Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clini-cal trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions—Drug, Biological, Dietary Supplement, or Combination Product—were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imag-ing or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to developrapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clini-cal trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions—Drug, Biological, Dietary Supplement, or Combination Product—were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imag-ing or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to developrapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clini-cal trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions—Drug, Biological, Dietary Supplement, or Combination Product—were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imag-ing or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to developrapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

Purpose: Vascular endothelial growth factor tyrosine kinase inhibitors (TKIs) have been the standard of care for advanced and metastatic clear cell renal cell carcinoma (ccRCC). However, the therapeutic effect of TKI monotherapy remains unsatisfactory given the high rates of acquired resistance to TKI therapy despite favorable initial tumor response. Materials and Methods: To define the TKI-resistance mechanism and identify new therapeutic target for TKI-resistant ccRCC, an integrative differential gene expression analysis was performed using acquired resistant cohort and a public dataset. Sunitinib-resistant RCC cell lines were established and used to test their malignant behaviors of TKI resistance through in vitro and in vivo studies. Immunohistochemistry was conducted to compare expression between the tumor and normal kidney and verify expression of pathway-related proteins. Results: Integrated differential gene expression analysis revealed increased interferon-induced transmembrane protein 3 (IFITM3) expression in post-TKI samples. IFITM3 expression was increased in ccRCC compared with the normal kidney. TKI-resistant RCC cells showed high expression of IFITM3 compared with TKI-sensitive cells and displayed aggressive biologic features such as higher proliferative ability, clonogenic survival, migration, and invasion while being treated with sunitinib. These aggressive features were suppressed by the inhibition of IFITM3 expression and promoted by IFITM3 overexpression, and these findings were confirmed in a xenograft model. IFITM3-mediated TKI resistance was associated with the activation of TRAF6 and MAPK/AP-1 pathways. Conclusions These results demonstrate IFITM3-mediated activation of the TRAF6/MAPK/AP-1 pathways as a mechanism of acquired TKI resistance, and suggest IFITM3 as a new target for TKI-resistant ccRCC.

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clini-cal trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions—Drug, Biological, Dietary Supplement, or Combination Product—were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imag-ing or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to developrapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

Purpose: Tyrosine kinase inhibitors (TKI) targeting vascular endothelial growth factor receptor (VEGFR) signaling pathways have been used for metastatic clear cell renal cell carcinoma (mCCRCC), but resistance to the drug develops in most patients. We aimed to explore the underlying mechanism of the TKI resistance with regard to programmed death-ligand 1 (PD-L1) and to investigate signaling pathway associated with the resistant mechanism. Materials and Methods: To determine the mechanism of resistance, 10 mCCRCC patients from whom tumor tissues were harvested at both the pretreatment and the TKI-resistant post-treatment period were included as the discovery cohort, and their global gene expression profiles were compared. A TKI-resistant renal cancer cell line was established by long-term treatment with sunitinib. Results: Among differentially expressed genes in the discovery cohort, increased PD-L1 expression in post-treatment tissues was noted in four patients. Pathway analysis showed that PD-L1 expression was positively correlated with the mammalian target of rapamycin (mTOR) signaling pathway. The TKI-resistant renal cancer cells showed increased expression of PD-L1 and mTOR signaling proteins and demonstrated aggressive tumoral behaviour. Treatment with mTOR inhibitors down-regulated PD-L1 expression and suppressed aggressive tumoral behaviour, which was reversed with stimulation of the mTOR pathway. Conclusion These results showed that PD-L1 expression may be increased in a subset of VEGFR-TKI–resistant mCCRCC patients via the mTOR pathway.

