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 aim of the present study was to evaluate the per-lesion sensitivity and specificity of the Korean Liver Cancer Association–National Cancer Center (KLCA-NCC) 2022 criteria for the noninvasive diagnosis of hepatocellular carcinoma (HCC), with intraindividual comparison of the diagnostic performance of magnetic resonance imaging with extracellular agents (ECA-MRI) and hepatobiliary agents (HBA-MRI). Materials and Methods: Patients at high risk for HCC who were referred to a tertiary academic institution for hepatic lesions with size ≥ 10 mm between July 2019 and June 2022 were enrolled. A total of 91 patients (mean age, 58.1 years; 76 men and 15 women) with 118 lesions who underwent both ECA-MRI and HBA-MRI were eligible for final analysis. The per-lesion sensitivities and specificities of the KLCA-NCC 2022 criteria using ECA-MRI and HBA-MRI were compared using McNemar’s test. Results: The 118 lesions were 93 HCCs, 4 non-HCC malignancies, and 21 benign lesions. On HBA-MRI, the “definite” HCC category showed significantly higher sensitivity than ECA-MRI (78.5% vs. 58.1%, p < 0.001), with identical specificity (92.0% vs. 92.0%, p > 0.999). For “probable” or “definite” HCC categories, there were no differences in the sensitivity (84.9% vs. 84.9%, p > 0.999) and specificity (84.0% vs. 84.0%, p > 0.999) between ECA-MRI and HBA-MRI. Conclusion The “definite” HCC category of the KLCA-NCC 2022 criteria showed higher sensitivity in diagnosing HCC on HBA-MRI compared with ECA-MRI, without compromising specificity. There were no significant differences in the sensitivity and specificity of “probable” or “definite” HCC categories according to ECA-MRI and HBA-MRI.

PURPOSE: The risk of posthepatectomy liver failure (PHLF) after right hepatectomy remains substantial. Additional parameters such as computed tomography volumetry, liver stiffness measurement by FibroScan, indocyanine green retention rate at 15 minutes, and platelet count used to properly assess future liver remnant volume quality and quantity are of the utmost importance. Thus, we compared the usefulness of these modalities for predicting PHLF among patients with hepatocellular carcinoma after right hepatectomy. METHODS: We retrospectively reviewed patients who underwent right hepatectomy for hepatocellular carcinoma between 2007 and 2013. PHLF was determined according to International Study Group of Liver Surgery consensus definition and severity grading. Grades B and C were defined as clinically relevant posthepatectomy liver failure (CRPHLF). The results were internally validated using a cohort of 97 patients. RESULTS: Among the 90 included patients, 15 (16.7%) had CRPHLF. Multivariate analysis confirmed that platelet count < 140 (109/L) (hazard ratio [HR], 24.231; 95% confidence interval [CI], 3.623–161.693; P = 0.001) and remnant liver volume-to-body weight (RVL/BW) ratio < 0.55 (HR, 25.600; 95% CI, 4.185–156.590; P < 0.001) were independent predictors of CRPHLF. Among the 12 patients with a platelet count < 140 (109/L) and RLV/BW ratio < 0.55, 9 (75%) had CRPHLF. Likewise, 5 of 38 (13.2%) with only one risk factor developed CRPHL versus 1 of 40 (2.5%) with no risk factors. These findings were confirmed by the validation cohort. CONCLUSION RLV/BW ratio and platelet count are more important than the conventional RLV/TFLV, indocyanine green retention rate at 15 minutes, and liver stiffness measurement in the preoperative risk assessment for CRPHLF.

