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    ์งˆ๋ณ‘ ๋ฐ”์ด์˜ค๋งˆ์ปค ๋ฐœ๊ตด ๋ฐ ๊ทธ์™€ ๊ฒฐํ•ฉํ•˜๋Š” ํ›„๊ฐ ์ˆ˜์šฉ์ฒด ํƒ์ƒ‰

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2020. 8. ๋ฐ•ํƒœํ˜„.Due to the development of medical technology and systems, the premature mortality rate due to disease has decreased significantly compared to the past. However, lethality from some incurable diseases including cancer is still high. Because it is difficult to feel conscious symptoms before the disease develops to a late stage, and the existing diagnosis method is inaccessible due to the invasive method and cost of examination. Due to this reason, the latest disease diagnosis technology is developing in the direction of improving accessibility, and in particular, the need for non-invasive and economic method is emerging. As a typical example, the technology for diagnosing a disease by detecting a specific volatile organic compounds enables simple diagnosis without pain because it can detect the signal of disease from exhaled breath, sweat, urine, and saliva as well as blood and body fluids. In particular, the bioelectronic sensor has demonstrated excellent selectivity and sensitivity by combining a primary transducer such as an olfactory receptor with a secondary transducer containing a nanostructured semiconductor such as carbon nanotubes or graphene. The purposes of this research are identification of disease biomarkers and screening, performance evaluation of olfactory receptors for the detection of biomarkers that are essential for development of bioeletronic sensor. The selected diseases for study are lung cancer, tuberculosis, and gastric cancer. First, the discovery of biomarkers for lung cancer and the screening of human olfactory receptors were performed. The lung cancer cell line and the normal lung cell line were cultured to compare the composition of headspace gas by GC / MS, and volatile organic compound 2-ethyl-1-hexanol, which is more frequently generated in lung cancer cell lines, was identified. In addition, human olfactory receptors capable of detecting this biomarker were screened using a dual-glo luciferase reporter gene assay. It was confirmed that the identified olfactory receptor sensitively and selectively detects the lung cancer biomarker, and then conducted olfactory nanovesicle generation and performance evaluation for use as a primary transducer of the bioelectronic sensor in the further study. In the second study, the screening of human olfactory receptors were carried out for identification of olfactory receptor capable of detecting 5 tuberculosis biomarkers found in urine [95]. The screening was conducted by transfectng the human olfactory receptor genes and the luciferase reporter gene into the HEK293 cell line to confirm