• Title/Summary/Keyword: Generation Z

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Standard Chemotherapy with Excluding Isoniazid in a Murine Model of Tuberculosis (마우스 결핵 모델에서 Isoniazid를 제외한 표준치료의 예비 연구)

  • Shim, Tae Sun;Lee, Eun Gae;Choi, Chang Min;Hong, Sang-Bum;Oh, Yeon-Mok;Lim, Chae-Man;Lee, Sang Do;Koh, Younsuck;Kim, Woo Sung;Kim, Dong Soon;Cho, Sang-Nae;Kim, Won Dong
    • Tuberculosis and Respiratory Diseases
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    • v.65 no.3
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    • pp.177-182
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    • 2008
  • Background: Isoniazid (INH, H) is a key drug of the standard first-line regimen for the treatment of tuberculosis (TB), yet some reports have suggested that treatment efficacy was maintained even though INH was omitted from the treatment regimen. Methods: One hundred forty C57BL/6 mice were infected with the H37Rv strain of M. tuberculosis with using a Glas-Col aerosol generation device, and this resulted in depositing about 100 bacilli in the lung. Four weeks after infection, anti-TB treatment was initiated with varying regimens for 4-8 weeks; Group 1: no treatment (control), Group 2 (4HREZ): 4 weeks of INH, rifampicin (R), pyrazinamide (Z) and ethambutol (E), Group 3: 1HREZ/3REZ, Group 4: 4REZ, Group 5: 4HREZ/4HRE, Group 6: 1HREZ/3REZ/4RE, and Group 7: 4REZ/4RE. The lungs and spleens were harvested at several time points until 28 weeks after infection, and the colony-forming unit (CFU) counts were determined. Results: The CFU counts increased steadily after infection in the control group. In the 4-week treatment groups (Group 2-4), even though the culture was negative at treatment completion, the bacilli grew again at the 12-week and 20-week time points after completion of treatment. In the 8-week treatment groups (Groups 5-7), the bacilli did not grow in the lung at 4 weeks after treatment initiation and thereafter. In the spleens of Group 7 in which INH was omitted from the treatment regimen, the culture was negative at 4-weeks after treatment initiation and thereafter. However, in Groups 5 and 6 in which INH was taken continuously or intermittently, the bacilli grew in the spleen at some time points after completion of treatment. Conclusion: TThe exclusion of INH from the standard first-line regimen did not affect the treatment outcome in a murine model of TB in the early stage of disease. Further studies using a murine model of chronic TB are necessary to clarify the role of INH in the standard first-line regimen for treating TB.

The Historical Study of Headache in Chinese Ming Dynasty (명대의가(明代醫家)들의 두통(頭痛)에 대한 인식변화에 관한 연구)

