No abstract

The number of sunspots shows the solar activity level. During the high solar activity, emissions of matter and electromagnetic fields from the Sun make it difficult for cosmic rays to penetrate the Earth. When solar energy is high, cosmic ray intensity is lower, so that the solar magnetic field and solar winds affect the Earth externally and originate new viruses. In this paper, we assess the possible effects of sunspot numbers on the world virus appearance. The literature has no sufficient results about these phenomena. Therefore, we try to relate solar ray extremum to virus generation and the history of pandemics. First, wavelet decomposition is used for smoothing the sunspot cycle to predict past pandemics and forecast the future time of possible virus generation. Finally, we investigate the geographical appearance of the virus in the world to show vulnerable places in the world. The result of the analysis of pandemics that occurred from 1750 to 2020 shows that world’s great viral pandemics like COVID-19 coincide with the relative extrema of sunspot number. Based on our result, 27 pandemic (from 36) incidences are on sunspot extrema. Then, we forecast future pandemics in the world for about 110 years or 10 cycles using presented multi-step autoregression (MSAR). To confirm these phenomena and the generation of new viruses because of solar activity, researchers should carry out experimental studies.

Early diagnosis of breast cancer is an important component of breast cancer therapy. A variety of diagnostic platforms can provide valuable information regarding breast cancer patients, including image-based diagnostic techniques. However, breast abnormalities are not always easy to identify. Mammography, ultrasound, and thermography are some of the technologies developed to detect breast cancer. Using image processing and artificial intelligence techniques, the computer enables radiologists to identify chest problems more accurately. The purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior. The use of machine learning methods is clearly beneficial in terms of improved performance, efficiency, and quality of images, which are crucial to the most innovative medical applications. According to a comparison of the process’s 905 images to those of other illnesses, 98.6% of the disorders are correctly identified. In summary, PSOWNNs, therefore, have a specificity of 98.8%. Furthermore, PSOWNNs have a precision of 98.6%, which means that, despite the high number of women diagnosed with breast cancer, only 830 (95.2%) are diagnosed. In other words, 95.2% of images are correctly classified. PSOWNNs are more accurate than other machine learning algorithms, SVM, KNN, and CNN.

Background and Aims We focused on determining the risk factors, thromboembolic events, and clinical course of New‐Onset Atrial Fibrillation (NOAF) among hospitalized coronavirus disease (COVID‐19) patients. Methods This retrospective study was conducted in the major referral centers in Tehran, Iran. Of 1764 patients enrolled in the study from January 2020 until July 2021, 147 had NOAF, and 1617 had normal sinus rhythm. Univariate and multivariate Logistic regressions were employed accordingly to evaluate NOAF risk factors. The statistical assessments have been run utilizing SPSS 25.0 (SPSS) or R 3.6.3 software. Results For the NOAF patients, the age was significantly higher, and the more prevalent comorbidities were metabolic syndrome, heart failure (HF), peripheral vascular disease, coronary artery disease, and liver cirrhosis. The multivariate analysis showed the established independent risk factors were; Troponin‐I (hazard ratio [HR] = 3.86; 95% confidence interval [CI] = 1.89−7.87; p < 0.001), HF (HR = 2.54; 95% CI = 1.61−4.02; p < 0.001), bilateral grand‐glass opacification (HR = 2.26; 95% CI = 1.68−3.05; p = 0.002). For cases with thromboembolic events, NOAF was the most important prognostic factor (odds ratio [OR] = 2.97; 95% CI = 2.03−4.33; p < 0.001). While evaluating the diagnostic ability of prognostic factors in detecting NOAF, Troponin‐I (Area under the curve [AUC] = 0.85), C‐Reactive Protein (AUC = 0.72), and d ‐dimer (AUC = 0.65) had the most accurate sensitivity. Furthermore, the Kaplan‐Meier curves demonstrated that the survival rates diminished more steeply for patients with NOAF history. Conclusion In hospitalized COVID‐19 patients with NOAF, the risk of thromboembolic events, hospital stay, and fatality are significantly higher. The established risk factors showed that patients with older age, higher inflammation states, and more severe clinical conditions based on CHADS2VASC‐score potentially need subsequent preventive strategies. Appropriate prophylactic anticoagulants, Initial management of cytokine storm, sufficient oxygen support, and reducing viral shedding could be of assistance in such patients.

Background: Breast carcinogenesis involves both genetic and epigenetic changes. DNA methylation, as well as micro-RNA regulations, are the significant epigenetic phenomena dysregulated in breast cancer. Herein, the expression of DACH1 as a tumor suppressor gene and its promoter methylation status was analyzed in breast cancer tumors. Also, the expression of three micro RNAs (miR-217, miR-6807-3p, and miR-552), which had been previously reported to target DACH1, was assessed.  Methods: The SYBR green-based Real-Time reverse transcription-PCR was used to determine DACH1 and micro-RNAs (miR-217, miR-6807-3p, and miR-552) expression in 120 ductal breast cancer tumors compared with standard control. Also, the promoter methylation pattern of DACH1 was investigated using the Methylation-specific PCR technique. Results: DACH1 expression was significantly down-regulated in breast tumors (p< 0.05). About 33.5% of tumors showed DACH1 promoter hyper-methylation. The studied micro-RNAs, expression was negatively correlated with DACH1 expression. The highest expressions of miRNAs and higher DACH1 promoter methylation were observed in advanced cancer situations. The Kaplan-Meier survival curves indicated that the overall survival was significantly poor in higher miRNAs and lower DACH1 expression in breast cancer patients (p<0.002). Conclusion: DACH1 down-regulation may be associated with a poor breast cancer prognosis. The DACH1 down-regulation may be due to epigenetic regulations such as promoter methylation, especially in triple-negative cases. Other factors, such as micro-RNAs (miR-217, miR-6807-3p, and miR-552), may also have an impact. The elevated expression of miR-217, miR-6807-3p, and miR-552, maybe candidates as possible poor prognostic biomarkers in breast cancer management for further consideration. 

Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it.

Figuring out the molecular mechanisms underlying breast cancer is essential for the diagnosis and treatment of this invasive disorder. Hence it is important to identify the most significant genes correlated with molecular events and to study their interactions in order to identify breast cancer mechanisms. Here we focus on the gene expression profiles, which we have detected in breast cancer. High-throughput genomic innovations such as microarray have helped us understand the complex dynamics of multisystem diseases such as diabetes and cancer. We performed an analysis using microarray datasets with Networkanalyst bioinformatics tool, based on a random effect model. We achieved pivotal differential expressed genes like ADAMTS5, SCARA5, IGSF10, C2orf40 that had the most down-regulation and also COL10A1, COL11A1, UHRF1 that they had most up-regulation in four-stage of breast cancer. We used CentiScape and AllegroMCODE plugins in CytoScape software in order to achieve better insight into and figure out hub genes in the protein-protein interactions network. Besides, we utilized DAVID online software to find involved biological pathways and Gene ontology, also used Expression2kinase software in order to find upstream regulatory transcription factors and kinases. In conclusion, we have found that the statistical network inference approach is useful in gene prioritization, is capable of contributing to practical network signature discovery, providing insights into the mechanisms relevant to the disease. Our study has also developed a new candidate genes, pathways, transcription factors, and kinases that may be candidates for diagnostic biomarkers and also for drug design after experimental examinations.