An integrated paper-based microfluidic platform for screening of early-stage Alzheimer's disease by detecting Aβ42.

Alzheimer's disease (AD) is the leading cause of dementia worldwide, and the development of early screening methods can address its significant health and social consequences. In this paper, we present a rotary-valve assisted paper-based immunoassay device (RAPID) for early screening of AD, featuring a highly integrated on-chip rotary micro-valve that enables fully automated and efficient detection of the AD biomarker (amyloid beta 42, Aβ42) in artificial plasma. The microfluidic paper-based analytical device (μPAD) of the RAPID pre-stores the required assay reagents on a μPAD and automatically controls the liquid flow through a single valve.

Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease.

Background: Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas.

Pneumatic piston hydrostatic bioreactor for cartilage tissue engineering.

During exercise, mechanical loads from the body are transduced into interstitial fluid pressure changes which are sensed as dynamic hydrostatic forces by cells in cartilage. The effects of these loading forces in health and disease are of interest to biologists, but the availability of affordable equipment for experimentation is an obstacle to research progress. Here, we report the development of a cost-effective hydropneumatic bioreactor system for research in mechanobiology.

Deep learning-assisted ultra-accurate smartphone testing of paper-based colorimetric ELISA assays.

Smartphone has long been considered as one excellent platform for disease screening and diagnosis, especially when combined with microfluidic paper-based analytical devices (μPADs) that feature low cost, ease of use, and pump-free operations. In this paper, we report a deep learning-assisted smartphone platform for ultra-accurate testing of paper-based microfluidic colorimetric enzyme-linked immunosorbent assay (c-ELISA). Different from existing smartphone-based μPAD platforms, whose sensing reliability is suffered from uncontrolled ambient lighting conditions, our platform is able to eliminate those random lighting influences for enhanced sensing accuracy.

Point-of-care Analysis for Non-invasive Diagnosis of Oral cancer (PANDORA): A technology-development proof of concept diagnostic accuracy study of dielectrophoresis in patients with oral squamous cell carcinoma and dysplasia.

Background: Delays in the identification and referral of oral cancer remain frequent. An accurate and non-invasive diagnostic test to be performed in primary care may help identifying oral cancer at an early stage and reduce mortality. Point-of-care Analysis for Non-invasive Diagnosis of Oral cancer (PANDORA) was a proof-of-concept prospective diagnostic accuracy study aimed at advancing the development of a dielectrophoresis-based diagnostic platform for oral squamous cell carcinoma (OSCC) and epithelial dysplasia (OED) using a novel automated DEPtech 3DEP analyser.