Efficient detection of single circulating tumor cell in blood using Raman mapping based on Aptamer-SERS bio-probe coupled with micropore membrane filtration

Intelligent identification of foodborne pathogenic bacteria by self-transfer deep learning and ensemble prediction based on single-cell Raman spectrum

Foodborne pathogenic infections pose a significant threat to human health. Accurate detection of foodborne diseases is essential in preventing disease transmission. This study proposed an AI model for precisely identifying foodborne pathogenic bacteria based on single-cell Raman spectrum. Self-transfer deep learning and ensemble prediction algorithms had been incorporated into the model framework to improve training efficiency and predictive performance, significantly improving prediction results. Our model can identify simultaneously gram-negative and positive, genus, species of foodborne pathogenic bacteria with an accuracy over 99.99 %, as well as recognized strain with over 99.49 %. At all four classification levels, unprecedented excellent predictive performance had been achieved. This advancement holds practical significance for medical detection and diagnosis of foodborne diseases by reducing false negatives.

Trends in Aptasensing and the Enhancement of Diagnostic Efficiency and Accuracy

The field of healthcare diagnostics is navigating complex challenges driven by evolving patient demographics and the rapid advancement of new technologies worldwide. In response to these challenges, these biosensors offer distinctive advantages over traditional diagnostic methods, such as cost-effectiveness, enhanced specificity, and adaptability, making their integration with point-of-care (POC) platforms more feasible. In recent years, aptasensors have significantly evolved in diagnostic capabilities through the integration of emerging technologies such as microfluidics, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) systems, wearable devices, and machine learning (ML), driving progress in precision medicine and global healthcare solutions. Moreover, these advancements not only improve diagnostic accuracy but also hold the potential to revolutionize early detection, reduce healthcare costs, and improve patient outcomes, especially in resource-limited settings. This Account examines key advancements, focusing on how scientific breakthroughs, including artificial intelligence (AI), have improved sensitivity and precision. Additionally, the integration of aptasensors with these technologies has enabled real-time monitoring and data analysis, fostering advances in personalized healthcare. Furthermore, the potential commercialization of aptasensor technologies could increase their availability in clinical settings and support their use as widespread solutions for global health challenges. Hence, this review discusses technological improvements, practical uses, and prospects while also focusing on the challenges surrounding standardization, clinical validation, and interdisciplinary collaboration for widespread application. Finally, ongoing efforts to address these challenges are key to ensure that aptasensors can be effectively implemented in diverse healthcare systems.

A revolutionary era in advancing precision immuno-oncology; role of circulating tumor cells

Despite a substantial progress in the development of strategies against cancer, cancer still remains a major global health issue due to a high recurrence rate, and severe side effects, leading basic medical scientists and clinical specialists toward more efficient diagnostics, prognostics, and therapeutics. Therefore, there is an imperative need for a comprehensive understanding on the cellular immunopathophysiology involved in the tumor microenvironment. In addition, results from a wide range of studies depicted that an aberration in the cellular mechanisms and immunopathophysiological interactions like Circulating Tumor Cells (CTCs) plays an indispensable role in the metastasis and tumor progression, revolutionizing cancer management by offering non-invasive detection methods and a real-time monitoring of tumor dynamics. Moreover, CTCs can clarify the tumor heterogeneity and the evolution of resistance mechanisms, aiding in the early detection of tumors and informing personalized treatment strategies. An increase in CTCs count can be associated with a worsened cancer prognosis, providing promising biomarkers for tumor phenotyping, tumor spreading or relapse, and monitoring the treatment response in patients with cancer. Hence, this systematic review aims to highlight the diagnostic, prognostic, and therapeutic potentials of CTCs, necessitating further investigations and an interdisciplinary collaboration among basic medical scientists and oncologists to address the current gaps in the strategies of cancer management, precisely improving patient-care and optimized clinical outcomes.

Vibration-assisted Microbead Production: A New Frontier for Biocompatible Surfaces

The encapsulation of active pharmaceutical ingredients (APIs) in microbeads is an essential step in drug delivery; however, it is also inherently associated with the need to control particle size and drug release profiles. Nevertheless, most conventional methods of microencapsulation fail to provide consistent results. A new method called vibration-assisted microbead coating is a novel unified technique utilizing mechanical vibrations to enable the controlled, uniform coating of microbeads on APIs. This chapter discusses the technology of vibration-assisted encapsulation performed by the authors through microbead formation and the physical activity of coating APIs. This chapter focuses on achieving uniform control of the final coated surface of the API, microbead shape, size, and loading through vibration parameters. Additionally, this chapter discusses the biocompatibility and stability of the final coated surface. This new means of encapsulation has high potential for drug delivery. This method reduces most of the traditional challenges of encapsulation, if not eliminates them, and is more reliable. Based on the abovementioned findings, the authors propose the following main areas for their further work: optimisation of vibration parameters for various APIs, research into the long-term stability of the loading–release profile, and possible use of the technique in targeted drug delivery.

Diagnostic use of circulating cells and sub-cellular bio-particles

In the bloodstream or other physiological fluids, "circulating cells and sub-cellular bio-particles" include many microscopic biological elements such as circulating tumor cells (CTCs), cell-free DNA (cfDNA), exosomes, microRNAs, platelets, immune cells, and proteins are the most well-known and investigated. These structures are crucial biomarkers in healthcare and medical research for the early detection of cancer and other disorders, enabling treatment to commence before the onset of clinical symptoms and enhancing the efficacy of treatments. As the size of these biomarkers to be detected decreases and their numbers in body fluids diminishes, the detection materials, ranging from visual inspection to advanced microscopy techniques, begin to become smaller, more sensitive, faster, and more effective, thanks to developing nanotechnology. This review first defines the circulating cells and subcellular bio-particles with their biological, physical, and mechanical properties and second focuses on their diagnostic importance, including their most recent applications as biomarkers, the biosensors that are utilized to detect them, the present obstacles that must be surmounted, and prospective developments in the domain. As technology advances and biomolecular pathways are deepens, diagnostic tests will become more sensitive, specific, and thorough. Finally, integrating recent advances in the diagnostic use of circulating cells and bioparticles into clinical practice is promising for precision medicine and patient outcomes.

