Behind the Paper
The real stories behind the latest research papers, from conception to publication, the highs and the lows
Filtered by: Bioengineering & Biotechnology
A comfortable and high-density digital healthcare platform by permeable 3D electronic skin
The development of permeable, three-dimensional integrated electronic skin (P3D-eskin) is quite a long journey. Ever since I was a freshman at university, I have had a strong curiosity and passion for digital healthcare technology using wearable electronics.
Beyond calories: assessing post-prandial oxidative stress using a micro-NMR system
Traditional views of adverse health effects of macronutrient have largely centered around calories and the intake-expenditure balance. Recent advancements in the biomedical technology have allowed measurements of metabolic and physiologic responses to macronutrient.
Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes Using Mellon
What constitutes a tissue based on single-cell data and how can we represent it computationally? This question led us to develop Mellon, a tool to quantify cell-state densities and reveal dynamics of cell-differentiation processes. Here's a behind-the-scenes look at our journey.
New tricks from old drugs: repurposing the 80s
A paper in Molecular Psychiatry describes a new cell biological mechanism regulated by antidepressants, showing how a single dose of a drug fluvoxamine may open up the brain for drug delivery. Can this finding signal the beginning of a brand new career for the 30+ year-old drugs?
A new automated, multiplex approach to studying shear stress mechanotransduction of circulating cells
As the mechanobiology of immune and cancer cells is gaining interest, we developed an easy to adopt, off-the-shelf system for subjecting many different samples to repeated cycles of fluid shear stress.
The Role of Smartwatch Data in Monitoring Non-Motor Symptoms of Parkinson’s Disease
Accurate and continuous monitoring of symptoms can enable better care and timely intervention. Wearables could facilitate this but are underexplored for monitoring of non-motor symptoms.
A single wearable sensor combined with machine learning estimates step length in older adults and patients with neurological disorders
We applied machine learning tools to estimate step length from features derived from a single, wearable, lower back sensor in older adults and people with altered gait patterns. Our results highlight the advantages of using machine learning for step length estimation and other related tasks.