Background and Aim: Achieving adhesion between restorative materials and dentin as a wet and dynamic surface is an important topic in restorative and especially in conservative dentistry. Adhesion of new dentin bonding systems depends on the formation of hybrid layer and micromechanical retention. Nevertheless, an ideal adhesive system has not yet been introduced .Recent studies reveal an increase in bonding stability when the collagen is removed from demineralized dentin surfaces. This study investigates the effect of collagen removal on the shear bond strength of four single bottle dentin bonding systems regarding their structural differences.
Materials and Methods: This experimental study was performed on 56 intact human premolar teeth. Smooth surfaces of dentin were prepared on buccal & lingual aspects of teeth, providing 112 dentin surfaces. The dentin surfaces were etched with 37% phosphoric acid for 15 seconds and then rinsed. The specimens were divided into 8 groups. Single bottle adhesive systems [Single Bond (3M), One-Step (Bisco), Prime & Bond NT (Dentsply), and Excite (Vivadent)] were then applied on the dentin surfaces of 4 groups using the wet bonding technique. In the other 4 groups, the demineralized dentin surfaces were treated with a 5.25% solution of sodium hypochlorite for one minute in order to remove the surface organic components. The adhesive systems mentioned before were applied to these 4 groups with the same wet bonding technique. A cylinder of Z100 (3M) dental composite with a 3 mm diameter and 2 mm height was placed on the adhesive covered dentin surface of all groups and light-cured (400 mW/cm2 ,40 sec on each side). The specimens were kept in distilled water at room temperature for one week and then thermocycled for 3000 times (5-55 oc). Shear bond strength of specimens was measured using an Instron (1495) universal mechanical testing machine with cross-head speed of 0.5 mm/minute and chisel form shearing blade. Data were analyzed by Two Way ANOVA and Tukey HSD tests with p<0.05 as the limit of significance.
Results: The mean & standard deviation of shear bond strengths (in Mpa unit) of all groups were as follows: One-Step = 19.60 1.83 One-step +H = 19.72 2.01 Single Bond =21.44 3.94 Single Bond +H =18.26 2.85 Prime&Bond NT=26.51 5.02 Prime&Bond NT+H =26.98 5.70 Excite =29.78 3.85 Excite +H =19.07 9.94 Analysis of the results revealed that the use of 5.25% sodium hypochlorite for one minute on the surface of demineralized dentin significantly decreased the shear bond strength of Excite and Single Bond (P<0.05). For Prime & Bond NT and One-Step, shear bond strength increased with this treatment but was not statistically significant (P>0.05).
Conclusion: Based on the results of this study, collagen removal from demineralized dentin surface caused a significant decrease in shear bond strength of alcohol & water/alcohol based bonding systems, while the bonding strength of the acetone based systems was not affected. Therefore, the effect of collagen removal on shear bond strength depends on the bonding system applied and its solvent type.
Background and Aim: Dental anxiety is a common problem in pediatric dentistry and results in behaviors like fear and anger that can negatively affect dental treatments. Exposure to various dental treatments and distressful experiences are reasons for anxiety during dental treatments. The aim of this study was to evaluate effect of cognitive behavioral interventions in reduction of stress during dental procedures in children.
Materials and Methods: In this clinical trial, 42 boys and girls, undergoing dental treatments were selected from dental clinics in Tehran. Patients were assigned to cognitive-behavioral interventions, placebo and control conditions. The fear scale, anger facial scale, pain facial scale and physiologic measure of pulse beat were evaluated. One way ANOVA and Tukey test were used to analyze the results and p<0.05 was the level of significance.
Results: Results showed significant differences between cognitive-behavioral interventions, placebo and control groups regarding fear, anger, pain and pulse beat. Comparison tests revealed that cognitive-behavioral interventions were more effective in reducing fear, anger, pain and pulse beat compared to the placebo or control.
Conclusion: According to the results of this study cognitive-behavioral interventions can be used to reduce distress of children undergoing dental procedures.
