• Journal of Acupuncture Research
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• v.17 no.3
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• pp.36-44
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• 2000

The purpose of this study is to arrange the acupuncture therapy on the asthma from the oriental medical literature. The results obtained as follows. 1. The cause of the asthma is the intrinsic factors and the over-sensitive reactions. The decreased function of the lung and the kidney are chronically affected to the asthma. 2. The typical sign of the asthma is tachypnea and wheezes to be heard throughout the lung field, particularly during expiration. 3. Bladder meridian, Conception vessel, and Lung meridian used frequently for the acupuncture therapy, and Bladder meridian, Conception vessel, Stomach meridian, and Lung meridian used frequently for the moxibusrion on the asthma. The acupuncture points of Lung meridian used most frequently on the asthma, but the acupuncture points of Heart meridian have not used. 4. Acupuncture point at B13, CV22, LI4, CV12, CV17, 536, B12 used used frequently for the acupuncture therapy, and the acupuncture point at B13, CV22, B12, CV17, B43 used frequently for the moxibustion on the asthma Particularly B13 and CV22 used most frequently for the acupuncture therapy on the asthma.

• Journal of Veterinary Clinics
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• v.15 no.1
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• pp.188-192
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• 1998

A 3-month old female Holstein calf was presented with about a month history of intermittent dyspnea, exercise intolerance and cough despite antibiotic therapy. Auscultation revealed prominent inspiratory and exploratory crackles and wheezes over the causal cervical trachea which were heard equally over both side of the chest.4 modest amount of forced exercise caused severe respiratory distress with stertorous noise and occasional honkinglike cough. Pasteurella spp. was isolated on the nasal swabs and a hemogram showed mild leucocytosis with a mature neutrophilia and mild monocytosis. Lateral radiographs of the neck and thorax revealed a marked narrowing of the tracheal lumen extending from the level of the fifth cervical to the second thoracic vertebra, and the lung field was judged to be within normal limitsi except very mild peribronchial thickening. The hypertrophic non-union fractures of the first pair of ribs were noted with a well delineatedr redundant callus formations and also the completely healed fractures were found on the next seven pairs of ribs. A diagnosis of tracheal collapse was made, which is thought to be a traumatic origin.4 poor prognosis was given. The calf was euthanatized and necropsied. The tracheal rings from 19th to 41s1 were collapsed dorsoventrally. Histologically, there was no difference between the collapsed and normal areas of the tracheae except the folding mucosal layer in the collapsed area. This report details a case of tracheal collapse in a calli and the literature is reviewed.

• Journal of Veterinary Clinics
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• v.27 no.2
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• pp.194-197
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• 2010

A 14-year-old gelded dressage horse weighing 500 kg was presented to the Equine Medical Center of the Seoul Racecourse of Korea Racing Authority (KRA) due to coughing and mucopurulent nasal discharge. The horse was initiated with empirical antibiotic in the first place. However, the clinical signs did not improve but were rather exacerbated even after 3 weeks of therapy. Extensive diagnostic procedures including transtracheal wash (TTW) fluid cytology were undertaken. The localized wheezes and crackles were auscultated and an increase in the amount of mucopurulent exudate in trachea was observed at endoscopy. Infiltration of neutrophils was observed in the TTW fluid cytology implying chronic obstructive pulmonary disease (COPD). Therefore, the systemic glucocorticoid therapy was to be given for 3 weeks with improved ventilation provided at the same time. The respiratory symptoms started to improve in 7 days of therapy and were fully resolved by when the therapy was terminated. The horse is clinically normal now and being monitored for development of any signs of chronic obstructive pulmonary disease.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.21-31
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• 2021

As the COVID-19 pandemic rapidly changes healthcare around the globe, the need for smart healthcare that allows for remote diagnosis is increasing. The current classification of respiratory diseases cost high and requires a face-to-face visit with a skilled medical professional, thus the pandemic significantly hinders monitoring and early diagnosis. Therefore, the ability to accurately classify and diagnose respiratory sound using deep learning-based AI models is essential to modern medicine as a remote alternative to the current stethoscope. In this study, we propose a deep learning-based respiratory sound classification model using data collected from medical experts. The sound data were preprocessed with BandPassFilter, and the relevant respiratory audio features were extracted with Log-Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). Subsequently, a Parallel CNN network model was trained on these two inputs using stacking ensemble techniques combined with various machine learning classifiers to efficiently classify and detect abnormal respiratory sounds with high accuracy. The model proposed in this paper classified abnormal respiratory sounds with an accuracy of 96.9%, which is approximately 6.1% higher than the classification accuracy of baseline model.