  • Chun, Duk-Bong;Maeng, Woong-Jae;Kim, Nam-Il
    • The Journal of Korean Medical History
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    • v.24 no.1
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    • pp.43-56
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    • 2011
  • Everyone once in a life experience headaches as symptoms are very common. According to a study in a country of more than a week and as many as those who have experienced a headache amounts to 69.4%. In addition, the high reported prevalence of migraine in 30s for 80% of all migraine sufferers daily life interfere with work or was affected. In Western medicine, the cause of headaches is traction or deformation of pain induced tissue like scalp, subcutaneous tissue, muscle, fascia, extracranial arteriovenous, nerves, periosteum. But it turns out there are not cause why pain induced tissue is being tracted or deformated. Therefore, most of the western-therapy is mainly conducted with regimen for a temporary symptom reduction. Therefore, I examined how it has been developed in Chinese Ming Dynasty, the perception of headache, change in disease stage and an etiological cause. Oriental medicine in the treatment of headache is a more fundamental way to have an excellent treatment. The recognition of head in "素問($s{\grave{u}}$ $w{\grave{e}}n$)" and "靈樞($l{\acute{i}}ng$ $sh{\bar{u}}$)" began to appear in 'Soul-神($sh{\acute{e}}n$) dwelling place' and 'where to gather all the Yang-'諸陽之會($zh{\bar{u}}$ $y{\acute{a}}ng$ $zh{\bar{i}}$ $hu{\grave{i}}$)'. Also, head was recognized as '六腑($li{\grave{u}}f{\check{u}}$) 淸陽之氣($q{\bar{i}}ng$ $y{\acute{a}}ng$ $zh{\bar{i}}$ $q{\grave{i}}$) and 五臟($w{\check{u}}$ $z{\grave{a}}ng$) 精血($j{\bar{i}}ng$ $xu{\grave{e}}$) gathering place'. More specific structures such as the brain is considered a sea of marrow(髓海-$su{\check{i}}$ $h{\check{a}}i$) in "內經($n{\grave{e}}i$ $j{\bar{i}}ng$)" and came to recognized place where a stroke occurs. Accompanying development of the recognition about head, there had been changed about the perception of headache and the recognition of the cause and mechanism of headache. And the recognition of headache began to be completed in Ming Dynasty through Jin, Yuan Dynasty. Chinese Ming Dynasty, specially 樓英($l{\acute{o}}u$ $y{\bar{i}}ng$), in "醫學綱目($y{\bar{i}}xu{\acute{e}}$ $g{\bar{a}}ngm{\grave{u}}$)", first enumerated prescription in detail by separating postpartum headache. and proposed treatment of headache especially due to postpartum sepsis(敗血-$b{\grave{a}}i$ $xu{\grave{e}}$). 許浚($x{\check{u}}$ $j{\grave{u}}n$) accepted a variety of views without impartial opinion in explaining one kind of headache in "東醫寶鑑($d{\bar{o}}ng-y{\bar{i}}$ $b{\check{a}}oji{\grave{a}}n)$" 張景岳($zh{\bar{a}}ng$ $j{\check{i}}ng$ $yu{\grave{e}}$), in "景岳全書($j{\check{i}}ng$ $yu{\grave{e}}$ $qu{\acute{a}}nsh{\bar{u}}$)", established his own unique classification system-新舊表裏($x{\bar{i}}nji{\grave{u}}$ $bi{\check{a}}ol{\check{i}}$)-, and offered a clear way even in treatment. Acupuncture treatment of headache in the choice of meridian has been developed as a single acupuncture point. Using the classification of headache to come for future generation as a way of locating acupoints were developed. Chinese Ming Dynasty, there are special treatments like 導引按蹻法($d{\check{a}}o$ y ${\check{i}}n$ ${\grave{a}}n$ $ji{\check{a}}o$ $f{\check{a}}$), 搐鼻法($ch{\grave{u}}$ $b{\acute{i}}$ $f{\check{a}})$, 吐法($t{\check{u}}$ $f{\check{a}}$), 外貼法($w{\grave{a}}i$ $ti{\bar{e}}$ $f{\check{a}}$), 熨法($y{\grave{u}}n$ $f{\check{a}}$), 點眼法($di{\check{a}}n$ $y{\check{a}}n$ $f{\check{a}}$), 熏蒸法($x{\bar{u}}nzh{\bar{e}}ng$ $f{\check{a}}$), 香氣療法($xi{\bar{a}}ngq{\grave{i}}$ $li{\acute{a}}of{\check{a}}$). Most of this therapy in the treatment of headache, it is not used here, but if you use a good fit for today's environment can make a difference.

Studies on the Morphology and Biology of a Parasitic Mite, Pyemotes tritici L.-F. & M. on the Cigarette Beetle(Lasioderma serricorne F.). (Pyemotes tritici L.-F. & M. 궐련벌레살이주머니응애의 형태(形態)와 생활사(生活史)에 관(關)한 연구(硏究))

  • Oh, M.H.;Kim, S.S.;Boo, K.S.
    • Korean journal of applied entomology
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    • v.24 no.1 s.62
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    • pp.15-18
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    • 1985
  • Studies were made on the morphology and life cycle of the mite, Pyemotes tritici $Lagr{\acute{e}}z$-Fossot & $Montagn{\acute{e}}$ (Trombidiformes; Pyemotidea) ,parasitic on larvae of cigarette beetle, Lasioderma serricorne F., which is one of the most serious pests of stored tobacco, cigar, and cigarette in Korea. One generation time was $20.9{\pm}0.7$ days out of which $9.5{\pm}0.3$ days were spent for feeding and $10.3{\pm}0.8$ days for reproduction of progenies. A female of this insect-parasitic mite produced $56.7{\pm}6.9$ progenies during her reproduction period. The body size of a newly-laid male or female was $280{\mu}m$ long and $85{\mu}m$ wide. As female of this mite sucked on, their abdomen grew larger and larger to reach $825{\mu}m$ in width and $0.346mm^3$ in volume after $9{\sim}10$ days. By sucking the humor of a host, the abdomen of a female mite became almost a global shape in two days. The increase rate of abdominal width was the biggest on the second or the third day of feeding while the volume of abdomen reached to the largest on $6{\sim}8$ days after feeding. The largest number of the daily young produced on 4-th day after a female began to reproduce.

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A Deep Learning Based Approach to Recognizing Accompanying Status of Smartphone Users Using Multimodal Data (스마트폰 다종 데이터를 활용한 딥러닝 기반의 사용자 동행 상태 인식)

  • Kim, Kilho;Choi, Sangwoo;Chae, Moon-jung;Park, Heewoong;Lee, Jaehong;Park, Jonghun
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.163-177
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    • 2019
  • As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.