Although considerable advances have been made in the early diagnosis and treatment of breast cancer, it is still one of the major causes of global cancer-related death in women over the last several decades. Phytochemicals have been shown to be promising agents in the prevention and treatment of breast cancer. Resveratrol is an important plant-derived polyphenolic compound, with a variety of potent biological activities. It has been suggested that resveratrol can be used in the prevention and treatment of various types of cancer, including breast cancer. Resveratrol can affect numerous signaling pathways in vitro, leading to the induction of cell cycle arrest and apoptosis, suppression of proliferation, reduction of inflammatory responses, and the inhibition of angiogenesis and metastasis. Nevertheless, studies of resveratrol in animal models of breast cancer have so far been disappointing.
Investigating the performance and productivity level of different energy consuming sectors in all countries is an inevitable action. This procedure will be conducted by comparing the energy input and the output of the system which is vital to ensure that the system is used properly. The proper utilization of systems will lead to more efficiency in the energy consumption section. One of the most important tasks in this type of study is the analysis of uncertainty indicators. The analysis and evaluation of uncertainty indices in energy consumption system is a tool that prioritizes the indicators in terms of importance and impact on each of the consumption targets. These consumption goals include energy, environmental, technical, economical, and social objectives. Ultimately, the output data of the uncertainty analysis will be very helpful for making the system more reliable and usable. In this study, we first introduced different sectors of the energy consumption system in Finland and examined each of these sectors in terms of physical and environmental goals. Then the uncertainty indexes in different sectors are extracted, evaluated qualitatively and quantified using the fuzzy logic method. Finally, indicators are prioritized based on the level of effectiveness and uncertainty. According to the results of this research, among 44 considered indices, the security of energy supply, carbon emission, equivalent annual cost, reliability, and political acceptability are respectively the most important indices for energy, environmental, economic, technical and social goals.
Objectives
We aimed to develop and validate a prognostic model to predict clinical deterioration defined as either death or intensive care unit admission of hospitalized COVID-19 patients.
Methods
This prospective, multicenter study investigated 172 consecutive hospitalized COVID-19 patients who underwent a chest computed tomography (CT) scan between March 20 and April 30, 2020 (development cohort), as well as an independent sample of 40 consecutive patients for external validation (validation cohort). The clinical, laboratory, and radiologic data were gathered, and logistic regression along with receiver operating characteristic (ROC) curve analysis was performed.
Results
The overall clinical deterioration rates of the development and validation cohorts were 28.4% (49 of 172) and 30% (12 of 40), respectively. Seven predictors were included in the scoring system with a total score of 15: CT severity score\(\ge\)15 (Odds Ratio (OR)=6.34, 4 points), pleural effusion (OR = 6.80, 2 points), symptom onset to admission ≤ 6 days (OR = 2.44, 2 points), age\(\ge\)70 years (OR = 2.44, 2 points), diabetes mellitus (OR = 2.24, 2 points), dyspnea (OR = 2.17, 1.5 points), and abnormal leukocyte count (OR = 1.89, 1.5 points). The area under the ROC curve for the scoring system in the development and validation cohorts was 0.823 (CI [0.751–0.895]) and 0.558 (CI [0.340–0.775]), respectively.
Conclusion
This study provided a new easy-to-calculate scoring system with external validation for hospitalized COVID-19 patients to predict clinical deterioration based on a combination of seven clinical, laboratory, and radiologic parameters.
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