Identification Of Cancer Cells Research Articles

Abstract A011: Identifying surface biomarkers from colorectal cancer-derived extracellular vesicles using a bead-based approach

Abstract Colorectal cancer (CRC) accounts for the second most common cause of cancer-related mortality globally. The search for prognostic biomarkers subserving to detected cancer at an early stage, to improve tumor classification and to stratify therapeutic patient management groups is actively ongoing. Surgical resections and chemotherapeutics remain a mainstay in the cure and control of solid tumors. Despite their usefulness in the clinical scenario, shortcomings are well recognized, leading researchers to investigate for a circulating source of tumor-derived materials in biofluids, including mostly circulating tumor DNA (ctDNA), circulating tumor cells (CTCs) and extracellular vesicles (EVs). Characterization of EVs surface markers and assessment of their molecular cargo, can lead to the identification of early metastatic colorectal cancer signatures. Cell surface markers of three CRC cell lines (HCT116, DLD1 and LOVO) were characterized using flow cytometry. EVs were isolated from 48-hour serum-deprived cell lines using the polymer pull down method (20% Polyethelene glycol (PEG)). The concentration and particle size distribution of isolated EVs was checked by the NanoSight NS300. Avidin-coated Polystyrene beads were conjugated with CD9 biotin antibodies; and were used to immunoprecipitated (IP) CD9+ EVs. Following a 2-hour incubation and a series of washing steps, the PE-labelled antibodies of interest (CD9, CD63, EpCAM, Vim, CD44, CD133 and CD29) were added and read using xMAP technology. To increase the number of surface markers that can be read in tandem, IP was multiplexed using EpCAM, CD9 and CD63. This was followed by the isolation of EVs from plasma of metastatic CRC patients (N=3) using size exclusion chromatography (Izon qEV1 columns-35nm in conjunction with the automatic fraction collector) and polymer pull down. The concentration and size were assessed by Nanosight NS300 and surface markers of tumor-derived EVs were assessed using the immunoprecipitation technique on the Luminex system. Comparison of signals between EVs isolated from cell lines and matched originator cells showed that cell surface signatures in EVs match closely with their originator cells. The mean fluorescence intensities quantified cell surface marker expression in the different exosome-bead complexes. Interestingly, one of the metastatic cancer patients with invasive colorectal adenocarcinoma and lung metastasis showed EpCAM and Vimentin positivity suggesting epithelial-mesenchymal plasticity. These results show the importance of multiplexing both at the level of immunoprecipitation and at end-measurement, given the dynamics of cell surface marker changes during disease progression. The use of the mesenchymal marker; Vimentin in a panel for cell surface markers stems from studies on biotinylated cell surface markers on various cancer cell lines and identification of Vimentin in such models. Interestingly we find Vimentin on EVs surface in this study. Citation Format: Jessica Debattista, Laura Grech, Christian Scerri, Malcolm Buhagiar, Godfrey Grech. Identifying surface biomarkers from colorectal cancer-derived extracellular vesicles using a bead-based approach [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A011.

Accurate Identification of Cancer Cells in Complex Pre‐Clinical Models Using a Deep‐Learning Neural Network: A Transfection‐Free Approach (Adv. Biology 11/2024)

Accurate Identification of Cancer Cells in Complex Pre‐Clinical Models Using a Deep‐Learning Neural Network: A Transfection‐Free Approach (Adv. Biology 11/2024)

Spiral shaped highly sensitive rectangular PCF-based cancer cells detector in terahertz regime

Abstract The increasing fatality rate of cancer in the modern world has become a global concern. To address this issue, early detection of cancer cells in human blood is essential. In this article, a rectangular spiral shaped photonic crystal fiber has been proposed for sensing cancer cells in terahertz regime. The proposed PCF based sensor features a square shaped core surrounded by rectangular air holes of different sizes in a spiral manner. Finite element method based COMSOL Multiphysics software has been used for the design and analysis of the model. The model is sensitive to refractive index variation for the identification of cancer cells. The values of effective refractive index, effective mode area, relative sensitivity, non-linearity, and confinement loss are obtained for MCF-7, MDA-MB-231, Jurkat, HeLa, Basal and PC-12. The PCF based sensor resulted in excellent results of relative sensitivity and Confinement loss as good as 99.377% and 8.59 × 10−12 dBm−1 respectively. Furthermore, the present PCF based sensor model is fabrication feasible, and we hope that it can be utilized at industrial level for biosensing applications in terahertz waveguide regime.

