Applications In Cancer Detection Research Articles

A novel simplified transperineal prostate biopsy guided by perineal ultrasound.

Prostate biopsies are mainly performed through transrectal or perineal approaches, while ultrasound probes are located in rectum for guidance. However, reports on the use of perineal ultrasound guided transperineal prostate biopsy (PG-TPPB) are few. A retrospective case-control study was designed. A total of 111 patients who underwent PG-TPPB from February 2019 to December 2020 were investigated retrospectively. Simultaneously, 188 patients who underwent transrectal prostate biopsy (TRPB) were included as control. The prostate cancer detection rates (PCDR), complication rates, and application values were compared between the two groups. The overall PCDR in the PG-TPPB and TRPB groups were 33.3% (37/111) and 39.9% (75/188), respectively (P = 0.258). There was no significant difference in the PCDR between the two groups under each PSA level (all P > 0.05). The single-needle PCDR in the PG-TPPB and TRPB groups were 21.5%（277/1 287）and 24.0% (513/2 134), respectively (P = 0.091). The incidence of complications in the PG-TPPB group was significantly lower than that in the TRPB group (8.1% vs 21.3%, P = 0.003). The PCDRs of PG-TPPB and TRPB were the same. However, the postoperative complication rate of PG-TPPB was significantly lower than that of TRPB. Moreover, PG-TPPB required simpler equipment and did not require enema administration, which is suitable for patients with rectal contraindications. The reports on perineal ultrasound guided transperineal prostate biopsy (PG-TPPB) are few. Our study indicated PG-TPPB reduced the postoperative complication rate. Moreover, PG-TPPB required simpler equipment. Importantly, PG-TPPB is suitable for patients with rectal contraindications.

Graphene-enhanced decagonal patch antenna for terahertz frequency operation in breast cancer detection

This paper presents a decagonal patch antenna loaded with graphene designed for terahertz (THz) frequency applications, with a specific emphasis on its potential for early breast cancer detection. The proposed antenna features a hybrid structure, integrating both copper and graphene materials. A decagonal graphene strip is intricately incorporated into the copper patch, yielding significant improvements in reflection coefficient, bandwidth, and gain. The antenna, with dimensions of 155µm×130µm×13µm, is designed on a polyimide substrate, characterized by a dielectric constant of 3.5 and a loss tangent of 0.0027. To ensure relevance in medical contexts, the design is optimized to operate within the frequency range of 2.1 to 5.7 THz, a critical spectrum for medical applications. Simulation results validate the effectiveness of the proposed antenna, demonstrating S11<−10dB within the frequency band of 2.1 to 5.7 THz (92.3% fractional bandwidth). The antenna exhibits an impressive bandwidth of 3.6 THz and a gain of 7.87 dBi at 4 THz. These findings establish the graphene-loaded decagonal patch antenna as a highly promising solution for breast cancer detection applications, showcasing its potential in the realm of medical diagnostics.

Multiparametric MRI for characterization of the tumour microenvironment.

Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.

Reactive Oxygen Species Amplifier for Apoptosis-Ferroptosis Mediated High-Efficiency Radiosensitization of Tumors.

Insufficient reactive oxygen species (ROS) production and radioresistance have consistently contributed to the failure of radiotherapy (RT). The development of a biomaterial capable of activating ROS-induced apoptosis and ferroptosis is a potential strategy to enhance RT sensitivity. To achieve precision and high-efficiency RT, the theranostic nanoplatform Au/Cu nanodots (Au/CuNDs) were designed for dual-mode imaging, amplifying ROS generation, and inducing apoptosis-ferroptosis to sensitize RT. A large amount of ROS is derived from three aspects: (1) When exposed to ionizing radiation, Au/CuNDs effectively absorb photons and emit various electrons, which can interact with water to produce ROS. (2) Au/CuNDs act as a catalase-like to produce abundant ROS through Fenton reaction with hydrogen peroxide overexpressed of tumor cells. (3) Au/CuNDs deplete overexpressed glutathione, which causes the accumulation of ROS. Large amounts of ROS and ionizing radiation further lead to apoptosis by increasing DNA damage, and ferroptosis by enhancing lipid peroxidation, significantly improving the therapeutic efficiency of RT. Furthermore, Au/CuNDs serve as an excellent nanoprobe for high-resolution near-infrared fluorescence imaging and computed tomography of tumors. The promising dual-mode imaging performance shows their potential application in clinical cancer detection and imaging-guided precision RT, minimizing damage to adjacent normal tissues during RT. In summary, our developed theranostic nanoplatform integrates dual-mode imaging and sensitizes RT via ROS-activated apoptosis-ferroptosis, offering a promising prospect for clinical cancer diagnosis and treatment.

