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Background: Besides maintaining health precautions, vaccination has been the only prevention from SARS-CoV-2, though no clinically proved 100% effective vaccine has been developed till date. At this stage, to withhold the debris of this pandemic-experts need to know the impact of the vaccine efficacy rates, the threshold level of vaccine effectiveness and how long this pandemic may extent with vaccines that have different efficacy rates. In this article, a mathematical model study has been done on the importance of vaccination and vaccine efficiency rate during an ongoing pandemic. Methods: We simulated a five compartment mathematical model to analyze the pandemic scenario in both California, and whole U.S. We considered four vaccines, Pfizer (95%), Moderna (94%), AstraZeneca (79%), and Johnson & Johnson (72%), which are being used rigorously to control the SARS-CoV-2 pandemic, in addition with two special cases: a vaccine with 100% efficacy rate and no vaccine under use. SARS-CoV-2 related data of California, and U.S. were used in this study. Findings: Both the infection and death rates are very high in California. Our model suggests that the pandemic situation in California will be under control in the last quartile of the year 2023 if vaccination program is continued with the Pfizer vaccine. During this time, six waves may happen from the beginning of the immunization where the case fatality and recovery rates will be 1.697% and 98.30%, respectively. However, according to the considered model, this period might be extended to the mid of 2024 when vaccines with lower efficacy rates are used. On the other hand, the daily cases and deaths in the U.S. will be under control at the end of 2026 with multiple waves. Although the number of susceptible people will fall down to none in the beginning of 2027, there is less chance to stop the vaccination program if vaccinated with a vaccine other than a 100% effective vaccine or Pfizer, and at that case vaccination program must run till the mid of 2028. According to this study, the unconfirmed-infectious and infected cases will be under control at the end of 2027 and at the mid of 2028, respectively. Interpretation: The more effective a vaccine is, the less people suffer from this malign infection. Vaccines which are less than 90% effective do not have notable contribution to control the pandemic besides hard immunity. Furthermore, specific groups of people are getting prioritized initially, mass vaccination and quick responses are required to control the spread of this disease.

Chloroflexus aurantiacus is a thermophilic bacterium that produces a multitude of proteinswithin its genome. Bioinformatics strategies can facilitate comprehending this organism throughfunctional and structural interpretation assessments.This study aimed to allocate the structure andfunction through an in-silico approach required for bacterial protein biosynthesis. This in-silicoviewpoint provides copious properties, including the physicochemical properties, subcellular location,three-dimensional structure, protein-protein interactions, and functional elucidation of the protein(WP_012256288.1). The STRING program is utilized for the explication of protein-proteininteractions. The in-silico investigation documented the protein's hydrophilic nature withpredominantly alpha (α) helices in its secondary structure.The tertiary-structure model of the proteinhas been shown to exhibit reasonably high consistency based on various quality assessmentmethods.The functional interpretation suggested that the protein can act as a translation initiationfactor, a protein required for translation and protein biosynthesis. Protein-protein interactions alsodemonstrated high credence that the protein interconnected with 30S ribosomal subunit involved inprotein synthesis. This study is bioinformatically examined that the protein (WP_012256288.1) isaffiliated in protein biosynthesis as a translation initiation factor IF-3 of C. aurantiacus.

There is a long ritual of acquired immunity using physical exercise, a balanced diet, and pharmaceutical medication to generate immunity against a particular disease insight into the human body. This paper has extensively reviewed the impact of exercise, daily life practice, food selection, and several other issues to improve the immune system that combat infection. Studying the effect of exercise in varying degrees on the immunity system of humans is well developed and exhibit in this study. It investigates the prevention of pandemics due to herd immunity and finds the perfect amount of exercise to boost immunity to its maximum. Besides the life practice, it is also explored that vaccination can improve and optimize herd immunity.

Background: The world is now in an emergency of preventing the drastic spread of COVID-19. After the infection was first reported in December 2019, almost every country did not pay attention to this highly contaminated disease and failed to react swiftly. Now the whole planet is in an vulnerable state, resulting to increase the mortality rate and facing difficulties in all socio-economic aspects. That is why we have the urge to develop an efficient mathematical model (quarantine) based on social consciousness to control the epidemic. Methods: This is a quarantine mathematical model. The outcome of the system is dependent on social consciousness. We have calculated the awareness level by considering various socio-economic factor of each country. In our model, the parameters are Education Index, Gross Domestic Product (GDP) per capita, population density, high literacy and stable economy. To maximize the efficiency of the model, it has to be implemented in initial stage. However, strictapplication of the method in vigorous stage of epidemic will also bring a satisfactory outcome. Results: In Spain, quarantine was effected on March 14, 2020. Spain experienced an increase in reported cases for 13 days of quarantine enforcement and from the 14th day, daily reported cases started to decrease with small fluctuation. Government ensured the social isolation through quarantine. After imposition of a quarantine on March 9, 2020 in Italy, within 13 days of lock-down, the maximum number of infection started to decrease. Similar results observed in France. Higher social consciousness would decrease the number of infected population dramatically while minimal or lower awareness will do a outburst. Conclusion: Outbreak will be in control of health care system which yields to reduce the death rate and will ensure social and economic stability.

In the last two decades the world had faced three respiratory syndrome outbreaks incurred by Coronavirus. Though the wild animals are the primary carriers of the virus, the human population managed to survive sacrificing more than 1,600 lives from 2002 to 2012. But the current virus outbreak has already taken more than 2,462 lives since 22 February 2020. In the first few days, when the cases were being introduced under light, there were no treatment for the infection and the unleashed spread demands to be analyzed to see the pattern of the outbreak. This manuscript aims to look into the growth map of the COVID-19 outbreak under mathematical growth functions and tries to understand which growth pattern assembles the scenario for the cases.

