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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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Iraqi Journal Of Physics
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Iraqi Journal Of Chemical And Petroleum Engineering
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Surfaces quality is one of the most specified customer requirements for machine parts. The major indication of surfaces quality on machined parts is surface roughness. The research aim is to study the cutting conditions and their effects on the surface roughness. This paper utilizes regression models to predict surface roughness over the machining time for variety of cutting conditions in turning. In the experimental part for turning, different types of materials (Aluminum alloy, Copper alloy, and Gray cast iron) were considered with different cutting speed ( ) and feed rate ( ). A mathematical Model depending on statistical-mathematical method between surface roughness (Rz ) and cutting condition ( , ) were derived, for the three materials

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