Early Detection Of Cancer Cells Research Articles

Early detection of cancer cells using high-sensitivity dual-side polished photonic crystal fiber biosensors based on surface plasmon resonance

Early detection of cancer cells using high-sensitivity dual-side polished photonic crystal fiber biosensors based on surface plasmon resonance

A Contact-Less Electrically Small Antenna Sensor for Retinal Cancer Cell Detection

A new noninvasive and portable diagnostic system for detecting ocular tumors has been proposed. The system uses a contact-less electrically small antenna sensor to detect retinal cancer cells. The antenna sensor is operated in the ISM (Industrial, Scientific, and Medical) 2.413 GHz band and has electrical dimensions of 8 × 16.2 × 0.35 mm3. The antenna sensor is fabricated on a biodegradable Teslin substrate and tested in an eye-mimicking phantom to compare numerical computations with measurements. The specific absorption rate (SAR) obtained at near and far-field distances under 1 g of tissue is 1.18 W/kg and 0.353 W/kg, and that under 10 g of tissue is 0.112 W/kg and 0.313 W/kg, respectively. Furthermore, to detect the ocular tumor using the proposed antenna sensor, the resonance frequency shift, and the unsupervised machine learning technique, principle component analysis (PCA) is employed on simulated and measured results. The resonance frequency shift for a 3.5 mm radius tumor is 70 MHz for a single tumor and 120 MHz for double tumors. The PCA generates clusters with and without tumors on the positive and negative sides of the two-dimensional plot. The proposed techniques are more impactful in distinguishing between healthy and malignant tissues. The proposed systematic approach could be a portable platform for early detection of cancerous cells inside the eye.

Au-TiO2 Coated Photonic Crystal Fiber Based SPR Refractometric Sensor for Detection of Cancerous Cells.

A surface plasmon resonance (SPR) refractometric sensor based on gold (Au) and titanium dioxide (TiO2) coated Photonic Crystal Fiber (PCF) is presented for the quick detection of various types of cancerous cells. The cancerous cells and their corresponding normal cells are both considered to be liquid cells each with their unique refractive index (RI). Normally these cells are found in liquid form in the suitable media (food) required to live the cancerous/normal cell lines. Also in our detection case, liquid samples are easy to pump into the sensing channel of the proposed PCF by employing either pressure or capillary forces.The proposed PCF sensor works on the SPR principle, with the Au coating serving as the plasmonic material. This sensor is investigated using the COMSOL Multiphysics software computational tool that is based on the full-vector finite element method (FEM). A TiO2 coating has been applied to enhance adhesion between the Au layer and the PCF surface. Above the Au coating, cancerous cells samples are filled into the PCF. When the core mode of the PCF is coupled with the surface plasmon polariton (SPP) mode under the specific resonance circumstances, SPR will occur on the interface of the gold-sample cell, and in the core mode, the loss peak is observed at the resonance wavelength. Cancerous cells samples have a distinct loss peak than normal cells samples therefore the cancerous cells can be diagnosed by measuring the shift in resonance wavelength corresponding to the loss peak of cancerous and their normal cells samples. The proposed sensor may identify various cancerous cells such as MDAMB-231, MCF-7, PC12, HeLa, and Jurkat for the diagnosis of breast cancer type-1, breast cancer type-2, adrenal glands, cervical, and blood cancer respectively. The computed wavelengths sensitivities of the proposed PCF are 9428.57nm/RIU, 10714.28nm/RIU, 7571.43nm/RIU, 5500nm/RIU, and 6000nm/RIU for the MDAMB-231, MCF-7, PC12, HeLa, and Jurkat cancerous cells, respectively. However, for various cancerous cells, the maximum amplitude sensitivity varies from -1387 RIU-1 to -1599 RIU-1. Moreover, the sensor resolution ranges between 0.93 ×10-5 RIU and 1.82 ×10-5 RIU with a 0.024 maximum detection limit. Because of its improved sensing capability, the presented SPR refractometric sensor is appropriate for the early detection of cancerous cells.

