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BackgroundAn outbreak of COVID-19 in Iran has spread throughout the country. Identifying the epidemiological characteristics of this disease will help to make appropriate decisions and thus control the epidemic. The aim of this study was characterization of the epidemiological features of COVID-19 in Iran.MethodsIn this retrospective study, data related to the epidemiological characteristics of COVID-19 patients admitted to Baqiyatallah Hospital in Tehran, Iran, from 19 February 2020 to 15 April 2020 have been analyzed and reported. Patient characteristics including age, gender and underlying diseases were investigated. Data were collected through patient records. Sex ratio, Case Fatality Rate (CFR) and daily trend of cases were also determined. A multiple logistic regression analysis was also performed to assess affecting factors on mortality.ResultsFrom February 19, 2020 to April 15, 2020, 12870 patients referred to the hospital emergency department, of which 2968 were hospitalized with COVID-19 diagnosis. The majority of cases were in the age group of 50 to 60 years of old. The male-to-female ratio was 1.93:1. A total of 239 deaths occurred among all cases for an overall CFR of 1.85% based on the total number of patients (both outpatient and inpatient) and 8.06% among hospitalized patients. Out of all patients 10.89% had comorbidity. Diabetes, chronic respiratory diseases, hypertension, cardiovascular diseases, chronic Kidney diseases and cancer were the most common comorbidities with 3.81, 2.02 , 1.99 , 1.25, 0.60 and 0.57 %, respectively. Male gender (OR=1.45, 95% CI: 1.08-1.96), older age (OR=1.05, 95% CI: 1.04-1.06) and having underlying diseases (OR=1.53, 95% CI: 1.04-2.24) were significantly associated with mortality.ConclusionsThe results of this study showed that Male gender, older age and having comorbidities were significantly associated with the risk of death among COVID-19 patients. It is important to pay special attention to male elderly patients with underlying diseases.

Melittin is a natural toxin used in traditional medicine as an anti-inflammatory drug. It seems that the anti-inflammatory properties of melittin are caused by suppressing the expression of inflammatory genes and inhibiting signaling pathways. However, the use of melittin is limited due to instability, rapid degradation, and impurity. The aim of this study was to investigate the intranasal administration of a melittin-encoded plasmid as a new melittin delivery method for allergic diseases. After the induction of a mouse model of allergic rhinitis, mice received intranasal melittin plasmid. After the final challenge with allergen and allergic symptom assessment, the required samples were collected, and transforming growth factor-beta (TGF-β), interferon-gamma (IFN-γ), and interleukin-4 (IL-4) cytokine levels, serum levels of ovalbumin-specific immunoglobulin (Ig) E and histopathological changes were assessed. In addition to investigating the immune response, the effect of melittin on the expression level of genes involved in apoptosis was also investigated. The melittin plasmid significantly improved nasal symptoms and decreased eosinophil infiltration into the nasal mucosa. Moreover, melittin decreased the expression levels of IL-4 and TGF-ß in nasal lavage fluid, while IFN-γ expression was increased. Regarding the expression level of genes involved in apoptosis, melittin led to an increase in BAX mRNA expression. These results suggest intranasal administration of a plasmid encoding melittin can suppress nasal symptoms, eosinophil infiltration, and immunomodulation of the immune response, which can be considered a promising approach in the treatment of inflammatory diseases. [ABSTRACT FROM AUTHOR]

Enzymes are protein molecules that play a crucial role in various biological processes in living organisms. They function as catalysts in biological reactions such as digestion, metabolism, DNA replication and other physiological processes. Furthermore, enzymes are widely used in food production, pharmaceuticals and biofuel production. In these industries, they accelerate desired chemical reactions as biocatalysts. Therefore, applying computational methods and data-driven algorithms to predict enzyme properties is essential. Over the past decade, deep learning has made remarkable advancements in science and technology. Deep learning is a subset of machine learning algorithms that rely on artificial neural networks. These algorithms can be employed for supervised, semi-supervised and unsupervised learning. Here, to provide an update on the current literature, we provide an overview of various deep learning algorithms and recent advancements in their application to enzyme science. These applications can generally be categorized into diverse subjects: function prediction, enzyme kinetic parameters prediction, enzyme-substrate identification, condition optimization, thermophilic property prediction, enzyme catalytic site prediction and enzyme design. In conclusion, we discuss the convergence of enzyme science and deep learning, highlighting the potential opportunities and challenges. Artificial intelligence algorithms have diverse applications in enzyme science. In this review paper, we focus on deep learning algorithms and their implications on various tasks, such as enzyme design, kinetic parameters prediction, etc. [ABSTRACT FROM AUTHOR]

Background and Aim: Health, according to the World Health Organization, encompasses complete physical, mental, and social well-being, not just the absence of disease and illness. This study aims to monitor and evaluate the scientific publications of military research centers in the health field and their researchers over the past 5 years (2016-2020). Methods: Military researchers were first identified using an advanced search strategy for various dimensions of health in the Science Direct database. Then, the complete output of their research articles were extracted from the Scopus database. Pearson's correlation coefficient and the Mann-Kendall trend test were used to analyze the data. R software version 4.2.2 was used for data analysis and VOSviewer software version 1.6.18 was utilized for the visualization of scientometrics approaches. Results: Overall, 3771 and 79069 research articles were identified from military research centers of Iran and the world, respectively, in the field of health during a period of 5 years (2016-2020). Military research centers of China and the United States were the leading countries in terms of scientific publications in the field of health. Iran, the United States, England, and Pakistan experienced significant research growth among the top countries during the period of 2016-2020 (P = 0.027). Additionally, the United States recorded near-significant growth in international collaboration (P = 0.086) and Iran showed near-significant growth in review researches (P = 0.086) during these years. Furthermore, a significant correlation was observed between the total number of articles and the ratio of review articles in Iran over time (P = 0.017). The military research centers of Iran and the world and their top researchers shared the priorities of the research topics of 'Medicine and Dentistry' and 'Biochemistry, Genetics, and Molecular Biology'. A shift in research focus towards the keyword 'Covid-19' was observed in Iran and the world in 2020. Conclusion: The main findings of the present study focused on the evaluation and visualization of the scientific publication of military research centers in Iran and worldwide, as well as the associated researchers. There is a need to review science and technology policies in order to strengthen weaknesses and sustain strengths. [ABSTRACT FROM AUTHOR]