• Archives of Pharmacal Research
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• v.10 no.4
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• pp.250-257
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• 1987

The development of physiologically based pharmacokinetic model for drug distribution and excretion is described. The physiological modeling procedure is useful in animal and clinical applications to obtain fundamental knowledge of the transport and metabolism of a substance in vivo. In this paper a review of physiologically based pharmacokinetics is presented in the hope of understanding and increasing the use of this modelling technique. The method of model development and the composition of equations based on the different models are explained. For the better understanding a physiological pharmacokinetic model of tenoxicam disposition in the rat is presented as an example of flow limited model.

• Sleep Medicine and Psychophysiology
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• v.16 no.1
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• pp.5-9
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• 2009

Relatively little is known about the neurobiology of insomnia, despite its wide prevalence and broad medical impact. Although much is still to be learned about the pathophysiology of the disorder, identification, systematic assessment, and appropriate treatment are clearly beneficial to patients. Recent research, using quantitative EEG, polysomnography (PSG), multiple sleep latency test (MSLT) and neuroimaging techniques, suggests that some broad areas can be identified as possible pathophysiological models. Sleep-wake homeostat model hypothesizes a failure in homeostatic regulation of sleep, an attenuated increase in sleep drive with time awake, and/or defective sensing of sleep need. Circadian clock model hypothesizes a dysfunctional circadian clock, resulting in changes in the timing of sleep-wake propensity that are incompatible with normal sleep. Intrinsic sleep-wake state mechanism model suggests that abnormal function of insomnia comprises the systems responsible for expression of the sleep states themselves. Extrinsic over-ride mechanism (stress-response) model suggests that insomnia reflects the consequences of overactivity of one of the systems considered "extrinsic" to normal sleep-wake control. Many current therapies for insomnia are based on these physiological models. Several attempts have been made to create a physiological model that would explain this disorder and could be used as a foundation for treatment. However, it appeared that no model can fully explain and clarify all aspects of insomnia. Future research should be necessary to expand our knowledge on the biological dimensions of insomnia.

• Research in Community and Public Health Nursing
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• v.15 no.2
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• pp.289-297
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• 2004

Purpose: This study was conducted to determine factors affecting workers' physiological somatic complain using the Job Stress Model proposed by the National Institute for Occupational Safety and Health (NIOSH). Method: Data were collected from the 1st to the 30th of December 1999. The subjects were 2.123 workers employed at 155 work sites. Collected date were analyzed through SAS/PC program. Result: According to individual characteristics, younger and women groups showed significantly higher physiological somatic complaint than elder men groups. By work condition, groups with higher physiological somatic complaint included workers of irregular shift work. Dark lighting, improper temperature in winter, improper ventilation, inappropriate humidity, unpleasant work environment and crowded work place were significantly related with physiological somatic complaint. By work-related factor, physiological somatic complaint was high in those with higher variance in work load, quantitative work load, role conflict, job burden, role ambiguity and future ambiguity. On the other hand, physiological somatic complaint was low in those with little underutilization of ability. As for the relationships between physiological somatic complaint and non-work related factors, physiological somatic complaint was high in workers who had a side job, were bringing up infants alone, cleaned the house alone, cared for the elderly and disabled persons, were studying, were volunteering at another organization, and were spending 5-10 hours in religious activities per week. Physiological somatic complain was in significantly negative correlations with overall social support, supervisory support and family support, but in significantly positive correlations with co-worker support. Conclusion: The main predictors of physiological somatic complain were gender, shift work pattern, overtime work, ventilation, role ambiguity, role conflict, future ambiguity, job control, variance in work load, overall social support, worker with side job, worker who cleans the house alone, worker who is studying. These predictors explained 19.10% of the total variance of physiological somatic complain.

• Korean Journal of Exercise Nutrition
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• v.24 no.4
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• pp.1-6
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• 2020

[Purpose] Exercise is a fundamental way to maintain and improve health and physical fitness. Many human studies have demonstrated the beneficial effects of exercise on various biological parameters. However, studies investigating the effects of exercise in dogs are limited. This review summarized the current data from studies that examined the effects of different exercise conditions (treadmill vs. non-treadmill and acute vs. chronic) on physiological and hematological parameters in dogs. [Methods] Papers addressing the effects of exercise in dogs published from January 2000 to October 2020 were retrieved from the online databases of Scopus, Google Scholar, and PubMed and were selected and reviewed. [Results] The exercise conditions differentially affected physiological and hematological responses and adaptation in dogs. Therefore, the development and comprehensive evaluation of scientific exercise programs for dogs are necessary. [Conclusion] The dog would be a valuable exercise science animal model, and studies aiming at the optimal health, well-being, and quality of life of dogs need to be conducted.