Background/Aims: To investigate whether serum Wisteria floribunda agglutinin-positive human Mac-2-binding protein (WFA+-M2BP) can predict the recurrence of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) after curative resection. Methods: Patients with chronic hepatitis B (CHB) who underwent curative resection for HCC between 2004 and 2015 were eligible for the study. Recurrence was sub-classified as early (<2 years) or late (≥2 years). Results: A total of 170 patients with CHB were selected. During the follow-up period (median, 22.6 months), 64 (37.6%) patients developed recurrence. In multivariate analyses, WFA+-M2BP level was an independent predictor of overall (hazard ratio [HR]=1.490), early (HR=1.667), and late recurrence (HR=1.416), together with male sex, des-gamma carboxyprothrombin level, maximal tumor size, portal vein invasion, and satellite nodules (all P<0.05). However, WFA+- M2BP level was not predictive of grade B-C posthepatectomy liver failure. The cutoff value that maximized the sum of sensitivity (30.2%) and specificity (90.6%) was 2.14 (area under receiver operating characteristic curve=0.632, P=0.010). Patients with a WFA+-M2BP level >2.14 experienced recurrence more frequently than those with a WFA+-M2BP level ≤2.14 (P=0.011 by log-rank test), and had poorer postoperative outcomes than those with a WFA+-M2BP level ≤2.14 in terms of overall recurrence (56.0 vs. 34.5%, P=0.047) and early recurrence (52.0 vs. 20.7%, P=0.001). Conclusions WFA+-M2BP level is an independent predictive factor of HBV-related HCC recurrence after curative resection. Further studies should investigate incorporation of WFA+-M2BP level into tailored postoperative surveillance strategies for patients with CHB.

Free fatty acids (FFAs) are important substrates for mitochondrial oxidative metabolism and ATP synthesis but also cause serious stress to various tissues, contributing to the development of metabolic diseases. CD36 is a major mediator of cellular FFA uptake. Inside the cell, saturated FFAs are able to induce the production of cytosolic and mitochondrial reactive oxygen species (ROS), which can be prevented by co-exposure to unsaturated FFAs. There are close connections between oxidative stress and organellar Ca²⁺ homeostasis. Highly oxidative conditions induced by palmitate trigger aberrant endoplasmic reticulum (ER) Ca²⁺ release and thereby deplete ER Ca²⁺ stores. The resulting ER Ca²⁺ deficiency impairs chaperones of the protein folding machinery, leading to the accumulation of misfolded proteins. This ER stress may further aggravate oxidative stress by augmenting ER ROS production. Secondary to ER Ca²⁺ release, cytosolic and mitochondrial matrix Ca²⁺ concentrations can also be altered. In addition, plasmalemmal ion channels operated by ER Ca²⁺ depletion mediate persistent Ca²⁺ influx, further impairing cytosolic and mitochondrial Ca²⁺ homeostasis. Mitochondrial Ca²⁺ overload causes superoxide production and functional impairment, culminating in apoptosis. This vicious cycle of lipotoxicity occurs in multiple tissues, resulting in β-cell failure and insulin resistance in target tissues, and further aggravates diabetic complications.
Adenosine Triphosphate ; Apoptosis ; Calcium* ; Cytosol ; Diabetes Complications ; Endoplasmic Reticulum ; Fatty Acids, Nonesterified ; Homeostasis ; Insulin Resistance ; Ion Channels ; Metabolic Diseases ; Metabolism ; Oxidative Stress* ; Protein Folding ; Reactive Oxygen Species ; Superoxides

PURPOSE: The authors evaluated the relationships between the clinical factors and resistive indexes (RIs) of prostate and urethral blood flows by using power Doppler transrectal ultrasonography (PDUS) in men with benign prostatic hyperplasia (BPH). METHODS: The data of 110 patients with BPH and lower urinary tract symptoms (LUTS) treated between January 2015 and July 2015 were prospectively collected. PDUS was used to identify the capsular and urethral arteries of the prostate in order to measure RIs. International Prostate Symptom Score (IPSS), maximal flow rate (Qmax), total prostate volume (TPV), transition zone volume (TZV), transition zone index (=TZV/TPV), presence of intravesical prostatic protrusion (IPP), and the RIs of capsular and urethral arteries were evaluated for all of the patients by one urologist. RESULTS: The 110 patients were categorized according to IPSS (mild symptoms, 0-7; moderate symptoms, 8-19; and severe symptoms, 20-35), Qmax (<10 and ≥10 mL/sec), TPV (<30 and ≥30 mL), and presence or absence of IPP. No significant relationship was found between the mean RI of any artery and IPSS or Qmax. The mean RIs of the urethral artery, and left and right capsular arteries were significantly dependent on prostate size and the presence of IPP. CONCLUSIONS: RI obtained by using PDUS correlated with the presence of IPP and prostate size. The RI of prostate blood flow can be used as a noninvasive diagnostic tool for BPH with LUTS.
Arteries ; Humans ; Lower Urinary Tract Symptoms ; Male ; Prospective Studies ; Prostate* ; Prostatic Hyperplasia* ; Ultrasonography