Circulating tumour cells (CTCs) are shed by primary tumours and are found in the peripheral blood of patients with metastatic cancers. Recent studies have shown that the number of CTCs corresponds with disease severity and prognosis. Therefore, detection and further functional analysis of CTCs are important for biomedical science, early diagnosis of cancer metastasis and tracking treatment efficacy in cancer patients, especially in point-of-care applications. Over the last few years, there has been an increasing shift towards not only capturing and detecting these rare cells, but also ensuring their viability for post-processing, such as cell culture and genetic analysis. High throughput lab-on-a-chip (LOC) has been fuelled up to process and analyse heterogeneous real patient samples while gaining profound insights for cancer biology. In this review, we highlight how miniaturisation strategies together with nanotechnologies have been used to advance LOC for capturing, separating, enriching and detecting different CTCs efficiently, while meeting the challenges of cell viability, high throughput multiplex or single-cell detection and post-processing. We begin this survey with an introduction to CTC biology, followed by description of the use of various materials, microstructures and nanostructures for design of LOC to achieve miniaturisation, as well as how various CTC capture or separation strategies can enhance cell capture and enrichment efficiencies, purity and viability. The significant progress of various nanotechnologies-based detection techniques to achieve high sensitivities and low detection limits for viable CTCs and/or to enable CTC post-processing are presented and the fundamental insights are also discussed. Finally, the challenges and perspectives of the technologies are enumerated.
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June 2025
Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, Maharashtra, India.
Integrated Circuits are made of various transistors that are embedded on a silicon wafer, these wafers are difficult to process and hence are prone to defects. Defecting these defects manually is a time consuming and labour-intensive task and hence automation is necessary. Deep Learning approach is better suited in this case as it is able to generalize defects if trained properly and can be a solution to segmentation and classification of defects automatically.
View Article and Find Full Text PDFBMC Genomics
January 2025
Cannabis Innovation and Research Center, Université de Moncton, Moncton, New-Brunswick, Canada.
Background: Due to its previously illicit nature, Cannabis sativa had not fully reaped the benefits of recent innovations in genomics and plant sciences. However, Canada's legalization of C. sativa and products derived from its flower in 2018 triggered significant new demand for robust genotyping tools to assist breeders in meeting consumer demands.
View Article and Find Full Text PDFMacromol Rapid Commun
January 2025
State Key Laboratory of Advanced Fiber Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 200051, China.
Mechanically responsive polymer materials have garnered significant interest due to their unique ability to respond to external forces, leading to groundbreaking applications in visual stress mapping and damage detection. However, their use in fibers remains relatively unexplored. In this study, a mechanoresponsive polymer is synthesized by incorporating a spiropyran (SP) mechanophore into a polyurethane backbone.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Dusseldorf, Germany.
Aim of this study was to proof the concept of optimizing the contrast between prostate cancer (PC) and healthy tissue by DWI post-processing using a quadrature method. DWI post-processing was performed on 30 patients (median age 67 years, prostate specific antigen 8.0 ng/ml) with PC and clear MRI findings (PI-RADS 4 and 5).
View Article and Find Full Text PDFPhysiol Meas
January 2025
Nanchang University, 1st Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330031, CHINA.
Background And Objective: In contrast to respiratory sound classification, respiratory phase and adventitious sound event detection provides more detailed and accurate respiratory information, which is clinically important for respiratory disorders. However, current respiratory sound event detection models mainly use convolutional neural networks to generate frame-level predictions. A significant drawback of the frame-based model lies in its pursuit of optimal frame-level predictions rather than the best event-level ones.
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