Optimized deep learning networks for accurate identification of cancer cells in bone marrow

Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for distinguishing between malignant and healthy bone is essential. Bones that have cancer have a different texture than bones in unaffected areas. Diagnosing hematological illnesses relies on correct labeling and categorizing nucleated cells in the bone marrow. However, timely diagnosis and treatment are hampered by pathologists' need to identify specimens, which can be sensitive and time-consuming manually. Humanity's ability to evaluate and identify these more complicated illnesses has significantly been bolstered by the development of artificial intelligence, particularly machine, and deep learning. Conversely, much research and development is needed to enhance cancer cell identification—and lower false alarm rates. We built a deep learning model for morphological analysis to solve this problem. This paper introduces a novel deep convolutional neural network architecture in which hybrid multi-objective and category-based optimization algorithms are used to optimize the hyperparameters adaptively. Using the processed cell pictures as input, the proposed model is then trained with an optimized attention-based multi-scale convolutional neural network to identify the kind of cancer cells in the bone marrow. Extensive experiments are run on publicly available datasets, with the results being measured and evaluated using a wide range of performance indicators. In contrast to deep learning models that have already been trained, the total accuracy of 99.7% was determined to be superior.

DNA logic gate for cancer cells identification, detection, and modulation of cell–cell interactions

The precise detection of cancer cells and modulation of cell–cell interactions would be highly desired for noninvasive and nontoxic cancer treatments but it remains challenging due to the existence of biomarkers on both normal and tumor cells. In this work, we engineered a logic-gated DNA molecular machine incorporating a membrane protein aptamer and an extracellular acid responsive split i-motif for the precise identification and detection of cancer cells, as well as the mediation of cell–cell interactions. In this system, only in the presence of both the membrane protein and protons, the logic-gated DNA molecular machine is activated to perform the “AND” logic operation to induce catalytic hairpin assembly (CHA) on the electrode, leading to the formation of G-quadruplex/hemin and the generation of an amplified electrochemical signal. On the other hand, the formed “AND” logic could hybridize with the DNA fragment on T-cell, effectively shorting the distance between T-cell and cancer cells. Benefiting from the DNA logic gate and CHA, this strategy could not only precisely identify cancer cells, but also show a sensitive analysis of cancer cells with a detection limit of 15 cells/mL. The logic-gated DNA molecular machine achieved the identification of cancer cells and modulation of intercellular interactions, providing a new idea for precise disease diagnosis and therapy.

Cell classification with phase-imaging meta-sensors.

The development of photonic technologies for machine learning is a promising avenue toward reducing the computational cost of image classification tasks. Here we investigate a convolutional neural network (CNN) where the first layer is replaced by an image sensor array consisting of recently developed angle-sensitive metasurface photodetectors. This array can visualize transparent phase objects directly by recording multiple anisotropic edge-enhanced images, analogous to the feature maps computed by the first convolutional layer of a CNN. The resulting classification performance is evaluated for a realistic task (the identification of transparent cancer cells from seven different lines) through computational-imaging simulations based on the measured angular characteristics of prototype devices. Our results show that this hybrid optoelectronic network can provide accurate classification (>90%) similar to its fully digital baseline CNN but with an order-of-magnitude reduction in the number of calculations.

A simple Ag-MoS2 hybrid nanozyme-based sensor array for colorimetric identification of biothiols and cancer cells.

The intracellular levels of biothiols are associated with various diseases including cancer, and biothiols are regarded as tumor biomarker. Due to the similarity of the molecular structure of biothiols, the development of simple, rapid, efficient, and sensitive colorimetric sensor arrays holds great promise for clinical cancer diagnosis. Here, we developed a simple Ag-MoS2 hybrid nanozyme-based sensor array for colorimetric identification of biothiols and cancer cells. The novel Ag-MoS2 nanoprobe was synthesized in a simple and efficient way through the in situ self-reduction reaction between MoS2 and noble metal precursor. Benefiting from to the formation of heterogeneous metal structures, the peroxidase (POD)-like catalytic activity of the synthesized Ag-MoS2 hybrid nanocomposites is significantly enhanced compared to MoS2 alone. Moreover, the catalytic activity of Ag-MoS2 nanozyme was correlated with the pH of the reaction solution and the inhibitory effects of the three biothiols on the nanozyme-triggered chromogenic system differed in the specific pH environments. Therefore, each sensing unit of this electronic tongue generated differential colorimetric fingerprints of different biothiols. After principal component analysis (PCA), the developed novel colorimetric sensor array can accurately discriminate biothiols between different types, various concentrations, and different mixture proportions. Further, the sensor array was used for the colorimetric identification of real serum and cellular samples, demonstrating its great potential in tumor diagnostic applications.

