Search Results (30)

This review evaluates the emerging role of circulating tumour cells (CTCs) as clinically meaningful, minimally invasive biomarkers for oral squamous cell carcinoma (OSCC). Despite advances in management, OSCC continues to demonstrate high morbidity and mortality, largely due to late diagnosis and the absence of validated biomarkers for early detection or real-time monitoring. Conventional diagnostic tools, tissue biopsy, and imaging provide only static snapshots and fail to capture tumour heterogeneity or evolving biological behaviour. CTCs offer a novel and significant opportunity to address these limitations. Key findings from recent studies highlight that CTC enumeration correlates with tumour burden, nodal metastasis, recurrence, and overall prognosis. Molecular and phenotypic characterisation further reveals dynamic traits such as epithelial–mesenchymal transition, stemness, and therapy resistance, providing insights into metastatic potential and treatment failure. Technological advances, including immunocytochemistry, microfluidic capture platforms, PCR-based assays, and next-generation sequencing, have enhanced the sensitivity and specificity of CTC detection and enabled detailed multi-omic profiling. Collectively, evidence suggests that integrating CTC analysis into OSCC clinical workflows could improve early detection, refine risk stratification, personalise therapeutic strategies, and support longitudinal monitoring of disease dynamics. As research progresses, CTC-based diagnostics represent a promising frontier in shifting OSCC management toward more precise, adaptive, and biologically informed care. Full article

Circulating tumor cells (CTCs) represent a powerful, minimally invasive window into tumor biology and disease evolution. Technological progress over the past decade has markedly improved the ability to isolate, preserve, and interrogate viable CTCs, transforming them from simple prognostic markers to functional tools for precision oncology. Advances in microfluidic platforms, immunomagnetic enrichment, aptamer-based capture, and nanostructured interfaces have expanded the efficiency and fidelity of CTC recovery, enabling comprehensive molecular profiling and ex vivo analysis. These innovations have paved the way for the development of CTC-derived preclinical models, including xenografts, organoids, and chorioallantoic membrane assays, which recapitulate patient-specific tumor heterogeneity and support individualized drug-sensitivity testing. In this review, we summarize current technologies for CTC isolation, outline recent achievements in functional and pharmacological characterization, and discuss the translational impact of CTC-derived models. We further identify persistent challenges and emerging opportunities, highlighting how integration of multi-omics platforms, artificial intelligence, and standardized workflows may accelerate the clinical implementation of CTC-guided personalized therapy. Full article

Circulating tumor cells (CTCs) are crucial biomarkers for lung cancer metastasis and recurrence, garnering significant clinical attention. Despite this, efficient and cost-effective detection methods remain scarce. Consequently, there is an urgent demand for the development of highly sensitive CTC detection technologies to enhance lung cancer diagnosis and treatment. This study utilized microspheres and A549 cells to model CTCs, assessing the impact of acoustic field forces on cell viability and proliferation and confirming capture efficiency. Subsequently, CTCs from the peripheral blood of patients with lung cancer were captured and identified using fluorescence in situ hybridization, and the results were compared to the immunomagnetic bead method to evaluate the differences between the techniques. Finally, epidermal growth factor receptor (EGFR) mutation analysis was conducted on CTC-positive samples. The findings showed that acoustic microfluidic technology effectively captures microspheres, A549 cells, and CTCs without compromising cell viability or proliferation. Moreover, EGFR mutation analysis successfully identified mutation types in four samples, establishing a basis for personalized targeted therapy. In conclusion, acoustic microfluidic technology preserves cell viability while efficiently capturing CTCs. When integrated with EGFR mutation analysis, it provides robust support for the precise diagnosis and treatment of lung cancer as well as personalized drug therapy. Full article

