12 research outputs found
4D inversion of time-lapse magnetotelluric data sets for monitoring geothermal reservoir
No full text
An analysis of changes in resistivity of general reservoir dams based on 4D inversion of time-lapse resistivity data
No full textAn Introduction to Real-time MWD/LWD Technologies for Geo-steering Driling:Based Mainly on Schlumberger’s Tools
No full textCorrelation of Bone Mineral Density with Pulmonary Function in Advanced Duchenne Muscular Dystrophy
No full textBackground: A relationship between bone mineral density (BMD) and physical function has been revealed in the general population and various diseases. However, there is a lack of research investigating the correlation between BMD and respiratory function, one of few measurable physical parameters in patients with advanced Duchenne muscular dystrophy (DMD).
Objective: To determine whether pulmonary function parameters, including respiratory muscle strength, are related to BMD.
Design: Retrospective observational study.
Setting: A tertiary university hospital.
Patients: DMD patients who were over 20 years of age, nonambulatory, and supported by mechanical ventilators.
Methods: The patients' age, weight, and pulmonary function as well as the BMD of the first and the fourth lumbar vertebra were assessed. Pulmonary function includes forced vital capacity (FVC), unassisted and assisted peak cough flow (UPCF and APCF), maximal expiratory pressure (MEP), and maximal inspiratory pressure (MIP).
Main outcome measures: A bivariate correlation for BMD and other pulmonary parameters was calculated, and hierarchical regression analysis was used to determine predictors of spine Z-score.
Results: It was observed that the decrease in the spine BMD was not significantly correlated with age. However, the body mass index (BMI) and all parameters of pulmonary function were correlated with BMD. Partial correlation analysis adjusted by BMI showed that UPCF and APCF were powerful predictors of spine BMD.
Conclusions: The BMD of the lumbar spine correlated with BMI and PCF in patients with DMD at an advanced stage.restrictio
Towards the imaging of deep land ore deposits with ERT-IP method – first results from a demonstration survey in Finland
No full textIn this study, we conducted a comprehensive geophysical survey near Kuusamo (Finland) to assess the potential of electrical resistivity methods in delineating mineral deposits at depths greater than 1 km. Preliminary investigations, including magnetic and gravity methods as well as drilling, revealed significant anomalous structures in the survey area. We employed multiple electrical and electromagnetic methods at the site, comprising controlled-source electromagnetic (CSEM), magnetotelluric (MT), electrical resistivity tomography (ERT), and induced polarization (IP). To obtain the geophysical data in very large-scale area, we used a total of 25 transmitter dipoles with 1km long using three distinct transmitter systems and recorded data at 119 receiver stations. In this paper, we present the acquisition and preliminary results from ERT-IP. Analysis of the resistivity and IP responses revealed notable IP signals at depths exceeding 1.5 km. Meanwhile, the resistivity data indicated generally very high values, around 10,000 ohm-m, with complex variations observed near the surface
Correlation of Bone Mineral Density with Pulmonary Function in Advanced Duchenne Muscular Dystrophy
No full textDetection of Aortic Dissection and Intramural Hematoma in Non-Contrast Chest Computed Tomography Using a You Only Look Once-Based Deep Learning Model
No full textBackground/Objectives: Aortic dissection (AD) and aortic intramural hematoma (IMH) are fatal diseases with similar clinical characteristics. Immediate computed tomography (CT) with a contrast medium is required to confirm the presence of AD or IMH. This retrospective study aimed to use CT images to differentiate AD and IMH from normal aorta (NA) using a deep learning algorithm. Methods: A 6-year retrospective study of non-contrast chest CT images was conducted at a university hospital in Seoul, Republic of Korea, from January 2016 to July 2021. The position of the aorta was analyzed in each CT image and categorized as NA, AD, or IMH. The images were divided into training, validation, and test sets in an 8:1:1 ratio. A deep learning model that can differentiate between AD and IMH from NA using non-contrast CT images alone, called YOLO (You Only Look Once) v4, was developed. The YOLOv4 model was used to analyze 8881 non-contrast CT images from 121 patients. Results: The YOLOv4 model can distinguish AD, IMH, and NA from each other simultaneously with a probability of over 92% using non-contrast CT images. Conclusions: This model can help distinguish AD and IMH from NA when applying a contrast agent is challenging
Clinical implication of maximal voluntary ventilation in myotonic muscular dystrophy
No full textPatients withmyotonicmusculardystrophytype 1 (DM1) tend to exhibit earlier respiratory insufficiency than patients with other neuromuscular diseases at similar or higher forced vital capacity (FVC). This study aimed to analyze several pulmonary function parameters to determine which factor contributes the most to early hypercapnia in patients with DM1.We analyzedventilationstatus monitoring, pulmonary function tests (including FVC,maximalvoluntaryventilation[MVV], andmaximalinspiratory and expiratory pressure), and polysomnography in subjects with DM1 who were admitted to a single university hospital. The correlation of each parameter with hypercapnia was determined. Subgroup analysis was also performed by dividing the subjects into 2 subgroups according to usage of mechanicalventilation.Final analysis included 50 patients with a mean age of 42.9 years (standard deviation = 11.1), 46.0% of whom were male. The hypercapnia was negatively correlated with MVV, FVC, forced expiratory volume in 1 second (FEV1), and their ratios to predicted values in subjects withmyotonicmusculardystrophytype 1. At the same partial pressure of carbon dioxide, the ratio to the predicted value was lowest for MVV, then FEV1, followed by FVC. Moreover, the P values for differences in MVV and its ratio to the predicted value between ventilator users and nonusers were the lowest.When screeningventilationfailure in patients with DM1, MVV should be considered alongside other routinely measured parameters.ope