ScCrab: A Reference-Guided Cancer Cell Identification Method based on Bayesian Neural Networks.

Cancer is a significant global public health concern, where early detection can greatly enhance curative outcomes. Therefore, the identification of cancer cells holds significant importance as the primary method for cancer diagnosis. The advancement of single-cell RNA sequencing (scRNA-seq) technology has made it possible to address the problem of cancer cell identification at the single-cell level more efficiently with computational methods, as opposed to the time-consuming and less reproducible manual identification methods. However, existing computational methods have shown suboptimal identification performance and a lack of capability to incorporate external reference data as prior information. Here, we propose scCrab, a reference-guided automatic cancer cell identification method, which performs ensemble learning based on a Bayesian neural network (BNN) with multi-head self-attention mechanisms and a linear regression model. Through a series of experiments on various datasets, we systematically validated the superior performance of scCrab in both intra- and inter-dataset predictions. Besides, we demonstrated the robustness of scCrab to dropout rate and sample size, and conducted ablation experiments to investigate the contributions of each component in scCrab. Furthermore, as a dedicated model for cancer cell identification, scCrab effectively captures cancer-related biological significance during the identification process.

Differential Sensing Approach as a Pattern-based Discrimination for Biological Samples.

The differential sensing approach uses fingerprint patterning to distinguish uncharacterized biological samples. Inspired by natural sensory systems, an array of cross-reactive sensors generates unique response fingerprint depending on the samples. Until today, this array system has been developed using various materials, including the library of surface-charged nanoparticles and chemosensors. Many differential array systems have demonstrated accurate identification of bacterial species, viral subtypes, and cancer cells, as well as distinguishing disease states in blood or urine. This capability is particularly important for distinguishing between normal and abnormal states when specific marker molecules have not yet been identified, providing a powerful diagnostic tool. In this concept, we summarized representative outcomes of differential sensing applications for biological sample discrimination.

Accurate Identification of Cancer Cells in Complex Pre-Clinical Models Using a Deep-Learning Neural Network: A Transfection-Free Approach.

3D co-cultures are key tools for in vitro biomedical research as they recapitulate more closely the in vivo environment while allowing a tighter control on the culture's composition and experimental conditions. The limited technologies available for the analysis of these models, however, hamper their widespread application. The separation of the contribution of the different cell types, in particular, is a fundamentalchallenge. In this work, ORACLE (OvaRiAn Cancer ceLl rEcognition) is presented, a deep neural network trained to distinguish between ovarian cancer and healthy cells based on the shape of their nucleus. The extensive validation that are conducted includes multiple cell lines and patient-derived cultures to characterize the effect of all the major potential confounding factors. High accuracy and reliability are maintained throughout the analysis (F1score> 0.9 and Area under the ROC curve -ROC-AUC-score = 0.99) demonstrating ORACLE's effectiveness with this detection and classification task. ORACLE is freely available (https://github.com/MarilisaCortesi/ORACLE/tree/main) and can be used to recognize both ovarian cancer cell lines and primary patient-derived cells. This feature is unique to ORACLE and thus enables for the first time the analysis of in vitro co-cultures comprised solely of patient-derivedcells.

Innovative Refractive Index Sensor Utilizing Terahertz Spectrum for Early Cancer Detection: a Photonic Crystal Fiber Approach.

