Toward Resolving Heterogeneous Mixtures of Nanocarriers in Drug Delivery Systems through Light Scattering and Machine Learning.

Nanocarriers (NCs) have emerged as a revolutionary approach in targeted drug delivery, promising to enhance drug efficacy and reduce toxicity through precise targeting and controlled release mechanisms. Despite their potential, the clinical adoption of NCs is hindered by challenges in their physicochemical characterization, essential for ensuring drug safety, efficacy, and quality control. Traditional characterization methods, such as dynamic light scattering and nanoparticle tracking analysis, offer limited insights, primarily focusing on particle size and concentration, while techniques like high-performance liquid chromatography and mass spectrometry are hampered by extensive sample preparation, high costs, and potential sample degradation.

Forecasting COVID-19 Severity by Intelligent Optical Fingerprinting of Blood Samples.

Forecasting COVID-19 disease severity is key to supporting clinical decision making and assisting resource allocation, particularly in intensive care units (ICUs). Here, we investigated the utility of time- and frequency-related features of the backscattered signal of serum patient samples to predict COVID-19 disease severity immediately after diagnosis. ICU admission was the primary outcome used to define disease severity.