Estrogens and the risk of breast cancer: A narrative review of literature.

In female mammals, the development and regulation of the reproductive system and non-reproductive system are significantly influenced by estrogens (oestrogens). In addition, lipid metabolism is another physiological role of estrogens. Estrogens act through different types of receptors to introduce signals to the target cell by affecting many estrogen response elements. Breast cancer is considered mostly a hormone-dependent disease. Approximately 70% of breast cancers express progesterone receptors and/or estrogen receptors, and they are a good marker for cancer prognosis. This review will discuss estrogen metabolism and the interaction of estrogen metabolites with breast cancer. The carcinogenic role of estrogen is discussed in light of both conventional and atypical cancers susceptible to hormones, such as prostate, endometrial, and lung cancer, as we examine how estrogen contributes to the formation and activation of breast cancer. In addition, this review will discuss other factors that can be associated with estrogen-driven breast cancer.

Human monkeypox virus in the shadow of the COVID-19 pandemic.

BACKGROUND: The end of smallpox in 1980 and the subsequent stopping of vaccination against smallpox was followed by the emergence of monkeypox (mpox), a viral disease of animal origin, meaning that it is transmitted from animal to human. The symptoms of mpox are similar to smallpox, except that they are less severe in terms of clinical features. In the case of public health, the mpox virus is one of the most important orthopoxviruses (such as variola, cowpox, and vaccinia) that come from the family Poxviridae. Mpox occurs mostly in central Africa and sometimes in tropical rainforests or some urban areas. Also, there are threats other than COVID-19, that must be addressed and prevented from spreading, as there has been an outbreak of mpox cases since May 7, 2022, throughout the USA, Europe, Australia, and part of Africa. OBJECTIVES: In this review, we will discuss mpox between the past, the present and during the COVID-19 pandemic. Also, it offers an updated summary of the taxonomy, etiology, transmission, and epidemiology of mpox illness. In addition, the current review aims to highlight the importance of emerging pandemics in the same era such as mpox and COVID-19. METHODS: A literature search was done for the study using online sources like PubMed and Google Scholar. Publications in English were included. Data for study variables were extracted. After the duplicate articles were eliminated, full-text screening was performed on the papers' titles and abstracts. RESULTS: The evaluation included a series documenting mpox virus outbreaks, and both prospective and retrospectiveinvestigations. CONCLUSIONS: monkeypox is a viral disease caused by the monkeypox virus (MPXV), which is primarily found in central and western Africa. The disease is transmitted from animals to humans and presents symptoms similar to those of smallpox, including fever, headache, muscle aches, and a rash. Monkeypox can lead to complications such as secondary integument infection, bronchopneumonia, sepsis, and encephalitis, as well as corneal infection that can result in blindness. There is no specific clinically proven treatment for monkeypox, and treatment is primarily supportive. However, antiviral drugs and vaccines are available for cross-protection against the virus, and strict infection control measures and vaccination of close contacts of affected individuals can help prevent and control outbreaks.

A cross-sectional study of the impact of COVID-19 pandemic on the physical activity of Jordanian population.

Background: Since the declaration of the COVID-19 pandemic on 11th March 2020, the public health recommendations have applied lockdowns and restrictions to limit the spread of the disease. These measures determined outdoor activities and access to many forms of exercise. Objective: The primary objective of this study was to examine the impact of the COVID-19 pandemic on the physical activity (PA) of the Jordanian population. Methods: A cross-sectional online survey was designed using Google Forms services and distributed on social media platforms during the first two weeks of November 2020 to evaluate the exercise activity changes during the COVID-19 pandemic in the study population. In this research, we included those who perform any form of physical activity (n = 1103). The data collected were analyzed using the Statistical Package for the Social Sciences (SPSS) version 26 (IBM SPSS Corp, SPSS Statistics ver. 26, USA). The categorical variables were summarized as frequencies and proportions and were compared using the Chi-square. For all analyses, P-value less than 0.05 was considered statistically significant. Results: A total of 1103 out of 2,511 (43.9%) respondents who participated in the survey were physically exercising during the COVID-19 pandemic. 41.8% were exercising at an irregular frequency, while 21.5% were exercising daily, 16.8% were exercising three to five times a week, and 19.9% were exercising once or twice a week. Moreover, 282 (25.6%) respondents started doing some form of exercise during the pandemic. Those significantly were less than 18-year-old, male gender, were single, were non-smokers, and had a diploma or bachelor's degree in a health-related major. These changes in the level of exercise have been attributed by 57.8% of respondents to the health aspects where they realized the importance of exercising in strengthening the immune system against diseases, including COVID-19. Conclusion: The current study showed that lockdown, due to the COVID-19 pandemic, had a positive impact on the healthy lifestyle of the Jordanian population which was attributed to their feeling of the importance of sports practice on the immune system and the availability of time for those activities. However, the younger and individuals were more aware of the importance of these practices which highlights the importance of considering other age groups in future studies of healthy behaviors.