the responsivity to the tuberculosis biomarkers. As a result, olfactory receptors recognizing each tuberculosis biomarker were selected, and their responsivity and selectivity were also analyzed. Third, a number of exhaled breath samples of gastric cancer patients and healthy subjects were collected and analyzed using GC/MS. As a result, butyl acid and propionic acid, which are volatile organic compounds found in relatively large amounts in the exhaled breath of gastric cancer patients, were identified. In particular, solid-phase microextraction (SPME) fibers were used as a instruments of collecting and concentrating volatile organic compounds to completely analyze the biomarkers containing a very small amount in the exhaled breath samples. To improve the reliability of the selected volatile organic compounds as biomarkers, we build a diagnostic model that distinguishes patients based on the amount of biomarkers in the exhaled breath through statistical analysis of overall data, and their sensitivity and selectivity were calculated. In addition, in order to identify a primary transducer of a bioelectronic sensor that detects biomarkers included in exhaled breath, the responsivity and selectivity of 2 human olfactory receptors known to detect butyric acid and propionic acid were estimated. Development of disease diagnosis technology is an inevitable process for universal welfare and extension of life expectancy. Diagnostic methods targeting disease-specific volatile organic compounds are attracting attention in academia as a next-generation diagnostic technology, and are actively being studied all over the world. In this thesis, several disease-specific volatile organic compounds have been newly identified, and the human olfactory receptors capable of recognizing disease biomarkers were screened. The above research results are expected to be useful for the development of sensitive and selective bioelectronic sensor for disease diagnosis.์˜๋ฃŒ๊ธฐ์ˆ ๊ณผ ์ฒด๊ณ„์˜ ๋ฐœ๋‹ฌ๋กœ ์ธํ•ด ์งˆ๋ณ‘์œผ๋กœ ์ธํ•œ ์กฐ๊ธฐ ์‚ฌ๋ง๋ฅ ์€ ๊ณผ๊ฑฐ์— ๋น„ํ•ด ํฌ๊ฒŒ ์ค„์–ด๋“ค์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์•”์„ ๋น„๋กฏํ•œ ์ผ๋ถ€ ๋‚œ์น˜์„ฑ ์งˆ๋ณ‘์œผ๋กœ ์ธํ•œ ์น˜์‚ฌ์œจ์€ ์—ฌ์ „ํžˆ ๋†’์€ ํŽธ์ด๋‹ค, ์ด๋Š” ์งˆ๋ณ‘์ด ์น˜๋ช…์ ์ธ ์ˆ˜์ค€๊นŒ์ง€ ๋ฐœ๋‹ฌํ•˜๊ธฐ ์ „์— ์ž๊ฐ์ฆ์ƒ์„ ๋Š๋ผ๊ธฐ ํž˜๋“ค๋‹ค๋Š” ์ ๊ณผ ๊ธฐ์กด์˜ ๊ฒ€์ง„ ๋ฐฉ๋ฒ•์ด ํŠน์œ ์˜ ์นจ์Šต์ ์ธ ๋ฐฉ์‹๊ณผ ๊ฒ€์‚ฌ ๋น„์šฉ ๋•Œ๋ฌธ์— ์ ‘๊ทผ์„ฑ์ด ๋–จ์–ด์ง„๋‹ค๋Š” ์ ์—์„œ ๋น„๋กฏ๋œ๋‹ค. ์ด๋Ÿฐ ์—ฐ์œ ๋กœ ์ตœ์‹  ์งˆ๋ณ‘ ์ง„๋‹จ ๊ธฐ์ˆ ์€ ์ ‘๊ทผ์„ฑ์˜ ํ–ฅ์ƒ์„ ์ถ”๊ตฌํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๋ฐœ์ „ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ํŠนํžˆ ๋น„ ์นจ์Šต์ ์ด๊ณ  ๊ฒฝ์ œ์ ์ธ ๋ฐฉ๋ฒ•์˜ ํ•„์š”์„ฑ์ด ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ๋Œ€ํ‘œ์ ์ธ ์˜ˆ์‹œ๋กœ, ํŠน์ด์ ์ธ ํœ˜๋ฐœ์„ฑ ์œ ๊ธฐ๋ฌผ์งˆ์„ ๊ฐ์ง€ํ•˜์—ฌ ์งˆ๋ณ‘์„ ์ง„๋‹จํ•˜๋Š” ๊ธฐ์ˆ ์€ ํ”ผ๋‚˜ ์ฒด์•ก ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋‚ ์ˆจ, ๋•€, ์†Œ๋ณ€, ์นจ ๋“ฑ์„ ๋งค๊ฐœ๋กœ ์™€๋ณ‘ ์—ฌ๋ถ€๋ฅผ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ๊ธฐ์— ๊ณ ํ†ต์ด ์ˆ˜๋ฐ˜๋˜์ง€ ์•Š๋Š” ๊ฐ„๋‹จํ•œ ์ง„๋‹จ์„ ๊ฐ€๋Šฅ์ผ€ ํ•œ๋‹ค. ํŠนํžˆ, ๋ฐ”์ด์˜ค ์ „์ž ์„ผ์„œ๋Š” ์นด๋ณธ๋‚˜๋…ธํŠœ๋ธŒ๋‚˜ ๊ทธ๋ผํ•€ ๊ฐ™์€ ๋‚˜๋…ธ ๊ตฌ์กฐ ๋ฐ˜๋„์ฒด๋ฅผ ํฌํ•จํ•œ 2์ฐจ ๋ณ€ํ™˜๊ธฐ์— ํ›„๊ฐ ์ˆ˜์šฉ์ฒด์™€ ๊ฐ™์€ 1์ฐจ ๋ณ€ํ™˜๊ธฐ๋ฅผ ๊ฒฐํ•ฉํ•˜์—ฌ ์šฐ์ˆ˜ํ•œ ์„ ํƒ๋„์™€ ๋ฏผ๊ฐ๋„๋ฅผ ์„ ๋ณด์ธ ๋ฐ” ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์งˆ๋ณ‘ ์ง„๋‹จ์šฉ ๋ฐ”์ด์˜ค ์ „์ž ์„ผ์„œ ์ œ์ž‘์„ ์œ„ํ•ด ํ•„์ˆ˜์ ์œผ๋กœ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•˜๋Š” ์งˆ๋ณ‘ ํ‘œ์ง€๋ฌผ์งˆ ์„ ์ •๊ณผ, ํ‘œ์ง€๋ฌผ์งˆ ํƒ์ง€๋ฅผ ์œ„ํ•œ ํ›„๊ฐ ์ˆ˜์šฉ์ฒด ๋ฐœ๊ตด ๋ฐ ์„ฑ๋Šฅ ํ‰๊ฐ€์ด๋‹ค. ์—ฐ๊ตฌ ๋Œ€์ƒ์œผ๋กœ ์„ ํƒํ•œ ์งˆ๋ณ‘์€ ํ์•”, ๊ฒฐํ•ต, ๊ทธ๋ฆฌ๊ณ  ์œ„์•”์ด๋‹ค. ๋จผ์ € ํ์•”์˜ ํ‘œ์ง€๋ฌผ์งˆ ๋ฐœ๊ตด๊ณผ ์ธ๊ฐ„ ํ›„๊ฐ ์ˆ˜์šฉ์ฒด ํƒ์ƒ‰์ด ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ํ์•” ์„ธํฌ์ฃผ์™€ ์ •์ƒ ํ ์„ธํฌ์ฃผ๋ฅผ ๋ฐฐ์–‘ํ•˜์—ฌ ๋‘๋ถ€๊ณต๊ฐ„์˜ ๊ฐ€์Šค ์กฐ์„ฑ์„ GC/MS๋กœ ๋น„๊ตํ•˜์˜€๊ณ , ํ์•” ์„ธํฌ์—์„œ ๋” ๋งŽ์ด ๋ฐœ์ƒํ•˜๋Š” ํœ˜๋ฐœ์„ฑ ์œ ๊ธฐ๋ฌผ์งˆ 2-์—ํ‹ธํ—ฅ์‚ฐ์˜ฌ์„ ํŠน์ •ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด ๋ฌผ์งˆ์„ ๊ฐ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ์ธ๊ฐ„ ํ›„๊ฐ ์ˆ˜์šฉ์ฒด๋ฅผ ์ด์ค‘๋ฐœ๊ด‘ ๋ฃจ์‹œํผ๋ ˆ์ด์ฆˆ ๊ฒ€์ •๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ํƒ์ƒ‰ํ•˜์˜€๋‹ค. ๋ฐœ๊ตด๋œ ํ›„๊ฐ ์ˆ˜์šฉ์ฒด๊ฐ€ ํ์•” ํ‘œ์ง€๋ฌผ์งˆ์„ ๋ฏผ๊ฐํ•˜๊ณ  ์„ ํƒ์ ์œผ๋กœ ๊ฐ์ง€ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ํ–ฅํ›„ ๋ฐ”์ด์˜ค ์ „์ž ์„ผ์„œ์˜ 1์ฐจ ์†Œ์ž๋กœ ์‚ฌ์šฉํ•˜๊ธฐ ์œ„ํ•œ ํ›„๊ฐ ๋‚˜๋…ธ๋ฒ ์‹œํด ์ƒ์‚ฐ ๋ฐ ์„ฑ๋Šฅ ํ‰๊ฐ€๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ์†Œ๋ณ€์—์„œ ๋ฐœ๊ฒฌ๋œ ๊ฒฐํ•ต ๊ด€๋ จ 5์ข…์˜ ํœ˜๋ฐœ์„ฑ ์œ ๊ธฐ๋ฌผ์งˆ๋“ค์„ ๊ฐ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ์ธ๊ฐ„ ํ›„๊ฐ ์ˆ˜์šฉ์ฒด๋ฅผ ํƒ์ƒ‰ํ•˜์˜€๋‹ค. ํƒ์ƒ‰ ๊ณผ์ •์€ HEK293 ์„ธํฌ์ฃผ์— ์ธ๊ฐ„ ํ›„๊ฐ ์ˆ˜์šฉ์ฒด ์œ ์ „์ž์™€ ๋ฃจ์‹œํผ๋ ˆ์ด์ฆˆ ๋ฆฌํฌํ„ฐ ์œ ์ „์ž๋ฅผ ํ˜•์งˆ๋„์ž…ํ•˜์—ฌ ๊ฒฐํ•ต ๋ฐ”์ด์˜ค๋งˆ์ปค๋“ค์— ๋Œ€ํ•œ ๋ฐ˜์‘์„ฑ์„ ํ™•์ธํ•จ์œผ๋กœ์จ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ ๊ฐ๊ฐ์˜ ๊ฒฐํ•ต ๋ฐ”์ด์˜ค๋งˆ์ปค์— ๋Œ€ํ•œ ํ›„๊ฐ ์ˆ˜์šฉ์ฒด๊ฐ€ ์„ ์ •๋˜์—ˆ์œผ๋ฉฐ, ๊ทธ ๋ฐ˜์‘์„ฑ๊ณผ ์„ ํƒ๋„ ๋˜ํ•œ ๋ถ„์„๋˜์—ˆ๋‹ค. ์„ธ๋ฒˆ์งธ๋กœ, ์œ„์•” ํ™˜์ž์™€ ๊ฑด๊ฐ•ํ•œ ์‚ฌ๋žŒ์˜ ๋‚ ์ˆจ ์ƒ˜ํ”Œ์„ ๋‹ค์ˆ˜ ์ฑ„์ทจํ•˜์—ฌ GC/MS ์žฅ๋น„๋ฅผ ์ด์šฉํ•ด ๋ถ„์„ํ•˜๊ณ  ๋น„๊ตํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์œ„์•” ํ™˜์ž์—๊ฒŒ์„œ ์ƒ๋Œ€์ ์œผ๋กœ ๋งŽ์ด ๋ฐœ๊ฒฌ๋˜๋Š” ํœ˜๋ฐœ์„ฑ ์œ ๊ธฐ๋ฌผ์งˆ์ธ ๋ทฐํ‹ธ์‚ฐ๊ณผ ํ”„๋กœํ”ผ์˜จ์‚ฐ์„ ํŠน์ •ํ•˜์˜€๋‹ค. ํŠนํžˆ, ๋‚ ์ˆจ ์ƒ˜ํ”Œ ๋‚ด์— ๋งค์šฐ ์ ์€ ์–‘์ด ํฌํ•จ๋œ ํ‘œ์ง€๋ฌผ์งˆ์„ ๋น ์ง์—†์ด ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ํœ˜๋ฐœ์„ฑ ์œ ๊ธฐ๋ฌผ์งˆ ์ฑ„์ทจ ๋ฐ ๋†์ถ• ์ˆ˜๋‹จ์œผ๋กœ ๊ณ ์ฒด ๋ฏธ์„ธ์ถ”์ถœ (SPME) ์„ฌ์œ ๋ฅผ ํ™œ์šฉํ•˜์˜€๋‹ค. ์„ ์ •ํ•œ ํœ˜๋ฐœ์„ฑ ์œ ๊ธฐ๋ฌผ์งˆ์˜ ํ‘œ์ง€๋ฌผ์งˆ๋กœ์„œ์˜ ์‹ ๋ขฐ๋„๋ฅผ ์ œ๊ณ ํ•˜๊ธฐ ์œ„ํ•ด, ์ „์ฒด ์ž๋ฃŒ์˜ ํ†ต๊ณ„ ๋ถ„์„ ๊ณผ์ •์„ ํ†ตํ•ด ๋‚ ์ˆจ ๋‚ด์˜ ํ‘œ์ง€๋ฌผ์งˆ ํฌํ•จ๋Ÿ‰์„ ๊ธฐ์ค€์œผ๋กœ ํ™˜์ž ์—ฌ๋ถ€๋ฅผ ๊ตฌ๋ถ„์ง“๋Š” ์ง„๋‹จ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜๊ณ  ๊ทธ ๋ฏผ๊ฐ๋„์™€ ์„ ํƒ๋„๋ฅผ ์‚ฐ์ถœํ•˜์˜€๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ, ํ–ฅํ›„ ์ง„ํ–‰ํ•  ๋‚ ์ˆจ์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์œ„์•” ์ง„๋‹จ์šฉ ๋ฐ”์ด์˜ค ์ „์ž ์„ผ์„œ ์ œ์ž‘์„ ์œ„ํ•ด, ๋ทฐํ‹ธ์‚ฐ๊ณผ ํ”„๋กœํ”ผ์˜จ์‚ฐ์„ ๊ฐ์ง€ํ•œ๋‹ค๊ณ  ์•Œ๋ ค์ง„ ์ธ๊ฐ„ ํ›„๊ฐ ์ˆ˜์šฉ์ฒด 2์ข…์˜ ๋ฐ˜์‘์„ฑ๊ณผ ์„ ํƒ๋„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์งˆ๋ณ‘ ์ง„๋‹จ๊ธฐ์ˆ  ๊ฐœ๋ฐœ์€ ์ธ๋ฅ˜์˜ ๋ณดํŽธ์  ๋ณต์ง€์™€ ํ‰๊ท ์ˆ˜๋ช… ์—ฐ์žฅ์„ ์œ„ํ•˜์—ฌ ํ•„์—ฐ์ ์œผ๋กœ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•˜๋Š” ๊ณผ์ •์ด๋‹ค. ์งˆ๋ณ‘ ํŠน์ด์  ํœ˜๋ฐœ์„ฑ ์œ ๊ธฐ๋ฌผ์งˆ์„ ๋Œ€์ƒ์œผ๋กœ ์‚ผ๋Š” ์ง„๋‹จ ๋ฐฉ์‹์€ ์ฐจ์„ธ๋Œ€ ์ง„๋‹จ๊ธฐ์ˆ ๋กœ์จ ํ•™๊ณ„์—์„œ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ์œผ๋ฉฐ, ์„ธ๊ณ„ ๊ฐ์ง€์—์„œ ํ™œ๋ฐœํ•˜๊ฒŒ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ช‡ ๊ฐ€์ง€ ์งˆ๋ณ‘ ํŠน์ด์  ํœ˜๋ฐœ์„ฑ ์œ ๊ธฐ๋ฌผ์งˆ์ด ์‹ ๊ทœ ๋ฐœ๊ตด๋˜์—ˆ์œผ๋ฉฐ, ๋˜ํ•œ ๊ธฐ์กด์— ์•Œ๋ ค์ง„ ์งˆ๋ณ‘ ํ‘œ์ง€๋ฌผ์งˆ์„ ๊ฐ์ง€ํ•˜๋Š” ๋Šฅ๋ ฅ์„ ๊ฐ€์ง„ ํ›„๊ฐ ์ˆ˜์šฉ์ฒด๋ฅผ ํƒ์ƒ‰ํ•˜๊ณ  ๊ทธ ๊ธฐ๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ƒ์ˆ ํ•œ ์—ฐ๊ตฌ ์„ฑ๊ณผ๋“ค์ด ๋ฏผ๊ฐํ•˜๊ณ  ์„ ํƒ์ ์ธ ์งˆ๋ณ‘ ์ง„๋‹จ์šฉ ์ƒ์ฒด ์†Œ์ž ๊ฐœ๋ฐœ์— ์œ ์šฉํ•˜๊ฒŒ ํ™œ์šฉ๋˜๊ธธ ๊ธฐ๋Œ€ํ•œ๋‹ค.Chapter 1. Research Background and Objectives 1 Chapter 2. Literature Review 4 2.1 Volatolomics 5 2.2 Biomarkers of disease 6 2.2.1 Volatile organic compounds related to disease 6 2.2.2 Sources and biochemical pathways of disease-related volatile organic compounds 7 2.3 Deorphanization and application of olfactory receptors 9 Chapter 3. Experimental Procedures 11 3.1 Collection and analysis of headspace gas from cell lines 12 3.1.1 Cell culture and headspace gas sampling 12 3.1.2 Headspace gas analysis with GC/MS 15 3.2 Identification of gastric cancer biomarkers from breath 17 3.2.1 Study groups and collection of clinical data 17 3.2.2 Sampling of exhaled breath and environmental gas 19 3.2.3 SPME-GC/MS analysis 19 3.2.4 Statistical analysis 20 3.3 Gene cloning 21 3.4 Production of olfactory receptor proteins 22 3.4.1 Expression of olfactory receptors in mammalian cells 22 3.4.2 Generation of olfactory nanovesicles 23 3.5 Characterization of olfactory receptor proteins 25 3.5.1 Immunocytochemistry 25 3.5.2 Western blot analysis 25 3.5.3 Calcium signaling assay 25 3.5.4 Dual-glo luciferase assay 28 Chapter 4. Identification of lung cancer biomarkers using a cancer cell line and screening of olfactory receptors for the biomarker detection 29 4.1 Introduction 30 4.2 Collection and analysis of headspace gas of lung cancer cell line 