A Compact Dual-polarized Probe-fed UWB Antenna System for Breast Cancer Detection Applications

AbstractThis article proposes an electrically small, probe-fed Ultra-Wideband (UWB) monopole antenna on a slotted truncated ground plane for breast cancer detection. The physical footprint of the proposed antenna element is 33 mm × 35 mm × 0.5 mm. This element is designed on the low-cost FR4 Epoxy substrate with a thickness of 0.5 mm. The proposed antenna has an electrical size of 0.33λ × 0.35λ × 0.005λ at the lowest frequency of operation; the radiator offers an impedance bandwidth of 8.34 GHz, which implies a fractional bandwidth of 115.5%. A compact dual-polarized antenna topology with two orthogonally placed probe-fed monopole antennas is proposed to achieve polarization diversity. The impedance characteristics of the individual radiating elements are maintained in spite of the electrical proximity of the radiators. The numerically computed and experimentally measured results of dual-polarized electrically small UWB antenna agree quite well. The proposed dual-polarized antenna topology is investigated for its utility in breast cancer detection in simulation.

The utilization of ELISA in early detection of cancer

The early detection of cancer is crucial in improving treatment success rates and survival rates. The primary focus of this research is the application of enzyme-linked immunosorbent assay (ELISA) in early cancer detection, as well as the measurement methods of cancer biomarkers. It can provide an overview of the currently used cancer biomarkers and their application in ELISA. Furthermore, this research will utilize the ELISA technique to determine whether a specific substance can serve as a cancer biomarker, with a particular emphasis on its application in early cancer detection. Through a comprehensive analysis of relevant research, this research elucidates the significance of ELISA in the measurement of cancer biomarkers and its potential as a robust cancer screening tool. Finally, this research will highlight the future directions of research, focusing on the implementation of ELISA technology in detecting and identifying a wider range of cancer biomarkers for achieving more accurate early cancer diagnosis.

Abstract 6258: Inference of gene expression using fragmentation patterns from targeted high-depth sequencing of cell-free DNA

Abstract Cell-free DNA (cfDNA) circulating in blood plasma arises primarily from cellular chromatin fragmentation and release due to cell death. Studies have demonstrated that the assessment of fragmentomic features of cfDNA enables gene expression inference and tissue-of-origin classification with potential applications for noninvasive cancer detection. However, due to low depth of coverage of sites of interest, current whole genome sequencing (WGS) methods have not been able to infer expression of individual genes or limited gene sets. Therefore, we developed a targeted sequencing approach that enables individual gene expression inference from cfDNA fragmentation patterns based on transcriptional start-site gene activation probability (TSS-GAP). To assess the sensitivity of this method for cfDNA-inferred gene expression profiling, we applied TSS-GAP to blends of micrococcal nuclease (MNase)-digested DNA from a cancer cell line (LS180) and sorted peripheral blood mononuclear cells (PBMC), with LS180 DNA at varying concentrations (0.1-10%). This enabled the determination of the limit of detection (LoD), relative to healthy donor background levels, on the basis of the genes’ activation probabilities (TSS-GAP scores). We found 936 genes with high inferred expression in the LS180 cancer cell line and low inferred expression in non-cancer samples (i.e. CD4, CD14, and plasma cfDNA from 4 healthy donors) and used those for healthy donor background and LoD assessment. Using these gene-level background values as cutoffs, blends were assessed to determine the lowest blend level at which expression of the gene was able to be differentiated from background. Out of the 936 genes, 326 (34.8%) were detectable at a blend level of 0.1%, 59 (6.3%) at 0.3%, 31 (3.3%) at 1%, 19 (2.0%) at 3%, and 39 (4.2%) at 10%. In total, 416 genes could be detected by TSS-GAP at levels 1% of or lower above background, highlighting the potential of our method for detecting expression of certain genes even from a small fraction of cells contributing cfDNA to liquid biopsy samples. Further expansion of this work could consider the utility of such a method for circulating tumor DNA (ctDNA) detection, cfDNA tissue-of-origin classification, and deconvolution of cell types contributing to cfDNA found in blood. Citation Format: Emily Leff, Alexander Tseng, Victoria Cheung, Ehsan Tabari, Kimberly Walter, Alex Lovejoy, Cheng-Ho J. Lin. Inference of gene expression using fragmentation patterns from targeted high-depth sequencing of cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6258.