Lung cancer classification based on support vector machine-recursive feature elimination and artificial bee colony


 
 
 
 Early detection of cancerous cells can increase survival rates for patients by more than 97%. Microarray data, used for cancer classification, are comp osed of many thousands of features and from tens to hundreds of instances. Handling these huge datasets is the most imp ortant challenge in data classification. Feature selection or reduction is therefore an essential task in data classification. We prop ose a cancer diagnostic to ol using a supp ort vector machine for classifier and feature selection. First, we use supp ort vector machine-recursive feature elimination to prefilter the genes. This was enhanced with the artificial b ee colony algorithm. We ran four simulations using Ontario and Michigan lung cancer datasets. This approach provides higher classification accuracy than those without feature selection, supp ort vector machine-recursive feature elimination, or the artificial b ee colony algorithm. The accuracy of a supp ort vector machine using a feature selection-based recursive feature elimination metho d combined with the artificial b ee colony algorithm reached 98% with 100 b est features for the Michigan lung cancer dataset and 97% with 70 b est features for the Ontario lung cancer dataset. SVM with RFE-ABC as the feature selection metho d gives us an accurate result to diagnose Lung cancer using microarray data.
 
 
 

Biosensor Design for the Detection of Circulating Tumor Cells Using the Quartz Crystal Resonator Technique.

A new mass-sensitive biosensing approach for detecting circulating tumor cells (CTCs) using a quartz crystal resonator (QCR) has been developed. A mathematical model was used to design a ring electrode-based QCR to eliminate the Gaussian spatial distribution of frequency response in the first harmonic mode, a characteristic of QCRs, without compromising the sensitivity of frequency response. An ink-dot method was used to validate the ring electrode fabricated based on our model. Furthermore, the ring electrode QCR was experimentally tested for its ability to capture circulating tumor cells, and the results were compared with a commercially available QCR with a keyhole electrode. An indirect method of surface immobilization technique was employed via modification of the SiO2 surface of the ring electrode using a silane, protein, and anti-EpCAM. The ring electrode successfully demonstrated eliminating the spatial nonuniformity of frequency response for three cancer cell lines, i.e., MCF-7, PANC-1, and PC-3, compared with the keyhole QCR, which showed nonuniform spatial response for the same cancer cell lines. These results are promising for developing QCR-based biosensors for the early detection of cancer cells, with the potential for point-of-care diagnosis for cancer screening.

Enhance the Design and Performance Analysis of a Highly Sensitive Twin-Core PCF SPR Biosensor with Gold Plating for the Early Detection of Cancer Cells

Enhance the Design and Performance Analysis of a Highly Sensitive Twin-Core PCF SPR Biosensor with Gold Plating for the Early Detection of Cancer Cells

On nano-solutions to overcome cancer hypoxia and resistance

Cancer is a leading cause of death globally, and current cancer therapies often fall short in reducing death and improving quality of life. Early detection of cancer cells and targeted drug application is crucial to optimal treatment. Nanotechnology shows promise in improving cancer diagnosis and treatment by reducing toxicity and refractory disease. In this essay, we focus on how nanotechnology can overcome resistance and hypoxic issues in cancer treatment.

Generation of the novel anti-FXYD5 monoclonal antibody and its application to the diagnosis of pancreatic and lung cancer

