Background: With evidence of sustained transmission in more than 190 countries, coronavirus disease 2019 (COVID-19) has been declared a global pandemic. Data are urgently needed about risk factors associated with clinical outcomes.Methods: A retrospective review of 323 hospitalized patients with COVID-19 in Wuhan was conducted. Patients were classified into 3 disease severity groups (nonsevere, severe, and critical), based on initial clinical presentation. Clinical outcomes were designated as favorable and unfavorable, based on disease progression and response to treatments. Logistic regression models were performed to identify risk factors associated with clinical outcomes, and log-rank test was conducted for the association with clinical progression.Results: Current standard treatments did not show significant improvement in patient outcomes. By univariate logistic regression analysis, 27 risk factors were significantly associated with clinical outcomes. Multivariate regression indicated age >65 years (P < .001), smoking (P = .001), critical disease status (P = .002), diabetes (P = .025), high hypersensitive troponin I (>0.04 pg/mL, P = .02), leukocytosis (>10 × 109/L, P < .001), and neutrophilia (>75 × 109/L, P < .001) predicted unfavorable clinical outcomes. In contrast, the administration of hypnotics was significantly associated with favorable outcomes (P < .001), which was confirmed by survival analysis.Conclusions: Hypnotics may be an effective ancillary treatment for COVID-19. We also found novel risk factors, such as higher hypersensitive troponin I, predicted poor clinical outcomes. Overall, our study provides useful data to guide early clinical decision making to reduce mortality and improve clinical outcomes of COVID-19.

Numerous methods are available to compare results of multiple microarray studies. One of the simplest but most effective of these procedures is to examine the overlap of resulting gene lists in a Venn diagram. Venn diagrams are graphical ways of representing interactions among sets to display information that can be read easily. Here we propose a simple but effective web application creating Venn diagrams from two or three gene lists. Each gene in the group list has link to the related information in NCBI?s Entrez Nucleotide database.

Background: Several classification and feature selection methods have been studied for the identification of differentially expressed genes in microarray data. Classification methods such as SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used in recent studies. The accuracy of these methods has been calculated with validation methods such as v-fold validation. However there is lack of comparison between these methods to find a better framework for classification, clustering and analysis of microarray gene expression results.Results: In this study, we compared the efficiency of the classification methods including; SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods. The v-fold cross validation was used to calculate the accuracy of the classifiers. Some of the common clustering methods including K-means, DBC, and EM clustering were applied to the datasets and the efficiency of these methods have been analysed. Further the efficiency of the feature selection methods including support vector machine recursive feature elimination (SVM-RFE), Chi Squared, and CSF were compared. In each case these methods were applied to eight different binary (two class) microarray datasets. We evaluated the class prediction efficiency of each gene list in training and test cross-validation using supervised classifiers.Conclusions: We presented a study in which we compared some of the common used classification, clustering, and feature selection methods. We applied these methods to eight publicly available datasets, and compared how these methods performed in class prediction of test datasets. We reported that the choice of feature selection methods, the number of genes in the gene list, the number of cases (samples) substantially influence classification success. Based on features chosen by these methods, error rates and accuracy of several classification algorithms were obtained. Results revealed the importance of feature selection in accurately classifying new samples and how an integrated feature selection and classification algorithm is performing and is capable of identifying significant genes.

Background: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. Results: In this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://database.cs.wayne.edu/proj/FGKA/index.htm. Conclusions: Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.

Lung cancer remains a major threat to human health. Low dose CT scan (LDCT) has become the main method of early screening for lung cancer due to the low sensitivity of chest X‐ray. However, LDCT not only has a high false positive rate, but also entails risks of overdiagnosis and cumulative radiation exposure. In addition, cumulative radiation by LDCT screening and subsequent follow‐up can increase the risk of lung cancer. Many studies have shown that long noncoding RNAs (lncRNAs) remain stable in blood, and profiling of blood has the advantages of being noninvasive, readily accessible and inexpensive. Serum or plasma assay of lncRNAs in blood can be used as a novel detection method to assist LDCT while improving the accuracy of early lung cancer screening. LncRNAs can participate in the regulation of various biological processes. A large number of researches have reported that lncRNAs are key regulators involved in the progression of human cancers through multiple action models. Especially, some lncRNAs can affect various hallmarks of lung cancer. In addition to their diagnostic value, lncRNAs also possess promising potential in other clinical applications toward lung cancer. LncRNAs can be used as predictive markers for chemosensitivity, radiosensitivity, and sensitivity to epidermal growth factor receptor (EGFR)‐targeted therapy, and as well markers of prognosis. Different lncRNAs have been implicated to regulate chemosensitivity, radiosensitivity, and sensitivity to EGFR‐targeted therapy through diverse mechanisms. Although many challenges need to be addressed in the future, lncRNAs have bright prospects as an adjunct to radiographic methods in the clinical management of lung cancer.

Background: At the end of December 2019, a novel strain of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) disease (COVID-19) has been identified in Wuhan, a central city in China, and then spread to every corner of the globe. As of October 8, 2022, the total number of COVID-19 cases had reached over 621 million worldwide, with more than 6.56 million confirmed deaths. Since SARS-CoV-2 genome sequences change due to mutation and recombination, it is pivotal to surveil emerging variants and monitor changes for improving pandemic management. Methods: 10,287,271 SARS-CoV-2 genome sequence samples were downloaded in FASTA format from the GISAID databases from February 24, 2020, to April 2022. Python programming language (version 3.8.0) software was utilized to process FASTA files to identify variants and sequence conservation. The NCBI RefSeq SARS-CoV-2 genome (accession no. NC_045512.2) was considered as the reference sequence. Results: Six mutations had more than 50% frequency in global SARS-CoV-2. These mutations include the P323L (99.3%) in NSP12, D614G (97.6) in S, the T492I (70.4) in NSP4, R203M (62.8%) in N, T60A (61.4%) in Orf9b, and P1228L (50.0%) in NSP3. In the SARS-CoV-2 genome, no mutation was observed in more than 90% of nsp11, nsp7, nsp10, nsp9, nsp8, and nsp16 regions. On the other hand, N, nsp3, S, nsp4, nsp12, and M had the maximum rate of mutations. In the S protein, the highest mutation frequency was observed in aa 508–635(0.77%) and aa 381–508 (0.43%). The highest frequency of mutation was observed in aa 66–88 (2.19%), aa 7–14, and aa 164–246 (2.92%) in M, E, and N proteins, respectively. Conclusion: Therefore, monitoring SARS-CoV-2 proteomic changes and detecting hot spots mutations and conserved regions could be applied to improve the SARS‐CoV‐2 diagnostic efficiency and design safe and effective vaccines against emerging variants.