AI-Based 3D Liver Segmentation and Volumetric Analysis in Living Donor Data.

This study investigated the application of deep learning for 3-dimensional (3D) liver segmentation and volumetric analysis in living donor liver transplantation. Using abdominal computed tomography data from 55 donors, this study aimed to evaluate the liver segmentation performance of various U-Net-based models, including 3D U-Net, RU-Net, DU-Net, and RDU-Net, before and after hepatectomy. Accurate liver volume measurement is critical in liver transplantation to ensure adequate functional recovery and minimize postoperative complications.

Non-invasive stroke diagnosis using speech data from dysarthria patients.

Acute Ischemic Stroke (AIS) is a major cause of disability and can lead to death in severe cases. A common symptom of AIS, dysarthria, significantly impacts the quality of life of patients. In this study, we developed a deep learning model using dysarthria data for cost-effective and non-invasive brain stroke diagnosis.

Predictive stress analysis in simplified spinal disc model using physics-informed neural networks.

This study develops a physics-informed neural network (PINN) model to predict stress distribution in a simplified spinal disc structure. The model incorporates 3D spatial inputs and enforces equilibrium conditions through a custom loss function. Trained on synthetic elasticity-based data, it achieves an MAE of 0.

An Analysis of the Efficacy of Deep Learning-Based Pectoralis Muscle Segmentation in Chest CT for Sarcopenia Diagnosis.

Sarcopenia is the loss of skeletal muscle function and mass and is a poor prognostic factor. This condition is typically diagnosed by measuring skeletal muscle mass at the L3 level. Chest computed tomography (CT) scans do not include the L3 level.

Automated Imaging of Cataract Surgery Using Artificial Intelligence.

This study proposes a state-of-the-art technology to estimate a set of parameters to automatically display an optimized image on a screen during cataract surgery. We constructed an architecture comprising two stages to estimate the parameters for realizing the optimized image. The Pix2Pix approach was first introduced to generate fake images that mimic the optimal image.

Comparative Effectiveness of Wearable Devices and Built-In Step Counters in Reducing Metabolic Syndrome Risk in South Korea: Population-Based Cohort Study.

Background: Mobile health technologies show promise in addressing metabolic syndrome, but their comparative effectiveness in large-scale public health interventions remains unclear.

Objective: This study aims to compare the effectiveness of wearable devices (wearable activity trackers) and mobile app-based activity trackers (built-in step counters) in promoting walking practice, improving health behaviors, and reducing metabolic syndrome risk within a national mobile health care program operated by the Korea Health Promotion Institute.

Methods: This retrospective cohort study analyzed data from 46,579 participants in South Korea's national mobile health care program (2020-2022).

Enhanced Capacitive Performance of Microwave-Driven CNTs on Carbonized Cigarette Filter Waste for Sustainable Energy Storage.

Microplastic pollution represents a significant global environmental issue, with cigarette filters being a major contributor due to their slow biodegradation. To address this issue while creating valuable materials, we developed a novel approach to synthesize nitrogen-doped carbon nanotubes on carbonized cigarette filter powder (NCNT@cCFP) using a microwave irradiation and nickel-catalyzed process. The successful incorporation of nitrogen (~6.

3D markerless tracking of speech movements with submillimeter accuracy.

Speech movements are highly complex and require precise tuning of both spatial and timing of oral articulators to support intelligible communication. These properties also make measurement of speech movements challenging, often requiring extensive physical sensors placed around the mouth and face that are not easily tolerated by certain populations such as young children. Recent progress in machine learning-based markerless facial landmark tracking technology demonstrated its potential to provide lip tracking without the need for physical sensors, but whether such technology can provide submillimeter precision and accuracy in 3D remains unknown.

Fabrication of Biaxially Aligned Nanofibers via Insulating Block-Assisted Electrospinning and Their Applications.

Electrospinning is a well-established and widely adopted process for producing fine and continuous nanofiber networks. Electrospun nanofibers have gained significant attention owing to their advantages, including nanoscale fiber uniformity, tunable pore size with bimodal distribution, and versatility in integrating various inorganic and organic compositions. Recently, considerable efforts have been made to align nanofibers and enhance their functionality with improved mechanical properties, faster charge transport, and more efficient mass transport in well-organized spatial structures.

Prevalence of Mortality and Vascular Complications in Older Patients with Diabetes in Korea.

Background: This study investigated the prevalence of diabetes mellitus (DM) and impaired fasting glucose, as well as their management and comorbidities among older Korean adults.

Methods: Data from 269,447 individuals aged 65 years and older from the Korean National Health Insurance Service between 2000 and 2019 were analyzed to evaluate trends in DM prevalence, healthcare utilization, mortality, and complications.

Results: Among 269,447 individuals, 18.

Quantification of salmon nasal cartilage extracts using liquid chromatography-tandem mass spectrometry.

Proteoglycans are high molecular weight glycoproteins with potential benefits in preventing osteoarthritis, reducing inflammation, enhancing immune function, and promoting skin health. Aggrecan, a key proteoglycan with glycosaminoglycan (GAG) chains, poses challenges in accurate quantification due to its complex structure. We hypothesize that by selecting target peptides from core proteins that exclude post-translational modifications such as GAG attachment, proteoglycans can be analyzed with high sensitivity and accuracy.

