Machine-learning-aided multiplexed nanoplasmonic biosensor for COVID-19 population immunity profiling

Antibodies testing, Immunity and immunity passport in Covid-19

The global outbreak of coronavirus disease-2019 (COVID-19) has affected most countries in the world. Our knowledge of diagnostic tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is evolving, and it is important to have a clear understanding of the nature of the tests and the interpretation of their findings. In this paper, the purpose is to interpret the results of two typ...

Multiplexed Nanoplasmonic Temporal Profiling of T-Cell Response under Immunomodulatory Agent Exposure

Immunomodulatory drugs-agents regulating the immune response-are commonly used for treating immune system disorders and minimizing graft versus host disease in persons receiving organ transplants. At the cellular level, immunosuppressant drugs are used to inhibit pro-inflammatory or tissue-damaging responses of cells. However, few studies have so far precisely characterized the cellular-level e...

Multiplex serum cytokine immunoassay using nanoplasmonic biosensor microarrays.

Precise monitoring of the rapidly changing immune status during the course of a disease requires multiplex analysis of cytokines from frequently sampled human blood. However, the current lack of rapid, multiplex, and low volume assays makes immune monitoring for clinical decision-making (e.g., critically ill patients) impractical. Without such assays, immune monitoring is even virtually impossi...

Optimizing Nanoplasmonic Biosensor Sensitivity with Orientated Single Domain Antibodies

Localized surface plasmon resonance (LSPR) spectroscopy and imaging are emerging biosensor technologies which tout label-free biomolecule detection at the nanoscale and ease of integration with standard microscopy setups. The applicability of these techniques can be limited by the restrictions that surface-conjugated ligands must be both sufficiently small and orientated to meet analyte sensiti...

Machine Learning-aided, Robust Wideband Spectrum Sensing for Cognitive Radios