The purpose of this study was to measure bone age using an artificial intelligence program based on the Greulich-Pyle (GP) method to find out the bone age corresponding to each stage of cervical vertebral maturation (CVM) and the middle phalanx of the third finger (MP3). This study was conducted on 3,118 patients who visited pediatric dentistry at Kyung Hee University Dental Hospital and Pusan National University Dental Hospital from 2013 to 2021. The CVM stage was divided into 5 stages according to the classification by Baccetti, and the MP3 stage was divided into 5 stages according to the methods of Hägg and Taranger. Based on the GP method, bone age was evaluated using an artificial intelligence program. The pubertal growth spurt in the CVM stage was CVM II and III. The mean bone age in CVM II was 11.00 ± 1.81 years for males and 10.00 ± 1.49 years for females, and in CVM III, 13.00 ± 1.46 years for males and 12.00 ± 1.44 years for females (p < 0.0001). The pubertal growth spurt in the MP3 stage was MP3 - G stage. The bone age at the MP3 - G stage was 13.14 ± 1.07 years for males and 11.40 ± 1.09 years for females (p < 0.0001). Bone age evaluation using artificial intelligence is worth using in clinical practice, and it is expected that a faster and more accurate diagnosis will be possible.

Purpose: Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage. Methods: Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups). Results: The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value. Conclusion This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.

Objective: Bipolar disorder (BD) is complex genetic disorder. Therefore, approaches using clinical phenotypes such as biological rhythm disruption could be an alternative. In this study, we explored the relationship between melatonin pathway genes with circadian and seasonal rhythms of BD. Methods: We recruited clinically stable patients with BD (n=324). We measured the seasonal variation of mood and behavior (seasonality), and circadian preference, on a lifetime basis. We analyzed 34 variants in four genes (MTNR1a, MTNR1b, AANAT, ASMT) involved in the melatonin pathway. Results: Four variants were nominally associated with seasonality and circadian preference. After multiple test corrections, the rs116879618 in AANAT remained significantly associated with seasonality (corrected p=0.0151). When analyzing additional variants of AANAT through imputation, the rs117849139, rs77121614 and rs28936679 (corrected p=0.0086, 0.0154, and 0.0092) also showed a significant association with seasonality. Conclusion This is the first study reporting the relationship between variants of AANAT and seasonality in patients with BD. Since AANAT controls the level of melatonin production in accordance with light and darkness, this study suggests that melatonin may be involved in the pathogenesis of BD, which frequently shows a seasonality of behaviors and symptom manifestations.

Objective: Bipolar disorder (BD) is complex genetic disorder. Therefore, approaches using clinical phenotypes such as biological rhythm disruption could be an alternative. In this study, we explored the relationship between melatonin pathway genes with circadian and seasonal rhythms of BD. Methods: We recruited clinically stable patients with BD (n=324). We measured the seasonal variation of mood and behavior (seasonality), and circadian preference, on a lifetime basis. We analyzed 34 variants in four genes (MTNR1a, MTNR1b, AANAT, ASMT) involved in the melatonin pathway. Results: Four variants were nominally associated with seasonality and circadian preference. After multiple test corrections, the rs116879618 in AANAT remained significantly associated with seasonality (corrected p=0.0151). When analyzing additional variants of AANAT through imputation, the rs117849139, rs77121614 and rs28936679 (corrected p=0.0086, 0.0154, and 0.0092) also showed a significant association with seasonality. Conclusion This is the first study reporting the relationship between variants of AANAT and seasonality in patients with BD. Since AANAT controls the level of melatonin production in accordance with light and darkness, this study suggests that melatonin may be involved in the pathogenesis of BD, which frequently shows a seasonality of behaviors and symptom manifestations.