Scene text understanding, serving as a cornerstone technology for autonomous navigation, document digitization, and accessibility tools, has witnessed a paradigm shift from traditional methods relying on handcrafted features and multi-stage processing pipelines to contemporary deep learning frameworks capable of learning hierarchical representations directly from raw image inputs. This survey distinctly categorizes modern scene text recognition (STR) methodologies into three principal paradigms: two-stage detection frameworks that employ region proposal networks for precise text localization, single-stage detectors designed to optimize computational efficiency, and specialized architectures tailored to handle arbitrarily shaped text through geometric-aware modeling techniques. Concurrently, an in-depth analysis of text recognition paradigms elucidates the evolutionary trajectory from connectionist temporal classification (CTC) and sequence-to-sequence models to transformer-based architectures, which excel in contextual modeling and demonstrate superior performance. In contrast to prior surveys, this work uniquely emphasizes several key differences and contributions. Firstly, it provides a comprehensive and systematic taxonomy of STR methods, explicitly highlighting the trade-offs between detection accuracy, computational efficiency, and geometric adaptability across different paradigms. Secondly, it delves into the nuances of text recognition, illustrating how transformer-based models have revolutionized the field by capturing long-range dependencies and contextual information, thereby addressing challenges in recognizing complex text layouts and multilingual scripts. Furthermore, the survey pioneers the exploration of critical research frontiers, such as multilingual text adaptation, enhancing model robustness against environmental variations (e.g., lighting conditions, occlusions), and devising data-efficient learning strategies to mitigate the dependency on large-scale annotated datasets. By synthesizing insights from technical advancements across 28 benchmark datasets and standardized evaluation protocols, this study offers researchers a holistic perspective on the current state-of-the-art, persistent challenges, and promising avenues for future research, with the ultimate goal of achieving human-level scene text comprehension. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains among the most aggressive malignancies in the United States. Advances in treatments have slowly increased survival rates; however, outcomes remain dismal, largely due to the insidious onset of the disease and lack of screening tests leading to diagnosis at more advanced disease stages. As we better understand the molecular mechanisms that drive PDAC, we can leverage this technology for early detection of new PDAC or recurrences and find more effective methods to track treatment response. Liquid biopsies are increasingly common for the treatment of many malignancies, leveraging better technology to detect scant quantities of circulating tumor cells (CTCs) or byproducts of tumor biology (e.g., exosomes and microRNA [miRNA]) in the blood stream. When combined with existing biomarkers like CA 19-9, there is promising research that improved diagnostic modalities may be available in the future. Furthermore, these technologies are being leveraged to better prognosticate patients with PDAC and potentially monitor treatment responses not captured by cross-sectional imaging, which may allow for real-time changes in therapeutic strategy. This manuscript will review the molecular mechanisms that drive PDAC development and the biomarkers available for diagnosis and prognostication. Much of the data presented is still investigational, though many trials are ongoing to translate these studies for clinical use. Full article

Circulating tumor cells (CTCs) are vital indicators of metastasis and provide a non-invasive method for early cancer diagnosis, prognosis, and therapeutic monitoring. However, their low prevalence and heterogeneity in the bloodstream pose significant challenges for detection. Microfluidic systems, or “lab-on-a-chip” devices, have emerged as a revolutionary tool in liquid biopsy, enabling efficient isolation and analysis of CTCs. These systems offer advantages such as reduced sample volume, enhanced sensitivity, and the ability to integrate multiple processes into a single platform. Several microfluidic techniques, including size-based filtration, dielectrophoresis, and immunoaffinity capture, have been developed to enhance CTC detection. The integration of machine learning (ML) with microfluidic systems has further improved the specificity and accuracy of CTC detection, significantly advancing the speed and efficiency of early cancer diagnosis. ML models have enabled more precise analysis of CTCs by automating detection processes and enhancing the ability to identify rare and heterogeneous cell populations. These advancements have already demonstrated their potential in improving diagnostic accuracy and enabling more personalized treatment approaches. In this review, we highlight the latest progress in the integration of microfluidic technologies and ML algorithms, emphasizing how their combination has changed early cancer diagnosis and contributed to significant advancements in this field. Full article

Glioblastoma is the most common form of brain cancer in adults, representing 35–40% of all malignant brain tumors. This highly aggressive malignancy originates in the central nervous system, and despite notable advancements in treatment strategies, it continues to be an incurable disease. The isolation of circulating tumor cells (CTC) at an early stage is challenging due to the low probability of their presence in peripheral blood. Detection and enumeration as early as possible can reportedly lead to more effective treatment. This study proposes a novel label-free, rapid, and continuous CTC separation device based on a lateral filter array microfluidic device for the highly efficient immunoaffinity isolation of CTCs. Our methodology successfully captured and isolated circulating tumor cells (CTCs) from the whole blood of glioblastoma (GBM) patients prior to surgery, achieving over 90% capture efficiency in under 40 min of analysis. These findings highlight the potential of this technology to enhance our understanding of the clinical significance of CTCs in the management of GBM in future research. Full article