An Integrated Inertial-Magnetophoresis Microfluidic Chip Online-Coupled with ICP-MS for Rapid Separation and Precise Detection of Circulating Tumor Cells

Circulating tumor cells (CTCs) are recognized as promising targets for liquid biopsy, which play an important role in early diagnosis and efficacy monitoring of cancer. However, due to the extreme scarcity of CTCs and partial size overlap between CTCs and white blood cells (WBCs), the separation and detection of CTCs from blood remain a big challenge. To address this issue, we fabricated a microfluidic chip by integrating a passive contraction-expansion array (CEA) inertial sorting zone and an active magnetophoresis zone with the trapezoidal groove and online coupled it with inductively coupled plasma mass spectrometry (ICP-MS) for rapid separation and precise detection of MCF-7 cells (as a model CTC) in blood samples. In the integrated microfluidic chip, most of the small-sized WBCs can be rapidly removed in the circular CEA inertial sorter, while the rest of the magnetically labeled WBCs can be further captured in the trapezoidal groove under the magnetic field. As a result, the rapid separation of MCF-7 cells from blood samples was achieved with an average recovery of 91.6% at a sample flow rate of 200 μL min-1. The developed online integrated inertial-magnetophoresis microfluidic chip-ICP-MS system has been applied for the detection of CTCs in real clinical blood samples with a fast analysis speed (5 min per 1 mL blood). CTCs were detected in all 24 blood samples from patients with different types of cancer, exhibiting excellent application potential in clinical diagnosis.

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.

Raman Scattering Enhancement through Pseudo-Cavity Modes

Raman spectroscopy plays a pivotal role in spectroscopic investigations. The small Raman scattering cross-section of numerous analytes, however, requires enhancement of the signal through specific structuring of the electromagnetic and morphological properties of the underlying surface. This enhancement technique is known as surface-enhanced Raman spectroscopy (SERS). Despite the existence of various proposed alternatives, the approach involving Fabry-Pérot cavities, which constitutes a straightforward method to enhance the electromagnetic field around the analyte, has not been extensively utilized. This is because, for the analyte to experience the maximum electric field, it needs to be embedded within the cavity. Consequently, the top mirror of the cavity will eventually shield it from the external laser source. Recently, an open-cavity configuration has been demonstrated to exhibit properties similar to the classic Fabry-Pérot configuration, with the added advantage of maintaining direct accessibility for the laser source. This paper showcases how such a simple yet innovative configuration can be effectively utilized to achieve remarkable Raman enhancement. The simple structure, coupled with its inexpensive nature and versatility in material selection and scalability, makes it an ideal choice for various analytes and integration into diverse Raman apparatus setups.

The integrated on-chip isolation and detection of circulating tumour cells

Circulating tumour cells (CTCs) are cancer cells shed from a primary tumour which intravasate into the blood stream and have the potential to extravasate into distant tissues, seeding metastatic lesions. As such, they can offer important insight into cancer progression with their presence generally associated with a poor prognosis. The detection and enumeration of CTCs is, therefore, critical to guiding clinical decisions during treatment and providing information on disease state. CTC isolation has been investigated using a plethora of methodologies, of which immunomagnetic capture and microfluidic size-based filtration are the most impactful to date. However, the isolation and detection of CTCs from whole blood comes with many technical barriers, such as those presented by the phenotypic heterogeneity of cell surface markers, with morphological similarity to healthy blood cells, and their low relative abundance (∼1 CTC/1 billion blood cells). At present, the majority of reported methods dissociate CTC isolation from detection, a workflow which undoubtedly contributes to loss from an already sparse population. This review focuses on developments wherein isolation and detection have been integrated into a single-step, microfluidic configuration, reducing CTC loss, increasing throughput, and enabling an on-chip CTC analysis with minimal operator intervention. Particular attention is given to immune-affinity, microfluidic CTC isolation, coupled to optical, physical, and electrochemical CTC detection (quantitative or otherwise).

Circulating Tumour Cells: Detection and Application in Advanced Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is one of the deadliest diseases worldwide. Tissue biopsy is the current gold standard for the diagnosis and molecular profiling of NSCLC. However, this approach presents some limitations due to inadequate tissue sampling, and intra- and intertumour heterogenicity. Liquid biopsy is a noninvasive method to determine cancer-related biomarkers in peripheral blood, and can be repeated at multiple timepoints. One of the most studied approaches to liquid biopsies is represented by circulating tumour cells (CTCs). Several studies have evaluated the prognostic and predictive role of CTCs in advanced NSCLC. Despite the limitations of these studies, the results of the majority of studies seem to be concordant regarding the correlation between high CTC count and poor prognosis in patients with NSCLC. Similarly, the decrease of CTC count during treatment may represent an important predictive marker of sensitivity to therapy in advanced NSCLC. Furthermore, molecular characterization of CTCs can be used to provide information on tumour biology, and on the mechanisms involved in resistance to targeted treatment. This review will discuss the current status of the clinical utility of CTCs in patients with advanced NSCLC, highlighting their potential application to prognosis and to treatment decision making.