Womxn In BME Journal

Authors: Diane N.H. Kim, Kevin T. Kim, Carolyn Kim, Michael A. Teitell, Thomas A. Zangle

Abstract: 

Microfluidic devices are widely used for biomedical applications based on microscopy or other optical detection methods. However, the materials commonly used for microfabrication typically have a high refractive index relative to water, which can create artifacts at device edges and limit applicability to applications requiring high-precision imaging or morphological feature detection. Here we present a soft lithography method to fabricate microfluidic devices out of MY133-V2000, a UV-curable, fluorinated polymer with low refractive index that is close to that of water (n = 1.33). The primary challenge in the use of this material (and fluorinated materials in general) is the low adhesion of the fluorinated material; we present several alternative fabrication methods we have tested to improve inter-layer adhesion. The close match between the refractive index of this material and aqueous solutions commonly used in biomedical applications enables fluorescence imaging at microchannel or other microfabricated edges without distortion. The close match in refractive index also enables quantitative phase microscopy imaging across the full width of microchannels without error-inducing artifacts for measurement of cell biomass. Overall, our results demonstrate the utility of low-refractive index microfluidics for biological applications requiring high-precision optical imaging.

Authors: David Y.Pereira, Ricky Y.T.Chiu, Samantha C.L.Zhang, Benjamin M.Wu, Daniel T.Kamei

Abstract: 

The lateral-flow immunoassay (LFA) is an inexpensive and rapid paper-based assay that can potentially detect infectious disease biomarkers in resource-poor settings. Despite its many advantages that make it suitable for point-of-care diagnosis, LFA is limited by its inferior sensitivity relative to sophisticated laboratory-based assays. Our group previously introduced the use of a micellar aqueous two-phase system (ATPS), comprised of the nonionic Triton X-114 surfactant, to concentrate biomarkers in a sample and enhance their detection with LFA. However, achieving complete phase separation and target concentration using the Triton X-114 system required many hours, and the concentrated sample needed to be manually extracted and applied to LFA. Here, we successfully integrated the concentration and detection steps into a single step that occurs entirely within a portable paper-based diagnostic strip. In a novel approach, we applied the micellar ATPS to a 3-D paper design and effectively reduced the macroscopic phase separation time from 8 h to approximately 3 min. The 3-D design was integrated with LFA to simultaneously concentrate and detect Plasmodium lactate dehydrogenase (pLDH), a malaria biomarker, in both phosphate-buffered saline and fetal bovine serum within 20 min at room temperature. Compared to a conventional LFA setup with a pLDH detection limit of 10 ng μL−1, our single-step diagnostic successfully detected pLDH at 1.0 ng μL−1, demonstrating a 10-fold detection limit improvement and resulting in a sensitive and user-friendly assay that can be used at the point-of-care. The integration of a micellar ATPS and LFA represents a new platform that can improve and promote the use of paper-based diagnostic assays for malaria and other diseases within resource-poor settings.

Tags: 

Authors: Yichen Wu†‡§○ , Aniruddha Ray†‡§∥○, Qingshan Wei†‡§# , Alborz Feizi†‡§, Xin Tong†, Eva Chen‡, Yi Luo†‡§, and Aydogan Ozcan*†‡§∥ 

Abstract: Aggregation-based assays, using micro- and nanoparticles have been widely accepted as an efficient and cost-effective biosensing tool, particularly in microbiology, where particle clustering events are used as a metric to infer the presence of a specific target analyte and quantify its concentration. Here, we present a sensitive and automated readout method for aggregation-based assays using a wide-field lens-free on-chip microscope, with the ability to rapidly analyze and quantify microscopic particle aggregation events in 3D, using deep learning-based holographic image reconstruction. In this method, the computation time for hologram reconstruction and particle autofocusing steps remains constant, regardless of the number of particles/clusters within the 3D sample volume, which provides a major throughput advantage, brought by deep learning-based image reconstruction. As a proof of concept, we demonstrate rapid detection of herpes simplex virus (HSV) by monitoring the clustering of antibody-coated microparticles, achieving a detection limit of ∼5 viral copies/μL (i.e., ∼25 copies/test).