인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Microfluidic impedance flow cytometry has been widely used in leukocyte differential and counting, but it faces a bottleneck due to the trade-off between impedance detection throughput and sensitivity. In this study, a microfluidic impedance flow cytometer based on a virtual constriction microchannel was reported, in which the virtual constriction microchannel was constructed by crossflow of conductive sample and insulated sheath fluids with underneath micro-electrodes for impedance measurements. Compared to conventional mechanical constriction microchannels, this virtual counterpart could effectively avoid direct physical contact between cells and the microchannel walls to maintain high throughputs, and significantly reduce the volume of the impedance detection region for sensitivity improvements. Using the developed microfluidic impedance flow cytometer, impedance pulses of three leukemia cell lines, K562, Jurkat, and HL-60, were detected, achieving a 99.8% differentiation accuracy through the use of a recurrent neural network. Furthermore, impedance pulses of four white blood cell subpopulations (neutrophils, eosinophils, monocytes, and lymphocytes) from three donors were detected, achieving a classification accuracy of ≥99.2%. A classification network model was established based on purified white blood cell and applied to impedance pulses of two white blood cell mixtures, resulting in proportional distributions of four leukocyte subpopulations within theoretical ranges. These results indicated that the developed microfluidic impedance flow cytometer based on the virtual constriction microchannel could achieve both high detection throughput and high sensitivity, showing great potentials for clinical diagnostics and blood analysis.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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