Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data. Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets. Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores. Conclusion This AI-driven software application provides a clinically valuable tool for predicting treatment response.While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.

Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data. Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets. Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores. Conclusion This AI-driven software application provides a clinically valuable tool for predicting treatment response.While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.

Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data. Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets. Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores. Conclusion This AI-driven software application provides a clinically valuable tool for predicting treatment response.While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.

Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data. Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets. Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores. Conclusion This AI-driven software application provides a clinically valuable tool for predicting treatment response.While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.

Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data. Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets. Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores. Conclusion This AI-driven software application provides a clinically valuable tool for predicting treatment response.While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.

Purpose: The treatment of male breast cancer (MBC) has been extrapolated from female breast cancer (FBC) because of its rarity despite their different clinicopathologic characteristics. We aimed to investigate the distribution of intrinsic subtypes based on immunohistochemistry, their clinical impact, and treatment pattern in clinical practice through a multicenter study in Korea. Materials and Methods: We retrospectively analyzed clinical data of 248 MBC patients from 18 institutions across the country from January 1995 to July 2016. Results: The median age of MBC patients was 63 years (range, 25 to 102 years). Among 148 intrinsic subtype classified patients, 61 (41.2%), 44 (29.7%), 29 (19.5%), and 14 (9.5%) were luminal A, luminal B, human epidermal growth factor receptor 2, and triple-negative breast cancer, respectively. Luminal A subtype showed trends for superior survival compared to other subtypes. Most hormone receptor-positive patients (166 patients, 82.6%) received adjuvant endocrine treatment. Five-year completion of adjuvant endocrine treatment was associated with superior disease-free survival (DFS) in patients classified with an intrinsic subtype (hazard ratio [HR], 0.15; 95% confidence interval [CI], 0.04 to 0.49; p=0.002) and in all patients (HR, 0.16; 95% CI, 0.05 to 0.54; p=0.003). Conclusion Distribution of subtypes of MBC was similar to FBC and luminal type A was most common. Overall survival tended to be improved for luminal A subtype, although there was no statistical significance. Completion of adjuvant endocrine treatment was associated with prolonged DFS in intrinsic subtype classified patients. MBC patients tended to receive less treatment. MBC patients should receive standard treatment according to guidelines as FBC patients.

The potent antioxidant and anti-hypertension activities have evidenced gastric enzymatic hydrolysates from flounder fish and their derived peptides. However, peptide composition and functional effect in various enzymatic hydrolysates differ by enzyme types, hydrolyzed times temperatures, etc. Therefore, we determined potential anti-hypertensive effect of hydrolysates produced from flounder fish using commercial enzymes such as Protamex, Flavourzyme, and Kojizyme which are common food grade proteases and characterized on its derived peptides. In this study, Protamex-mediated hydrolysate showed a more potent anti-hypertension effect than other commercial enzymes. Protamex-mediated hydrolysate was fractionated into three ranges of molecular weight (above 10 kDa (FPH-I), 5-10 kDa (FPH-II), and below 5 kDa (FPH-III)). The FPH-III exhibited the strongest anti-hypertensive effect, and it was revealed that three active peptides, valine-phenylalanine-serine-glycinetryptophan-alanine-alanine (VFSGWAA), leucine-histidine-phenylalanine (LHF) and tryptophan-proline-tryptophan (WPW) were contained. The activities were confirmed via angiotensin-converting enzyme (ACE) inhibition and molecular docking simulation. Among the three peptides, LHF and WPW have a molecular structure stability against the gastrointestinal digestion. LHF showed a significant anti-hypertension effect at 9 h after oral administration in spontaneously hypertensive rats (SHRs). Therefore, we suggest that Protamex-mediated hydrolysate would be an excellent anti-hypertensive agent due to the existence of stabilized functional peptides, including LHF and WPW.

