Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Background: Many pregnant women receive antibiotic treatment for infections. We investigated the association between quinolone use in the first trimester of pregnancy and the risk of adverse health outcomes for the child in Korea. Materials and Methods: This nationwide, population-based cohort study used data on mother-child pairs from the National Health Insurance claims database. This study cohort included 2,177,765 pregnancies from January 1, 2011, to December 31, 2020, and 87,456 women were prescribed quinolones during pregnancy. After propensity score matching, the final number of study subjects was 84,365 for both quinolone and non-antibiotic users. We examined the subjects’ exposure to quinolone antibiotics. The main outcome measures were absolute and relative risks of atopic dermatitis, asthma, and allergies. We adjusted for potential confounders. Results: Quinolones were prescribed at least once during the first trimester in 4.01% of pregnancies. Quinolone users had significantly higher absolute risks than non-antibiotic users for atopic dermatitis, asthma, and allergies, with significantly elevated risk ratios (RRs) for these conditions (atopic dermatitis: RR, 1.09; 95% confidence interval [CI], 1.08–1.11, asthma: RR, 1.04; 95% CI, 1.03–1.05, and allergies: RR, 1.10; 95% CI, 1.08–1.13). Conclusion We found that quinolone exposure during the first trimester of pregnancy increased the risk of atopic dermatitis, asthma, and allergies. This study could provide physicians with useful information when selecting antibiotics for pregnant women.

The study aimed to investigate the role of peripheral NLR family pyrin domain-containing 3 protein (NLRP3) in inflammatory pain development in the orofacial area. Male Sprague–Dawley rats were used in experiments, with orofacial formalin-induced pain behavior and complete Freund’s adjuvant (CFA)-induced thermal hyperalgesia as chronic inflammatory pain models. Administration of 5% formalin produced biphasic nociceptive behavior, and subcutaneous pretreatment with MCC950 (50 and 100 μg/50 μL), an NLRP3 inhibitor, remarkably attenuated nociceptive behavior during the second phase. Subcutaneous CFA injection induced thermal hyperalgesia 1 day after injection, which persisted for 7 days. Five days after CFA injection, subcutaneous treatment with MCC950 (50 and 100 μg/50 μL) significantly attenuated thermal hyperalgesia. Additionally, subcutaneous injection of BMS-986299 (50 and 100 μg/50 μL), an NLRP3 agonist, induced significant nociceptive behavior for 1 hour in naïve rats. Pretreatment with an interleukin-1β (IL-1β) receptor antagonist blocked the nociceptive behavior produced by subcutaneous injection of BMS-986299 (100 μg/50 μL);however, treatment with a hypoxia-inducible factor 1α inhibitor did not. These findings suggest the involvement of the peripheral NLRP3 and IL-1β pathway in chronic inflammatory pain development in the orofacial area, highlighting the potential of blocking this pathway as a strategy for developing future inflammatory pain treatment drugs.

Background: Many pregnant women receive antibiotic treatment for infections. We investigated the association between quinolone use in the first trimester of pregnancy and the risk of adverse health outcomes for the child in Korea. Materials and Methods: This nationwide, population-based cohort study used data on mother-child pairs from the National Health Insurance claims database. This study cohort included 2,177,765 pregnancies from January 1, 2011, to December 31, 2020, and 87,456 women were prescribed quinolones during pregnancy. After propensity score matching, the final number of study subjects was 84,365 for both quinolone and non-antibiotic users. We examined the subjects’ exposure to quinolone antibiotics. The main outcome measures were absolute and relative risks of atopic dermatitis, asthma, and allergies. We adjusted for potential confounders. Results: Quinolones were prescribed at least once during the first trimester in 4.01% of pregnancies. Quinolone users had significantly higher absolute risks than non-antibiotic users for atopic dermatitis, asthma, and allergies, with significantly elevated risk ratios (RRs) for these conditions (atopic dermatitis: RR, 1.09; 95% confidence interval [CI], 1.08–1.11, asthma: RR, 1.04; 95% CI, 1.03–1.05, and allergies: RR, 1.10; 95% CI, 1.08–1.13). Conclusion We found that quinolone exposure during the first trimester of pregnancy increased the risk of atopic dermatitis, asthma, and allergies. This study could provide physicians with useful information when selecting antibiotics for pregnant women.

Background: Many pregnant women receive antibiotic treatment for infections. We investigated the association between quinolone use in the first trimester of pregnancy and the risk of adverse health outcomes for the child in Korea. Materials and Methods: This nationwide, population-based cohort study used data on mother-child pairs from the National Health Insurance claims database. This study cohort included 2,177,765 pregnancies from January 1, 2011, to December 31, 2020, and 87,456 women were prescribed quinolones during pregnancy. After propensity score matching, the final number of study subjects was 84,365 for both quinolone and non-antibiotic users. We examined the subjects’ exposure to quinolone antibiotics. The main outcome measures were absolute and relative risks of atopic dermatitis, asthma, and allergies. We adjusted for potential confounders. Results: Quinolones were prescribed at least once during the first trimester in 4.01% of pregnancies. Quinolone users had significantly higher absolute risks than non-antibiotic users for atopic dermatitis, asthma, and allergies, with significantly elevated risk ratios (RRs) for these conditions (atopic dermatitis: RR, 1.09; 95% confidence interval [CI], 1.08–1.11, asthma: RR, 1.04; 95% CI, 1.03–1.05, and allergies: RR, 1.10; 95% CI, 1.08–1.13). Conclusion We found that quinolone exposure during the first trimester of pregnancy increased the risk of atopic dermatitis, asthma, and allergies. This study could provide physicians with useful information when selecting antibiotics for pregnant women.

Background/Aims: Chronic hepatitis B (CHB) is related to an increased risk of extrahepatic malignancy (EHM), and antiviral treatment is associated with an incidence of EHM comparable to controls. We compared the risks of EHM and intrahepatic malignancy (IHM) between entecavir (ETV) and tenofovir disoproxil fumarate (TDF) treatment. Methods: Using data from the National Health Insurance Service of Korea, this nationwide cohort study included treatment-naïve CHB patients who initiated ETV (n=24,287) or TDF (n=29,199) therapy between 2012 and 2014. The primary outcome was the development of any primary EHM. Secondary outcomes included overall IHM development. E-value was calculated to assess the robustness of results to unmeasured confounders. Results: The median follow-up duration was 5.9 years, and all baseline characteristics were well balanced after propensity score matching. EHM incidence rate differed significantly between within versus beyond 3 years in both groups (P<0.01, Davies test). During the first 3 years, EHM risk was comparable in the propensity score-matched cohort (5.88 versus 5.84/1,000 person-years; subdistribution hazard ratio [SHR]=1.01, 95% confidence interval [CI]=0.88–1.17, P=0.84). After year 3, however, TDF was associated with a significantly lower EHM incidence compared to ETV (4.92 versus 6.91/1,000 person-years; SHR=0.70, 95% CI=0.60–0.81, P<0.01; E-value for SHR=2.21). Regarding IHM, the superiority of TDF over ETV was maintained both within (17.58 versus 20.19/1,000 person-years; SHR=0.88, 95% CI=0.81–0.95, P<0.01) and after year 3 (11.45 versus 16.20/1,000 person-years; SHR=0.68, 95% CI=0.62–0.75, P<0.01; E-value for SHR=2.30). Conclusions TDF was associated with approximately 30% lower risks of both EHM and IHM than ETV in CHB patients after 3 years of antiviral therapy.