iLoF: An intelligent Lab on Fiber Approach for Human Cancer Single-Cell Type Identification

Shining a Light on Cancer - Photonics in Microfluidic Tumor Modelling and Biosensing

Microfluidic platforms represent a powerful approach to miniaturizing important characteristics of cancers, improving in vitro testing by increasing physiological relevance. Different tools can manipulate cells and materials at the microscale, but few offer the efficiency and versatility of light and optical technologies. Moreover, light-driven technologies englobe a broad toolbox for quantifying critical biological phenomena. Herein, we review the role of photonics in microfluidic 3D cancer modeling and biosensing from three major perspectives. First, we look at optical-driven technologies that allow biomaterials and living cells to be manipulated with micro-sized precision and the opportunities to advance 3D microfluidic models by engineering cancer microenvironments' hallmarks, such as their architecture, cellular complexity, and vascularization. Second, we delve into the growing field of optofluidics, exploring how optical tools can directly interface microfluidic chips, enabling the extraction of relevant biological data, from single fluorescent signals to the complete 3D imaging of diseased cells within microchannels. Third, we review advances in optical cancer biosensing, focusing on how light-matter interactions can detect biomarkers, rare circulating tumor cells, and cell-derived structures such as exosomes. We overview photonic technologies' current challenges and caveats in microfluidic 3D cancer models, outlining future research avenues that may catapult the field. This article is protected by copyright. All rights reserved.

Recent Advances in Biomedical Photonic Sensors: A Focus on Optical-Fibre-Based Sensing

In this invited review, we provide an overview of the recent advances in biomedical photonic sensors within the last five years. This review is focused on works using optical-fibre technology, employing diverse optical fibres, sensing techniques, and configurations applied in several medical fields. We identified technical innovations and advancements with increased implementations of optical-fibre sensors, multiparameter sensors, and control systems in real applications. Examples of outstanding optical-fibre sensor performances for physical and biochemical parameters are covered, including diverse sensing strategies and fibre-optical probes for integration into medical instruments such as catheters, needles, or endoscopes.

Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers

The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.

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. We developed a stacking ensemble machine learning model including the backscattered signal features (optical fingerprint), patient comorbidities, and age (AUROC = 0.80), which significantly outperformed the predictive value of clinical and laboratory variables available at hospital admission (AUROC = 0.71). The information derived from patient optical fingerprints was not strongly correlated with any clinical/laboratory variable, suggesting that optical fingerprinting brings unique information for COVID-19 severity risk assessment. Optical fingerprinting is a label-free, real-time, and low-cost technology that can be easily integrated as a front-line tool to facilitate the triage and clinical management of COVID-19 patients.