Myopia, particularly high myopia, is a significant risk factor for several ocular pathologies including cataract, glaucoma, and retinal detachment. Excessive axial elongation associated with high myopia can induce biomechanical stretching, increasing the risk of serious complications like posterior staphyloma and myopic maculopathy. Global meta-analyses estimate that approximately 10 million people were visually impaired due to myopic maculopathy in 2015, with 3 million being blind. Recent nationwide surveys in South Korea revealed a prevalence of 65.4% for myopia and 6.9% for high myopia in children and adolescents, highlighting the urgent need for effective management. Delaying the onset and slowing the progression of myopia during childhood and adolescence is crucial for reducing the potential lifetime risk of these complications. This consensus statement, prepared by the Korean Myopia Society for the Korean Association for Pediatric Ophthalmology and Strabismus (KAPOS), reviews the current evidence for myopia control interventions and provides management strategies applicable to the South Korean clinical setting. Key interventions covered include lifestyle modifications (outdoor time, near work adjustment), optical methods (myopia-control spectacle lenses, dual-focus soft contact lenses, orthokeratology), and pharmacologic treatment (low-concentration atropine), as well as combination therapies. The statement also addresses patient selection, treatment outcome evaluation using spherical equivalent and axial length changes, and the crucial aspects related to treatment cessation and the rebound effect.

Betulinic acid (3-beta-hydroxy-lup20[29]-en-28-oic acid) has attracted significant attention due to its diverse biological and pharmacological activities, including anti-inflammatory, antimicrobial, antiviral, antidiabetic, antimalarial, anti-human immunodeficiency virus, and antitumor effects. However, its effectiveness against oral cancer remains unknown. This study aimed to evaluate the effect of betulinic acid on the induction of apoptosis in FaDu human pharyngeal carcinoma cells by performing 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay, LIVE/DEAD stain, 4',6-diamidino-2-phenylindole (DAPI) stain and western blot. In the MTT assay, LIVE/DEAD stain, and DAPI stain analyses, betulinic acid increased FaDu cell apoptosis in a concentration-dependent manner. Apoptosis induced by betulinic acid in FaDu cells was mediated by the expression of Fas and the activation of caspase-8, caspase-3, and poly (ADP-ribose) polymerase. Western blotting revealed that B-cell lymphoma 2 (Bcl-2) and B-cell lymphoma-extra large were downregulated, while Bcl-2-associated death promoter and Bcl-2-associated X protein were upregulated by betulinic acid in FaDu cells. These findings indicate that betulinic acid inhibits cell proliferation in FaDu human pharyngeal carcinoma cells and induces apoptosis through both apoptotic receptor-mediated exogenous apoptosis and mitochondrialmediated endogenous apoptosis pathways.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Background: The benefits of transradial access (TRA) over transfemoral access (TFA) for bifurcation percutaneous coronary intervention (PCI) are uncertain because of the limited availability of device selection. This study aimed to compare the procedural differences and the in-hospital and long-term outcomes of TRA and TFA for bifurcation PCI using secondgeneration drug-eluting stents (DESs). Methods: Based on data from the Coronary Bifurcation Stenting Registry III, a retrospective registry of 2,648 patients undergoing bifurcation PCI with second-generation DES from 21 centers in South Korea, patients were categorized into the TRA group (n = 1,507) or the TFA group (n = 1,141). After propensity score matching (PSM), procedural differences, in-hospital outcomes, and device-oriented composite outcomes (DOCOs; a composite of cardiac death, target vessel-related myocardial infarction, and target lesion revascularization) were compared between the two groups (772 matched patients each group). Results: Despite well-balanced baseline clinical and lesion characteristics after PSM, the use of the two-stent strategy (14.2% vs. 23.7%, P = 0.001) and the incidence of in-hospital adverse outcomes, primarily driven by access site complications (2.2% vs. 4.4%, P = 0.015), were significantly lower in the TRA group than in the TFA group. At the 5-year follow-up, the incidence of DOCOs was similar between the groups (6.3% vs. 7.1%, P = 0.639). Conclusion The findings suggested that TRA may be safer than TFA for bifurcation PCI using second-generation DESs. Despite differences in treatment strategy, TRA was associated with similar long-term clinical outcomes as those of TFA. Therefore, TRA might be the preferred access for bifurcation PCI using second-generation DES.

Purpose: This study aimed to describe the desperate situation where the clinician should make decisions to further manage patients having experienced adverse drug reaction (ADR) to lamotrigine that is indicated to not easily controlled neuropsychiatric diseases. Methods: A descriptive analysis was done by thoroughly reviewing medical records of patients who were reported to have ADR to lamotrigine in a regional drug-safety center between 2010 and 2018. Results: Eighty-four cases of lamotrigine-related ADRs occurred in 80 patients. Skin lesions were most commonly observed in 70 cases (83.3%) and 14 cases (16.7%) had severe ADRs. Sixty-three subjects (78.8%) discontinued lamotrigine, while 17 (21.3%) continued it.At the time of discontinuation, 30.0% were prescribed aromatic antiepileptic drugs. Among 4 subjects who were eventually prescribed lamotrigine again after a period of discontinuation, 3 (75.0%) experienced its recurrence. Among patients who had taken alternative medications, the incidence of ADRs was higher in those being prescribed aromatic antiepileptic drugs than in the others being prescribed other than aromatic antiepileptic drugs (P = 0.013). Regarding the control of underlying diseases, as many as 65 (86.7%) and 68 (90.7%) failed to reach maintaining the resolved state from 6 months and 12 months after the substitution, respectively. Conclusion Patients can be easily trapped between the recurrence of ADRs and the treatment failure to a certain drug like lamotrigine, in which we can hardly find a reasonable alternative to manage them.

Hericium erinaceus, also known as lion’s mane mushroom, is an edible and medicinal mushroom that belongs to the family Hericiaceae. We previously reported hericene A as an anti-diabetic constituent of H. erinaceus and the effect of cultivation substrates on its content was investigated. As the continuation, the contents of five major compounds such as hericenes A-D, which exerted α-glucosidase inhibitory activity, together with ergosterol were investigated depending on cultivation stages. H. erinaceus was cultured for 25 days (5 stages) to induce fruiting bodies, and the contents of the compounds at each stage were quantified. All the five compounds were detected in fruiting body by HPLC analysis. Among the hericene derivatives in the mushroom, the content of hericene A was the highest, followed by hericene C and the content of hericenes B and D was relative low. All four hericene derivatives present in the highest content at stage 4 whereas the content of ergosterol was highest at stage 5. The highest α-glucosidase inhibitory activity of H. erinaceus was measured at stage 4, which correlates with the contents of hericene derivatives. Conclusively, H. erinaceus with better efficacy and high content of active constituents can be secured by the optimization of cultivation conditions.