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Association between self‐reported oral disease/conditions and symptoms of depression among Iraqi individuals
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Abstract<sec><title>Aims

The negative impact of oral diseases on the function, economy, and general health of the population is well‐documented. In the last decades, evidence linking increased expression of depression and oral diseases/conditions has significantly increased. The aim of this study is to assess the association between oral disease/conditions and self‐reported symptoms of depression individuals.

Methods

A specially designed questionnaire was distributed via social media for 1 week. It consisted of two main sections; the first section was dedicated to collect demographic variables and self‐reported symptoms of oral diseases. The second section, Patient Health Questionnaire‐9 (PHQ‐9), was used to assess the severity of depression via nine questions using a 4‐point Likert scale. Association between depression and oral disease was determined by linear regression analysis.

Results

A total of 1975 participants responded fully to the questionnaire and were included in the final analysis. The majority of participants, about 60%, showed mild to moderate symptoms of depression, while 8.9% expressed severe symptoms. Oral diseases positively associated with depression were caries, missing teeth, gingival bleeding, gingival recession, teeth mobility, and dry mouth (R2 = .155). In contrast, increased esthetic level of teeth/gingiva significantly decreased the feeling of depression.

Conclusions

Results indicated that oral diseases, particularly those adversely affecting function and esthetics, were associated with symptoms of depression in Iraqi individuals. These findings highlighted the importance of maintaining oral health as part of the general psychological wellbeing of the population.

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Thu Oct 22 2020
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2020 4th International Symposium On Multidisciplinary Studies And Innovative Technologies (ismsit)
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Al-kindy College Medical Journal
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Wed Apr 20 2011
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Chaos in a harvested prey-predator model with infectious disease in the prey
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A harvested prey-predator model with infectious disease in preyis investigated. It is assumed that the predator feeds on the infected prey only according to Holling type-II functional response. The existence, uniqueness and boundedness of the solution of the model are investigated. The local stability analysis of the harvested prey-predator model is carried out. The necessary and sufficient conditions for the persistence of the model are also obtained. Finally, the global dynamics of this model is investigated analytically as well as numerically. It is observed that, the model have different types of dynamical behaviors including chaos.

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Publication Date
Mon Jun 11 2018
Journal Name
Internationaljournalofresearchinpharmaceuticalsciences
Treatment satisfaction with disease-modifying therapy in relapsing-remit-tingmultiple sclerosis in Iraq
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Multiplesclerosis(MS)isachronic,inflammatory,immune-mediateddiseaseof the central nervous system (CNS). More than 2 million people worldwidehave MS. The goal of the present study was to compare Iraqi patients' treat-ment satisfaction with three different disease-modifying therapies (DMTs),administeredorally,subcutaneously,andbyslowinfusion;namely,fin-golimod, interferon beta-1b (IFNβ-1b), and natalizumab, respectively. Aswell as to assess the individual differences among these therapies about theireffectiveness, convenience and global satisfaction also to assess the role ofcertain predictors on treatment satisfaction. Patient satisfaction with medi-cation assessed by the Treatment Satisfaction Questi

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A missing data imputation method based on salp swarm algorithm for diabetes disease
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Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

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Enhancing Disease Prediction in Healthcare through Explainable AI in Clinical Decision Support Systems
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Immune Cytokines IFN-γ, TGF-β, TNF-α, and IL-1β Modulate the Pathophysiological Markers in Idiopathic Parkinson’s Disease
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Emotion management among the headmaster of intermediate schools
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The Authority Concept among University students of Baghdad
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