Clinical Application of Neural Network for Cancer Detection Application


 INTRODUCTION: The field of medical diagnostics is currently confronted with a significant obstacle in the shape of cancer, a disease that tragically results in the loss of millions of lives each year. Ensuring the administration of appropriate treatment to cancer patients is of paramount significance for medical practitioners.
 OBJECTIVES: Hence, the accurate identification of cancer cells holds significant importance. The timely identification of a condition can facilitates prompt diagnosis and intervention. Numerous researchers have devised multiple methodologies for the early detection of cancer.
 METHODS: The accurate anticipation of cancer has consistently posed a significant and formidable undertaking for medical professionals and researchers. This article examines various neural network technologies utilised in the diagnosis of cancer.
 RESULTS: Neural networks have emerged as a prominent area of research within the medical science field, particularly in disciplines such as cardiology, radiology, and oncology, among others.
 CONCLUSION: The findings of this survey indicate that neural network technologies demonstrate a high level of efficacy in the diagnosis of cancer. A significant proportion of neural networks exhibit exceptional precision when it comes to categorizing tumours cells.

PH response mechanism of bifunctional fluorescent carbon quantum dots and application in cancer detection and bioself-targeting imaging

The development of fluorescent probes suitable for lysosome visualisation as well as real-time tracking is crucial because lysosomes play a crucial role in numerous metabolic activities and diseases. In this paper, nitrogen-doped carbon quantum dots (NCQDs) enriched with a large number of hydroxyl, amino, and carboxyl groups were prepared by a simple solvothermal method using tartaric acid and 4-aminosalicylic acid as raw materials. The fluorescence intensity of NCQDs decreased with increasing pH (1–13) due to protonation and deprotonation as well as hydrolysis of surface groups in alkaline environment and detachment of chromophores. This enabled the NCQDs to achieve label-free and rapid visualisation of pH in cells. The disparity in pH levels between cancer cells and normal cells resulted in varying fluorescence brightness of NCQDs observed in these two cells, which enabled the differentiation of cancer cells from normal cells. Most importantly, when NCQDs entered the cell, lysosomes could provide protons for NCQDs, and the protonation of NCQDs' surface groups led to stronger fluorescence after endocytosis of NCQDs in lysosomes. Thus, lysosomal targeted imaging could be achieved without the need to introduce lysosomal-targeted ligands, and NCQDs had a higher biocompatibility and stability compared to commonly used commercial dyes, which made it the ideal choice of lysosomal-targeted fluorescent probes.

Advancements in Pancreatic Cancer Detection: Integrating Biomarkers, Imaging Technologies, and Machine Learning for Early Diagnosis.

Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting. Through the analysis of imaging data, biomarker profiles, and clinical information, AI algorithms excel in discerning subtle abnormalities indicative of pancreatic cancer with remarkable precision. Moreover, machine learning (ML) algorithms facilitate the amalgamation of diverse data sources to optimize patient care. However, despite its huge potential, the implementation of AI in pancreatic cancer detection faces various challenges. Issues such as the scarcity of comprehensive datasets, biases in algorithm development, and concerns regarding data privacy and security necessitate thorough scrutiny. While AI offers immense promise in transforming pancreatic cancer detection and management, ongoing research and collaborative efforts are indispensable in overcoming technical hurdles and ethical dilemmas. This review delves into the evolution of AI, its application in pancreatic cancer detection, and the challenges and ethical considerations inherent in its integration.

