Evaluation of the effects of photodynamic therapy consisted of the blue laser and zinc oxide QDs on MDA-MB-231 cancer cells by inhibiting cancer markers and inducing apoptosis.

The increasing number of cancer patients has cast attention on developing new anti-cancer modalities. Photodynamic therapy is a safe anti-cancer approach, which encompasses (1) local administration of a photosensitizer and (2) light irradiation. Zinc oxide (ZnO) quantum dots (QDs) are photosensitizers that can be utilized for this purpose. In the present study, to better appreciate the likely more efficient cytotoxic effect of the combination of ZnO QDs and the visible 470-nm blue light in comparison to the QDs alone, several assays were to be conducted upon breast cancer MDA-MB 231 cells. MTT assay showed that in certain groups the combination displayed higher cytotoxic effects compared to those following QD treatment alone. LDH leakage and lipid peroxidation rates by the combination were significantly higher than treatment with either the blue laser or QDs. Although the combination managed to meaningfully reduce the number of colonies and CAT activity compared to QD treatment, there were no palpable differences between them. Lastly, the combination was able to increase the apoptotic genes, including BAX, TP53, caspase 3, and caspase 9 compared to QD, while, in the case of Bcl-2, an anti-apoptotic gene, none of the groups managed to make any tangible differences on its expression levels. Our findings propose that there may be synergistic effects between the blue laser and QD that can possibly be adopted in anti-cancer therapy in the future. However, further investigations regarding this matter are of the essence.

Study of DACH1 Expression and its Epigenetic Regulators as Possible Breast Cancer-Related Biomarkers.

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.

Risk factors, thromboembolic events, and clinical course of New-Onset Atrial Fibrillation among COVID-19 hospitalized patients: A multicenter cross-sectional analysis in Iran.

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.

EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms.

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.

PSOWNNs-CNN: A Computational Radiology for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning Methods.

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.

SARS-CoV-2 triggering autoimmune diseases.

Autoimmunity, hyperstimulation of the immune system, can be caused by a variety of reasons. Viruses are thought to be important environmental elements that contribute to the development of autoimmune antibodies. It seems that viruses cause autoimmunity with mechanisms such as molecular mimicry, bystander activation of T cells, transient immunosuppression, and inflammation, which has also been seen in post-Covid-19 autoimmunity. Infection of respiratory epithelium by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) dysregulates the immune response, triggers both innate and acquired immunity that led to the immune system's hyperactivation, excessive cytokine secretion known as "cytokine storm," and finally acute respiratory distress syndrome (ARDS) associated with high mortality. Any factor in the body that triggers chronic inflammation can contribute to autoimmune disease, which has been documented during the Covid-19 pandemic. It has been observed that some patients produce autoantibody and autoreactive CD4+ and CD8+ T cells, leading to the loss of self-tolerance. However, there is a scarcity of evidence defining the precise molecular interaction between the virus and the immune system to elicit autoreactivity. Here, we present a review of the relevant immunological findings in Covid-19 and the current reports of autoimmune disease associated with the disease.

Revealing the relationship between solar activity and COVID-19 and forecasting of possible future viruses using multi-step autoregression (MSAR).

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.