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. Three of these antigens, including KP-SCLC-29 (NOL4), KP-SCLC-59 (CCDC83), and KP-SCLC-69 (KIF20B), were CT antigens. RT-PCR and western blot analysis showed that NOL4 was frequently present in small-cell lung cancer cell lines (8/9, 8/9). In addition, NOL4 mRNA was weakly, or at a low frequency, or not detected in various cancer cell lines. Our results reveal that NOL4 was expressed at protein levels in small-cell lung cancer tissues (10/10) but not detected in lung adenocarcinoma and squamous cell carcinoma by immunohistochemical analysis. Serological response to NOL4 was also evaluated by western blot assay using NOL4 recombinant protein. A humoral response against NOL4 proteins was detected in 75% (33/44) of small-cell lung cancer patients and in 65% (13/20) of healthy donors by a serological western blot assay. These data suggest that NOL4 is a specific target that may be useful for diagnosis and immunotherapy in SCLC.

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. This experimental analysis used OCT and fundus image data of postmortems from the Project Macula. The DL based on OCT, fundus, and combination of OCT and fundus were invented to diagnose AMD. These models consisted of pre-trained VGG-19 and transfer learning using random forest. Following the data augmentation and training process, the DL using OCT alone showed diagnostic efficiency with area under the curve (AUC) of 0.906 (95% confidence interval, 0.891-0.921) and 82.6% (81.0-84.3%) accuracy rate. The DL using fundus alone exhibited AUC of 0.914 (0.900-0.928) and 83.5% (81.8-85.0%) accuracy rate. Combined usage of the fundus with OCT increased the diagnostic power with AUC of 0.969 (0.956-0.979) and 90.5% (89.2-91.8%) accuracy rate. The Delong test showed that the DL using both OCT and fundus data outperformed the DL using OCT alone (P value < 0.001) and fundus image alone (P value < 0.001). This multimodal random forest model showed even better performance than a restricted Boltzmann machine (P value = 0.002) and deep belief network algorithms (P value = 0.042). According to Duncan's multiple range test, the multimodal methods significantly improved the performance obtained by the single-modal methods. In this preliminary study, a multimodal DL algorithm based on the combination of OCT and fundus image raised the diagnostic accuracy compared to this data alone. Future diagnostic DL needs to adopt the multimodal process to combine various types of imaging for a more precise AMD diagnosis. Graphical abstract The basic architectural structure of the tested multimodal deep learning model based on pre-trained deep convolutional neural network and random forest using the combination of OCT and fundus image.

Ten-year prediction of suicide death using Cox regression and machine learning in a nationwide retrospective cohort study in South Korea.

BACKGROUND: Death by suicide is a preventable public health concern worldwide. The aim of this study is to investigate the probability of suicide death using baseline characteristics and simple medical facility visit history data using Cox regression, support vector machines (SVMs), and deep neural networks (DNNs). METHOD: This study included 819,951 subjects in the National Health Insurance Service (NHIS)-Cohort Sample Database from 2004 to 2013. The dataset was divided randomly into two independent training and validation groups. To improve the performance of predicting suicide death, we applied SVM and DNN to the same training set as the Cox regression model. RESULTS: Among the study population, 2546 people died by intentional self-harm during the follow-up time. Sex, age, type of insurance, household income, disability, and medical records of eight ICD-10 codes (including mental and behavioural disorders) were selected by a Cox regression model with backward stepwise elimination. The area of under the curve (AUC) of Cox regression (0.688), SVM (0.687), and DNN (0.683) were approximately the same. The group with top .5% of predicted probability had hazard ratio of 26.21 compared to that with the lowest 10% of predicted probability. LIMITATIONS: This study is limited by the lack of information on suicidal ideation and attempts, other potential covariates such as information of medication and subcategory ICD-10 codes. Moreover, predictors from the prior 12-24 months of the date of death could be expected to show better performances than predictors from up to 10 years ago. CONCLUSIONS: We suggest a 10-year probability prediction model for suicide death using general characteristics and simple insurance data, which are annually conducted by the Korean government. Suicide death prevention might be enhanced by our prediction model.

Risk factors of suicide attempt among people with suicidal ideation in South Korea: a cross-sectional study.

