Purpose: This study aimed to develop and evaluate the effectiveness of the Trauma-nursing Education and Skill Support (TESS) program based on the ADDIE model (Analysis, Design, Development, Implementation, Evaluation model). The program was designed to enhance trauma nurses’ clinical competencies, including trauma-related knowledge, self-efficacy, and problem-solving ability, through the integration of theoretical education and simulation-based practice. Methods: A quasi-experimental study using a non-equivalent control group pretest–posttest design was conducted. Participants included 108 trauma nurses from regional trauma centers, military trauma centers, and emergency care facilities, who were assigned to an experimental group (n=52) or a control group (n=56). The TESS program consisted of a 2-day, 14-hour blended-learning course that included eight lecture sessions and four simulation-based practice stations. Data were collected at baseline, immediately after the intervention, and at 6 months using validated instruments measuring trauma-related knowledge, self-efficacy, and problem-solving ability. Two-way repeated-measures analysis of variance was used for data analysis. Results: The experimental group demonstrated significant improvements in trauma-related knowledge, self-efficacy, and problem-solving ability compared with baseline (all p<.001). These improvements were sustained at 6 months, although trauma-related knowledge scores showed a slight decline compared with immediate posttest levels. Between-group analyses confirmed significant group-by-time interaction effects for all outcomes: trauma-related knowledge (η2=0.12, p<.001), self-efficacy (η2=0.09, p=.002), and problem-solving ability (η2=0.08, p=.003). Conclusion The TESS program effectively enhanced trauma nurses’ trauma-related knowledge, self-efficacy, and problem-solving ability, with effects sustained for up to 6 months. Incorporating blended learning and simulation-based training into standardized trauma nursing education may strengthen clinical competencies and ultimately contribute to improved patient outcomes.

Immunosenescence, an age-associated decline in immune function, is increasingly recognized as a central determinant of health and disease in older adults. Characterized by thymic involution, loss of naïve T cells, contraction of T cell receptor diversity, accumulation of senescent and exhausted lymphocytes, and a chronic inflammatory state known as inflammaging, immunosenescence compromises both innate and adaptive immune responses. Immunosenescence contributes to the pathogenesis of diverse age-related diseases. In autoimmune and metabolic diseases, premature accumulation of senescent T cells and impaired regulatory T cell function drive chronic inflammation and tissue damage, while in neurodegenerative diseases, microglial aging and sustained neuroinflammation exacerbate neuronal loss. These findings highlight immunosenescence as a unifying mechanism linking aging to systemic and organ-specific pathologies. Advances in biomarker discovery, including phenotypic markers, telomere attrition, and epigenetic signatures, have enabled the quantitative assessment of immune aging, while emerging therapeutic strategies, such as cytokine modulation, mTOR inhibition, senolytics, and epigenetic reprogramming, show promise in restoring immune competence. Here, we summarize recent research on immunosenescence in various diseases, particularly chronic inflammatory, metabolic, and neurodegenerative diseases, and suggest novel strategies for the development of senolytic drugs.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Purpose: To compare near-distance reading speeds across different intraocular lens (IOL) types using a Korean reading speed measurement application. Methods: Patients who underwent bilateral cataract surgery and received trifocal IOL (Panoptix), extended depth of focus (EDoF) IOL (Vivity), or enhanced monofocal IOL (Eyhance) were enrolled. Korean reading speed and reading & speaking speed were measured using a Korean reading speed measurement application. Additionally, satisfaction with visual function in everyday life was assessed using a questionnaire. Results: No statistically significant differences were observed in postoperative corrected distance visual acuity among the three groups (p = 0.71). The Panoptix group demonstrated significantly higher distance-corrected near visual acuity than the other two groups (p < 0.05). For letter sizes of logMAR 0.5 or larger, the Panoptix group exhibited significantly higher reading and reading & speaking speeds than the other two groups. For letter sizes of logMAR 0.8 or larger, the Vivity group showed significantly higher reading and reading & speaking speeds than the Eyhance group (p < 0.05). The Panoptix group showed significantly greater satisfaction with visual function in work at near distances, including that for activities such as reading a book. Conclusion The Panoptix trifocal IOL is expected to provide superior near-vision function and patient satisfaction than other IOL types.

Background: Atherogenic dyslipidemia, which is frequently associated with type 2 diabetes (T2D) and insulin resistance, contributes to the development of vascular complications. Statin therapy is the primary approach to dyslipidemia management in T2D, however, the role of non-statin therapy remains unclear. Ezetimibe reduces cholesterol burden by inhibiting intestinal cholesterol absorption. Fibrates lower triglyceride levels and increase high-density lipoprotein cholesterol (HDL-C) levels via peroxisome proliferator- activated receptor alpha agonism. Therefore, when combined, these drugs effectively lower non-HDL-C levels. Despite this, few clinical trials have specifically targeted non-HDL-C, and the efficacy of triple combination therapies, including statins, ezetimibe, and fibrates, has yet to be determined. Methods: This is a multicenter, prospective, randomized, open-label, active-comparator controlled trial involving 3,958 eligible participants with T2D, cardiovascular risk factors, and elevated non-HDL-C (≥100 mg/dL). Participants, already on moderate-intensity statins, will be randomly assigned to either Ezefeno (ezetimibe/fenofibrate) addition or statin dose-escalation. The primary end point is the development of a composite of major adverse cardiovascular and diabetic microvascular events over 48 months. Conclusion This trial aims to assess whether combining statins, ezetimibe, and fenofibrate is as effective as, or possibly superior to, statin monotherapy intensification in lowering cardiovascular and microvascular disease risk for patients with T2D. This could propose a novel therapeutic approach for managing dyslipidemia in T2D.

Objective: Dysphagia is a common clinical condition characterized by difficulty in swallowing. It is sub-classified into oropharyngeal dysphagia, which refers to problems in the mouth and pharynx, and esophageal dysphagia, which refers to problems in the esophageal body and esophagogastric junction. Dysphagia can have a significant negative impact one’s physical health and quality of life as its severity increases. Therefore, proper assessment and management of dysphagia are critical for improving swallowing function and preventing complications. Thus a guideline was developed to provide evidence-based recommendations for assessment and management in patients with dysphagia. Methods: Nineteen key questions on dysphagia were developed. These questions dealt with various aspects of problems related to dysphagia, including assessment, management, and complications. A literature search for relevant articles was conducted using Pubmed, Embase, the Cochrane Library, and one domestic database of KoreaMed, until April 2021. The level of evidence and recommendation grade were established according to the Grading of Recommendation Assessment, Development and Evaluation methodology. Results: Early screening and assessment of videofluoroscopic swallowing were recommended for assessing the presence of dysphagia. Therapeutic methods, such as tongue and pharyngeal muscle strengthening exercises and neuromuscular electrical stimulation with swallowing therapy, were effective in improving swallowing function and quality of life in patients with dysphagia. Nutritional intervention and an oral care program were also recommended. Conclusion This guideline presents recommendations for the assessment and management of patients with oropharyngeal dysphagia, including rehabilitative strategies.