Background and Aims: In this in-vitro study, the effect of multiple adhesive coating on the microshear bond strength of composite to dentin and surface microhardness of dentin after treatment with four adhesives (One Step Plus, One Step, Single Bond, Single Bond 2) were evaluated.
Materials and Methods: One hundred intact human molars were cut to obtain disks of dentin having 2 mm thickness. For the microshear bond test, sixty disks were randomly divided into four groups. In each group one type of adhesive was used. In one half of a disk two layers and in another half six layers of adhesive were applied. Cylinders with 1mm height was filled with a composite and light cured. The cross-head speed was 0.5 mm/min. Vickers microhardness was tested on forty dentin disks which divided into four groups and prepared in the same manner used for microshear bond test. Data were analyzed by Two-way ANOVA and Tukey tests.
Results: The highest and lowest bond strength were recorded as 29.49 ± 5.74 MPa (One Step Plus 6 layers), and 21.23 ± 4.83 MPa (One Step Plus 2 layers), respectively. The results indicated that One Step Plus bond strength in 6 Layers was significantly higher than 2 layers. The highest and lowest dentin hardness values were
39.08 ± 8.34VHN (Single Bond 2 layers) and 28.53 ± 5.98 VHN (One Step Plus 6 layers). None of the adhesives exhibited significant difference in hardness with regards to the layers applied (P>0.05). Presence of filler in adhesives had no significant effect on bond strength (P=0.05) whereas caused significant decrease in the dentin microhardness (P<0.05). In addition, type of solvent had significant effect on the bond strength and bond strength was significantly higher in acetone-base adhesives (P<0.05). However, dentin microhardness was significantly higher in the ethanol-base adhesives (P<0.05).
Conclusion: Multiple adhesive coating had no influence on the microshear bond strength of composite to dentin and dentin surface microhardness. It was dependent on the type of adhesive used.
Background and Aims: The aim of this study was to evaluate the quality of an experimental hydrofluoric acid (HF) for preparation of porcelain and to compare it with two commercial hydrofluoric acids in Iranian trademark.
Materials and Methods: A- Evaluation of etch pattern of experimental HF using scanning electron microscope (SEM): 6 feldespathic discs were divided into 3 groups. Each group was etched with related HF (experimental, Ultradent and Kimia) for 1 minute. SEM images were recorded at 3 magnifications. B- Bond strength test: 18 feldespathic discs were considered for each acidic group. Then the porcelain surfaces were etched and bonded to composite with unfilled resin. Consequently, the microshear test was done. C- Microleakage test: 54 discs were divided into 3 groups (n=18). Then the porcelain surfaces were etched and bonded to composite with unfilled resin and finally observed under stereomicroscope. The data were analyzed with one-way ANOVA and Smirnov tests.
Results: SEM analysis showed no difference between groups in terms of etch pattern. Microshear bond strength values for experimental, Kimia, and Ultradent HF were 28.53 (±4.92), 28.21 (±6.61), and 26.14 (±7.61) MPa, respectively. There was no significant difference between the bond strength of test groups (P<0.05). Furthermore, no significant difference was found between the microleakage of test groups (P>0.05).
Conclusion: Quality of experimental HF in terms of etch pattern, microshear bond strength and microleakage of composite/porcelain interface was similar to that of two commercial hydrofluoric acids.
Background and Aims: The present study aimed to evaluate the barriers to the production of scientific dental articles in dental schools in Iran based on the opinions of dental postgraduate students.
Materials and Methods: A self-administered questionnaire was distributed among postgraduate students of all Iranian dental schools in June 2010. The respondents rated their agreement with eight sentences about what hinder them from producing scientific dental articles based on a 5-grade Likert scale. The data were analyzed using Chi-square test.