BACKGROUND: Suicide is a serious public health concern worldwide, and the fourth leading cause of death in Korea. Few studies have focused on risk factors for suicide attempt among people with suicidal ideation. The aim of the present study was to investigate the risk factors and develop prediction models for suicide attempt among people with suicidal ideation in the Korean population. METHOD: This study included 1567 men and 3726 women aged 20 years and older who had suicidal ideation from the Korea National Health and Nutrition Examination Survey from 2007 to 2012. Among them, 106 men and 188 women attempted suicide. Multivariate logistic regression analysis with backward stepwise elimination was performed to find risk factors for suicide attempt. Sub-group analysis, dividing participants into under 50 and at least 50 years old was also performed. RESULTS: Among people with suicidal ideation, age, education, cancer, and depressive disorder were selected as risk factors for suicide attempt in men. Age, education, national basic livelihood security, daily activity limitation, depressive disorder, stress, smoking, and regular exercise were selected in women. Area under curves of our prediction models in men and women were 0.728 and 0.716, respectively. CONCLUSIONS: It is important to pay attention to populations with suicidal ideation and the risk factors mentioned above. Prediction models using the determined risk factors could be useful to detect high-risk groups early for suicide attempt among people with suicidal ideation. It is necessary to develop specific action plans for these high-risk groups to prevent suicide.

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. The present study aimed to investigate the association between snoring and fragility fractures. METHODS: This study included 2969 men and 3220 women aged 40 years and older from the Ansung and Ansan cohort studies in Korea. During a 10-year follow-up period, 129 and 273 fracture cases were reported in men and women, respectively. RESULTS: Severe snoring (6-7 nights per week or sleep disturbance by snoring in the next room) was a statistically significant risk factor for fracture (p = 0.006, hazard ratio 1.68, 95% confidence interval 1.16-2.43) after adjusting for covariates related to fragility fracture in women. However, both snoring and severe snoring groups did not show significant associations with the fracture risk in men. CONCLUSIONS: Thus, information on the frequency of snoring in women may improve the accuracy of fragility fracture risk prediction, which can help in deciding whether intervention or treatment is necessary.

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. METHODS: The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models. RESULTS: The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001). CONCLUSIONS: The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk.

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. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

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. The authors conducted a retrospective cohort study via medical record review of patients admitted between March 2001 and April 2013 at the Severance Hospital, Seoul, South Korea. Patients diagnosed by a conventional 2-step method were evaluated according to the presence of adverse outcomes (neonatal hypoglycemia, hyperbilirubinemia, and hyperinsulinemia; admission to the neonatal intensive care unit; large for gestational age; gestational insulin therapy; and gestational hypertension). Of 802 women who had an abnormal 50-g, 1-hour glucose challenge test, 306 were diagnosed with GDM and 496 did not have GDM (false-positive group). In the GDM group, 218 women (71.2%) had adverse outcomes. In contrast, 240 women (48.4%) in the false-positive group had adverse outcomes. Women with adverse outcomes had a significantly higher body mass index (BMI) at entry (P = 0.03) and fasting blood glucose (FBG) (P = 0.03). Our logistic regression model derived from 2 variables, BMI at entry and FBG, predicted GDM adverse outcome with an area under the curve of 0.642, accuracy of 61.3%, sensitivity of 57.2%, and specificity of 66.9% compared with the conventional 2-step method with an area under the curve of 0.610, accuracy of 59.1%, sensitivity of 47.6%, and specificity of 74.4%. Our model performed better in predicting GDM adverse outcomes than the conventional 2-step method using only BMI at entry and FBG. Moreover, our model represents a practical, inexpensive, efficient, reproducible, easy, and patient- and clinician-friendly approach.

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. MATERIALS AND METHODS: We systematically evaluated 6 machine learning algorithms and 3 feature selection methods using 5-fold cross-validation and a grid search on microarray data obtained from the National Center for Biotechnology Information. To validate the models and selected biomarkers, expression profiles were analyzed in tissue samples obtained from the Yonsei University College of Medicine. RESULTS: The best accuracy of the optimal machine learning model was 97.3%. In addition, 5 features, including the expression of the putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and serous ovarian carcinoma groups. Different expression levels of SNTN and AOX1 were validated by real-time quantitative reverse-transcription polymerase chain reaction, Western blotting, and immunohistochemistry. A multinomial logistic regression model using SNTN and AOX1 alone was used to construct a simple-to-use equation that gave a diagnostic test accuracy of 91.9%. CONCLUSIONS: We identified 2 biomarkers, SNTN and AOX1, that are likely involved in the pathogenesis and progression of ovarian tumors. An accurate diagnosis of ovarian tumor subclasses by application of the equation in conjunction with expression analysis of SNTN and AOX1 would offer a new accurate diagnosis tool in conjunction with frozen-section diagnosis within 30 minutes.

