Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

Hypoxia-inducible factor-1 alpha (HIF-1α) is a transcription factor activated under hypoxic conditions, and it plays a crucial role in cellular stress regulation.While HIF-1α activity is essential in normal tissues, its presence in the tumor microenvironment represents a significant risk factor as it can induce angiogenesis and confer resistance to anti-cancer drugs, thereby contributing to poor prognoses. Typically, HIF-1α undergoes rapid degradation in normoxic conditions via oxygen-dependent degradation mechanisms. However, certain cancer cells can express HIF-1α even under normoxia. In this study, we observed an inclination toward increased normoxic HIF-1α expression in cancer cell lines exhibiting increased HDAC6 expression, which prompted the hypothesis that HDAC6 may modulate HIF-1α stability in normoxic conditions. To prove this hypothesis, several cancer cells with relatively higher HIF-1α levels under normoxic conditions were treated with ACY-241, a selective HDAC6 inhibitor, and small interfering RNAs for HDAC6 knockdown. Our data revealed a significant reduction in HIF-1α expression upon HDAC6 inhibition. Moreover, the downregulation of HIF-1α under normoxic conditions decreased zinc finger E-box-binding homeobox 1 expression and increased E-cadherin levels in lung cancer H1975 cells, consequently suppressing cell invasion and migration. ACY-241 treatment also demonstrated an inhibitory effect on cell invasion and migration by reducing HIF-1α level. This study confirms that HDAC6 knockdown and ACY-241 treatment effectively decrease HIF-1α expression under normoxia, thereby suppressing the epithelial– mesenchymal transition. These findings highlight the potential of selective HDAC6 inhibition as an innovative therapeutic strategy for lung cancer.

The etiology of disease pathogenesis can be largely explained by genetic variations and several types of environmental factors. In genetically disease-susceptible individuals, subsequent environmental triggers may induce disease development. The human body is colonized by complex commensal microbes that have co-evolved with the host immune system. With the adaptation to modern lifestyles, its composition has changed depending on host genetics, changes in diet, overuse of antibiotics against infection and elimination of natural enemies through the strengthening of sanitation. In particular, commensal microbiota is necessary in the development, induction and function of T cells to maintain host immune homeostasis. Alterations in the compositional diversity and abundance levels of microbiota, known as dysbiosis, can trigger several types of autoimmune and inflammatory diseases through the imbalance of T-cell subpopulations, such as Th1, Th2, Th17 and Treg cells. Recently, emerging evidence has identified that dysbiosis is involved in the progression of rheumatoid arthritis, type 1 and 2 diabetic mellitus, and asthma, together with dysregulated T-cell subpopulations. In this review, we will focus on understanding the complicated microbiota-T-cell axis between homeostatic and pathogenic conditions and elucidate important insights for the development of novel targets for disease therapy.
Anti-Bacterial Agents ; Arthritis, Rheumatoid ; Asthma ; Colon ; Diet ; Dysbiosis ; Genetic Variation ; Genetics ; Homeostasis* ; Human Body ; Immune System ; Life Style ; Microbiota* ; Sanitation ; T-Lymphocytes* ; T-Lymphocytes, Regulatory

Purpose: Epidemiological data on injuries resulting from ice hockey and their management are lacking in Korea. A comprehensive analysis of such data is crucial for the effective prevention and management of ice hockey injuries. This study aimed to determine the epidemiological profile of ice hockey injuries and their management among elite Korean players. Methods: The descriptive epidemiological study involved three semiprofessional male ice hockey teams and used a retrospective self-reported questionnaire for assessment. The data collected included demographic characteristics such as player positions and stick-side preferences, injured body parts, injury types, treatment methods, and the decision-maker for returning to sports. Results: A total of 68 players were included in the study, of whom 58 (85.3%) experienced moderate-to-severe orthopedic injuries. Among the reported injuries, 93 (77.5%) occurred during the games, with player-to-player contact being the most frequent cause of such injuries. The decision to return to sports in 53 cases (44.2%) was made by the medical staff, whereas players and nonmedical staff made that decision in 67 cases (55.8%). The decision-making process of the medical staff for allowing players to return to sports was significantly associated with the player’s position and whether the injury required surgery. Conclusion The study emphasizes the high prevalence of orthopedic injuries among elite ice hockey players in Korea and the importance of injury prevention strategies. It also highlights the need for increased involvement of medical staff in return-to-play decisions to ensure successful recovery of players and their reintegration into the competition.

