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Inflammatory Bowel Disease (IBD), Crohn's disease (CD) and ulcerative colitis (UC), requires a combination of procedures and tests in diagnosis and discrimination. This study aimed to delineate specific serum metabolomic biomarkers that diagnose IBD and differentiate IBD subgroups. Serum samples and clinical metadata of the participants, IBD patients and Normal Controls (NC), were collected. Untargeted and targeted metabolomic analyses by high-resolution mass spectrometry and multivariate statistical approaches were applied. Further, Receiver Operating Characteristic (ROC) curves, pathways, and network analyses were conducted. Distinct clustering separated IBD patients from the NC, although the CD and UC subgroups overlapped in the non-targeted profiling. Targeted metabolomics revealed elevated tryptophan and indole-3-acetic acid levels and reduced primary-to-secondary bile acid ratios in both CD and UC patients. The differences in specific tryptophan metabolites between CD and UC were identified. The ROC analysis underscored the discriminatory power of the biomarkers (AUC values: NC vs. CD = 0.9738; NC vs. UC = 0.9887; UC vs. CD = 0.7140). Pathway analysis revealed alterations in glycerolipid metabolism, markedly differentiating UC from CD. Network analysis correlated metabolomic markers with the clinical phenotypes of IBD. Serum metabolomic biomarkers can precisely identify IBD, discriminate IBD subtypes, and further reveal the phenotypes of IBD. [ABSTRACT FROM AUTHOR]

Auscultation is a method that involves listening to sounds from the patient's body, mainly using a stethoscope, to diagnose diseases. The stethoscope allows for non-invasive, real-time diagnosis, and it is ideal for diagnosing respiratory diseases and first aid. However, accurate interpretation of respiratory sounds using a stethoscope is a subjective process that requires considerable expertise from clinicians. To overcome the shortcomings of existing stethoscopes, research is actively being conducted to develop an artificial intelligence deep learning model that can interpret breathing sounds recorded through electronic stethoscopes. Most recent studies in this area have focused on CNN-based respiratory sound classification models. However, such CNN models are limited in their ability to accurately interpret conditions that require longer overall length and more detailed context. Therefore, in the present work, we apply the Transformer model-based Audio Spectrogram Transformer (AST) model to our actual clinical practice data. This prospective study targeted children who visited the pediatric departments of two university hospitals in South Korea from 2019 to 2020. A pediatric pulmonologist recorded breath sounds, and a pediatric breath sound dataset was constructed through double-blind verification. We then developed a deep learning model that applied the pre-trained weights of the AST model to our data with a total of 194 wheezes and 531 other respiratory sounds. We compared the performance of the proposed model with that of a previously published CNN-based model and also conducted performance tests using previous datasets. To ensure the reliability of the proposed model, we visualized the classification process using Score-Class Activation Mapping (Score-CAM). Our model had an accuracy of 91.1%, area under the curve (AUC) of 86.6%, precision of 88.2%, recall of 76.9%, and F1-score of 82.2%. Ultimately, the proposed transformer-based model showed high accuracy in wheezing detection, and the decision-making process of the model was also verified to be reliable. The artificial intelligence deep learning model we have developed and described in this study is expected to help accurately diagnose pediatric respiratory diseases in real-world clinical practice. [ABSTRACT FROM AUTHOR]

Background: Vertebral fractures are a common medical issue. Physical activity helps improve bone health and manage the risk of fractures. However, much controversy exists about the association between physical activity and vertebral fractures. Therefore, we aimed to investigate the association between changes in regular physical activity (RPA) and vertebral fractures. Methods: This study was a nationwide, observational cohort study based on claim data from the Korean National Health Insurance Service. Physical activity level was obtained from self-reported questionnaires from 2009 to 2012. Participants were divided depending on their levels of physical activity and the occurrence of vertebral fractures from 2013 to 2016 was recorded. Results: The group with sufficient RPA, compared to the Insufficient group (incidence rate showed a decrease of 1.93/1,000 PY; adjusted HR (aHR): 0.83; 95% CI: 0.81–0.84), had a reduced risk of vertebral fracture. From the perspective of RPA changes, the Continuous Sufficient group had an aHR of 0.74, 95% CI 0.72–0.76, and the Increased group had an aHR of 0.87, 95% CI 0.85–0.88. The Decreased group had an aHR of 0.94, 95% CI 0.92–0.95. Conclusion: The degree of RPA could reduce the risk of vertebral fracture. Continuous sufficient RPA helped lower the risk of vertebral fracture. [ABSTRACT FROM AUTHOR]

Microfocused ultrasound (MFU) and (MRF) are non-invasive modalities widely used for skin rejuvenation and are often combined with injectables, including neuromodulators and soft tissue fillers. However, large-scale, long-term safety data on such combination therapies are lacking. To address this gap, we conducted a retrospective chart review at two private practice dermatology clinics in South Korea from June 2005 to December 2023. A total of 1,040 patients were included, comprising 515 (49.5%) and 525 (50.5%) patients undergoing MFU and MRF, respectively. Regarding combination therapy, the patients received neuromodulators (76.9%) or dermal filler injections using hyaluronic acid (HA) (17.3%) or poly-L-lactic acid (PLLA) (5.8%) in conjunction with MFU/MRF. Documented adverse events included one case of paradoxical bulging and three cases of eyelid ptosis among those treated with MFU/MRF and neuromodulators (overall incidence: 0.1% and 0.4%, respectively), two cases of surface irregularity among those treated with MFU/MRF and HA fillers (overall incidence: 1.1%), and one case of cheek asymmetry among those treated with MFU/MRF and PLLA injections (overall incidence: 1.7%). No adverse events such as infection, necrosis, burn, filler migration, or unexpected premature loss of filler were reported. While limited by its retrospective design and the inclusion of only two dermatology clinics, which restricts the generalizability of the findings, our study highlights the favorable long-term safety profile of combining MFU and MRF with cosmetic injectables. Our study offers reassurance to clinicians and patients considering these approaches. [ABSTRACT FROM AUTHOR]

Background/Objectives: Patient-specific instrumentation (PSI) in total knee arthroplasty (TKA) uses preoperative three-dimensional imaging to create cutting blocks tailored to patient anatomy. However, there is debate regarding the additional benefits of PSI in terms of improved alignment or functional outcomes compared to using conventional instruments. Although PSI design has undergone continuous development, the improvements have not been incorporated. Therefore, the aim of this study was to compare the surgical time and radiological outcomes between advanced-design PSI and conventional instruments. Methods: We conducted a retrospective review of 328 patients who underwent primary TKAs using PSI for osteoarthritis and compared them with 328 matched patients who underwent TKA performed with conventional instruments during the same period (March 2023 to August 2024). We compared the surgical time and component alignment between the advanced-design PSI group and the conventional instrument group. Results: The average surgical time was significantly shorter in the advanced-design PSI group (47.6 ± 12.4 min) compared to the conventional instrument group (59.2 ± 14.2 min, p < 0.05). The advanced PSI design group had a significantly lower occurrence of outliers in hip–knee–ankle alignment (7%) compared to the conventional instrument group (36.3%). This trend was also observed in femoral coronal alignment, tibial coronal alignment, and femoral sagittal alignment. Conclusions: The use of advanced-design PSI demonstrated significantly reduced surgical time and improved alignment compared to conventional instruments. This highlights that proper design is a key factor for PSI to achieve superior biomechanical effects. Our study shows that advanced-design PSI technology has the potential to replace conventional instruments in TKA, though further research is required to determine its clinical outcomes and economic benefits. [ABSTRACT FROM AUTHOR]