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. RESULTS: The mean ELF-MF exposures were 0.11 ± 0.07 µT for laparoscopic surgeries and 0.12 ± 0.10 µT for robotic surgeries. There were no significant differences between the laparoscopic and robotic surgeries. CONCLUSIONS: Patients' mean ELF-MF exposure levels in laparoscopic and robotic surgeries were lower than 0.2 µT, which is considered safe according to previous studies. However, because many medical devices have been implemented for multiple purposes in hospitals, the MF environment in hospitals regarding patient health should not be overlooked. Copyright © 2015 John Wiley & Sons, Ltd.

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. SETTING: Gynecologic surgeries at the Yonsei University Health System in Seoul, Korea from July to October in 2014. PATIENTS: Ten laparoscopic gynecologic surgeries and 10 robotic gynecologic surgeries. INTERVENTION: The intensity of ELF-MF exposure to surgeons was measured every 4 seconds during 10 laparoscopic gynecologic surgeries and 10 robotic gynecologic surgeries using portable ELF-MF measuring devices with logging capability. MEASUREMENT AND MAIN RESULTS: The mean ELF-MF exposures were .1 ± .1 mG for laparoscopic gynecologic surgeries and .3 ± .1 mG for robotic gynecologic surgeries. ELF-MF exposure levels to surgeons during robotic gynecologic surgery were significantly higher than those during laparoscopic gynecologic surgery (p < .001) after adjustment for duration of measurement. CONCLUSION: The present study demonstrated low levels of ELF-MF exposure to surgeons during robotic gynecologic surgery and conventional laparoscopic surgery, hoping to alleviate concerns regarding the hazards of MF exposure posed to surgeons and hospital staff.

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.The ELF-MF intensities that clinicians and pregnant women were exposed to were measured every 10 seconds for 40 prenatal ultrasound examinations using Philips iU22 or Accuvix V20 Prestige machines and 20 patient consultations in a consulting room using portable ELF-MF measurement devices. The mean ELF-MF exposure of both clinicians and pregnant women was 0.18 ± 0.06 mG during prenatal ultrasound examination. During patient consultation, the mean ELF-MF exposures of clinicians and pregnant women were 0.10 ± 0.01 and 0.11 ± 0.01 mG, respectively. Mean ELF-MF exposures during prenatal ultrasound examination were significantly higher than those during patient consultations (P < 0.001 by Mann-Whitney U test).Our results provide basic reference data on the ELF-MF exposure of both clinicians and pregnant women during prenatal ultrasound monitoring from 2 different ultrasound devices and patient consultation, all of which were below 2 mG, the most stringent level considered safe in many studies, thus relieving any anxiety of clinicians and pregnant women regarding potential risks of ELF-MFs.

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. Heart rates of ten subjects were measured using the RF systems during sleep. All three RF devices detected heart rates at 0.2 to 1 mm distance from the skin of the wrist over clothes made of cotton fabric. The wrist pulse signals of a flexible RF single resonator were consistent with the signals obtained by a portable piezoelectric transducer as a reference. Then, we confirmed that the heart rate after sleep onset time significantly decreased compared to before sleep. In conclusion, the RF system can be utilized as a noncontact nonintrusive method for measuring heart rates during sleep.

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. However, there has been no report regarding how much ELF-MF surgeons are exposed to during laparoscopic and robotic surgeries. In this observational study, we aimed to measure and compare the ELF-MFs that surgeons are exposed to during laparoscopic and robotic surgery.The intensities of the ELF-MFs surgeons are exposed to were measured every 4 seconds for 20 cases of laparoscopic surgery and 20 cases of robotic surgery using portable ELF-MF measuring devices with logging capability.The mean ELF-MF exposures were 0.6 ±â€Š0.1 mG for laparoscopic surgeries and 0.3 ±â€Š0.0 mG for robotic surgeries (significantly lower with P < 0.001 by Mann-Whitney U test).Our results show that the ELF-MF exposure levels of surgeons in both robotic and conventional laparoscopic surgery were lower than 2 mG, which is the most stringent level considered safe in many studies. However, we should not overlook the effects of long-term ELF-MF exposure during many surgeries in the course of a surgeon's career.

