Effect of coffee roasting level on tooth discoloration.

Purpose: Coffee consumption is a well-known contributor to tooth discoloration, and the extent of staining is influenced by the chemical composition of the coffee. This study investigated the associations of coffee roasting level, chlorogenic acid (CGA) content, absorbance level, and their combined effects with tooth discoloration.

Methods: Bovine tooth enamel specimens were immersed in light, medium, and dark roasts of four coffee types (two Arabica and two Robusta coffees) for 72 h.

Inhibiting lipid droplet biogenesis enhances host protection against hypervirulent Klebsiella pneumoniae infections.

Hypervirulent Klebsiella pneumoniae (hvKp), an emerging Kp subtype, has become a serious global pathogen. However, the information regarding host interactions and innate immune responses during hvKp infection is limited. Here, we found that hvKp clinical strains increased triacylglycerol synthesis, resulting in lipid droplets (LDs) formation via the mammalian target of rapamycin signaling pathway in RAW264.

Deep Learning Models for Anatomical Location Classification in Esophagogastroduodenoscopy Images and Videos: A Quantitative Evaluation with Clinical Data.

Background/objectives: During gastroscopy, accurately identifying the anatomical locations of the gastrointestinal tract is crucial for developing diagnostic aids, such as lesion localization and blind spot alerts.

Methods: This study utilized a dataset of 31,403 still images from 1000 patients with normal findings to annotate the anatomical locations within the images and develop a classification model. The model was then applied to videos of 20 esophagogastroduodenoscopy procedures, where it was validated for real-time location prediction.

Effects of the Strength, Assistance in walking, Rise from a chair, Climb stairs, and Falls score on postoperative clinical outcomes following colorectal cancer surgery: a retrospective study.

Background/objectives: Sarcopenia has known negative effects on clinical and oncological outcomes in patients with colorectal cancer (CRC). The use of the Strength, Assistance in walking, Rise from a chair, Climb stairs, and Falls (SARC-F) questionnaire to determine the effects of sarcopenia on postoperative complications of CRC has not been reported to date. Therefore, this study aimed to investigate the relationship of SARC-F score with clinicopathologic outcomes after CRC surgery.

Automated Association for Osteosynthesis Foundation and Orthopedic Trauma Association classification of pelvic fractures on pelvic radiographs using deep learning.

High-energy impacts, like vehicle crashes or falls, can lead to pelvic ring injuries. Rapid diagnosis and treatment are crucial due to the risks of severe bleeding and organ damage. Pelvic radiography promptly assesses fracture extent and location, but struggles to diagnose bleeding.

The autophagy-targeting compound V46 enhances antimicrobial responses to Mycobacteroides abscessus by activating transcription factor EB.

Mycobacteroides abscessus (Mabc) is a rapidly growing nontuberculous mycobacterium that poses a considerable challenge as a multidrug-resistant pathogen causing chronic human infection. Effective therapeutics that enhance protective immune responses to Mabc are urgently needed. This study introduces trans-3,5,4'-trimethoxystilbene (V46), a novel resveratrol analogue with autophagy-activating properties and antimicrobial activity against Mabc infection, including multidrug-resistant strains.

Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography.

Background: We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography.

Methods: Panoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0.

Intraoperative detection of parathyroid glands using artificial intelligence: optimizing medical image training with data augmentation methods.

Background: Postoperative hypoparathyroidism is a major complication of thyroidectomy, occurring when the parathyroid glands are inadvertently damaged during surgery. Although intraoperative images are rarely used to train artificial intelligence (AI) because of its complex nature, AI may be trained to intraoperatively detect parathyroid glands using various augmentation methods. The purpose of this study was to train an effective AI model to detect parathyroid glands during thyroidectomy.

Re-hospitalization factors and economic characteristics of urinary tract infected patients using machine learning.

Objective: Urinary tract infection is one of the most prevalent bacterial infectious diseases in outpatient treatment, and 50-80% of women experience it more than once, with a recurrence rate of 40-50% within a year; consequently, preventing re-hospitalization of patients is critical. However, in the field of urology, no research on the analysis of the re-hospitalization status for urinary tract infections using machine learning algorithms has been reported to date. Therefore, this study uses various machine learning algorithms to analyze the clinical and nonclinical factors related to patients who were re-hospitalized within 30 days of urinary tract infection.

Allergy Wheal and Erythema Segmentation Using Attention U-Net.

The skin prick test (SPT) is a key tool for identifying sensitized allergens associated with immunoglobulin E-mediated allergic diseases such as asthma, allergic rhinitis, atopic dermatitis, urticaria, angioedema, and anaphylaxis. However, the SPT is labor-intensive and time-consuming due to the necessity of measuring the sizes of the erythema and wheals induced by allergens on the skin. In this study, we used an image preprocessing method and a deep learning model to segment wheals and erythema in SPT images captured by a smartphone camera.