Evolution and Trends of Aptamers in Cancer Research: A Bibliometric Analysis of the Literature over the Last Decade.

Aptamers are increasingly applied in cancer research. Here, we have performed the first bibliometric analysis to demonstrate the evolution of aptamers in cancer research over the past decade and to reveal future trends. Original articles and reviews on aptamers in cancer research published from 2013 to 2022 were retrieved from the Web of Science Core Collection database. VOSviewer, CiteSpace, and R software were used for bibliometric analysis of the literature and visualization of the results. A total of 1627 eligible publications were analyzed. Annual and cumulative publications have been found to be steadily increased. China was the most productive country (884 publications) and Hunan University was the most productive institution (97 publications). The United States had the highest level of international collaboration (betweenness centrality = 0.55). Wei-Hong Tan was the most productive author (68 publications) and Craig Tuerk was the most cited author (387 citations). Analytical Chemistry and Biosensors and Bioelectronics were the most influential journals in this field. Three major themes were identified: aptamer selection techniques, aptamer-targeted drug delivery, and aptasensors for cancer detection. The research hotspots have shifted from aptamer selection, targeted drug delivery, molecular imaging, and biomarker detection to electrochemical aptasensors and therapeutic applications. The future may focus on high specificity and affinity in aptamer selection, aptasensor fabrication, aptamer- targeted drug delivery, and therapeutic aptamer development. The field of aptamers in cancer research has been steadily developing over the past decade, and future research may focus on aptamer application in cancer detection and therapy and the improvement of aptamer selection.

Ethical considerations for artificial intelligence indermatology: a scoping review.

The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.

AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis

(1) Background: Lung cancer’s high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI’s performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI’s role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI’s potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI’s accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI’s potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future.

A High-Resolution 3D Ultrasound Imaging System Oriented towards a Specific Application in Breast Cancer Detection Based on a 1 × 256 Ring Array.

This paper presents the design and development of a high-resolution 3D ultrasound imaging system based on a 1 × 256 piezoelectric ring array, achieving an accuracy of 0.1 mm in both ascending and descending modes. The system achieves an imaging spatial resolution of approximately 0.78 mm. A 256 × 32 cylindrical sensor array and a digital phantom of breast tissue were constructed using the k-Wave toolbox. The signal is acquired layer by layer using 3D acoustic time-domain simulation, resulting in the collection of data from each of the 32 layers. The 1 × 256 ring array moves on a vertical trajectory from the chest wall to the nipple at a constant speed. A data set was collected at intervals of 1.5 mm, resulting in a total of 32 data sets. Surface rendering and volume rendering algorithms were used to reconstruct 3D ultrasound images from the volume data obtained via simulation so that the smallest simulated reconstructed lesion had a diameter of 0.3 mm. The reconstructed three-dimensional image derived from the experimental data exhibits the contour of the breast model along with its internal mass. Reconstructable dimensions can be achieved up to approximately 0.78 mm. The feasibility of applying the system to 3D breast ultrasound imaging has been demonstrated, demonstrating its attributes of resolution, precision, and exceptional efficiency.

Updated Progress on Mass Spectrometry Imaging and its Application in Cancer Treatment and Drug Discovery.

Mass spectrometry imaging (MSI) is an imaging method based on mass spectrometry technology that can simultaneously visualize the spatial distribution of various biological molecules. The use of MSI in cancer detection and drug discovery has been extensively investigated in recent years. This review aims to summarize the latest advances of MSI and its specific applications in cancer detection and drug discovery, providing a basic understanding of the development and application of MSI in the past five years and offering references for the further application of MSI in cancer detection and drug discovery. In the database, "mass spectrometry imaging", "cancer treatment", and "drug discovery" were used as keywords for literature retrieval, and the time range was limited to "2018- 2023". After organizing and analyzing the literature and patents, a review was conducted. Based on the literature, it was found that the updated progress of MSI in the past five years mostly focused on concrete methods, operation procedures, facilities, and composite applications. The patents of MSI were mainly correlated with the mass spectrometry imaging system and its application in cancer treatment. MSI is conducive to investigating the therapeutic schedule of cancer and searching for new drugs. MSI is a convenient, fast and powerful technology that has made great progress in sample preparation, instrumentation, quantitation, and multimodal imaging. MSI has emerged as a powerful technique in various biomedical applications, which has strong potential in cancer detection, treatment, formation mechanism research, discovery of biomarkers, and drug discovery process.