Circulating tumor cells (CTCs) are a type of cancer cell that spreads from the main tumor to the bloodstream, and they are often the most important among the various entities that can be isolated from the blood. For the diagnosis of cancer, conventional biopsies are often invasive and unreliable, whereas a liquid biopsy, which isolates the affected item from blood or lymph fluid, is a less invasive and effective diagnostic technique. Microfluidic technologies offer a suitable channel for conducting liquid biopsies, and this technology is utilized to extract CTCs in a microfluidic chip by physical and bio-affinity-based techniques. This effort uses functionalized magnetic nanoparticles (MNPs) in a unique microfluidic chip to collect CTCs using a hybrid (physical and bio-affinity-based/guided magnetic) capturing approach with a high capture rate. Accordingly, folic acid-functionalized Fe₃O₄ nanoparticles have been used to capture MCF-7 (breast cancer) CTCs with capture efficiencies reaching up to 95% at a 10 µL/min flow rate. Moreover, studies have been conducted to support this claim, including simulation and biomimetic investigations. Full article

The early and non-invasive diagnosis of tumor diseases has been widely investigated by the scientific community focusing on the development of sensors/biomarkers that act as a way of recognizing the adhesion of circulating tumor cells (CTCs). As a challenge in this area, strategies for CTCs capture and enrichment currently require improvements in the sensors/biomarker’s selectivity. This can be achieved by understanding the biological recognition factors for different cancer cell lines and also by understanding the interaction between surface parameters and the affinity between macromolecules and the cell surface. To overcome some of these concerns, electrochemical sensors have been used as precise, fast-response, and low-cost transduction platforms for application in cytosensors. Additionally, distinct materials, geometries, and technologies have been investigated to improve the sensitivity and specificity properties of the support electrode that will transform biochemical events into electrical signals. This review identifies novel approaches regarding the application of different specific biomarkers (CD44, Integrins, and EpCAm) for capturing CTCs. These biomarkers can be applied in electrochemical biosensors as a cytodetection strategy for diagnosis of cancerous diseases. Full article

The clinical utility of circulating tumor cells (CTC) as a non-invasive multipurpose biomarker is broadly recognized. The earliest methods for enriching CTCs from whole blood rely on antibody-based positive selection. The prognostic utility of CTC enumeration using positive selection with the FDA-approved CellSearch^TM system has been demonstrated in numerous studies. The capture of cells with specific protein phenotypes does not fully represent cancer heterogeneity and therefore does not realize the prognostic potential of CTC liquid biopsies. To avoid this selection bias, CTC enrichment based on size and deformability may provide better fidelity, i.e., facilitate the characterization of CTCs with any phenotype. In this study, the recently FDA-approved Parsortix^® technology was used to enrich CTCs from prostate cancer (PCa) patients for transcriptome analysis using HyCEAD^TM technology. A tailored PCa gene panel allowed us to stratify metastatic castration-resistant prostate cancer (mCRPC) patients with clinical outcomes. In addition, our findings suggest that targeted CTC transcriptome profiling may be predictive of therapy response. Full article

The rise of end-to-end (E2E) speech recognition technology in recent years has overturned the design pattern of cascading multiple subtasks in classical speech recognition and achieved direct mapping of speech input signals to text labels. In this study, a new E2E framework, ResNet–GAU–CTC, is proposed to implement Mandarin speech recognition for air traffic control (ATC). A deep residual network (ResNet) utilizes the translation invariance and local correlation of a convolutional neural network (CNN) to extract the time-frequency domain information of speech signals. A gated attention unit (GAU) utilizes a gated single-head attention mechanism to better capture the long-range dependencies of sequences, thus attaining a larger receptive field and contextual information, as well as a faster training convergence rate. The connectionist temporal classification (CTC) criterion eliminates the need for forced frame-level alignments. To address the problems of scarce data resources and unique pronunciation norms and contexts in the ATC field, transfer learning and data augmentation techniques were applied to enhance the robustness of the network and improve the generalization ability of the model. The character error rate (CER) of our model was 11.1% on the expanded Aishell corpus, and it decreased to 8.0% on the ATC corpus. Full article