Despite recent advances in cancer treatments, pancreatic cancer has a dismal prognosis globally. Early detection of cancer cells and effective treatments for recalcitrant tumors are required, but the innovative therapeutic tools remain in development. Cancer-specific antigens expressed only on cancer cells may help resolve these problems, and antibodies to such antigens have potential in basic research and clinical applications. To generate specific antibodies that bind to proteins expressed on the surface of pancreatic cancer cells, we immunized mice with human pancreatic cancer MIA PaCa-2 cells, and isolated a hybridoma that produces a monoclonal antibody (mAb), named 12–13.8. This antibody was applied to molecular biological experiments such as immunocytochemistry, immunoblotting, flow cytometry, and immunoprecipitation. In addition, we showed that mAb 12–13.8 could accumulate in tumors, through in vivo experiments using cancer-bearing mice. Immunohistochemical staining of pancreatic and lung tumor tissues indicated that the increase of the staining strength by mAb 12–13.8 positively and inversely correlated with the patients' cancer recurrence and survival rate, respectively. We identified the FXYD5 protein as the target protein of mAb 12–13.8, by a human protein array screening system. The FXYD5 protein is overexpressed in various types of cancer and is modified by O-linked glycosylation. We confirmed the binding of the FXYD5 protein to mAb 12–13.8 by using FXYD5-knockout MIA PaCa-2 cells, and detailed epitope mapping identified amino acid residues 45–52 as the minimal peptide sequence. Our results indicate that mAb 12–13.8 could be a valuable tool for FXYD5 studies, and useful in diagnostic and drug delivery applications for cancer patients.

A Review on Nanotechnology in Cancer Diagnosis and Therapy: it’s Application

Cancer is a leading cause of death and poor quality of life globally. Even though several strategies are devised to reduce deaths, reduce chronic pain and improve the quality of life, there remains a shortfall in the adequacies of these cancer therapies. Among the cardinal steps towards ensuring optimal cancer treatment are early detection of cancer cells and drug application with high specificity to reduce toxicities. Due to increased systemic toxicities and refractoriness with conventional cancer diagnostic and therapeutic tools, other strategies including nanotechnology are being employed to improve diagnosis and mitigate disease severity. Over the years, immunotherapeutic agents based on nanotechnology have been used for several cancer types to reduce the invasiveness of cancerous cells while sparing healthy cells at the target site. Nanomaterials including carbon nanotubes, polymeric micelles and liposomes have been used in cancer drug design where they have shown considerable pharmacokinetic and pharmacodynamic benefits in cancer diagnosis and treatment. In this review, we outline the commonly used nanomaterials which are employed in cancer diagnosis and therapy. We have highlighted the suitability of these nanomaterials for cancer management based on their physicochemical and biological properties. We further reviewed the challenges that are associated with the various nanomaterials which limit their uses and hamper their translatability into the clinical setting in certain cancer types. Keywords: Nanomaterials, Nanotechnology, Cancer, Diagnosis, Treatment.

Diagnosis of Breast Cancer with Hybrid Artificial Intelligence Method

According to the data of 2020, it is seen that 1 of every eight cancers diagnosed worldwide and the 5th among cancers that cause death is breast cancer. Cancer can spread to different organs and reach an incurable stage in patients who are not diagnosed and treated at the right time. Therefore, reducing the time taken for breast cancer diagnosis and reducing mortality rates are of great importance for accurate and early diagnosis of the disease. This study aims to improve the accuracy of cancer detection by using various machine learning algorithms and methods for artificial intelligence-based breast cancer diagnosis. By using ultrasonography images taken from 780 people, image information processed with statistical parameters was extracted. Artificial intelligence-based breast cancer detection was performed by applying three different machine learning algorithms and the hybrid machine learning algorithm designed as a combination of these algorithms on the extracted data set. In this way, early detection of cancerous cells will be carried out without creating advanced risks for the individual, and treatment will be possible.

Structural characterization and bioimaging of Zn2+ using meta-benziporphodimethene analogue.

