PURPOSE: This study assessed the effect of storage time on chemical structure of a single-bottle and a two-bottle experimental ceramic primer and micro-shear bond strength (µSBS) of composite to ceramic. MATERIALS AND METHODS: This study was conducted on 60 sintered zirconia and 60 feldspathic porcelain blocks. Half of the specimens (n = 30) were subjected to surface treatment with the single-bottle Clearfil ceramic primer (n = 15) and two-bottle experimental primer (n = 15) after 24 hours. The remaining half received the same surface treatments after 6 months storage in distilled water. Composite cylinders were bonded to the ceramics, and they were then subjected to µSBS test. Also, the primers underwent Fourier-transform infrared spectroscopy (FTIR) after 24 hours and 6 months to assess their chemical structure. Data were analyzed with 3-way ANOVA and adjusted Bonferroni test (alpha = 0.05). RESULTS: The µSBS of both ceramics significantly decreased at 6 months in one-bottle ceramic primer group (P = .001), but it was not significantly different from the two-bottle experimental primer group (P = .635). FTIR showed hydrolysis of single-bottle primer, cleavage of silane and 10-MDP bonds, and formation of siloxane bonds after 6 months. CONCLUSION Six months of storage caused significant degradation of singlebottle ceramic primer, and consequently had an adverse effect on µSBS.

OBJECTIVES: Recent studies have shown that the rate of needlestick and sharps injuries (NSIs) is unacceptably high in Iranian hospitals. The aim of the present study was to use a systematic approach to predict and reduce these injuries.METHODS: This cross-sectional study was conducted in 5 hospitals in Tehran, Iran. Eleven variables thought to affect NSIs were categorized based on the Human Factors Analysis and Classification System (HFACS) framework and modeled using a Bayesian network. A self-administered validated questionnaire was used to collect the required data. In total, 343 cases were used to train the model and 50 cases were used to test the model. Model performance was assessed using various indices. Finally, using predictive reasoning, several intervention strategies for reducing NSIs were recommended.RESULTS: The Bayesian network HFACS model was able to predict 86% of new cases correctly. The analyses showed that safety motivation and fatigue were the most important contributors to NSIs. Supervisors' attitude toward safety and working hours per week were the most important factors in the unsafe supervision category. Management commitment and staffing were the most important organizational-level factors affecting NSIs. Finally, promising intervention strategies for reducing NSIs were identified and discussed.CONCLUSIONS: To reduce NSIs, both management commitment and sufficient staffing are necessary. Supervisors should encourage nurses to engage in safe behavior. Excessive working hours result in fatigue and increase the risk of NSIs.
Accident Prevention ; Bayes Theorem ; Classification ; Cross-Sectional Studies ; Fatigue ; Humans ; Iran ; Motivation ; Needlestick Injuries ; Organization and Administration

OBJECTIVES: Recent studies have shown that the rate of needlestick and sharps injuries (NSIs) is unacceptably high in Iranian hospitals. The aim of the present study was to use a systematic approach to predict and reduce these injuries. METHODS: This cross-sectional study was conducted in 5 hospitals in Tehran, Iran. Eleven variables thought to affect NSIs were categorized based on the Human Factors Analysis and Classification System (HFACS) framework and modeled using a Bayesian network. A self-administered validated questionnaire was used to collect the required data. In total, 343 cases were used to train the model and 50 cases were used to test the model. Model performance was assessed using various indices. Finally, using predictive reasoning, several intervention strategies for reducing NSIs were recommended. RESULTS: The Bayesian network HFACS model was able to predict 86% of new cases correctly. The analyses showed that safety motivation and fatigue were the most important contributors to NSIs. Supervisors' attitude toward safety and working hours per week were the most important factors in the unsafe supervision category. Management commitment and staffing were the most important organizational-level factors affecting NSIs. Finally, promising intervention strategies for reducing NSIs were identified and discussed. CONCLUSIONS: To reduce NSIs, both management commitment and sufficient staffing are necessary. Supervisors should encourage nurses to engage in safe behavior. Excessive working hours result in fatigue and increase the risk of NSIs.
Accident Prevention ; Bayes Theorem ; Classification ; Cross-Sectional Studies ; Fatigue ; Humans ; Iran* ; Motivation ; Needlestick Injuries* ; Organization and Administration

OBJECTIVES: Recent studies have shown that the rate of needlestick and sharps injuries (NSIs) is unacceptably high in Iranian hospitals. The aim of the present study was to use a systematic approach to predict and reduce these injuries. METHODS: This cross-sectional study was conducted in 5 hospitals in Tehran, Iran. Eleven variables thought to affect NSIs were categorized based on the Human Factors Analysis and Classification System (HFACS) framework and modeled using a Bayesian network. A self-administered validated questionnaire was used to collect the required data. In total, 343 cases were used to train the model and 50 cases were used to test the model. Model performance was assessed using various indices. Finally, using predictive reasoning, several intervention strategies for reducing NSIs were recommended. RESULTS: The Bayesian network HFACS model was able to predict 86% of new cases correctly. The analyses showed that safety motivation and fatigue were the most important contributors to NSIs. Supervisors' attitude toward safety and working hours per week were the most important factors in the unsafe supervision category. Management commitment and staffing were the most important organizational-level factors affecting NSIs. Finally, promising intervention strategies for reducing NSIs were identified and discussed. CONCLUSIONS To reduce NSIs, both management commitment and sufficient staffing are necessary. Supervisors should encourage nurses to engage in safe behavior. Excessive working hours result in fatigue and increase the risk of NSIs.