KCNQ2 mutations induce a neonatal-onset epileptic encephalopathy of widely varying severity, ranging from benign familial neonatal epilepsy to severe refractory epileptic encephalopathy. Refractory seizures with KCNQ2 mutations have a positive response to sodium-channel blockers. Recently, a distinctive ictal pattern has been reported during amplitude-integrated electroencephalographic (aEEG) monitoring in infants with KCNQ2 encephalopathy. Herein, we describe a case of KCNQ2 encephalopathy with this distinctive ictal aEEG pattern, which was confirmed using conventional electroencephalography (EEG). A 3-day-old female infant presented with neonatal seizures accompanied by cyanosis and desaturation. Her seizure semiology was tonic and focal clonic. Her ictal aEEG demonstrated a sudden rise in amplitude followed by a suppressed background pattern. This pattern was also confirmed on conventional EEG. Her seizures were refractory despite the administration of multiple conventional antiepileptic drugs. Finally, c.794C>T; p. (Ala265Val) mutation was observed in the KCNQ2 gene on genetic testing, and she was diagnosed with KCNQ2 encephalopathy. Identifying this distinctive ictal pattern on aEEG monitoring facilitates the early detection of KCNQ2 encephalopathy and timely targeted treatment in patients with refractory seizures.

Purpose: We investigated the association of effector memory (EM) CD8 + T cell and CD4 + T cell immunity with metabolic syndrome (MS). Methods: Surface and intracellular staining of peripheral blood mononuclear cells was performed. Anti-interleukin-7 receptor-alpha (IL-7Rα) and CX3CR1 antibodies were used to stain the subsets of EM CD8 + T cells, while anti-interferon-gamma (IFN-γ), interleukin-17 (IL-17), and forkhead box P3 (FOXP3) antibodies were used for CD4 + T cell subsets. Results: Of the 47 obese children, 11 were female. Children with MS had significantly higher levels of serum insulin (34.8±13.8 vs. 16.4±6.3 μU/mL, p<0.001) and homeostasis model assessment of insulin resistance (8.9±4.1 vs. 3.9±1.5, p<0.001) than children without MS.Children with MS revealed significantly higher frequencies of IL-7Rα low CD8+ T cells (60.1 ±19.1% vs. 48.4±11.5%, p=0.047) and IL-7Rα low CX3CR1 + CD8 + T cells (53.8±20.1% vs. 41.5 ±11.9%, p=0.036) than children without MS. As the serum triglyceride levels increased, the frequency of IL-7Rα low CX3CR1 + and IL-7Rα high CX3CR1 – CD8 + T cells increased and decreased, respectively (r=0.335, p=0.014 and r=−0.350, p=0.010, respectively), in 47 children. However, no CD4 + T cell subset parameters were significantly different between children with and without MS. Conclusion In obese children with MS, the changes in immunity due to changes in EM CD8 + T cells might be related to the morbidity of obesity.

KCNQ2 mutations induce a neonatal-onset epileptic encephalopathy of widely varying severity, ranging from benign familial neonatal epilepsy to severe refractory epileptic encephalopathy. Refractory seizures with KCNQ2 mutations have a positive response to sodium-channel blockers. Recently, a distinctive ictal pattern has been reported during amplitude-integrated electroencephalographic (aEEG) monitoring in infants with KCNQ2 encephalopathy. Herein, we describe a case of KCNQ2 encephalopathy with this distinctive ictal aEEG pattern, which was confirmed using conventional electroencephalography (EEG). A 3-day-old female infant presented with neonatal seizures accompanied by cyanosis and desaturation. Her seizure semiology was tonic and focal clonic. Her ictal aEEG demonstrated a sudden rise in amplitude followed by a suppressed background pattern. This pattern was also confirmed on conventional EEG. Her seizures were refractory despite the administration of multiple conventional antiepileptic drugs. Finally, c.794C>T; p. (Ala265Val) mutation was observed in the KCNQ2 gene on genetic testing, and she was diagnosed with KCNQ2 encephalopathy. Identifying this distinctive ictal pattern on aEEG monitoring facilitates the early detection of KCNQ2 encephalopathy and timely targeted treatment in patients with refractory seizures.