Cancer Testis Antigen, NOL4, Is an Immunogenic Antigen Specifically Expressed in Small-Cell Lung Cancer.

To identify cancer/testis (CT) antigens and immunogenic proteins, immunoscreening of testicular and small-cell lung cancer cell line NCI-H889 cDNA libraries was performed using serum obtained from a small-cell lung cancer (SCLC) patient. We obtained 113 positive cDNA clones comprised of 74 different genes, designated KP-SCLC-1 through KP-SCLC-74. Of these genes, 59 genes were found to be related to cancers by EMBASE analysis.

The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment.

Recently, researchers have built new deep learning (DL) models using a single image modality to diagnose age-related macular degeneration (AMD). Retinal fundus and optical coherence tomography (OCT) images in clinical settings are the most important modalities investigating AMD. Whether concomitant use of fundus and OCT data in DL technique is beneficial has not been so clearly identified.

Increased fragility fracture risk in Korean women who snore: a 10-year population-based prospective cohort study.

Background: Snoring is frequently associated with obstructive sleep apnea (OSA). Previous studies have shown that bone mineral density was significantly lower in patients with OSA than in controls; however, these studies did not focus on fractures. Fragility fractures can lead to long-term disabilities and a decrease in quality of life.

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study.

Background: Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA.

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification.

Development of a Screening Tool for Predicting Adverse Outcomes of Gestational Diabetes Mellitus: A Retrospective Cohort Study.

Gestational diabetes mellitus (GDM) is a common disease in pregnancy causing maternal and fetal complications. To prevent these adverse outcomes, optimal screening and diagnostic criteria must be adequate, timely, and efficient. This study suggests a novel approach that is practical, efficient, and patient- and clinician-friendly in predicting adverse outcomes of GDM.

Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning.

Objectives: Serous borderline ovarian tumors (SBOTs) are a subtype of serous ovarian carcinoma with atypical proliferation. Frozen-section diagnosis has been used as an intraoperative diagnosis tool in supporting the fertility-sparing surgery by diagnosing SBOTs with accuracy of 48% to 79%. Using DNA microarray technology, we designed multicategory classification models to support frozen-section diagnosis within 30 minutes.

Exposure of Surgeons to Magnetic Fields during Laparoscopic and Robotic Gynecologic Surgeries.

Study Objective: To measure and compare levels of extremely-low-frequency magnetic field (ELF-MF) exposure to surgeons during laparoscopic and robotic gynecologic surgeries.

Design: Prospective case-control study.

Design Classification: Canadian Task Force I.

Safety of Exposure From Extremely Low Frequency Magnetic Fields During Prenatal Ultrasound Examinations in Clinicians and Pregnant Women.

Investigations into the safety of ultrasonography in pregnancy have focused on the potential harm of ultrasound itself. However, no data have been published regarding the electromagnetic fields that ultrasound devices might produce. This study is the first to measure extremely low-frequency magnetic field (ELF-MF) exposure of clinicians and pregnant women during prenatal ultrasound examinations in the examination room from 2 different ultrasound devices and compare them with ELF-MFs during patient consultation in the consulting room.

Patient exposure to extremely low-frequency magnetic fields during laparoscopic and robotic surgeries.

Background: Laparoscopic and robotic surgeries require many electronic devices, and the hazard of extremely low-frequency magnetic fields (ELF-MFs) from these devices to humans remains uncertain. This study aimed to measure and compare patients' exposure levels to ELF-MFs in laparoscopic and robotic surgeries.

Methods: The intensity of ELF-MF exposure to patients was measured every 10 s during 30 laparoscopic surgeries and 30 robotic surgeries using portable ELF-MF measuring devices with logging capabilities.

Heart Rate Detection During Sleep Using a Flexible RF Resonator and Injection-Locked PLL Sensor.

Novel nonintrusive technologies for wrist pulse detection have been developed and proposed as systems for sleep monitoring using three types of radio frequency (RF) sensors. The three types of RF sensors for heart rate measurement on wrist are a flexible RF single resonator, array resonators, and an injection-locked PLL resonator sensor. To verify the performance of the new RF systems, we compared heart rates between presleep time and postsleep onset time.