In this article, we have presented a new cancer sensor with a square core Photonic Crystal Fiber (PCF) to detect the cancerous tissues of the cervix, breast, and skin. This process is thus streamlined and separated by PCF due to its excellent detection characteristics. All required configurations using the finite element method are developed, and various performances of the model are studied using MATLAB. The results depict a mathematical analysis regarding the effectiveness of the sensor within the frequency range of 1.0-2.8 THz. Its relative sensitivity becomes around 99.85% at 2.2 THz with 8.49 × 10-14 dB/m for CL. This PCF has a spot size 3.06 × 10-4 μm that further contributes an effective area of 9.078 × 10-8 m2. Moreover, it has a very small EML of 0.00182 cm-1. This device uses the unique photonic properties of cancer cells to provide quick, reliable, and really very accurate methods for cancer cell identification, such as in breast, cervical, and skin cancers. Due to small size and flexibility, only minimally invasive operations are possible. Real-time monitoring can also be provided, hence improving immediate evaluation and therapy efficacy. This article introduces a novel integration of PCF technology with THz radiation to create a highly sensitive sensor for early cancer detection. By utilizing THz waves' non-invasive and high-resolution properties, this sensor overcomes the sensitivity limitations of traditional methods. It also addresses scattering issues from conventional air hole shapes through optimized geometric configurations, setting a new standard in biomedical sensing and potentially revolutionizing early cancer diagnostics.

Machine Learning for Identification of Cancer Cells Based on Electrical Property Using Microfluidic Impedance Flow Cytometry

Machine Learning for Identification of Cancer Cells Based on Electrical Property Using Microfluidic Impedance Flow Cytometry

IGF1R signaling induces epithelial-mesenchymal plasticity via ITGAV in cutaneous carcinoma

BackgroundEarly cutaneous squamous cell carcinomas (cSCCs) generally show epithelial differentiation features and good prognosis, whereas advanced cSCCs present mesenchymal traits associated with tumor relapse, metastasis, and poor survival. Currently, the mechanisms involved in cSCC progression are unclear, and the established markers are suboptimal for accurately predicting the clinical course of the disease.MethodsUsing a mouse model of cSCC progression, expression microarray analysis, immunofluorescence and flow cytometry assays, we have identified a prognostic biomarker of tumor relapse, which has been evaluated in a cohort of cSCC patient samples. Phosphoproteomic analysis have revealed signaling pathways induced in epithelial plastic cancer cells that promote epithelial-mesenchymal plasticity (EMP) and tumor progression. These pathways have been validated by genetic and pharmacological inhibition assays.ResultsWe show that the emergence of epithelial cancer cells expressing integrin αV (ITGAV) promotes cSCC progression to a mesenchymal state. Consistently, ITGAV expression allows the identification of patients at risk of cSCC relapse above the currently employed clinical histopathological parameters. We also demonstrate that activation of insulin-like growth factor-1 receptor (IGF1R) pathway in epithelial cancer cells is necessary to induce EMP and mesenchymal state acquisition in response to tumor microenvironment-derived factors, while promoting ITGAV expression. Likewise, ITGAV knockdown in epithelial plastic cancer cells also blocks EMP acquisition, generating epithelial tumors.ConclusionsOur results demonstrate that ITGAV is a prognostic biomarker of relapse in cSCCs that would allow improved patient stratification. ITGAV also collaborates with IGF1R to induce EMP in epithelial cancer cells and promotes cSCC progression, revealing a potential therapeutic strategy to block the generation of advanced mesenchymal cSCCs.Graphical During cSCC progression, cancer cells evolve from the epithelial to the mesenchymal state, which is associated with poor prognosis. The current investigation reveals that, at intermediate cSCC stages (MD/PD-SCC), epithelial cancer cells activate IGF1R and ITGAV signaling to acquire EMP and progress to the aggressive mesenchymal state in response to TME-derived factors. In addition, ITGAV allows the identification of these epithelial plastic cancer cells and functions as a prognostic biomarker of tumor relapse.

High-confidence calling of normal epithelial cells allows identification of a novel stem-like cell state in the colorectal cancer microenvironment.