Resiliency Assessment of Power Systems Using Deep Reinforcement Learning.

Evaluating the resiliency of power systems against abnormal operational conditions is crucial for adapting effective actions in planning and operation. This paper introduces the level-of-resilience (LoR) measure to assess power system resiliency in terms of the minimum number of faults needed to produce a system outage (blackout) under sequential topology attacks. Four deep reinforcement learning (DRL)-based agents: deep Q-network (DQN), double DQN, the REINFORCE (Monte-Carlo policy gradient), and REINFORCE with baseline are used to determine the LoR. In this paper, three case studies based on IEEE 6-bus test system are investigated. The results demonstrate that the double DQN network agent achieved the highest success rate, and it was the fastest among the other agents. Thus, it can be an efficient agent for resiliency evaluation.

Disease characteristics, determinants, and perception of use of sunscreen and sun-protective behaviors among patients of color with melasma: A cross-sectional study.

BACKGROUND: Melasma is an acquired state of hyperpigmentation that most commonly affects the face. The use of sunscreen is essential in melasma treatment. We sought to investigate patients' perspectives and behaviors toward sunscreen usage. METHODS: A cross-sectional hospital-based study targeted 418 melasma patients from May 2019 to May 2021. Data regarding socio-demographic characteristics and the knowledge, attitude, and behavior toward sunscreen and sun exposure were collected and analyzed. Furthermore, a complete clinical assessment was done. RESULTS: The mean age of the patients was 35.4 ± 8.6 years. Melasma distribution was mostly centro-facial (49.8%). The mean duration of the disease was 22.3 ± 11.6 months, with a mean Melasma Area and Severity Index (MASI) score of 18.6 ± 8.9. Only 170 patients believed that sun exposure played a role in their disease. Skin darkening was the most recognized effect of sun exposure by 92.9% of participants. 58.6% reported using sunscreen with a higher female predominance (p < .001), while males reported more broad-spectrum sunscreen usage (p < .001). The reason that was reported the most behind not using sunscreen was the high cost (94%). CONCLUSIONS: Our study showed a sound level of knowledge regarding the effects of sun exposure, and sunscreen advantages and disadvantages, with relatively inadequate translation into their attitudes and practices regarding sun-protective behaviors. The use of sunscreen among the patients was average in numbers with a higher female predominance.

Short-Time Wind Speed Forecast Using Artificial Learning-Based Algorithms.

The need for an efficient power source for operating the modern industry has been rapidly increasing in the past years. Therefore, the latest renewable power sources are difficult to be predicted. The generated power is highly dependent on fluctuated factors (such as wind bearing, pressure, wind speed, and humidity of surrounding atmosphere). Thus, accurate forecasting methods are of paramount importance to be developed and employed in practice. In this paper, a case study of a wind harvesting farm is investigated in terms of wind speed collected data. For data like the wind speed that are hard to be predicted, a well built and tested forecasting algorithm must be provided. To accomplish this goal, four neural network-based algorithms: artificial neural network (ANN), convolutional neural network (CNN), long short-term memory (LSTM), and a hybrid model convolutional LSTM (ConvLSTM) that combines LSTM with CNN, and one support vector machine (SVM) model are investigated, evaluated, and compared using different statistical and time indicators to assure that the final model meets the goal that is built for. Results show that even though SVM delivered the most accurate predictions, ConvLSTM was chosen due to its less computational efforts as well as high prediction accuracy.

Attack Graph Modeling for Implantable Pacemaker.

Remote health monitoring systems are used to audit implantable medical devices or patients' health in a non-clinical setting. These systems are prone to cyberattacks exploiting their critical vulnerabilities. Thus, threatening patients' health and confidentiality. In this paper, a pacemaker automatic remote monitoring system (PARMS) is modeled using architecture analysis and design language (AADL), formally characterized, and checked using the JKind model checker tool. The generated attack graph is visualized using the Graphviz tool, and classifies security breaches through the violation of the security features of significance. The developed attack graph showed the essentiality of setting up appropriate security measures in PARMS.