Microfluidics has provided clinicians with new technologies to detect and analyze circulating tumor biomarkers in order to further improve their understanding of disease mechanism, as well as to improve patient management. Among these different biomarkers, circulating tumor cells have proven to be of high interest for different types of cancer and in particular for breast cancer. Here we focus our attention on a breast cancer subtype referred as HER2-positive breast cancer, this cancer being associated with an amplification of HER2 protein at the plasma membrane of cancer cells. Combined with therapies targeting the HER2 protein, HER2-HER3 dimerization blockade further improves a patient’s outcome. In this work, we propose a new approach to CTC characterization by on-chip integrating proximity ligation assay, so that we can quantify the HER2-HER3 dimerization event at the level of single CTC. To achieve this, we developed a microfluidic approach combining both CTC capture, identification and HER2-HER3 status quantification by Proximity Ligation Assay (PLA). We first optimized and demonstrated the potential of the on-chip quantification of HER2-HER3 dimerization using cancer cell lines with various levels of HER2 overexpression and validated its clinical potential with a patient’s sample treated or not with HER2-targeted therapy. Full article

Gastrointestinal (GI) cancers constitute a group of highest morbidity worldwide, with colorectal cancer (CRC) and gastric cancer being among the most frequently diagnosed. The majority of gastrointestinal cancer patients already present metastasis by the time of diagnosis, which is widely associated with cancer-related death. Accumulating evidence suggests that epithelial-to-mesenchymal transition (EMT) in cancer promotes circulating tumor cell (CTCs) formation, which ultimately drives metastasis development. These cells have emerged as a fundamental tool for cancer diagnosis and monitoring, as they reflect tumor heterogeneity and the clonal evolution of cancer in real-time. In particular, EMT phenotypes are commonly associated with therapy resistance. Thus, capturing these CTCs is expected to reveal important clinical information. However, currently available CTC isolation approaches are suboptimal and are often targeted to capture epithelial CTCs, leading to the loss of EMT or mesenchymal CTCs. Here, we describe size-based CTCs isolation using the RUBYchip™, a label-free microfluidic device, aiming to detect EMT biomarkers in CTCs from whole blood samples of GI cancer patients. We found that, for most cases, the mesenchymal phenotype was predominant, and in fact a considerable fraction of isolated CTCs did not express epithelial markers. The RUBYchip™ can overcome the limitations of label-dependent technologies and improve the identification of CTC subpopulations that may be related to different clinical outcomes. Full article

Analysis of circulating tumor cells (CTCs) from blood samples provides a non-invasive approach for early cancer detection. However, the rarity of CTCs makes it challenging to establish assays with the required sensitivity and specificity. We combine a highly sensitive CTC capture assay exploiting the cancer cell binding recombinant malaria VAR2CSA protein (rVAR2) with the detection of colon-related mRNA transcripts (USH1C and CKMT1A). Cancer cell transcripts are detected by RT-qPCR using proprietary Target Enrichment Long-probe Quantitative Amplified Signal (TELQAS) technology. We validate each step of the workflow using colorectal cancer (CRC) cell lines spiked into blood and compare this with antibody-based cell detection. USH1C and CKMT1A are expressed in healthy colon tissue and CRC cell lines, while only low-level expression can be detected in healthy white blood cells (WBCs). The qPCR reaction shows a near-perfect amplification efficiency for all primer targets with minimal interference of WBC cDNA. Spike-in of 10 cancer cells in 3 mL blood can be detected and statistically separated from control blood using the RT-qPCR assay after rVAR2 capture (p < 0.01 for both primer targets, Mann-Whitney test). Our results provide a validated workflow for highly sensitive detection of magnetically enriched cancer cells. Full article

HER2 is a prognostic and predictive biomarker in breast cancer, normally assessed in tumour biopsy and used to guide treatment choices. Circulating tumour cells (CTCs) escape the primary tumour and enter the bloodstream, exhibiting great metastatic potential and representing a real-time snapshot of the tumour burden. Liquid biopsy offers the unique opportunity for low invasive sampling in cancer patients and holds the potential to provide valuable information for the clinical management of cancer patients. This study assesses the performance of the RUBYchip™, a microfluidic system for CTC capture based on cell size and deformability, and compares it with the only FDA-approved technology for CTC enumeration, CellSearch^®. After optimising device performance, 30 whole blood samples from metastatic breast cancer patients were processed with both technologies. The expression of HER2 was assessed in isolated CTCs and compared to tissue biopsy. Results show that the RUBYchip^TM was able to isolate CTCs with higher efficiency than CellSearch^®, up to 10 times more, averaging all samples. An accurate evaluation of different CTC subpopulations, including HER2+ CTCs, was provided. Liquid biopsy through the use of the RUBYchip^TM in the clinic can overcome the limitations of histological testing and evaluate HER2 status in patients in real-time, helping to tailor treatment during disease evolution. Full article