王致丞^1,2、紀紹文²、莊漢聲^2,3

¹奇美醫學中心 外科部 泌尿科；²成功大學 生物醫學工程研究所，³醫材研發中心

 

Jhih-Cheng Wang^1,2, Shao-Wen Chi², Han-Sheng Chuang^2,3

Divisions of Urology, Department of Surgery, Chi Mei Medical Center¹, Tainan, Taiwan;

Department of Biomedical Engineering² and Medical Device Innovation Center³, National Cheng Kung University, Tainan, Taiwan

 

Purpose: The aim of his study is to develop a novel method for quickly quantify and monitor the drugs sensitivity of the microorganisms by using optical diffusometry and microparticle-based immunoassay.

Materials and Methods: Optical diffusometry requires only a microscope and a camera to quantify the Brownian motion of particles. Because Brownian motion is a random and self-driven physical phenomenon, this technique can avoid the aforementioned limitations. In our concept of design, as bacteria grow and attach to particles, the measured Brownian motion tends to vary in response to the increased equivalent particle diameter. When bacteria are sensitive to an antibiotic, the change will then be halted, which can be associated with the minimum inhibitory concentration (MIC) of the drug. In an attempt with P. aeruginosa, we demonstrated that an AST process can be complete within 2 h. In addition, the minimum requirement of the sample volume is only 0.5 μL while the initial bacteria count is as low as 50 CFU per droplet (10⁵ CFU/mL).

Results: An assessment of binding specificity showed that 92 ± 2.2% of the anti-P. aeruginosa polyclonal antibody modified particles remained attached firmly to P. aeruginosa after 1 h (Fig and Video). The bacterium-particle complex was verified using a SEM. The rough surfaces of the antibody-conjugated particles were attributed to the matrix formed by the antibodies. The SEM images provided visual evidence showing the successful binding between the bacteria and the particles. The incubation time and binding efficacy here were consistent with those in previous studies based on the bead-based immunoassay

Conclusions: Then we go into clinical practice stage, we select E-coli as the pathogen in the infectious event to test how correct our theory could be in predicting antibiotics’ sensitivity or resistance for the pathogen when compared to the clinical urine culture results. The prediction rate is around 80 ~85%.