Deep learning-based prediction of possibility for immediate implant placement using panoramic radiography.

In this study, we investigated whether deep learning-based prediction of immediate implant placement is possible. Panoramic radiographs of 201 patients with 874 teeth (Group 1: 440 teeth difficult to place implant immediately after extraction, Group 2: 434 teeth possible of immediate implant placement after extraction) for extraction were evaluated for the training and testing of a deep learning model. DenseNet121, ResNet18, ResNet101, ResNeXt101, InceptionNetV3, and InceptionResNetV2 were used.

Peripheral blood proteome biomarkers distinguish immunosuppressive features of cancer progression.

Immune status critically affects cancer progression and therapy responses. This study aimed to identify plasma proteome changes in immunosuppressive cancer and potential biomarkers predicting systemic immunosuppression. Mouse models of syngeneic breast tumors (benign 67NR and malignant 4T1) were used to collect plasma samples.

Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection.

Employing two standard mammography views is crucial for radiologists, providing comprehensive insights for reliable clinical evaluations. This study introduces paired mammogram view based-network(PMVnet), a novel algorithm designed to enhance breast lesion detection by integrating relational information from paired whole mammograms, addressing the limitations of current methods. Utilizing 1,636 private mammograms, PMVnet combines cosine similarity and the squeeze-and-excitation method within a U-shaped architecture to leverage correlated information.

Radiological safety assessment of monazite industry worker due to NORM inhalation.

Monazite is used as a raw material for the production of rare earth elements. The workers in the monazite industry are exposed to radiation from the raw material. Therefore, internal radiation dose should be assessed for radiological safety assessment of the workers.

Comparative Analysis of U-Net and U-Net3 + for Retinal Exudate Segmentation: Performance Evaluation Across Regions.

Diabetic retinopathy is a major complication of diabetes, with its prevalence nearly doubling to approximately 10.5% by 2021. Exudates, the characteristic lesions of diabetic retinopathy, are crucial for assessing disease progression and severity.

Corrigendum: Enhancing enzyme-mediated cellulose hydrolysis by incorporating acid groups onto the lignin during biomass pretreatment.

[This corrects the article DOI: 10.3389/fbioe.2020.

Effects of blood pressure lowering in patients treated with intravenous thrombolysis before endovascular thrombectomy.

Background: The effects of blood pressure (BP) lowering in patients treated with intravenous tissue plasminogen activator (IV tPA) before endovascular thrombectomy (EVT) are unclear.

Aims: This study aims to investigate whether intensive and conventional BP management affects outcomes differently, depending on IV tPA administration before EVT.

Methods: In this subgroup analysis of the Outcome in Patients Treated with Intra-Arterial Thrombectomy-Optimal Blood Pressure Control (OPTIMAL-BP; ClinicalTrials.

Using Artificial Intelligence in the Comprehensive Management of Spinal Cord Injury.

Spinal cord injury (SCI) frequently results in persistent motor, sensory, or autonomic dysfunction, and the outcomes are largely determined by the location and severity of the injury. Despite significant technological progress, the intricate nature of the spinal cord anatomy and the difficulties associated with neuroregeneration make full recovery from SCI uncommon. This review explores the potential of artificial intelligence (AI), with a particular focus on machine learning, to enhance patient outcomes in SCI management.

Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types.

Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheless, the broader categorization of renal tissue into non-neoplastic normal tissue, benign tumor and malignant tumor remains understudied.

Prediction of cccDNA dynamics in hepatitis B patients by a combination of serum surrogate markers.

Quantification of intrahepatic covalently closed circular DNA (cccDNA) is a key for evaluating an elimination of hepatitis B virus (HBV) in infected patients. However, quantifying cccDNA requires invasive methods such as a liver biopsy, which makes it impractical to access the dynamics of cccDNA in patients. Although HBV RNA and HBV core-related antigens (HBcrAg) have been proposed as surrogate markers for evaluating cccDNA activity, they do not necessarily estimate the amount of cccDNA.

Developing a Robust Fuzzy Inference Algorithm in a Dog Disease Pre-Diagnosis System for Casual Owners.

While the pet market is continuously rapidly increasing in Korea, pet dog owners feel uncomfortable in coping with pet dog's health problems in time. In this paper, we propose a pre-diagnosis system based on neuro-fuzzy learning, enabling non-expert users to monitor their pets' health by inputting observed symptoms. To develop such a system, we form a disease-symptom database based on several textbooks with veterinarians' guidance and filtering.

Unveiling Trade-Off and Synergy in Simultaneous Removal of NO, CO, and NH on Mixed Metal Oxide Nanostructure Catalysts.

The simultaneous removal reaction (SRR) is a pioneering approach for achieving the simultaneous removal of anthropogenic NO and CO pollutants through catalytic reactions. To facilitate this removal across diverse industrial fields, it is crucial to understand the trade-offs and synergies among the multiple reactions involved in the SRR process. In this study, we developed mixed metal oxide nanostructures derived from layered double hydroxides as catalysts for the SRR, achieving high catalytic conversions of 93.