'Prevention is better than cure, especially when something has no cure.' Cancer, in most patients is detected at the stage beyond which it becomes noncurative. Therefore, the early detection of cancer cells can play a crucial role in enhancing the chances of a patient's survival. In this light, we present a nonfluorescent receptor used for the detection of Zn2+ ion in MDA-MB-231 carcinoma cells that exhibits fluorescence turn-on behaviour upon binding with the metal ion. In this work, the synthesis of 11,16-bis(2,6-difluorobenzene)-6,6,21,21-tetramethyl-meta-benziporpho-6,21-dimethene and its Zn2+ chloride complex have been reported. The compounds were fully characterized using UV-visible, nuclear magnetic resonance (NMR), infrared (IR) and mass spectrometry. Furthermore, the X-ray polymorphs of a meta-benziporphodimethene analogue were added. The study of its bioimaging applications in MDA-MB-231 breast cancer cells for the detection of Zn2+ ions is reported.

Diagnosis of breast cancer for modern mammography using artificial intelligence

The diagnosis of breast cancer, one of the most common types of cancer worldwide, is still a challenging task. Localisation of the breast mass and accurate classification is crucial in detecting breast cancer at an early stage. In machine learning-based classification models, performance is dependent on the accuracy of extracted features and is susceptible to saturation problems. Deep learning methods are currently used to learn self-regulating top-level features and achieve remarkable accuracy. It has long been recognised that mammography is competent for the early detection of cancer cells. Thus the technique of image segmentation and artificial intelligence can be applied to the initial stage diagnosis of breast cancer. The proposed method is composed of two major approaches. In the first, the transfer learning method is employed. In the second, convolution neural network architecture is constructed, and its hyper-parameters are adjusted to achieve accurate classification. The result indicates that the proposed methods achieve significant accuracy for MIAS (95.95%), DDSM (99.39%), INbreast (96.53%), and combined datasets (92.27%). Comparison of results of the proposed approach with current schemes demonstrates its efficiency.

Recent Advances in Electrochemical Sensing of Hydrogen Peroxide (H2O2) Released from Cancer Cells.

Cancer is by far the most common cause of death worldwide. There are more than 200 types of cancer known hitherto depending upon the origin and type. Early diagnosis of cancer provides better disease prognosis and the best chance for a cure. This fact prompts world-leading scientists and clinicians to develop techniques for the early detection of cancer. Thus, less morbidity and lower mortality rates are envisioned. The latest advancements in the diagnosis of cancer utilizing nanotechnology have manifested encouraging results. Cancerous cells are well known for their substantial amounts of hydrogen peroxide (H2O2). The common methods for the detection of H2O2 include colorimetry, titration, chromatography, spectrophotometry, fluorimetry, and chemiluminescence. These methods commonly lack selectivity, sensitivity, and reproducibility and have prolonged analytical time. New biosensors are reported to circumvent these obstacles. The production of detectable amounts of H2O2 by cancerous cells has promoted the use of bio- and electrochemical sensors because of their high sensitivity, selectivity, robustness, and miniaturized point-of-care cancer diagnostics. Thus, this review will emphasize the principles, analytical parameters, advantages, and disadvantages of the latest electrochemical biosensors in the detection of H2O2. It will provide a summary of the latest technological advancements of biosensors based on potentiometric, impedimetric, amperometric, and voltammetric H2O2 detection. Moreover, it will critically describe the classification of biosensors based on the material, nature, conjugation, and carbon-nanocomposite electrodes for rapid and effective detection of H2O2, which can be useful in the early detection of cancerous cells.

Computational Study of a Compact and High Sensitive Photonic Crystal for Cancer Cells Detection

In this paper, a computational study was performed to design a compact and high sensitive photonic crystal for the early detection of cancer cells. The plane wave expansion (PWE) and finite-difference time-domain (FDTD) methods were used to determine the dispersion and optical properties of light under variable refractive indices, respectively. The proposed PC-based sensor provided a wide band structure and very-good confinement of light in the sensing area, corresponding to vary sensitive refractive index variations. The effect of lattice constant of PC and radius of the sensing region on the sensitivity of the biosensor were studied. A sensitivity of 15 085 nm/RIU and Q-factor of 81.58 were obtained, ensuing a low detection limit of 0.01 and a high Figure of Merit (FOM) of 159.54 RIU^−1. Such combinations made an attribute to the proposed structure to perform label-free bio-sensing in cancer cells detection and other points of medical care diagnosis applications.

