Confinement-guided ultrasensitive optical assay with artificial intelligence for disease diagnostics

Abstract

<p>The necessity for ultrasensitive detection is becoming increasingly apparent as it plays a pivotal role in disease early diagnostics and health management, particularly when it comes to detecting and monitoring low-abundance biomarkers or precious samples with tiny volumes. In many disease cases, such as cancer, infectious disease, autoimmune disorder, and neurodegenerative disease, low-abundant target biomarkers like circulating tumor cells (CTCs), extracellular vesicle (EV) subpopulations, and post-translational modified proteins (PTMs) are commonly existing and can be served as early indicators of disease onset or progression. However, these biomarkers often exist in ultra-low quantities in body fluids, surpassing the detection limits of conventional diagnostic tools like enzyme-linked immunosorbent assay (ELISA). This leads to the inability to probe disease evolution at a very early stage from molecular pathology perspective. In such regard, ultrasensitive optical assays have emerged as a solution to overcome these limitations and have witnessed significant progress in recent decades. This review provides a comprehensive overview of the recent advancements in ultrasensitive optical detection for disease diagnostics, particularly focusing on the conjunction of confinement within micro-/nano-structures and signal amplification to generate distinguishable optical readouts. The discussion begins with a meticulous evaluation of the advantages and disadvantages of these ultra-sensitive optical assays. Then, the spotlight is turned towards the implementation of artificial intelligence (AI) algorithms. The ability of AI to process large volumes of visible reporter signal and clinical data has proven invaluable in identifying unique patterns across multi-center cohort samples. Looking forward, the review underscores future advancements in developing convergent biotechnology (BT) and information technology (IT) toolbox, especially optical biosensors for high-throughput biomarker screening, point-of-care (PoC) testing with appropriate algorithms for their clinical translation are highlighted.</p>

Preliminary optimization study on the PFA-based GSHCAL for the CEPC

“…According to the related studies shown in the CEPC CDR, a BMR of better than 4% can meet most requirements for Higgs measurements, but the further improvement of the BMR is still necessary for the flavor and new physics measurements. Benefiting from the continuous development of novel materials [13] and photoelectric detection [14,15], a conceptual design of high-granularity glass scintillator hadronic calorimeter (GSHCAL) based on particle flow paradigm has been proposed recently and can achieve a BMR of around 3.38% with an initial parameter configuration [16], which shows great potential to improve the BMR significantly. However, this initial configuration in the conceptual design is a relatively ideal scheme, which still need more optimization to balance the key physics performance (i.e.…”