Advances in far-infrared research: therapeutic mechanisms of disease and application in cancer detection.

Far infrared (FIR) irradiation is commonly used as a convenient, non-contact, non-invasive treatment for diseases such as myocardial ischemia, diabetes, and chronic kidney disease. In this review, we focus on reviewing the potential therapeutic mechanisms of FIR and its cutting-edge applications in cancer detection. Firstly, we searched the relevant literature in the last decade for systematic screening and briefly summarized the biophysical properties of FIR. We then focused on the possible mechanisms of FIR in wound healing, cardiovascular diseases, and other chronic diseases. In addition, we review recent applications of FIR in cancer detection, where Fourier transform infrared spectroscopy and infrared thermography provide additional diagnostic methods for the medical diagnosis of cancer. Finally, we conclude and look into the future development of FIR for disease treatment and cancer detection. As a high-frequency non-ionizing wave, FIR has the advantages of safety, convenience, and low cost. We hope that this review can provide biological information reference and relevant data support for those who are interested in FIR and related high-frequency non-ionizing waves, to promote the further application of FIR in the biomedical field.

A comprehensive review on singlet oxygen generation in nanomaterials and conjugated polymers for photodynamic therapy in the treatment of cancer.

A key role in lessening humanity's continuous fight against cancer could be played by photodynamic therapy (PDT), a minimally invasive treatment employed in the medical care of a range of benign disorders and malignancies. Cancerous tissue can be effectively removed by using a light source-excited photosensitizer. Singlet oxygen and reactive oxygen species are produced via the photosensitizer as a result of this excitation. In the recent past, researchers have put in tremendous efforts towards developing photosensitizer molecules for photodynamic treatment (PDT) to treat cancer. Conjugated polymers, characterized by their efficient fluorescence, exceptional photostability, and strong light absorption, are currently under scrutiny for their potential applications in cancer detection and treatment through photodynamic and photothermal therapy. Researchers are exploring the versatility of these polymers, utilizing sophisticated chemical synthesis and adaptable polymer structures to create new variants with enhanced capabilities for generating singlet oxygen in photodynamic treatment (PDT). The incorporation of photosensitizers into conjugated polymer nanoparticles has proved to be beneficial, as it improves singlet oxygen formation through effective energy transfer. The evolution of nanotechnology has emerged as an alternative avenue for enhancing the performance of current photosensitizers and overcoming significant challenges in cancer PDT. Various materials, including biocompatible metals, polymers, carbon, silicon, and semiconductor-based nanomaterials, have undergone thorough investigation as potential photosensitizers for cancer PDT. This paper outlines the recent advances in singlet oxygen generation by investigators using an array of materials, including graphene quantum dots (GQDs), gold nanoparticles (Au NPs), silver nanoparticles (Ag NPs), titanium dioxide (TiO2), ytterbium (Yb) and thulium (Tm) co-doped upconversion nanoparticle cores (Yb/Tm-co-doped UCNP cores), bismuth oxychloride nanoplates and nanosheets (BiOCl nanoplates and nanosheets), and others. It also stresses the synthesis and application of systems such as amphiphilic block copolymer functionalized with folic acid (FA), polyethylene glycol (PEG), poly(β-benzyl-L-aspartate) (PBLA10) (FA-PEG-PBLA10) functionalized with folic acid, tetra(4-hydroxyphenyl)porphyrin (THPP-(PNIPAM-b-PMAGA)4), pyrazoline-fused axial silicon phthalocyanine (HY-SiPc), phthalocyanines (HY-ZnPcp, HY-ZnPcnp, and HY-SiPc), silver nanoparticles coated with polyaniline (Ag@PANI), doxorubicin (DOX) and infrared (IR)-responsive poly(2-ethyl-2-oxazoline) (PEtOx) (DOX/PEtOx-IR NPs), particularly in NIR imaging-guided photodynamic therapy (fluorescent and photoacoustic). The study puts forward a comprehensive summary and a convincing justification for the usage of the above-mentioned materials in cancer PDT.