BACKGROUND/AIMS: Colorectal cancer incidence among patients aged ≤50 years is increasing. This study aimed to develop and validate an advanced colorectal neoplasm (ACRN) screening model for young adults aged <50 years in Korea. METHODS: This retrospective cross-sectional study included 59,575 consecutive asymptomatic Koreans who underwent screening colonoscopy between 2003 and 2012 at a single comprehensive health care center. Young Adult Colorectal Screening (YCS) score was developed as an optimized risk stratification model for ACRN using multivariate analysis and was internally validated. The predictive power and diagnostic performance of YCS score was compared with those of Asia-Pacific Colorectal Screening (APCS) and Korean Colorectal Screening (KCS) scores. RESULTS: 41,702 and 17,873 subjects were randomly allocated into the derivation and validation cohorts, respectively, by examination year. ACRN prevalence was 0.9% in both cohorts. YCS score comprised sex, age, alcohol, smoking, obesity, glucose metabolism abnormality, and family history of CRC, with score ranges of 0 to 10. In the validation cohort, ACRN prevalence was 0.6% in the low-risk tier (score, 0–4), 1.5% in the moderate-risk tier (score, 5–7), and 3.4% in the high-risk tier (score, 8–10). ACRN risk increased 2.5-fold (95% confidence interval [CI], 1.8–3.4) in the moderate-risk tier and 5.8-fold (95% CI, 3.4–9.8) in the high-risk tier compared with the low-risk tier. YCS score identified better balanced accuracy (53.9%) than APCS (51.5%) and KCS (50.7%) scores and had relatively good discriminative power (area under the curve=0.660). CONCLUSIONS: YCS score based on clinical and laboratory risk factors was clinically effective and beneficial for predicting ACRN risk and targeting screening colonoscopy in adults aged <50 years.
Adult ; Cohort Studies ; Colonoscopy ; Colorectal Neoplasms ; Comprehensive Health Care ; Cross-Sectional Studies ; Early Detection of Cancer ; Glucose ; Humans ; Incidence ; Korea ; Mass Screening ; Metabolism ; Multivariate Analysis ; Obesity ; Prevalence ; Retrospective Studies ; Risk Assessment ; Risk Factors ; Smoke ; Smoking ; Young Adult

This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns (normal, alveolar, bronchial, and unstructured interstitial) were obtained from 512 thoracic radiographs of 252 dogs and 65 cats. Forty-four texture parameters based on eight methods of texture analysis (first-order statistics, spatial gray-level-dependence matrices, gray-level-difference statistics, gray-level run length image statistics, neighborhood gray-tone difference matrices, fractal dimension texture analysis, Fourier power spectrum, and Law's texture energy measures) were used to extract textural features from the ROIs. The texture parameters of each lung pattern were compared and used for training and testing of artificial neural networks. Classification performance was evaluated by calculating accuracy and the area under the receiver operating characteristic curve (AUC). Forty texture parameters showed significant differences between the lung patterns. The accuracy of lung pattern classification was 99.1% in the training dataset and 91.9% in the testing dataset. The AUCs were above 0.98 in the training set and above 0.92 in the testing dataset. Texture analysis and machine learning algorithms may potentially facilitate the evaluation of medical images.
Animals ; Area Under Curve ; Cats ; Classification ; Dataset ; Dogs ; Fourier Analysis ; Fractals ; Lung ; Machine Learning ; Neural Networks (Computer) ; Pattern Recognition, Visual ; Radiography, Thoracic ; Residence Characteristics ; ROC Curve

Gene-gene interaction (GGI) analysis is known to play an important role in explaining missing heritability. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in a case-control design. In this study, we developed “Hierarchical structural CoMponent analysis of Gene-Gene Interactions” (HisCoM-GGI) software for GGI analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and single nucleotide polymorphisms (SNPs), enabling both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various types of genomic data and supports data management and multithreading to improve the efficiency of genome-wide association study data analysis. We expect that HisCoM-GGI software will provide advanced accessibility to researchers in genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.
Case-Control Studies ; Genome-Wide Association Study ; Methods ; Phenotype ; Polymorphism, Single Nucleotide ; Statistics as Topic

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.
Body Mass Index* ; Decision Support Techniques ; Genome-Wide Association Study ; Humans ; Korea ; Learning ; Linear Models