Breast cancer is the most common cancer among women over the world. To reducing reoccurrence and mortality rates, adjuvant hormonal therapy (AHT) is used for a long period. The major barrier to the effectiveness of the treatment is adherence. Adherence to medicines among patients is challenging. Patient beliefs in medications can be positively or negatively correlated to adherence. Objectives: To investigate the extent of adherence and factors affecting adherence, as well as to investigate the association between beliefs and adherence in women with breast cancer taking AHT. Method: A cross-sectional study included 124 Iraqi women with breast cancer recruited from Middle Euphrates Cancer Center. Morisky medication adherence scale (MMAS) and beliefs about medication questionnaires (BMQ) are used to assess adherence and beliefs respectively. Result: 25% of women were fully adherent (MMAS = 8). 83.06% of all women developed side effects from medications received. Side effects and unemployed women were significantly associated with non-adherence. Additionally, there is no significant association between beliefs in medications and adherence. conclusion The enormous percent of poor adherence caused by side effects suggests the need for interventions by educating patients about the importance of their treatment and how to overcome side effects.
The aim of this paper is to translate the basic properties of the classical complete normed algebra to the complete fuzzy normed algebra at this end a proof of multiplication fuzzy continuous is given. Also a proof of every fuzzy normed algebra without identity can be embedded into fuzzy normed algebra with identity and is an ideal in is given. Moreover the proof of the resolvent set of a non zero element in complete fuzzy normed space is equal to the set of complex numbers is given. Finally basic properties of the resolvent space of a complete fuzzy normed algebra is given.
In This paper, we introduce the associated graphs of commutative KU-algebra. Firstly, we define the KU-graph which is determined by all the elements of commutative KU-algebra as vertices. Secondly, the graph of equivalence classes of commutative KU-algebra is studied and several examples are presented. Also, by using the definition of graph folding, we prove that the graph of equivalence classes and the graph folding of commutative KU-algebra are the same, where the graph is complete bipartite graph.
In this research, we highlight the most important research related to the mixed ligand complexes of the drug trimethoprim (TMP), and for the past 7 years where this drug has been used as a chelating ligand and gives stability to the complexes with ions of metal elements where these complexes, prepared and diagnosed, and for some research the bacterial activity was studied against different types of bacteria.
In this research, we highlight the most important research related to the mixed ligand complexes of the drug trimethoprim (TMP), and for the past 7 years where this drug has been used as a chelating ligand and gives stability to the complexes with ions of metal elements where these complexes, prepared and diagnosed, and for some research the bacterial activity was studied against different types of bacteria
Image Fusion Using A Convolutional Neural Network
This study sought to determine malformation caused by Ochratoxin-A (OTA) on mouse embryos. Twenty adult female white Swiss mice (mus msculus) were divided into four groups, with five females per group, and with one male placed with two females in a cage. Avaginal plug was observed in the early morning and the day of mating was considered as day of pregnancy followed by the first day of pregnancy. Three sub lethal concentrations of OTA were applied to the respective groups (other than the control), 1mg/kg, 2mg/kg and 4mg/kg. The animals were given 0.1 ml per 10 gm body weight per concentration of OTA once a day during days 7-14 of pregnancy. The control group animals were given distilled water. The pregnant mice were dissected, and the embry
... Show MoreOver the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time. In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as D
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