Advanced strategies for CRISPR/Cas9 delivery and applications in gene editing, therapy, and cancer detection using nanoparticles and nanocarriers.

Cancer remains one of the deadliest diseases, and is characterised by the uncontrolled growth of modified human cells. Unlike infectious diseases, cancer does not originate from foreign agents. Though a variety of diagnostic procedures are available; their cost-effectiveness and accessibility create significant hurdles. Non-specific cancer symptoms further complicate early detection, leading to belated recognition of certain cancer. The lack of reliable biomarkers hampers effective treatment, as chemotherapy, radiation therapy, and surgery often result in poor outcomes and high recurrence rates. Genetic and epigenetic mutations play a crucial role in cancer pathogenesis, necessitating the development of alternate treatment methods. The advent of CRISPR/Cas9 technology has transformed molecular biology and exhibits potential for gene modification and therapy in various cancer types. Nonetheless, obstacles such as safe transport, off-target consequences, and potency must be overcome before widespread clinical use. Notably, this review delves into the multifaceted landscape of cancer research, highlighting the pivotal role of nanoparticles in advancing CRISPR/Cas9-based cancer interventions. By addressing the challenges associated with cancer diagnosis and treatment, this integrated approach paves the way for innovative solutions and improved patient outcomes.

Breast Cancer Detection using Image Processing and Machine Learning: A Comprehensive Review and Improved Segmentation Approach

Breast cancer stands as one of the most prevalent health concerns for women. Early detection of breast cancer can significantly improve the chances of survival. In developed countries, more than 19.9% of women will die per year due to breast cancer. Regular breast cancer screening is an important way to detect cancer early. Image processing techniques are highly used for different types of cancer detection applications with medical screening. Segmentation of the breast tumor region is a critical step in image processing related to this manner. Lots of research work can be found on developing ways for detecting breast cancers. However still, there is a need for a standard and robust cancer region segmentation method. Right cancer region segmentation is significant for better feature extraction and better classification. This study presents a comprehensive literature review about technologies used with image enhancement and tumor segmentation. Further, the study proposes a robust approach to image enhancement and cancer region segmentation for breast cancer detection using image processing techniques and machine learning. In this work, mammograms are enhanced using Contrast Limited Adaptive Histogram Equalization and denoised using the Median blurring filter. This study enables an effective image segmentation approach with two stages: removing the background using thresholding, tumor region segmentation using a combination of thresholding, and Canny edge detection. Segmented tumor region’s features are extracted using the Gabor filter. Here the accuracy of the approach is compared with three main machine learning classifiers: Decision Tree, Random Forest, Multinomial Logistic Regression and mammograms are classified into three classes (malignant, benign and normal). Finally, an ensemble approach is proposed using the hard voting mechanism to improve the accuracy. With the dataset of mini-MIAS this proposed approach achieved 78.89% accuracy. Results demonstrated that the proposed breast cancer detection approach improves the performance of segmentation breast tumor regions.

3D digital holographic polarimetry of diffuse optically anisotropic biological tissue object fields

The experimental validation of methods for the digital holographic 3D layer-by-layer reconstruction of complex amplitude distributions in different phase planes of diffuse biological tissue histological section microscopic images with a subsequent reconstruction of azimuth and ellipticity polarization maps is presented. Polarization dependencies of the integral and layer-by-layer vector structure of the laser object field azimuth and ellipticity distributions in two types of biological layer samples—the fibrous myocardium and parenchymal liver—have been investigated. Scenarios and dynamics of changes in the values of statistical moments of the first to fourth orders, which characterize the integral and layer-by-layer polarization maps of diffuse biological tissue histological section microscopic images with different morphological structures, have been determined. Criteria for selecting the polarization components of the object field that are formed by acts of scattering of different multiplicities have been identified. An example of biomedical application in cancer detection of biological tissues is provided.