Despite the advances in diagnosis and management of breast cancer, metastasis has been responsible for the staggering percentage of breast cancer-related death. Mortality threat can be explained mostly by the lack of proper understanding of the diversity of pathological features and underlying mechanism of breast cancer metastasis and effective targeted therapy. Breast cancer stem cells (BCSCs) are the potential source of tumor cells spread to distant organs. BCSCs targeted therapy can suppress the breast cancer progression to metastasis. Spreading of tumor cells to the bone, lung, liver, and brain occurs through a distinct non-random process; called metastasis organotropism. Recently, brain metastasis in breast cancer patients has been detected more frequently, causing a significant clinical burden. BRCA1 and BRCA2 associated breast cancers carry a remarkably higher propensity of CNS metastasis. BRCA1 and BRCA2 associated breast cancers commonly have the propensity to be the triple-negative (TN) and hormone receptors (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative molecular subtypes, respectively. Regardless of molecular subtypes, metastasis is most commonly evident at the bone. Heterogeneity is a critical pathological feature, leads to therapeutic resistance. BCSCs, biomarkers expression patterns, and mutations contribute to heterogeneity. In this paper, we discuss crucial pathological features of breast cancer metastasis, emphasizing metastasis organotropism and heterogeneity; and mechanisms of breast cancer metastasis, highlighting the pathways of metastasis to the brain. We consider that this paper reinforces future research areas and benefits the general readers, physicians, and researchers to identify potential areas to develop targeted therapies.

The human pathogen Mycobacterium tuberculosis (MTB) is indeed one of the renowned, important, longtime infectious diseases, tuberculosis (TB). Interestingly, MTB infection has become one of the world’s leading causes of human death. In trehalose synthase, the protein NCGM 946K2 146 found in MTB has an important role. For carbohydrate transport and metabolism, trehalose synthase is required. The protein is not clarified yet, though. In this research, an in silico approach was, therefore, formulated for functional and structural documentation of the uncharacterized protein NCGM946K2_146.Three distinct servers, including Modeller, Phyre2, and Swiss Model, were used to evaluate the predicted tertiary structure. The top materials are selected using structural evaluations conducted with the analysis of Ramachandran Plot, Swiss-Model Interactive Workplace, ProSA-web, Verify 3D, and Z scores. This analysis aimed to uncover the value of the NCGM946K2_146 protein of MTB. This research will, therefore, improve our pathogenesis awareness and give us a chance to target the protein compound.

Automated visual identification of Benign and Malignant breast tumors even with ultrasound (US) B-Mode image is still an open area of research. This paper presents parametric image-based approach to classify and detect benign and malignant breast tumors from ultrasound images using a custom-made convolutional neural network architecture. The Rician Inverse Gaussian (RiIG) distribution is presented here as a suitable model for describing the statistics of the ultrasound images in the Contourlet Transform domain. Locally computed values of the dispersion parameters of the RiIG distribution in various contourlet sub-bands yield parametric images those are classified using the proposed convolutional neural network. Experiments are conducted on a publicly available dataset of 250 images, of 100 belong to the benign Fibroadenoma and 150 in the malignant category. The proposed method provides accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV) of 96%, 97.3%, 94.12%, 96% and 96%, respectively. It is also shown that the accuracy obtained by the Proposed Method is higher than several recently reported results.

Segmentation or lesion boundary detection in classification of Benign and Malignant breast tumors from B-Mode ultrasound image analysis is a challenging one. In this study, first a suitable frame is chosen by strain and velocity imaging from a raw radio frequency (RF) echo of clinical cases. The consequent B-Mode ultrasound (US) image is calculated and binarized. Finally, the lesion boundary is well-defined by binary dilation technique using MATLAB. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 5(1), Dec 2018 P 75-78

The role of TRIM24 in HCC and the mechanisms underlying the dysregulated expression of TRIM24 is poorly understood. Here, we systematically analyzed TRIM24 expression and its prognostic role in HCC using various open databases. More precisely, we found an up-regulated gene expression profile of TRIM24 in hepatocellular carcinoma by using Oncomine, cBioPortal, TCGA, and the Human Protein Atlas. The higher expression of TRIM24 is allied with various clinicopathological conditions such as cancer stages, tumor grade, nodal metastasis status, histological subtypes, and also mutant TP53 status based on the TCGA database. In addition to the mRNA expression profile, the correlation between mRNA expression and TRIM24 promoter methylation is entrenched using TCGA datasets from the UALCAN web and subsequently, we examined the liaison of TRIM24 promoter methylation with different clinicopathological conditions in HCC patients. We also clarified a negative correlation of the TRIM24 mRNA expression with antioxidant responsive genes using the TCGA cBioPortal database. Data mining via Kaplan-Meier Plotter and the Human Protein Atlas symbolized a positive tie-up among the higher TRIM24 gene expression, protein expression, and poor survival of HCC patients. Besides, we also identified a highly co-expressed gene, SMARCC1 with TRIM24 in the most predominant correlation fashion in HCC using UALCAN, cBioPortal, UCSC Xena database. Finally, we identified the co-expressed genes with gene oncological features, signaling pathways, and ENCODE ChIP-seq coordination in HCC development. Altogether, this study establishes that elevated TRIM24 expression might accelerate the cancer progression and poor prognosis in HCC patients in a concerted fashion with SMARCC1 expression, which would allow them as a potential therapeutic biomarker in HCC cases.