• Journal of Korean Academy of Nursing
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• v.47 no.2
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• pp.257-266
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• 2017

Purpose: The aims of this study were to construct a hypothetical structural model which explains premenstrual coping in university students and to test the fitness with collected data. Methods: Participants were 206 unmarried women university students from 3 universities in A and B cities. Data were collected from March 29 until April 30, 2016 using self-report structured questionnaires and were analyzed using IBM SPSS 23.0 and AMOS 18.0. Results: Physiological factor was identified as a significant predictor of premenstrual syndrome (t=6.45, p<.001). This model explained 22.1% of the variance in premenstrual syndrome. Psychological factors (t=-2.49, p=.013) and premenstrual syndrome (t=8.17, p<.001) were identified as significant predictors of premenstrual coping. Also this model explained 30.9% of the variance in premenstrual coping in university students. A physiological factors directly influenced premenstrual syndrome (${\beta}=.41$, p=.012). Premenstrual syndrome (${\beta}=.55$, p=.005) and physiological factor (${\beta}=.23$, p=.015) had significant total effects on premenstrual coping. Physiological factor did not have a direct influence on premenstrual coping, but indirectly affected it (${\beta}=.22$, p=.007). Psychological factors did not have an indirect or total effect on premenstrual coping, but directly affected it (${\beta}=-.17$, p=.036). Conclusion: These findings suggest that strategies to control physiological factors such as menstrual pain should be helpful to improve premenstrual syndrome symptoms. When developing a program to improve premenstrual coping ability and quality of menstrual related health, it is important to consider psychological factors including perceived stress and menstrual attitude and premenstrual syndrome.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.309-316
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• 2018

Objectives: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. Methods: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. Results: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. Conclusions: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1061-1065
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• 2002

Sensors need to be implanted to obtain necessary information for LVAS (Left Ventricular Assist System) operations. Size of the sensors can prevent them from being implanted in a patient and reliabilities of the sensors are questionable for a long term use. In this wort we utilize a developed pump model to estimate flow and pressure difference across the pump without implanted sensors and present a method to obtain the physiological variables as aorta pressure and left ventricle pressure from the pump model and pulsatility of flow estimate or pressure difference estimate. These estimated variables can be used for LVAS control as an index or indices.

• Journal of the Ergonomics Society of Korea
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• v.27 no.2
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• pp.1-7
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• 2008

This study quantified 7 trunk muscles' physiological cross-sectional areas (PCSAs) and developed prediction equations for the physiological cross-sectional area as a function of anthropometic variables for Korean people. Nine females and nine males were participated in the magnetic resonance imaging (MRI) scans approximately from S1 through T8. Muscle fiber angle corrected cross-sectional areas (anatomical cross sectional areas: ACSAs) were recorded at each vertebral level and maximum value of ACSAs were determined as physiological cross sectional area (PCSA). There was a significant gender difference in PCSAs of all muscles (p<0.05). Stepwise linear regression techniques using anthropometric measures (e.g., height, weight, trunk depths and widths) as independent variables were conducted to develop prediction equations for the PCSA for each muscle. For males, six muscles' significant prediction equations (p<0.05) were developed except quadratus lumborum. For females, three prediction equations were developed for psoas, quadratus lumborum, and erector spinae muscles (p<0.05).

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.331-338
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• 2001

In this paper, we have proposed a new computer retina model reflecting the mechanism of transient amacrine cell on the basis of a conventional computer retina model to understand mechanism of visual information processing. The conventional computer retina model contained most of mechanism for other retina models and it was verified with the physiological data. However, we found that a conventional computer retina model doesn't have the mechanism of amacrine cell that was likely to respond to moving stimulus. In proposed model, therefore, a conventional computer model that considered from photoreceptors to bipolar cells and a new computer model that considered for transient amacrine cell and ganglion cell was combined. As we compared the physiological data with the results of computer simulation of transient amacrine cell about fixed stimulus and moving stimulus, we confirmed that the proposed new computer retina model was normally operated.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.1.1-5
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• 1994

In the single sweep record of event-related potential (ERP), the peak latency of P300, which is one of the most prominent positive peaks in the ERP record, might fluctuate according to the recording conditions. The fluctuation of the peak latency (measurement fluctuation) is the summation of the fluctuation caused by physiological factor (physiological fluctuation) and one by noise of background EEG (noise fluctuation). We propsed a method for estimating the interval of the physiological fluctuation based on a limited number of single sweep records. The noise fluctuation was estimated by using the relationship between the signal-to-noise (SN) ratio and the noise fluctuation based on the P300 model and the background EEG model. The interval estimate of the physiological fluctuation were obtained by subtracting the interval estimate of the noise fluctuation from that of the measurement fluctuation. The proposed method was tested by using simulation data of ERP and applied to actual ERP and data of normal subjects, and gave satisfactory results.