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. Data for postmenopausal Korean women aged 50 years or older were obtained from the Korea National Health and Nutrition Examination Survey 2008-2011. Osteoporosis was diagnosed using dual energy X-ray absorptiometry, while hypertension was diagnosed using blood pressure data. The odds ratios for osteoporosis and hypertension were calculated using logistic regression analysis for quartiles of the daily calcium intake, daily sodium intake, and PTH levels. Women with hypertension had a high coexistence of osteoporosis (43.6 vs. 36.5 %; P = 0.022), and vice versa (21.1 vs. 16.6 %; P = 0.022). PTH was significantly associated with osteoporosis and hypertension, and a high intake of calcium was strongly correlated with a low incidence of osteoporosis. This is the first study to report the characteristics of postmenopausal Korean women who have high dietary sodium intake and low dietary calcium intake, in association with the incidence of osteoporosis and hypertension. Osteoporosis and hypertension were strongly associated with each other, and PTH appears to be a key mediator of both diseases, suggesting a possible pathogenic link.

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. To predict the four ATLS classes, three popular machine learning algorithms with four feature selection methods for multicategory classification were applied to a rat model of acute hemorrhage. A total of 78 anesthetized rats were divided into four groups for ATLS classification based on blood loss (in percent). The support vector machine one-versus-one model with the Kruskal-Wallis feature selection method performed best, with 80.8% accuracy, relative classifier information of 0.629, and a kappa index of 0.732. The new hemorrhage-induced severity index (lactate concentration/perfusion index), diastolic blood pressure, mean arterial pressure, and the perfusion index were selected as the optimal variables for predicting the four ATLS classes by support vector machine one-versus-one with the Kruskal-Wallis method. These four variables were also selected for binary classification to predict ATLS classes I and II versus III and IV for blood transfusion requirement. The suggested ATLS classification system would be helpful to first responders by indicating the severity of patients, allowing physicians to prepare suitable resuscitation before hospital arrival, which could hasten treatment initiation.

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. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n = 4685) were used for training and internal validation, while data from KNHANES 2011 (n = 4566) were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729) and the screening score model (0.712), respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.

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. However, very few provocation studies have examined the health effects of WCDMA mobile phone radiation on teenagers. METHODS: In this double-blind study, two volunteer groups of 26 adults and 26 teenagers were simultaneously investigated by measuring physiological changes in heart rate, respiration rate, and heart rate variability for autonomic nervous system (ANS), eight subjective symptoms, and perception of RF-EMFs during sham and real exposure sessions to verify its effects on adults and teenagers. Experiments were conducted using a dummy phone containing a WCDMA module (average power, 250 mW at 1950 MHz; specific absorption rate, 1.57 W/kg) within a headset placed on the head for 32 min. RESULTS: Short-term WCDMA RF-EMFs generated no significant changes in ANS, subjective symptoms or the percentages of those who believed they were being exposed in either group. CONCLUSIONS: Considering the analyzed physiological data, the subjective symptoms surveyed, and the percentages of those who believed they were being exposed, 32 min of RF radiation emitted by WCDMA mobile phones demonstrated no effects in either adult or teenager subjects.

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. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n = 4,685) were used for training and internal validation, while data from KNHANES 2011 (n = 4,566) were used for external validation. We developed a model to screen for pre-diabetes using support vector machine (SVM), and performed a systematic evaluation of the SVM model using internal and external validation. We compared the performance of the SVM model with that of a screening score model based on logistic regression analysis for pre-diabetes that had been developed previously. Backward elimination logistic regression resulted in associations between pre-diabetes and age, sex, waist circumference, body mass index, alcohol intake, family history of diabetes, and hypertension. The areas under the curves (AUCs) for the SVM model in the internal and external datasets were 0.761 and 0.731, respectively, while the AUCs for the screening score model were 0.734 and 0.712, respectively. The SVM model developed in this study performed better than the screening score model that had been developed previously and may be more effective for pre-diabetes screening.

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. Using DNA microarray technology, we designed multicategory classification models to discriminate ovarian cancer subclasses. To develop multicategory classification models with optimal parameters and features, we systematically evaluated three machine learning algorithms and three feature selection methods using five-fold cross validation and a grid search. The study included 22 subjects with normal ovarian surface epithelial cells, 12 with SBOTs, and 79 with SOCs according to microarray data with 54,675 probe sets obtained from the National Center for Biotechnology Information gene expression omnibus repository. Application of the optimal model of support vector machines one-versus-rest with signal-to-noise as a feature selection method gave an accuracy of 97.3%, relative classifier information of 0.916, and a kappa index of 0.941. In addition, 5 features, including the expression of putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and SOC groups. An accurate diagnosis of ovarian tumor subclasses by application of multicategory machine learning would be cost-effective and simple to perform, and would ensure more effective subclass-targeted therapy.