Single-cell analyses can be confounded by assigning unrelated groups of cells to common developmental trajectories. For instance, cancer cells and admixed normal epithelial cells could adopt similar cell states thus complicating analyses of their developmental potential. Here, we develop and benchmark CCISM (for Cancer Cell Identification using Somatic Mutations) to exploit genomic single nucleotide variants for the disambiguation of cancer cells from genomically normal non-cancer cells in single-cell data. We find that our method and others based on gene expression or allelic imbalances identify overlapping sets of colorectal cancer versus normal colon epithelial cells, depending on molecular characteristics of individual cancers. Further, we define consensus cell identities of normal and cancer epithelial cells with higher transcriptome cluster homogeneity than those derived using existing tools. Using the consensus identities, we identify significant shifts of cell state distributions in genomically normal epithelial cells developing in the cancer microenvironment, with immature states increased at the expense of terminal differentiation throughout the colon, and a novel stem-like cell state arising in the left colon. Trajectory analyses show that the new cell state extends the pseudo-time range of normal colon stem-like cells in a cancer context. We identify cancer-associated fibroblasts as sources of WNT and BMP ligands potentially contributing to increased plasticity of stem cells in the cancer microenvironment. Our analyses advocate careful interpretation of cell heterogeneity and plasticity in the cancer context and the consideration of genomic information in addition to gene expression data when possible.

Controlling glycolysis to generate characteristic volatile organic compounds of lung cancer cells

Characteristic volatile organic compounds (VOCs) are anticipated to be used for the identification of lung cancer cells. However, to date, consistent biomarkers of VOCs in lung cancer cells have not been obtained through direct comparison between cancer and healthy groups. In this study, we regulated the glycolysis, a common metabolic process in cancer cells, and employed solid phase microextraction gas chromatography mass spectrometry (SPME–GC–MS) combined with untargeted analysis to identify the characteristic VOCs shared by cancer cells. The VOCs released by three types of lung cancer cells (A549, PC-9, NCI-H460) and one normal lung epithelial cell (BEAS-2B) were detected using SPME–GC–MS, both in their resting state and after treatment with glycolysis inhibitors (2-Deoxy-d-glucose, 2-DG/3-Bromopyruvic acid, 3-BrPA). Untargeted analysis methods were employed to compare the VOC profiles between each type of cancer cell and normal cells before and after glycolysis regulation. Our findings revealed that compared to normal cells, the three types of lung cancer cells exhibited three common differential VOCs in their resting state: ethyl propionate, acetoin, and 3-decen-5-one. Furthermore, under glycolysis control, a single common differential VOC—acetoin was identified. Notably, acetoin levels increased by 2.60–3.29-fold in all three lung cancer cell lines upon the application of glycolysis inhibitors while remaining relatively stable in normal cells. To further elucidate the formation mechanism of acetoin, we investigated its production by blocking glutaminolysis. This interdisciplinary approach combining metabolic biochemistry with MS analysis through interventional synthetic VOCs holds great potential for revolutionizing the identification of lung cancer cells and paving the way for novel cytological examination techniques.

Development and Enhancement of PCF-based Sensors for Terahertz-frequency Region Breast Cancer Cell Detection.

In today's medical research, breast cancer is a severe problem, so it is imperative to develop a reliable and efficient approach for identifying cancerous breast cells. PCF, with its exceptional sense-making abilities, simplifies and distinguishes that procedure. The research presents a unique structural hybrid PCF for detecting breast cancer cells using sensors based on PCF that are specifically built for the terahertz-frequency range. The improvement in sensor sensitivity and specificity in identifying cancer cells at these frequencies is a notable progress compared to conventional approaches, which could potentially result in earlier and more precise diagnosis. In our analysis, we discovered the most common malignancies in breast cancer. We investigate the features of the cancerous cell detector using the COMSOL-Multiphysics 5.6 software. This PCF detector achieves a Confinement Loss of 4.75 × 10-12 and 3.42 × 10-13 dB/m for Type-1 and Type-2 cancer cells, respectively, at 1.2 THz, as well as about 99.946% and 99.969% relative sensitivity. This sensor ensures the highest level of sensitivity for the identification of cancerous breast cells. This sensor's physical architecture is quite straightforward, making it simple to build using current manufacturing techniques. Therefore, it seems that this sensor will pave a new path for identifying and treating cancerous cells.

Nanozyme-Inhibited SERS Multichannel Paper-Based Sensor Array for the Quantification and Identification of Biothiols and Cancer Cells Based on Three Ag-Based Nanomaterials.