To discuss the advantages and limitations of hair follicle-derived cell transplantation (FCT) in vitiligo, compared to the epidermal cell transplantation (ECT), and the knowledge gap which is required to be bridged. The papers relevant to the purpose was reviewed. Surgical approaches for treating vitiligo are based on the idea of replenishing lost melanocytes.Skin and hair follicles as the main sources of melanocytes have been applied for this purpose transferring the whole tissue or tissue-derived cell suspension to the vitiligo lesions. Considering the differences between hair follicle and epidermis in terms of the constituting cell populations, phenotype and function of melanocytes, and micro-environmental factors, different response of vitiligo patients to treatment with FCT or ECT would be expected theoretically. However, there is currently a lack of evidence on such a difference. However, ECT appears to be a more feasible, less time-consuming, and more comfortable treatment for both physicians and patients. Although the current evidence has not shown a significant difference between ECT and FCT in terms of efficacy, ECT appears to be more feasible specifically in the treatment of large lesions. However, further randomized controlled clinical trials with larger sample sizes and longer follow-up durations are required to be conducted to draw a definite conclusion on comparing FCT with ECT in terms of the safety, efficacy, durability of the therapeutic effects, and indications in vitiligo patients.

To discuss the advantages and limitations of hair follicle-derived cell transplantation (FCT) in vitiligo, compared to the epidermal cell transplantation (ECT), and the knowledge gap which is required to be bridged. The papers relevant to the purpose was reviewed. Surgical approaches for treating vitiligo are based on the idea of replenishing lost melanocytes.Skin and hair follicles as the main sources of melanocytes have been applied for this purpose transferring the whole tissue or tissue-derived cell suspension to the vitiligo lesions. Considering the differences between hair follicle and epidermis in terms of the constituting cell populations, phenotype and function of melanocytes, and micro-environmental factors, different response of vitiligo patients to treatment with FCT or ECT would be expected theoretically. However, there is currently a lack of evidence on such a difference. However, ECT appears to be a more feasible, less time-consuming, and more comfortable treatment for both physicians and patients. Although the current evidence has not shown a significant difference between ECT and FCT in terms of efficacy, ECT appears to be more feasible specifically in the treatment of large lesions. However, further randomized controlled clinical trials with larger sample sizes and longer follow-up durations are required to be conducted to draw a definite conclusion on comparing FCT with ECT in terms of the safety, efficacy, durability of the therapeutic effects, and indications in vitiligo patients.

Purpose: Recent advancements in artificial intelligence (AI), particularly tools such as ChatGPT developed by OpenAI, a U.S.-based AI research organization, have transformed the healthcare and education sectors. This study investigated the effectiveness of ChatGPT in answering dentistry exam questions, demonstrating its potential to enhance professional practice and patient care. Materials and Methods: This study assessed the performance of ChatGPT 3.5 and 4 on U.S. dental exams -specifically, the Integrated National Board Dental Examination (INBDE), Dental Admission Test (DAT), and Advanced Dental Admission Test (ADAT) - excluding image-based questions. Using customized prompts,ChatGPT’s answers were evaluated against official answer sheets. Results: ChatGPT 3.5 and 4 were tested with 253 questions from the INBDE, ADAT, and DAT exams. For the INBDE, both versions achieved 80% accuracy in knowledge-based questions and 66-69% in case history questions.In ADAT, they scored 66-83% in knowledge-based and 76% in case history questions. ChatGPT 4 excelled on the DAT, with 94% accuracy in knowledge-based questions, 57% in mathematical analysis items, and 100% in comprehension questions, surpassing ChatGPT 3.5’s rates of 83%, 31%, and 82%, respectively. The difference was significant for knowledge-based questions (P = 0.009). Both versions showed similar patterns in incorrect responses. Conclusion Both ChatGPT 3.5 and 4 effectively handled knowledge-based, case history, and comprehension questions, with ChatGPT 4 being more reliable and surpassing the performance of 3.5. ChatGPT 4’s perfect score incomprehension questions underscores its trainability in specific subjects. However, both versions exhibited weakerperformance in mathematical analysis, suggesting this as an area for improvement.

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures. Materials and Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command. Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images.The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913). Conclusion This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.

Purpose: Recent advancements in artificial intelligence (AI), particularly tools such as ChatGPT developed by OpenAI, a U.S.-based AI research organization, have transformed the healthcare and education sectors. This study investigated the effectiveness of ChatGPT in answering dentistry exam questions, demonstrating its potential to enhance professional practice and patient care. Materials and Methods: This study assessed the performance of ChatGPT 3.5 and 4 on U.S. dental exams -specifically, the Integrated National Board Dental Examination (INBDE), Dental Admission Test (DAT), and Advanced Dental Admission Test (ADAT) - excluding image-based questions. Using customized prompts,ChatGPT’s answers were evaluated against official answer sheets. Results: ChatGPT 3.5 and 4 were tested with 253 questions from the INBDE, ADAT, and DAT exams. For the INBDE, both versions achieved 80% accuracy in knowledge-based questions and 66-69% in case history questions.In ADAT, they scored 66-83% in knowledge-based and 76% in case history questions. ChatGPT 4 excelled on the DAT, with 94% accuracy in knowledge-based questions, 57% in mathematical analysis items, and 100% in comprehension questions, surpassing ChatGPT 3.5’s rates of 83%, 31%, and 82%, respectively. The difference was significant for knowledge-based questions (P = 0.009). Both versions showed similar patterns in incorrect responses. Conclusion Both ChatGPT 3.5 and 4 effectively handled knowledge-based, case history, and comprehension questions, with ChatGPT 4 being more reliable and surpassing the performance of 3.5. ChatGPT 4’s perfect score incomprehension questions underscores its trainability in specific subjects. However, both versions exhibited weakerperformance in mathematical analysis, suggesting this as an area for improvement.

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures. Materials and Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command. Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images.The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913). Conclusion This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.