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.

Objective: The primary objective of this study was to investigate the effect of methylphenidate (MPH) on height, weight, and body mass index (BMI) in drug-naive children and adolescents with attention deficit hyperactivity disorder (ADHD) over 24 months. The secondary objective was to investigate whether the age of MPH initiation and sex act as risk factors for growth retardation. Methods: A total of 82 patients with ADHD were included. Weight, height, and BMI were measured at baseline and every 6 months up to 24 months. Weight, height, and BMI data were converted to z-scores and analyzed using two-way repeated-measures ANOVA and multiple linear regression. Results: The z-score of height, weight and BMI decreased from the baseline values. The z-scores of height were at baseline 0.002; 6 months -0.100; 12 months -0.159; 18 months -0.159; 24 months -0.186. The z-scores of weight were at baseline 0.104; 6 months -0.155; 12 months -0.256; 18 months -0.278; 24 months -0.301. Here were no age and sex differences of height, weight, and BMI. Conclusion The use of MPH was associated with attenuation of weight and height gain rates in children and adolescents with ADHD.

Objective: The aim of this study was to investigate differentially expressed genes and their functions in the hippocampus and striatum after heroin administration in cynomolgus macaques of different ages. Methods: Cynomolgus monkeys were divided by age as follows: 1 year (A1, n = 2); 3 to 4 years (A2, n = 2); 6 to 8 years (A3, n = 2); and older than 11 years (A4, n = 2). After heroin was injected intramuscularly into the monkeys (0.6 mg/kg), we performed large-scale transcriptome profiling in the hippocampus (H) and striatum (S) using RNA sequencing technology. Some genes were validated with real-time quantitative PCR. Results: In the hippocampus, the gene expression of A1H was similar to that of A4H, while the gene expression of A2H was similar to that of A3H. Genes associated with the mitogen-activated protein kinase signaling pathway (STMN1, FGF14, and MAPT) and -aminobutyric acid-ergic synapses (GABBR2 and GAD1) were differentially expressed among control and heroin-treated animals. Differential gene expression between A1S and A4S was the least significant, while differential gene expression between A3S and A2S was the most significant. Genes associated with the neurotrophin signaling pathway (NTRK1 and NGFR), autophagy (ATG5), and dopaminergic synapses (AKT1) in the striatum were differentially expressed among control and heroin-treated animals. Conclusion These results suggest that even a single heroin exposure can cause differential gene expression in the hippocampus and striatum of nonhuman primates at different ages.

Purpose: The aim of this study was to evaluate the clinical efficacy of a Tanner-Whitehouse 3 (TW3)-based fully automated bone age assessment system on hand-wrist radiographs of Korean children and adolescents. Materials and Methods: Hand-wrist radiographs of 80 subjects (40 boys and 40 girls, 7-15 years of age) were collected. The clinical efficacy was evaluated by comparing the bone ages that were determined using the system with those from the reference standard produced by 2 oral and maxillofacial radiologists. Comparisons were conducted using the paired t-test and simple regression analysis. Results: The bone ages estimated with this bone age assessment system were not significantly different from those obtained with the reference standard (p>0.05) and satisfied the equivalence criterion of 0.6 years within the 95% confidence interval ( - 0.07 to 0.22), demonstrating excellent performance of the system. Similarly, in the comparisons of gender subgroups, no significant difference in bone age between the values produced by the system and the reference standard was observed (p>0.05 for both boys and girls). The determination coefficients obtained via regression analysis were 0.962, 0.945, and 0.952 for boys, girls, and overall, respectively (p=0.000); hence, the radiologist-determined bone ages and the system-determined bone ages were strongly correlated. Conclusion This TW3-based system can be effectively used for bone age assessment based on hand-wrist radiographs of Korean children and adolescents.

OBJECTIVE: To determine whether fragment removal on in vitro fertilization (IVF) day 2 improved the subsequent development and pregnancy outcomes of fragmented embryos compared to similar-grade embryos without fragment removal. METHODS: This study was a retrospective analysis involving 191 IVF cycles in which all embryos had over 10% fragmentation (grade 3 or 4) on day 2 of the IVF-embryo transfer cycle from March 2015 to December 2017. IVF cycles were divided into the fragment removal group (n=87) and the no fragment removal group (n=104) as a control cohort. Before fragment removal, embryos with fragmentation on day 2 were incubated in Ca2+- and Mg2+-free biopsy medium under paraffin oil for 30 minutes. Microsurgical fragment removal was performed with later-assisted hatching and a handmade suction micropipette that had an outer diameter of 30 µm. RESULTS: There were no significant differences in the characteristics of the patients between the control and the fragment removal groups. After fragment removal and subsequent in vitro culture for 24 hours, the number of blastomeres (7.1±1.7 vs. 6.9±1.6) was comparable between the transferred embryos in the two groups, but the morphological grade of the embryos in the fragment removal group (1.9±0.7) was significantly higher than that of the control group (3.1±0.5, p < 0.01). The clinical pregnancy (43.7%) and implantation rates (25.8%) in the fragment removal group were significantly higher than those in the control group (28.8% and 14.0%, respectively; p < 0.05). CONCLUSION: Early fragment removal on day 2 significantly improved the subsequent development and pregnancy outcomes of fragmented embryos.
Biopsy ; Blastomeres ; Cohort Studies ; Embryonic Structures* ; Female ; Fertilization in Vitro* ; Humans* ; In Vitro Techniques* ; Paraffin ; Pregnancy ; Pregnancy Outcome ; Retrospective Studies ; Suction

Fanconi syndrome is a dysfunction of the proximal renal tubules that results in impaired reabsorption and increased urinary loss of phosphate and other solutes. The pathophysiology of drug-induced Fanconi syndrome is unclear. Here we report the case of a 36-year-old woman who presented with pain in multiple bones and proteinuria. She had a 7-year history of taking adefovir at 10 mg/day for chronic hepatitis B. Three years previously she had received surgery for a nontraumatic right femur neck fracture, after which she continued to complain of pain in multiple bones, and proteinuria, glycosuria, and phosphaturia were noted. The findings of a light-microscope examination of a renal biopsy sample were normal, but mitochondrial damage of the proximal tubules was evident in electron microscopy. Western blot analysis revealed that the level of serum fibroblast growth factor 23 (FGF23) was lower than in normal controls. After 2 months of treatment, hypophosphatemia and proximal tubular dysfunction were reversed, and serum FGF23 had normalized. This case suggests that direct mitochondrial damage in proximal tubules can cause drug-induced Fanconi syndrome associated with osteomalacia.
Adult ; Biopsy ; Blotting, Western ; Fanconi Syndrome* ; Female ; Femoral Neck Fractures ; Fibroblast Growth Factors ; Glycosuria ; Hepatitis B, Chronic ; Humans ; Hypophosphatemia ; Hypophosphatemia, Familial ; Kidney Tubules, Proximal ; Microscopy, Electron ; Mitochondria ; Osteomalacia* ; Proteinuria