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

Advances of 3D Cell Co-Culture Technology Based on Microfluidic Chips

Cell co-culture technology aims to study the communication mechanism between cells and to better reveal the interactions and regulatory mechanisms involved in processes such as cell growth, differentiation, apoptosis, and other cellular activities. This is achieved by simulating the complex organismic environment. Such studies are of great significance for understanding the physiological and pathological processes of multicellular organisms. As an emerging cell cultivation technology, 3D cell co-culture technology, based on microfluidic chips, can efficiently, rapidly, and accurately achieve cell co-culture. This is accomplished by leveraging the unique microchannel structures and flow characteristics of microfluidic chips. The technology can simulate the native microenvironment of cell growth, providing a new technical platform for studying intercellular communication. It has been widely used in the research of oncology, immunology, neuroscience, and other fields. In this review, we summarize and provide insights into the design of cell co-culture systems on microfluidic chips, the detection methods employed in co-culture systems, and the applications of these models.

Co-axial hydrogel spinning for facile biofabrication of prostate cancer-like 3D models

Glandular cancers are amongst the most prevalent types of cancer, which can develop in many different organs, presenting challenges in their detection as well as high treatment variability and failure rates. For that purpose, anticancer drugs are commonly tested in cancer cell lines grown in 2D tissue culture on plastic dishesin vitro, or in animal modelsin vivo. However, 2D culture models diverge significantly from the 3D characteristics of living tissues and animal models require extensive animal use and time. Glandular cancers, such as prostate cancer-the second leading cause of male cancer death-typically exist in co-centrical architectures where a cell layer surrounds an acellular lumen. Herein, this spatial cellular position and 3D architecture, containing dual compartments with different hydrogel materials, is engineered using a simple co-axial nozzle setup, in a single step utilizing prostate as a model of glandular cancer. The resulting hydrogel soft structures support viable prostate cancer cells of different cell lines and enable over-time maturation into cancer-mimicking aggregates surrounding the acellular core. The biofabricated cancer mimicking structures are then used as a model to predict the inhibitory efficacy of the poly ADP ribose polymerase inhibitor, Talazoparib, and the antiandrogen drug, Enzalutamide, in the growth of the cancer cell layer. Our results show that the obtained hydrogel constructs can be adapted to quickly obtain 3D cancer models which combine 3D physiological architectures with high-throughput screening to detect and optimize anti-cancer drugs in prostate and potentially other glandular cancer types.

Meticulous research for design of plasmonics sensors for cancer detection and food contaminants analysis via machine learning and artificial intelligence

Cancer is one of the leading causes of death worldwide, making early detection and accurate diagnosis critical for effective treatment and improved patient outcomes. In recent years, machine learning (ML) has emerged as a powerful tool for cancer detection, enabling the development of innovative algorithms that can analyze vast amounts of data and provide accurate predictions. This review paper aims to provide a comprehensive overview of the various ML algorithms and techniques employed for cancer detection, highlighting recent advancements, challenges, and future directions in this field. The main challenge is finding a safe, auditable and reliable analysis method for fundamental scientific publication. Food contaminant analysis is a process of testing food products to identify and quantify the presence of harmful substances or contaminants. These substances can include bacteria, viruses, toxins, pesticides, heavy metals, allergens, and other chemical residues. Machine learning (ML) and artificial intelligence (A.I) proposed as a promising method that possesses excellent potential to extract information with high validity that may be overlooked with conventional analysis techniques and for its capability in a wide range of investigations. A.I technology used in meta-optics can develop optical devices and systems to a higher level in future. Furthermore (M.L.) and (A.I.) play key roles as a health Approach for nano materials NMs safety assessment in environment and human health research. Beside, benefits of ML in design of plasmonic sensors for different applications with improved resolution and detection are convinced.

Dynamic Flow Control over Optical Properties of Liquid Crystal-Quantum Dot Hybrids in Microfluidic Devices

In this paper, we report developing approaches to tuning the optical behavior of microfluidic devices by infusing smart hybrids of liquid crystal and quantum dots into microchannel confinement. We characterize the optical responses of liquid crystal-quantum dot composites to polarized and UV light in single-phase microflows. In the range of flow velocities up to 10 mm/s, the flow modes of microfluidic devices were found to correlate with the orientation of liquid crystals, dispersion of quantum dots in homogeneous microflows and the resulting luminescence response of these dynamic systems to UV excitation. We developed a Matlab algorithm and script to quantify this correlation by performing an automated analysis of microscopy images. Such systems may have application potential as optically responsive sensing microdevices with integrated smart nanostructural components, parts of lab-on-a-chip logic circuits, or diagnostic tools for biomedical instruments.

Boosting the Clinical Translation of Organ-on-a-Chip Technology

Organ-on-a-chip devices have become a viable option for investigating critical physiological events and responses; this technology has matured substantially, and many systems have been reported for disease modeling or drug screening over the last decade. Despite the wide acceptance in the academic community, their adoption by clinical end-users is still a non-accomplished promise. The reasons behind this difficulty can be very diverse but most likely are related to the lack of predictive power, physiological relevance, and reliability necessary for being utilized in the clinical area. In this Perspective, we briefly discuss the main attributes of organ-on-a-chip platforms in academia and how these characteristics impede their easy translation to the clinic. We also discuss how academia, in conjunction with the industry, can contribute to boosting their adoption by proposing novel design concepts, fabrication methods, processes, and manufacturing materials, improving their standardization and versatility, and simplifying their manipulation and reusability.