32 4.3 Screening of human olfactory receptors recognizing 2-ethyl-1-hexanol 37 4.4 Generation and characterization of olfactory nanovesicles 39 4.5 Conclusions 41 Chapter 5. Screening of human olfactory receptors to detect tuberculosis-specific volatile organic compounds in urine 42 5.1 Introduction 43 5.2 Screening of human olfactory receptors 45 5.3 Characterization of olfactory receptors recognizing biomarkers of tuberculosis 51 5.5 Conclusions 54 Chapter 6 Identification and validation of gastric cancer biomarkers and assessment of human olfactory receptors for the biomarker detection 55 6.1 Introduction 56 6.2 Selection of SPME fiber type 58 6.3 Sampling and Analysis of Exhaled Breath 60 6.4 Changes in the amounts of VOCs in the breath of gastric cancer patients before and after surgery 65 6.5 Statistical analysis for construction of diagnostic model 70 6.6 Cell-based assay for characterization of human olfactory receptors recognizing gastric cancer biomarkers 74 6.7 Conclusions 77 Chapter 7. Overall Discussion and further suggestions 78 References 84 Appendix 1. Comparative evaluation of sensitivity to hexanal between human and canine olfactory receptors 102 A1.1 Abstract 103 A1.2 Introduction 103 A1.3 Cloning of hOR2W1 and cfOR0312 genes 105 A1.4 Expression of human and canine olfactory receptors on HEK293 cell surface 107 A1.5 Comparison of human and canine OR sensitivity to hexanal 109 A1.6 Conclusions 113 References 114 Abstract 118Docto

    Diagnosis of Smear-Negative Pulmonary Tuberculosis using Ensemble Method: A Preliminary Research

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    Indonesia is one of 22 countries with the highest burden of Tuberculosis in the world. According to WHOโ€™s 2015 report, Indonesia was estimated to have one million new tuberculosis (TB) cases per year. Unfortunately, only one-third of new TB cases are detected. Diagnosis of TB is difficult, especially in the case of smear-negative pulmonary tuberculosis (SNPT). The SNPT is diagnosed by TB trained doctors based on physical and laboratory examinations. This study is preliminary research that aims to determine the ensemble method with the highest level of accuracy in the diagnosis model of SNPT. This model is expected to be a reference in the development of the diagnosis of new pulmonary tuberculosis cases using input in the form of symptoms and physical examination in accordance with the guidelines for tuberculosis management in Indonesia. The proposed SNPT diagnosis model can be used as a cost-effective tool in conditions of limited resources. Data were obtained from medical records of tuberculosis patients from the Jakarta Respiratory Center. The results show that the Random Forest has the best accuracy, which is 90.59%, then Adaboost of 90.54% and Bagging of 86.91%