Nanotechnology, A Tool for Diagnostics and Treatment of Cancer.

Nanotechnology has shown promising advancements in the field of drug development and its delivery. In particular, the applications of nanoparticles for treatment and diagnostics of cancer reached such a precision that it can detect a single cancer cell and target it to deliver a payload for the treatment of that cancerous cell. Conventional cancer therapy methods have side effects, and diagnostics techniques are time-consuming and expensive. Nanoparticles (NPs) such as polymeric nanoparticles (nanogels, nanofibers, liposomes), metallic nanoparticles such as gold NP (GNPs), sliver NP (AgNP), calcium nanoparticles (CaNPs), carbon nanotubes (CNTs), graphene, and quantum dots (QDs) have revolutionized cancer diagnostics and treatments due to their high surface charge, size, and morphology. Functionalization of these nanoparticles with different biological molecules, such as antibodies, helps them to target delivery and early detection of cancer cells through their plasmon resonance properties. While some of the magnetic properties of nanoparticles such as iron (Fe), copper (Cu), and carbon NT were also evaluated for detection and treatments of cancer cells. An advanced type of nanoparticles, such as nanobubbles and oxygen-releasing polymers, are helping to address the hypoxia conditions in the cancer microenvironment, while others are employed in photodynamic therapy (PDT) and photothermal therapy (PTT) due to their intrinsic theranostic properties. The green synthesis of nanoparticles has further increased biocompatibility and broadened their applications. In this review paper, we discussed the advancement in nanotechnology and its applications for cancer treatment and diagnostics and highlighted challenges for translation of these advanced nano-based techniques for clinical applications and their green synthesis.

Application of Carbon Nanoparticles in Oncology and Regenerative Medicine.

Currently, carbon nanoparticles play a large role as carriers of various types of drugs, and also have applications in other fields of medicine, e.g., in tissue engineering, where they are used to reconstruct bone tissue. They also contribute to the early detection of cancer cells, and can act as markers in imaging diagnostics. Their antibacterial and anti-inflammatory properties are also known. This feature is particularly important in dental implantology, where various types of bacterial infections and implant rejection often occur. The search for newer and more effective treatments may lead to future use of nanoparticles on a large scale. In this work, the current state of knowledge on the possible use of nanotubes, nanodiamonds, and fullerenes in therapy is reviewed. Both advantages and disadvantages of the use of carbon nanoparticles in therapy and diagnostics have been indicated.

Silicon nanostructure-based photonic MEMS sensor for biosensing application

We explored the design of an optical pressure sensor. The objective is to create a photonic microelectromechanical system-based cantilever sensor design to detect prostate-specific antigen, a protein biomarker associated with prostate cancer. Sensor’s performance for the early detection of cancerous cells is dependent on sensitivity. The designed sensor consists of a hexagonal ring integrated with microcantilevers. Two types of microcantilever such as rectangular profile and V profile with integrated photonic sensing layer are investigated for sensitivity, quality factor, and resonant wavelength. The analysis shows that a geometrical modification of microcantilever has a significant effect on sensitivity enhancement. The finite difference time domain tool is used for designing photonic ring resonator patches. Numerical analysis of microcantilever is considered to investigate surface stress behavior. The cantilever deformation due to applied pressure resulted in a change in the index of refraction. Results show that the sensitivity of 55 nm / MPa for a triangular-shaped microcantilever obtained is higher compared to the rectangular-shaped microcantilever with a sensitivity of 0.19 nm / MPa. The designed structures have significantly higher sensitivities than the previously published sensitivity of 3.27 nm / MPa at wavelength 1550 nm. The maximum quality factor obtained for the rectangular shape is 2852 and 77,510 for the triangular shape. Triangular-shaped microcantilever can minimize winding inflexibility or stiffness. This capability of the triangular-shaped microcantilever enhances feasibility in making the final packaging and future fabrication.