Biothiols play essential roles in maintaining normal physiological functions, resisting oxidative stress, and protecting cell health. Establishing an effective and reliable sensor array for the accurate quantification and discrimination of diverse biothiols is extremely meaningful. In this work, Ag/Mn3O4, Ag3PO4, and Ag3Cit with excellent oxidase-mimetic activity and surface-enhanced Raman scattering (SERS)-enhanced features have been prepared and loaded onto Whatman filter paper (WFP) to build SERS paper chips as three sensing channels, which can induce 3,3',5,5'-tetramethylbenzidine (TMB) oxidation to SERS-active reporters (TMBox) and concurrently generate prominent SERS signals. Nevertheless, the addition of biothiols can suppress conversion from TMB to TMBox, which can cause the reduction of the SERS signal from TMBox. Interestingly, each SERS sensing channel can generate different TMBox signals' variations due to differences in the oxidative inhibition abilities of diverse biothiols and exclusive properties of each paper chip, which can be plotted as specific fingerprint patterns of each biothiol and further translated into intuitive two-dimensional (2D) clustering profiles through linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA) techniques for precise identification of these six biothiols with the minimum concentration of 1 μM. More importantly, this SERS sensor array is exploited for the precise quantification of intracellular glutathione (GSH), and can differentiate between normal and cancer cells based on different intracellular GSH contents and even identify different types of tumor cells, demonstrating its powerful application prospects in disease diagnosis.

Intelligent Reconfiguration-Promoted Cellular Internalization of Core-Shell DNA Nanoprobe Equipped with Successive Dual Stimuli-Responsive Protective Satellites for Amplification Fluorescence Imaging of Tumor Cells.

Although DNA probes have attracted increasing interest for precise tumor cell identification by imaging intracellular biomarkers, the requirement of commercial transfection reagents, limited targeting ligands, and/or non-biocompatible inorganic nanostructures has hampered the clinic translation. To circumvent these shortcomings, a reconfigurable ES-NC (Na+-dependent DNAzyme (E)-based substrate (S) cleavage core/shell DNA nanocluster (NC)) entirely from DNA strands is assembled for precise imaging of cancerous cells in a successive dual-stimuli-responsive manner. This nanoprobe is composed of a strung DNA tetrahedral satellites-based protective (DTP) shell, parallelly aligned target-responsive sensing (PTS) interlayer, and hydrophobic cholesterol-packed innermost layer (HCI core). Tetrahedral axial rotation-activated reconfiguration of DTP shell promotes the exposure of interior hydrophobic moieties, enabling cholesterol-mediated cellular internalization without auxiliary elements. Within cells, over-expressed glutathione triggers the disassembly of the DTP protective shell (first stimulus), facilitating target-stimulated signal transduction/amplification process (second stimuli). Target miRNA-21 is detected down to 10.6 fM without interference from coexisting miRNAs. Compared with transfection reagent-mediated counterpart, ES-NC displays a higher imaging ability, resists nuclease degradation, and has no detectable damage to healthy cells. The blindtest demonstrates that the ES-NC is suitable for the identification of cancerous cells from healthy cells, indicating a promising tool for early diagnosis and prediction of cancer.

Emerging Biohybrids of Aptamer-Based Nano-Biosensing Technologies for Effective Early Cancer Detection.

Cancer is a leading global cause of mortality, which underscores the imperative of early detection for improved patient outcomes. Biorecognition molecules, especially aptamers, have emerged as highly effective tools for early and accurate cancer cell identification. Aptamers, with superior versatility in synthesis and modification, offer enhanced binding specificity and stability compared with conventional antibodies. Hence, this article reviews diagnostic strategies employing aptamer-based biohybrid nano-biosensing technologies, focusing on their utility in detecting cancer biomarkers and abnormal cells. Recent developments include the synthesis of nano-aptamers using diverse nanomaterials, such as metallic nanoparticles, metal oxide nanoparticles, carbon-derived substances, and biohybrid nanostructures. The integration of these nanomaterials with aptamers significantly enhances sensitivity and specificity, promising innovative and efficient approaches for cancer diagnosis. This convergence of nanotechnology with aptamer research holds the potential to revolutionize cancer treatment through rapid, accurate, and non-invasive diagnostic methods.

Synthesis and Characterization of Magnetic Hybrid Microgels for Identification of Breast Cancer Cells

Synthesis and Characterization of Magnetic Hybrid Microgels for Identification of Breast Cancer Cells