    Recent Advances in Volatile Organic Compound Analysis as Diagnostic Biomarkers

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    Volatile organic compounds (VOCs) are a diverse group of carbon-based molecules that are volatile at ambient temperatures and are emitted by an organism as a result of metabolic processes of cells and associated microbiome. The qualitative and quantitative profile of VOCs in biological fluids can vary depending on the physiological changes. Therefore, the pattern of volatile metabolites may reflect the presence of several diseases. This has been intensively investigated in the last few decades, resulting in an increasing number of studies focused on new volatile biomarker discovery.This reprint aimed to summarize the recent findings related to VOCs detected in various biological fluids such as breath, urine and feces for biomedical applications. The content covers various topics, including but not limited to biomedical/medical application of VOC analysis, biomarker discovery, and novel approaches for sampling and analyzing VOCs

    Simultaneous Proton Transfer Reaction-Mass Spectrometry and electronic nose study of the volatile compounds released by Plasmodium falciparum infected red blood cells in vitro

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    The discovery that Volatile Organic Compounds (VOCs) can be biomarkers for several diseases has led to the conception of their possible application as diagnostic tools. In this study, we aimed at defining of diagnostic signatures for the presence of malaria transmissible stages in infected individuals. To do this, we compared VOCs released by asexual and sexual stage cultures of Plasmodium falciparum, the deadliest species of malaria, with those emitted by uninfected red blood cells (RBCs). VOC analysis was carried out with an innovative set-up, where each sample was simultaneously analysed by proton transfer reaction time of flight mass spectrometry (PTR-ToF-MS) and an electronic nose. PTR-Tof-MS results show that sexual stages are characterized by a larger emission of hexanal, compared with uninfected or asexual stage-infected RBCs, which makes them clearly identifiable. PTR-Tof-MS analysis also detected differences in VOC composition between asexual stages and uninfected RBCs. These results have been substantially replicated by the electronic nose analysis and may open the possibility to develop sensitive and easy-to-use devices able to detect sexual parasite stages in infected individuals. This study also demonstrates that the combination of mass spectrometry with electronic noses is a useful tool to identify markers of diseases and to support the development of optimized sensors
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