Background/Aims: Posthepatectomy liver failure (PHLF) is a major complication that increases mortality in patients with hepatocellular carcinoma after surgical resection. The aim of this retrospective study was to evaluate the utility of magnetic resonance elastography-assessed liver stiffness (MRE-LS) for the prediction of PHLF and to develop an MRE-LS-based risk prediction model. Methods: A total of 160 hepatocellular carcinoma patients who underwent surgical resection with available preoperative MRE-LS data were enrolled. Clinical and laboratory parameters were collected from medical records. Logistic regression analyses were conducted to identify the risk factors for PHLF and develop a risk prediction model. Results: PHLF was present in 24 patients (15%). In the multivariate logistic analysis, high MRE-LS (kPa; odds ratio [OR] 1.49, 95% confidence interval [CI] 1.12 to 1.98, p=0.006), low serum albumin (≤3.8 g/dL; OR 15.89, 95% CI 2.41 to 104.82, p=0.004), major hepatic resection (OR 4.16, 95% CI 1.40 to 12.38, p=0.014), higher albumin-bilirubin score (>–0.55; OR 3.72, 95% CI 1.15 to 12.04, p=0.028), and higher serum α-fetoprotein (>100 ng/mL; OR 3.53, 95% CI 1.20 to 10.40, p=0.022) were identified as independent risk factors for PHLF. A risk prediction model for PHLF was established using the multivariate logistic regression equation. The area under the receiver operating characteristic curve (AUC) of the risk prediction model was 0.877 for predicting PHLF and 0.923 for predicting grade B and C PHLF. In leave-one-out cross-validation, the risk model showed good performance, with AUCs of 0.807 for all-grade PHLF and 0. 871 for grade B and C PHLF. Conclusions Our novel MRE-LS-based risk model had excellent performance in predicting PHLF, especially grade B and C PHLF.

Purpose: Neuroinflammation is considered an important pathway associated with several diseases that result in cognitive decline. 18F-THK5351 positron emission tomography (PET) signals might indicate the presence of neuroinflammation, as well as Alzheimer’s disease-type tau aggregates. β-amyloid (Aβ)-negative (Aβ–) amnestic mild cognitive impairment (aMCI) may be associated with non-Alzheimer’s disease pathophysiology. Accordingly, we investigated associations between 18F-THK5351 PET positivity and cognitive decline among Aβ– aMCI patients. Materials and Methods: The present study included 25 amyloid PET negative aMCI patients who underwent a minimum of two follow-up neuropsychological evaluations, including clinical dementia rating-sum of boxes (CDR-SOB). The patients were classified into two groups: 18F-THK5351-positive and -negative groups. The present study used a linear mixed effects model to estimate the effects of 18F-THK5351 PET positivity on cognitive prognosis among Aβ– aMCI patients. Results: Among the 25 Aβ– aMCI patients, 10 (40.0%) were 18F-THK5351 positive. The patients in the 18F-THK5351-positive group were older than those in the 18F-THK5351-negative group (77.4±2.2 years vs. 70.0±5.5 years; p<0.001). There was no difference between the two groups with regard to the proportion of apolipoprotein E ε4 carriers. Interestingly, however, the CDR-SOB scores of the 18F-THK5351-positive group deteriorated at a faster rate than those of the 18F-THK5351-negative group (B=0.003, p=0.033). Conclusion The results of the present study suggest that increased 18F-THK5351 uptake might be a useful predictor of poor prognosis among Aβ– aMCI patients, which might be associated with increased neuroinflammation (ClinicalTrials.gov NCT02656498).

Incidental thyroid nodules are commonly detected on ultrasonography (US). This has contributed to the rapidly rising incidence of low-risk papillary thyroid carcinoma over the last 20 years. The appropriate diagnosis and management of these patients is based on the risk factors related to the patients as well as the thyroid nodules. The Korean Society of Thyroid Radiology (KSThR) published consensus recommendations for US-based management of thyroid nodules in 2011 and revised them in 2016. These guidelines have been used as the standard guidelines in Korea. However, recent advances in the diagnosis and management of thyroid nodules have necessitated the revision of the original recommendations. The task force of the KSThR has revised the Korean Thyroid Imaging Reporting and Data System and recommendations for US lexicon, biopsy criteria, US criteria of extrathyroidal extension, optimal thyroid computed tomography protocol, and US follow-up of thyroid nodules before and after biopsy. The biopsy criteria were revised to reduce unnecessary biopsies for benign nodules while maintaining an appropriate sensitivity for the detection of malignant tumors in small (1–2 cm) thyroid nodules. The goal of these recommendations is to provide the optimal scientific evidence and expert opinion consensus regarding US-based diagnosis and management of thyroid nodules.