Enhancing saliva diagnostics: The impact of amylase depletion on MALDI-ToF MS profiles as applied to COVID-19.

Introduction: Human saliva contains a wealth of proteins that can be monitored for disease diagnosis and progression. Saliva, which is easy to collect, has been extensively studied for the diagnosis of numerous systemic and infectious diseases. However, the presence of amylase, the most abundant protein in saliva, can obscure the detection of low-abundance proteins by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-ToF MS), thus reducing its diagnostic utility.

MALDI-ToF protein profiling as a potential rapid diagnostic platform for COVID-19.

More than a year after the COVID-19 pandemic was declared, the need still exists for accurate, rapid, inexpensive and non-invasive diagnostic methods that yield high specificity and sensitivity towards the current and newly emerging SARS-CoV-2 strains. Compared to the nasopharyngeal swabs, several studies have established saliva as a more amenable specimen type for early detection of SARS-CoV-2. Considering the limitations and high demand for COVID-19 testing, we employed MALDI-ToF mass spectrometry in the analysis of 60 gargle samples from human donors and compared the resultant spectra against COVID-19 status.

Design of Turn-On Near-Infrared Fluorescent Probes for Highly Sensitive and Selective Monitoring of Biopolymers.

Simple, sensitive, and selective detection of specific biopolymers is critical in a broad range of biomedical and technological areas. We present a design of turn-on near-infrared (NIR) fluorescent probes with intrinsically high signal-to-background ratio. The fluorescent signal generation mechanism is based on the aggregation/de-aggregation of phthalocyanine chromophores controlled by selective binding of small-molecule "anchor" groups to a specific binding site of a target biopolymer.

Multidimensional Tunability of Nucleic Acids Enables Sensing over Unknown Backgrounds.

A longstanding challenge in quantitative analysis is the relationship between a sensor's dynamic range and a background: the response range must align with the target's background value. If this condition is not met, a reliable measurement is impossible. The requirement is especially critical for sensing systems displaying sharp responses.