Detection of Single Cancer Cells in Blood with Artificially Intelligent Nanoarray.

Detection and monitoring of single cancer cells (SCCs), such as circulating tumor cells (CTCs), would be of aid in an efficient early detection of cancer, a tailored (personalized) therapy, and in a fast bedside assessment of treatment efficacy. Nevertheless, currently available techniques, which mostly rely on the isolation of SCCs based on their physical or biological properties, suffer from low sensitivity, complicated technical procedures, low cost-effectiveness, and being unsuitable for continuous monitoring. We report here on the design and use of an artificially intelligent nanoarray based on a heterogeneous set of chemisensitive nanostructured films for the detection of SCCs using volatile organic compounds emanating in the air trapped above blood samples containing SCCs. For demonstration purposes, we have focused on samples containing A549 lung cancer cells (hereafter, SCCA549). The nanoarray developed to detect SCCA549 has >90% accuracy, >85% sensitivity, and >95% specificity. Detection works irrespective of the medium and/or the environment. These results were validated by complementary mass spectrometry. The ability to continuously record, store, and preprocess the signals increases the chances that this nanotechnology might also be useful in the early detection of cancer cells in the blood and continuous monitoring of their possible progression.

Breast Cancer Detection Using Deep learning and IoT Technologies.

In today’s world fostering health is of utmost importance. The prevalence of health issues has posted a serious burden on the doctors across the globe and because of this treating every patient efficiently becomes difficult. To ease the burden on the doctors there have been significant revolutionizing ideas that has changed the healthcare industry. With the advancements in computer technology, accurate and faster results can be generated and accordingly the patients can be treated. Detection of cancer in early stages is still an issue in the healthcare. According to the World Health Organization, the diagnosis and eradication of cancer cells can be done, if they are detected in their early stages. Hence, to mitigate the issues pertaining to cancer, early detection of cancer cells is must. Of all the different types of cancer the patients being affected with breast cancer is at the top. Many women are vulnerable to breast cancer and hence the detection of those cancer cells becomes important to eradicate them from the patient’s body. According to the WHO, around a million women died in 2011 because of breast cancer and this issue is burgeoning. Breast cancer is the second most common cancer in women and men worldwide. In 2012, it represented about 12 percent of all new cancer cases and 25 percent of all cancers in women.Breast cancer enhances when cells in the breast begin to grow out of control. These cells usually form a tumour that can often be seen on an x-ray or felt as a lump. The tumour is malignant (cancer) if the cells can grow into (invade) encompassing tissues or spread (metastasize) to far off territories of the body. The survival from breast cancer is also less. This paper tries to put forward a solution that could help the patient determine whether she is vulnerable to breast cancer at an early stage itself so that with proper treatment, the breast cancer cells can be eradicated.

Smart Digital Mammographic Screening System for Bulk Image Processing

Treating breast cancer is easier at early stages. However, proper diagnosis is essential for this purpose. Mammography helps in early detection of cancer cells. Existence of masses, calcification and mammogram are the evidences that help radiologists in early cancer identification. This paper proposes a smart digital mammographic screening system for processing images in large volumes irrespective of the nature of images. Watershed segmentation is performed based on appropriate selection of internal and external markers using multiple threshold extended maxima transformations in this technique. Distinguishing between healthy breast tissue and masses can be performed efficiently using a two-stage classifier. Extreme Learning Machine based single layer feed forward network along with Bayesian classifier is used for reducing false positive areas. Feature vector with features like texture and contrast are calculated using these approaches. Digital Mammography Screening database (DMS) is created with 100 mammographic images for the purpose of evaluation. Further, online databases like Breast Cancer Database (BCDB) and BreakHis are also used for analysis. Overall sensitivity of the datasets using the Bayesian classifier and Extreme Learning Machine is found to be 85% and 90% respectively.