Search results

This paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients.

A 3D numerical model consisting of simplified organ geometries is used to simulate the electrical impedance changes in the ICG-relevant domain of the human torso. The Bayesian probability theory is used for detecting an aortic dissection, which provides information about the probabilities for both cases, a dissected and a healthy aorta. Thus, the reliability and the uncertainty of the disease identification are found by this method and may indicate further diagnostic clarification.

The Bayesian classification shows that the enhanced multi-sensors ICG is more reliable in detecting aortic dissection than conventional ICG. Bayesian probability theory allows a rigorous quantification of all uncertainties to draw reliable conclusions for the medical treatment of aortic dissection.

This paper presents a non-invasive and reliable method based on a numerical simulation that could be beneficial for the medical management of aortic dissection patients. With this method, clinicians would be able to monitor the patient’s status and make better decisions in the treatment procedure of each patient.

We previously examined the efficacy of rTMS for major depressive disorder in an applied clinical practice. Clinical response was related to severity of depression as well as the rTMS instrument utilized suggesting a relationship to instrument or magnetic field parameters and individual factors. The effectiveness of repetitive transcranial magnetic stimulation (rTMS) in the treatment of major depressive disorder was further evaluated using Log-Rank statistics for time to remission outcomes. A follow-up retrospective medical records study was carried out on patients with major depressive disorder undergoing rTMS therapy at AwakeningsKC Clinical Neuroscience Institute (CNI), a suburban tertiary psychiatric clinic. Cox Proportional Hazard with Log-Rank statistics were applied and the time course to clinical remission was evaluated over a 6-week period with respect to age, gender, and depression severity. Clinical response was observed referencing two different rTMS instruments (MagVenture; NeuroStar). Time to remission studies of 247 case reports (N=98 males; N=149 females) showed consistently greater clinically defined remission rates after 6 weeks of rTMS treatment for patients using the MagVenture vs NeuroStar instrument. Patients previously admitted for inpatient psychiatric hospitalization exhibited higher response rates when treated with the MagVenture rTMS unit. Stepwise Cox Proportional Hazards Regression final model of time to remission included rTMS unit, inpatient psychiatric hospitalization and obese body habitus. Response to rTMS in applied clinical practice is related to severity of psychiatric illness and may require consideration of magnetic field parameters of the rTMS unit with respect to individual factors such as sex or body composition.

Adolescents being in a stage of growth need good sleep, but, today, they suffer from sleep deprivation due to such extrinsic factor as a smartphone which they enjoy spending time using the device. However, the effects of smartphone output power (SOP) on the duration of good sleep remains unclear. The purpose of this paper is to investigate the correlation of the SOP and sleep loss in high school students.

The time-series study was conducted among 145 high school students in Chiang Mai Province who completed a sleep diary which applied by the Pittsburg Sleep Quality Index. The SOP was corrected by a smartphone application and transmitted by e-mail to a researcher every day. The completed data set contains 12,969 entries. Headache, anxiety and depression were also assessed. Data were analyzed using the generalized estimating equation adjusted for demographic data, smartphone use and other factors.

Most of the study subjects are female, 17.4 years old on average. The prevalence of sleep loss (<8 h) was 52.9 percent with averagely 7.4 ±1.7 h of sleep duration and poor sleep at 32.1 percent. Anxiety, depression, headache had relationships with sleep loss. The daily dose, evening and nocturnal SOP in the range of ≥ 2.00 × 10^‒5 mW had stronger relationships with sleep loss than their effects in the range of ≤ 1.79 × 10^‒5 mW (OR_adj1.32; 95% CI: 1.26–1.76, OR_adj1.34; 95% CI: 1.07–1.17 and OR_adj1.41; 95% CI: 1.07–1.17, respectively). Meanwhile, morning Lag_2 and daytime Lag_1 in the range of ≥ 2.00 × 10^‒5 mW appeared to have a strong relationship with sleep loss (OR_adj1.60; 95% CI: 1.26–1.76, OR_adj1.36; 95% CI: 1.07–1.17). The relationship between Lag_4 daily dose and sleep loss took the form of a reverse dose-response.

Sleep loss in adolescents has an increasing trend of prevalence and has been found to be correlated with the highest SOP group (≥ 2.00 × 10^‒5 mW range). These results confirmed that increased and longer smartphone use result in reduced sleep time. This causes them to be exposed to smartphone electromagnetic radiation and smartphone screen lighting. This disturbs brain waves and nervous system controlling sleep balance mechanisms. The findings recommended parents setting time and boundaries around technology use at home to reduce contact with electromagnetic radiation and smartphone screen lighting, thereby increasing sleeping time in order to create good sleep quality.