Results: Totally, 270 filled questionnaires from 14 dental schools were received. Of all respondents, 53% were male, the mean age were 29.6 ± 3.8. About half of the respondents reported at least one published article. Less than half of the respondents reported producing an article from undergraduate thesis more women than men and more younger than older students (P<0.03). About two-third of the respondents rated absence of an English editing center, no financial incentives, no appropriate environment, and no competency for scientific writing as most prevalent barriers to the production of scientific dental articles.
Conclusion: To expand the share of Iran in the production of scientific dental documents, the potential of postgraduate dental students must be regarded and suitable condition for scientific writing must be provided. Specifically, based on the findings of the present study, provision of an English editing facility, establishing financial incentives, and providing the students with appropriate environment and efficient scientific writing education are of utmost importance.
Background and Aims: Dental students’ evaluation of teachers’ educational activity is crucial for the improvement of dental school’s performance. The process of the evaluation needs a valid and reliable tool. This study aimed to produce and validate a questionnaire for the evaluation of dental school teachers by students.
Materials and Methods: A group of 15 teachers in the Shahid Beheshti dental school gave their opinions regarding the characteristics of an ideal teacher using nominal group technique. These characteristics together with characteristics gathered from similar studies made the base of a questionnaire which later underwent a validity and reliability assessment by means of the calculation of Content Validity Index (CVI), Content Validity Ratio (CVR), and Cronbach’s alpha coefficient.
Results: The preliminary questionnaire included 94 items in four categories naming: ethics, educational capability, practical capability, and managemet. After calculating CVR and CVI for each item, 23 items with CVR<0.33 and 20 items with CVI<0.79 have been excluded from the questionnaire leaving a questionnaire with 54 items. The overall reliability of this questionnaire using Chronbach’s alpha coefficient was 0.96.
Conclusion: Consulting an expert group that are familiar with different aspects of educational performance of faculty teachers, it was concluded that applying various methods for validity and reliability, and considering local culture values are useful for the preparation of the questionnaire for similar studies in Iran.
The rapid integration of artificial intelligence (AI) into various aspects of human life raises important questions about its potential benefits and drawbacks. As AI reduces our reliance on cognitive processes, we must consider its long-term effects on human cognition. While, the general use of AI is still relatively new, significant discussions and studies have begun to explore its impact on cognitive function. This letter aims to summarize findings from several studies, highlighting the dual nature of AI's cognitive effects and the necessity for a balanced and targeted approach to its use.
Some studies indicated that AI could enhance cognitive abilities. For instance, Haider et al. (1) conducted a cross-sectional study revealing that AI tools such as memory enhancement platforms and adaptive learning systems improved short-term and long-term memory, analytical thinking, and decision-making efficiency. A review (2) also found that AI-based interventions like brain training programs, promoted neuroplasticity and alleviate anxiety, particularly in older adults.
Conversely, another research highlights the negative effects of AI on cognitive function. Zhai et al. (3) conducted a systematic review demonstrating that prolonged use of AI conversational systems in education could hinder independent problem-solving and analytical reasoning, introducing risks such as algorithmic bias, privacy violations, and plagiarism. Furthermore, excessive reliance on AI has been linked to diminished critical thinking, creativity, and work ethic, potentially fostering psychological dependence and reducing motivation for deeper learning (1). Increased screen time and reliance on AI may also alter brain structure, resulting in reduced gray matter in frontal regions and impairing attention, memory, and socioemotional regulation (2).
The impact of AI varies across different age groups. In children, unsupervised AI use can impede language development and attention span. Among young people, social media and AI tools may contribute to anxiety, loneliness, and poor academic performance. For older adults, while cognitive training through AI shows promise, it necessitates support for digital literacy (2).
In summary, while AI can enhance cognition in certain contexts, its unchecked use poses risks to creativity, independent thinking, and ethical standards. For optimal outcomes, AI usage should be accompanied by human judgment and critical thinking skills must be integrated, and also overall screen time should be monitored and limited. Future research should prioritize prospective studies and develop specific guidelines for AI users across different ages and demographic groups, including students.
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