Airway and Airway Obstruction Site Segmentation Study Using U-Net with Drug-Induced Sleep Endoscopy Images.

Obstructive sleep apnea is characterized by a decrease or cessation of breathing due to repetitive closure of the upper airway during sleep, leading to a decrease in blood oxygen saturation. In this study, employing a U-Net model, we utilized drug-induced sleep endoscopy images to segment the major causes of airway obstruction, including the epiglottis, oropharynx lateral walls, and tongue base. The evaluation metrics included sensitivity, specificity, accuracy, and Dice score, with airway sensitivity at 0.

Interfacial Engineering of Mn Porphyrin/Au Electrodes for Identifying MnO as the Active Species under Oxygen Evolution Reactions.

Understanding the influence of surface structural features at a molecular level is beneficial in guiding an electrode's mechanistic proposals for electrocatalytic reactions. The relationship between structural stability and catalytic activity significantly impacts reaction performance, even though atomistic knowledge of active sites remains a topic of discussion. In this context, this work presents scanning tunneling microscopy (STM) results for the highly ordered arrangement of manganese porphyrin molecules on a Au(111) surface; STM allows us to monitor active sites at a molecular level to focus on long-standing issues with the electrocatalytic process, especially the exact nature of the real active sites at the interfaces.

Secular changes in dental development of Korean children aged 4 to 16 years over a 10-year period.

This study evaluated 10-year secular changes in dental maturity and dental development among Korean children. A retrospective analysis of panoramic radiograph samples from Korean children (4-16 years old) taken in 2010 and 2020 was conducted. The 2010 group consisted of 3491 radiographs (1970 boys and 1521 girls), and the 2020 group included 5133 radiographs (2825 boys and 2308 girls).

Radiomics and machine learning analysis of liver magnetic resonance imaging for prediction and early detection of tumor response in colorectal liver metastases.

Purpose: The aim of this study was to demonstrate the effectiveness of a machine learning-based radiomics model for distinguishing tumor response and overall survival in patients with unresectable colorectal liver metastases (CRLM) treated with targeted biological therapy.

Methods: We prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy.

Deep Learning-Based Nystagmus Detection for BPPV Diagnosis.

In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystagmus detection models. Among the four deep learning architectures used in this study, the CNN1D model proposed as a nystagmus detection model demonstrated the best performance, exhibiting a sensitivity of 94.

Alterations in pH of Coffee Bean Extract and Properties of Chlorogenic Acid Based on the Roasting Degree.

Factors influencing the sour taste of coffee and the properties of chlorogenic acid are not yet fully understood. This study aimed to evaluate the impact of roasting degree on pH-associated changes in coffee bean extract and the thermal stability of chlorogenic acid. Coffee bean extract pH decreased up to a chromaticity value of 75 but increased with higher chromaticity values.

Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs.

Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock. Pelvic radiography can quickly diagnose pelvic fractures, and the Association for Osteosynthesis Foundation and Orthopedic Trauma Association (AO/OTA) classification system is useful for evaluating pelvic fracture instability. This study aimed to develop a radiomics-based machine-learning algorithm to quickly diagnose fractures on pelvic X-ray and classify their instability.

Deep-learning-based pelvic automatic segmentation in pelvic fractures.

With the recent increase in traffic accidents, pelvic fractures are increasing, second only to skull fractures, in terms of mortality and risk of complications. Research is actively being conducted on the treatment of intra-abdominal bleeding, the primary cause of death related to pelvic fractures. Considerable preliminary research has also been performed on segmenting tumors and organs.

Image segmentation of impacted mesiodens using deep learning.

This study aimed to evaluate the performance of deep learning algorithms for the classification and segmentation of impacted mesiodens in pediatric panoramic radiographs. A total of 850 panoramic radiographs of pediatric patients (aged 3-9 years) was included in this study. The U-Net semantic segmentation algorithm was applied for the detection and segmentation of mesiodens in the upper anterior region.

Fully automated deep learning approach to dental development assessment in panoramic radiographs.

Background: Dental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian's method using deep learning.

Methods: The study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021.

Deep Learning-Based Multi-Class Segmentation of the Paranasal Sinuses of Sinusitis Patients Based on Computed Tomographic Images.

Accurate paranasal sinus segmentation is essential for reducing surgical complications through surgical guidance systems. This study introduces a multiclass Convolutional Neural Network (CNN) segmentation model by comparing four 3D U-Net variations-normal, residual, dense, and residual-dense. Data normalization and training were conducted on a 40-patient test set (20 normal, 20 abnormal) using 5-fold cross-validation.

Correlation between dental and skeletal maturity in Korean children based on dental maturity percentile: a retrospective study.

Background: The correlation between dental maturity and skeletal maturity has been proposed, but its clinical application remains challenging. Moreover, the varying correlations observed in different studies indicate the necessity for research tailored to specific populations.

Aim: To compare skeletal maturity in Korean children with advanced and delayed dental maturity using dental maturity percentile.