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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kea-Tiong Tang Shih-Wen Chiu Chung-Hung Shih Chia-Ling Chang Chia-Min Yang Da-Jeng Yao Jen-Huo Wang Chien-Ming Huang Hsin Chen Kwuang-Han Chang Chih-Cheng Hsieh Ting-Hau Chang Meng-Fan Chang Chia-Min Wang Yi-Wen Liu Tsan-Jieh Chen Chia-Hsiang Yang Herming Chiueh Jyuo-Min Shyu |
| Copyright Year | 2014 |
| Description | Author affiliation: Taipei Med. Univ., Taipei, Taiwan (Chung-Hung Shih) || Nat. Chiao Tung Univ., Hsinchu, Taiwan (Tsan-Jieh Chen; Chia-Hsiang Yang; Herming Chiueh) || Nat. Tsing Hua Univ., Hsinchu, Taiwan (Kea-Tiong Tang; Shih-Wen Chiu; Chia-Ling Chang; Chia-Min Yang; Da-Jeng Yao; Jen-Huo Wang; Chien-Ming Huang; Hsin Chen; Kwuang-Han Chang; Chih-Cheng Hsieh; Ting-Hau Chang; Meng-Fan Chang; Chia-Min Wang; Yi-Wen Liu; Jyuo-Min Shyu) |
| Abstract | Ventilator-associated pneumonia (VAP) is the most frequently acquired infection among patients that receive mechanical ventilation in the intensive-care unit (ICU). The mortality rate for VAP lies in the 20-to-50% range and could be even higher in some ICUs. A standard operation procedure to VAP treatment includes a sequence of chest radiography, sputum gram stain, sputum culture, and empiric therapy, initially with antibiotics covering broad pathogens. However, collection of the gram stain and culture of lower respiratory tract specimen is usually not time-efficient (up to 5 days), delaying the initiation of therapy and unacceptable for critically ill patients. A rapid and accurate diagnosis for VAP is therefore crucial, but still unavailable. It is known that microorganisms generate complex metabolites during infection. Fast detection is feasible by examining metabolic wastes in proximal end of the expiratory device, demanding a miniaturized, battery-powered, gas-sensing device. In this work, a fully integrated low-power nose-on-a-chip with a robust learning kernel is developed for such a vital clinical need. |
| Starting Page | 420 |
| Ending Page | 421 |
| File Size | 4995937 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781479909186 |
| ISSN | 01936530 |
| e-ISBN | 9781479909209 |
| DOI | 10.1109/ISSCC.2014.6757496 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-02-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Sensors Kernel Reduced instruction set computing Robustness Mesoporous materials Accuracy |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering |
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