Objectives: ：The high lifetime prevalence of depression in Korea is related to problems such as suicide and decreased productivity, as well as the cost of disease due to increased use of medical services, which can cause great socioeconomic loss. Therefore, in this study, the burden of disease of depression and the importance of managing mental health diseases, which are increasing day by day, are suggested to be helpful in determining priorities in health policy establishment. Methods: ：In this study, the socio-economic cost of depression was calculated by dividing it into direct cost and indirect cost. For statistical data, data from the National Health Insurance Service of the public and statistics on diseases of national interest were mainly used. Results: ：As a result, the socio-economic cost of depression in 2019 estimated in this study was calculated to be a total of KRW 4.83 trillion, with direct costs 692.9 billion won and indirect costs 4.13 trillion won. Among them, the cost due to decrease in work performance accounted for the largest portion, accounting for 65.5%. Conclusions ：As the socio-economic burden due to depression is expected to increase in the future, it is necessary to establish a systematic funding plan for the treatment and management of depressed patients in daily life.

Objective: This study aimed to compare the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV) profiles of children with attention deficit/hyperactivity disorder (ADHD) and typically-developing children (TC) in Korea. Methods: The Korean version of the WISC-IV and the Advanced Test of Attention (ATA) were administered to 377 children and adolescents: 224 with ADHD (age 8.2±2.1 years, 182 boys) and 153 TC (age 8.7±2.4 years, 68 boys). Partial correlation and an analysis of covariance were used to investigate the relationship between the scores of the WISC-IV and the ATA. Results: The mean score of the full-scale intelligence quotient was lower in ADHD children than in TC (p<0.001). In analyses controlling for gender and with the full-scale intelligence quotient as a covariate, the working memory index (WMI) (p<0.001) and values of the Digit span subtest (p=0.001) of the WISC-IV were lower in the ADHD group than in TC. The WMI (r=-0.26, p<0.001) and its subtest Arithmetic scores (r=-0.25, p<0.001) were negatively correlated with Commission errors on the auditory ATA. Conclusion Children with ADHD have significantly lower WMI scores, which were clinically correlated with Commission errors on the auditory task of the ATA. Thus, the WMI is an indicator of attention deficit in children with ADHD.

BACKGROUND: The Internet has advantages in terms of accessibility and amount of information, and the search for health information over the Internet is increasing exponentially. The purpose of this study is to analyze the information generated about some dental treatment on the internet by year. METHODS: Naver Knowledge (JisikIn in Korean) which is an interactive search service was selected as the first search site in Korea. Scaling, wisdom tooth extraction, and endodontic treatment that can be paid by Korean health insurance were selected. Finally, 4,729 questions about scaling, 23,963 wisdom teeth extraction questions and 17,733 endodontic treatment questions were extracted. The question contents, the information about the questioner and the answerer, and an error of answers were investigated. Frequency analysis was used and chi-square test was used if necessary. RESULTS: The most frequently asked questions were discomfort and dissatisfaction after the treatment. The need for treatment was the second in questions of the wisdom tooth extraction and endodontic treatment, but the health insurance benefit was the second in dental scaling. Most of the questioners didn't disclose personal information. The public answered the most in 2013~2014, but the highest percentage of the respondents was experts in 2017. Responses were mostly personal experience, but showed a tendency to decrease with years, and professional knowledge showed an increasing tendency. The error of the answer has also gradually decreased. CONCLUSION: Questions about dental care over the Internet are increasing exponentially, experts are responding increasingly, and errors in answers are decreasing. Nevertheless, it is necessary to pay attention to the related expert group to prevent misinformation.
Dental Care ; Dental Scaling ; Humans ; Insurance, Health ; Internet ; Korea ; Molar, Third ; Surveys and Questionnaires

Green discoloration of the urine after propofol administration is a rare clinical phenomenon. Although the exact incidence of propofol-induced green urine is not known, the reported incidence is thought to be less than 1%. In most reported cases of propofol-induced green urine, the clinical effects were benign and reversible. However, many clinicians are unfamiliar with this rare side effect of propofol. Here, we present the case of a patient who showed green urine following two-staged repair of a thoracoabdominal aortic aneurysm with propofol infusion. His urine had a normal yellowish color after the first operation, but appeared green immediately after the second surgery. Because propofol is a commonly used sedative agent, knowing that green urine can be attributed to propofol administration and that its clinical effect is mostly benign will help clinicians with patient management, as such knowledge will also reduce unnecessary concerns and laboratory tests.
Anesthesia, General* ; Aortic Aneurysm, Thoracic ; Humans ; Incidence ; Propofol*