Objective: The increasing concern over adolescent suicide necessitates suicide prevention training for school teachers, as students spend a significant portion of their time at school. This study’s objective is to develop a suicide prevention program tailored for teachers. Methods: The program was developed by a multidisciplinary research team, drawing on a review of both domestic and international suicide prevention programs, related scholarly articles, and Korean psychological autopsy interviews of adolescents. This was complemented by a survey of teachers to assess the program’s practicality and usability. Results: The developed program comprises three parts, consistent with other versions: Careful Observation, Active Listening, and Risk Evaluation and Expert Referral. Careful Observation focuses on training teachers to recognize verbal, behavioral, and situational warning signs of suicidal ideation in students; Active Listening involves strategies for encouraging students to express their suicidal thoughts and techniques for being an empathetic and attentive listener; Risk Evaluation and Expert Referral provides instruction on how to assess suicide risk and assist students safely. Conclusion It is anticipated that this program will equip teachers with valuable knowledge and skills, contributing to a reduction in adolescents suicide rates.

This study aimed to assess the competency of artificial intelligence (AI) in pediatric dentistry and compare it with that of dentists. We used open-source data obtained from the Korea Health Personnel Licensing Examination Institute. A total of 32 item multiple-choice pediatric dentistry exam questions were included. Two AI-based chatbots (ChatGPT 3.5 and Gemini) were evaluated. Each chatbot received the same questions seven times in separate chat sessions initiated on April 25, 2024. The accuracy was assessed by measuring the percentage of correct answers, and consistency was evaluated using Cronbach’s alpha coefficient. Both ChatGPT 3.5 and Gemini demonstrated similar accuracy, with no significant differences observed between them. However, neither chatbot achieved the minimum passing score set by the Pediatric Dentistry National Examination. However, both chatbots exhibited acceptable consistency in their responses. Within the limits of this study, both AI-based chatbots did not sufficiently answer the pediatric dentistry exam questions. This finding suggests that pediatric dentists should be aware of the advantages and limitations of this new tool and effectively utilize it to promote patient health.

This study aimed to assess the competency of artificial intelligence (AI) in pediatric dentistry and compare it with that of dentists. We used open-source data obtained from the Korea Health Personnel Licensing Examination Institute. A total of 32 item multiple-choice pediatric dentistry exam questions were included. Two AI-based chatbots (ChatGPT 3.5 and Gemini) were evaluated. Each chatbot received the same questions seven times in separate chat sessions initiated on April 25, 2024. The accuracy was assessed by measuring the percentage of correct answers, and consistency was evaluated using Cronbach’s alpha coefficient. Both ChatGPT 3.5 and Gemini demonstrated similar accuracy, with no significant differences observed between them. However, neither chatbot achieved the minimum passing score set by the Pediatric Dentistry National Examination. However, both chatbots exhibited acceptable consistency in their responses. Within the limits of this study, both AI-based chatbots did not sufficiently answer the pediatric dentistry exam questions. This finding suggests that pediatric dentists should be aware of the advantages and limitations of this new tool and effectively utilize it to promote patient health.

This study aimed to assess the competency of artificial intelligence (AI) in pediatric dentistry and compare it with that of dentists. We used open-source data obtained from the Korea Health Personnel Licensing Examination Institute. A total of 32 item multiple-choice pediatric dentistry exam questions were included. Two AI-based chatbots (ChatGPT 3.5 and Gemini) were evaluated. Each chatbot received the same questions seven times in separate chat sessions initiated on April 25, 2024. The accuracy was assessed by measuring the percentage of correct answers, and consistency was evaluated using Cronbach’s alpha coefficient. Both ChatGPT 3.5 and Gemini demonstrated similar accuracy, with no significant differences observed between them. However, neither chatbot achieved the minimum passing score set by the Pediatric Dentistry National Examination. However, both chatbots exhibited acceptable consistency in their responses. Within the limits of this study, both AI-based chatbots did not sufficiently answer the pediatric dentistry exam questions. This finding suggests that pediatric dentists should be aware of the advantages and limitations of this new tool and effectively utilize it to promote patient health.

Background: The gut–brain axis (GBA) in Parkinson’s disease (PD) has only been investigated in limited mice models despite dysbiosis of the gut microbiota being considered one of the major treatment targets for neurodegenerative disease. Therefore, this study examined the compositional changes of fecal microbiota in novel transgenic (Tg) mice overexpressing human α-synuclein (hαSyn) proteins under the neuron-specific enolase (NSE) to analyze the potential as GBA model. Results: The expression level of the αSyn proteins was significantly higher in the substantia nigra and striatum of NSEhαSyn Tg mice than the Non-Tg mice, while those of tyrosine hydroxylase (TH) were decreased in the same group. In addition, a decrease of 72.7% in the fall times and a 3.8-fold increase in the fall number was detected in NSE-hαSyn Tg mice. The villus thickness and crypt length on the histological structure of the gastrointestinal (GI) tract decreased in NSE-hαSyn Tg mice. Furthermore, the NSE-hαSyn Tg mice exhibited a significant increase in 11 genera, including Scatolibacter, Clostridium, Feifania, Lachnoclostridium, and Acetatifactor population, and a decrease in only two genera in Ligilactobacillus and Sangeribacter population during enhancement of microbiota richness and diversity. Conclusions The motor coordination and balance dysfunction of NSE-hαSyn Tg mice may be associated with compositional changes in gut microbiota. In addition, these mice have potential as a GBA model.