top 25 countries from April of 2020 to February of 2021. We calculated the eigenvalues and eigenvectors of the cross-correlation matrix of the changes in daily accumulated data over monthly time windows. The largest eigenvalue describes the overall evolution dynamics of the COVID-19 and indicates that evolution was faster in April of 2020 than in any other period. By using the first two PC coefficients, we can identify the group dynamics of the COVID-19 evolution. We observed groups under critical states in the loading plot and found that American and European countries are represented by strong clusters in the loading plot. The first PC plays an important role and the correlations (C1) between the normalized logarithmic changes in deaths or confirmed cases and the first PCs may be used as indicators of different phases of the COVID-19. By varying C1 over time, we identified different phases of the COVID-19 in the analyzed countries over the target time period.

Severity of SARS-CoV-2 infection is linked to double-negative (CD27- IgD-) B cell subset numbers.

OBJECTIVES: The role of B cells in COVID-19, beyond the production of specific antibodies against SARS-CoV-2, is still not well understood. Here, we describe the novel landscape of circulating double-negative (DN) CD27- IgD- B cells in COVID-19 patients, representing a group of atypical and neglected subpopulations of this cell lineage. METHODS: Using multiparametric flow cytometry, we determined DN B cell subset amounts from 91 COVID-19 patients, correlated those with cytokines, clinical and laboratory parameters, and segregated them by principal components analysis. RESULTS: We detected significant increments in the DN2 and DN3 B cell subsets, while we found a relevant decrease in the DN1 B cell subpopulation, according to disease severity and patient outcomes. These DN cell numbers also appeared to correlate with pro- or anti-inflammatory signatures, respectively, and contributed to the segregation of the patients into disease severity groups. CONCLUSION: This study provides insights into DN B cell subsets' potential role in immune responses against SARS-CoV-2, particularly linked to the severity of COVID-19.

Evolution trace of SARS-CoV-2 from January 19 to March 12, 2020, in the United States.

As a kind of human betacoronavirus, SARS-CoV-2 has endangered globally public health. As of January 2021, the virus had resulted in 2,209,195 deaths. By studying the evolution trend and characteristics of 265 SARS-CoV-2 strains in the United States from January to March, it is found that the strains can be divided into six clades, USA clade-1, USA clade-2, USA clade-3, USA clade-4, USA clade-5, and USA clade-6, in which US clade-1 may be the most ancestral clade, USA clade-2 is an interim clade of USA clade-1 and USA clade-3, the other three clades have similar codon usage pattern, while USA clade-6 is the newest and most adaptable clade. Mismatch analysis and protein alignment showed that the evolution of the clades arises from some special mutations in viral proteins, which may help the strain to invade, replicate, transcribe and so on. Compared with previous research and classifications, we suggest that clade O in GISAID should not be an independent clade and Wuhan-Hu-1 (EPI_ISL_402125) should not be an ancestral reference sequence. Our study decoded the evolutionary dynamic of SARS-CoV-2 in the early stage from the United States, which give some clues to infer the current evolution trend of SARS-CoV-2 and study the function of viral mutational protein.

The network limits of infectious disease control via occupation-based targeting.

Policymakers commonly employ non-pharmaceutical interventions to reduce the scale and severity of pandemics. Of non-pharmaceutical interventions, physical distancing policies-designed to reduce person-to-person pathogenic spread - have risen to recent prominence. In particular, stay-at-home policies of the sort widely implemented around the globe in response to the COVID-19 pandemic have proven to be markedly effective at slowing pandemic growth. However, such blunt policy instruments, while effective, produce numerous unintended consequences, including potentially dramatic reductions in economic productivity. In this study, we develop methods to investigate the potential to simultaneously contain pandemic spread while also minimizing economic disruptions. We do so by incorporating both occupational and contact network information contained within an urban environment, information that is commonly excluded from typical pandemic control policy design. The results of our methods suggest that large gains in both economic productivity and pandemic control might be had by the incorporation and consideration of simple-to-measure characteristics of the occupational contact network. We find evidence that more sophisticated, and more privacy invasive, measures of this network do not drastically increase performance.