Parathyroid hormone, calcium, and sodium bridging between osteoporosis and hypertension in postmenopausal Korean women.

The coexistence of osteoporosis and hypertension, which are considered distinct diseases, has been widely reported. In addition, daily intake of calcium and sodium, as well as parathyroid hormone levels (PTH), is known to be associated with osteoporosis and hypertension. This study aimed to determine the association of low calcium intake, high sodium intake, and PTH levels with osteoporosis and hypertension in postmenopausal Korean women.

Exposure of surgeons to extremely low-frequency magnetic fields during laparoscopic and robotic surgeries.

The development of new medical electronic devices and equipment has increased the use of electrical apparatuses in surgery. Many studies have reported the association of long-term exposure to extremely low-frequency magnetic fields (ELF-MFs) with diseases or cancer. Robotic surgery has emerged as an alternative tool to overcome the disadvantages of conventional laparoscopic surgery.

Multicategory classification of 11 neuromuscular diseases based on microarray data using support vector machine.

We applied multicategory machine learning methods to classify 11 neuromuscular disease groups and one control group based on microarray data. To develop multicategory classification models with optimal parameters and features, we performed a systematic evaluation of three machine learning algorithms and four feature selection methods using three-fold cross validation and a grid search. This study included 114 subjects of 11 neuromuscular diseases and 31 subjects of a control group using microarray data with 22,283 probe sets from the National Center for Biotechnology Information (NCBI).

Classification of serous ovarian tumors based on microarray data using multicategory support vector machines.

Ovarian cancer, the most fatal of reproductive cancers, is the fifth leading cause of death in women in the United States. Serous borderline ovarian tumors (SBOTs) are considered to be earlier or less malignant forms of serous ovarian carcinomas (SOCs). SBOTs are asymptomatic and progression to advanced stages is common.

Screening for pre-diabetes using support vector machine model.

The global prevalence of diabetes is rapidly increasing. Studies support screening and interventions for pre-diabetes, which results in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for pre-diabetes that could assist with decreasing the prevalence of diabetes through early identification and subsequent interventions.

Prediction of ATLS hypovolemic shock class in rats using the perfusion index and lactate concentration.

It is necessary to quickly and accurately determine Advanced Trauma Life Support (ATLS) hemorrhagic shock class for triage in cases of acute hemorrhage caused by trauma. However, the ATLS classification has limitations, namely, with regard to primary vital signs. This study identified the optimal variables for appropriate triage of hemorrhage severity, including the peripheral perfusion index and serum lactate concentration in addition to the conventional primary vital signs.

Screening for prediabetes using machine learning models.

The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes.

Effects of short-term radiation emitted by WCDMA mobile phones on teenagers and adults.

Background: With the rapid increasing use of third generation (3 G) mobile phones, social concerns have arisen concerning the possible health effects of radio frequency-electromagnetic fields (RF-EMFs) emitted by wideband code division multiple access (WCDMA) mobile phones in humans. The number of people, who complain of various symptoms such as headache, dizziness, and fatigue, has also increased. Recently, the importance of researches on teenagers has been on the rise.

Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning.

Purpose: A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools.

Materials And Methods: We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys.

Interpretation of movement during stair ascent for predicting severity and prognosis of knee osteoarthritis in elderly women using support vector machine.

Several studies have demonstrated that pathologic movement changes in knee osteoarthritis (OA) may contribute to disease progression. The aim of this study was to investigate the association between movement changes during stair ascent and pain, radiographic severity, and prognosis of knee OA in the elderly women using machine learning (ML) over a seven-year follow-up period. Eighteen elderly female patients with knee OA and 20 healthy controls were enrolled.

Osteoporosis risk prediction using machine learning and conventional methods.

A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1).

Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques.

This study sought to determine a mortality prediction model that could be used for triage in the setting of acute hemorrhage from trauma. To achieve this aim, various machine learning techniques were applied using the rat model in acute hemorrhage. Thirty-six anesthetized rats were randomized into three groups according to the volume of controlled blood loss.