scholarly journals Nanogenomics and Artificial Intelligence: A Dynamic Duo for the Fight Against Breast Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Batla S. Al-Sowayan ◽  
Alaa T. Al-Shareeda
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Human Performance ◽  
Estrogen Receptor Status ◽  
Automated Analysis ◽  
Breast Cancers ◽  
Fast Evaluation ◽  
Different Types ◽  
Metastasis Site ◽  
Whole Slide Images

Application software is utilized to aid in the diagnosis of breast cancer. Yet, recent advances in artificial intelligence (AI) are addressing challenges related to the detection, classification, and monitoring of different types of tumors. AI can apply deep learning algorithms to perform automated analysis on mammographic or histologic examinations. Large volume of data generated by digitalized mammogram or whole-slide images can be interoperated through advanced machine learning. This enables fast evaluation of every tissue patch on an image, resulting in a quicker more sensitivity, and more reproducible diagnoses compared to human performance. On the other hand, cancer cell-exosomes which are extracellular vesicles released by cancer cells into the blood circulation, are being explored as cancer biomarker. Recent studies on cancer-exosome-content revealed that the encapsulated miRNA and other biomolecules are indicative of tumor sub-type, possible metastasis and prognosis. Thus, theoretically, through nanogenomicas, a profile of each breast tumor sub-type, estrogen receptor status, and potential metastasis site can be constructed. Then, a laboratory instrument, fitted with an AI program, can be used to diagnose suspected patients by matching their sera miRNA and biomolecules composition with the available template profiles. In this paper, we discuss the advantages of establishing a nanogenomics-AI-based breast cancer diagnostic approach, compared to the gold standard radiology or histology based approaches that are currently being adapted to AI. Also, we discuss the advantages of building the diagnostic and prognostic biomolecular profiles for breast cancers based on the exosome encapsulated content, rather than the free circulating miRNA and other biomolecules.

scholarly journals Electric charge conversable drug liposomes enable to enhance treatment efficacy of breast cancer

2019 ◽  
Vol 2 (3) ◽  
pp. 317-343 ◽  
Author(s):  
Yao Zhao ◽  
Jing Bai ◽  
Qian Luo ◽  
Jing-Ying Zhang ◽  
Jia-Rui Xu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Electric Charge ◽  
Treatment Efficacy ◽  
Breast Cancer Cells ◽  
Alpha Tocopherol ◽  
Breast Cancers ◽  
Different Types ◽  
Acid Succinate ◽  
Intrinsic Drug Resistance

Intrinsic drug resistance has been demonstrated in different types of breast cancer cells, leading to the recurrence of disease after treatment. Here, we report a functional drug liposome that enables electric charge conversion in the weak acidic milieu of cancer to enhance the treatment efficacy of different breast cancers. The functional drug liposomes were developed by encapsulating daunorubicin and rofecoxib, and modified with new functional material, D-alpha tocopherol acid succinate-polyethylene glycol-glutarate (TPGS1000-glutarate). The results demonstrated that the liposomes promoted the effects of cellular uptake and lysosomal escape, followed by targeting the mitochondria. Consequently, the electric charge conversable drug liposomes significantly enhanced the treatment efficacy by initiating a cascade of reactions through inducing autophagy and apoptosis in different breast cancer cells. In conclusion, the electric charge conversable drug liposomes enable to enhance treatment efficacy of different breast cancers, and hence the study could offer a broadly applicable strategy to enhance efficacy against heterogeneous and refractory cancer cells.

scholarly journals Use of Artificial Intelligence in Healthcare and Medicine

2020 ◽  
Author(s):  
Deekshaa Khanna
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Cognitive Functions ◽  
Paradigm Shift ◽  
Human Performance ◽  
Analytical Techniques ◽  
Medical Field ◽  
Healthcare Data ◽  
Wide Range ◽  
Different Types

Artificial Intelligence is a field of computer science that mimic human cognitive functions. It has brought a paradigm shift in the medical field mostly due to the increase in healthcare data and rapid increase of analytical techniques. In recent years AI has surpassed human performance in several medical field areas, and this is a great adoption in healthcare. Also, through the use of analytical techniques, AI has the capabilities to prevent, detect, diagnose, and treat a wide range of diseases. This research paper will discuss different types of Artificial Intelligence techniques and how AI has been used in healthcare. Also, it will provide a view of the future of Artificial Intelligence in healthcare.

scholarly journals An investigation towards understanding the role of CDH1 gene in different types of Breast Cancers

2021 ◽  
Author(s):  
Antara Sengupta ◽  
Raja Banerjee
Keyword(s):  
Breast Cancer ◽  
Potential Role ◽  
Regular Function ◽  
Breast Cancers ◽  
Cdh1 Gene ◽  
Different Types ◽  
Genetic Aberration ◽  
Breast Tissues

AbstractAt recent age breast cancer attracts the attention of both the medical and the scientific community for its deadly occurrence throughout the globe as it is considered to be happened due to genetic aberration. Out of several genes expressed, it is found that cadherin 1, type 1 (CDH1) is responsible in several ways to control the metabolic order in human. Hence we focus on CDH1 gene whether any deviation in it especially alteration/modification in its sequence is responsible for the occurrence of this deadly disease. Towards this end study of the available genomic sequences of CDH1 gene of several patients, suffering from various types of breast cancer (obtained from the Sanger Database), are studied. The results emphasizes that alternation/modification in the sequence of the CDH1 gene affect its regular function which may have a potential role in damaging the different types of breast tissues, causing malfunction and leading to breast cancers in patients.

scholarly journals Management of breast cancer patients leptomeningeal carcinomatosis: experience with Ommaya catheter

2021 ◽  
Vol 1 (3) ◽  
pp. 125-130
Author(s):  
Felipe De Lacerda Pereira ◽  
Letícia Assad Maia Sandoval ◽  
Luiza Bernardes Ferreira ◽  
Ana Paula Teixeira Leite ◽  
Juliana Moreira Batista ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Leptomeningeal Carcinomatosis ◽  
Meningeal Carcinomatosis ◽  
Breast Cancers ◽  
Breast Cancer Patients ◽  
Disease Response ◽  
Systemic Involvement ◽  
The Poor ◽  
Different Types ◽  
And Control

Leptomeningeal Carcinomatosis is a recurrent complication in different types of tumors with systemic involvement, especially breast cancers. Thus, given the different forms of treatment for this disease, this article presents the effectiveness of using Ommaya catheter as a way of administering chemotherapy. Two cases of breast cancer were studied and their therapeutic evolution reported. Despite the poor prognosis associated with meningeal carcinomatosis, these cases had a disease response and control for a longer period than the expected median.

scholarly journals Integrative multiomics-histopathology analysis for breast cancer classification

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yasha Ektefaie ◽  
William Yuan ◽  
Deborah A. Dillon ◽  
Nancy U. Lin ◽  
Jeffrey A. Golden ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Visual Information ◽  
Estrogen Receptor Status ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Breast Cancers ◽  
Her2 Receptor ◽  
Mutation Status ◽  
Receptor Status

AbstractHistopathologic evaluation of biopsy slides is a critical step in diagnosing and subtyping breast cancers. However, the connections between histology and multi-omics status have never been systematically explored or interpreted. We developed weakly supervised deep learning models over hematoxylin-and-eosin-stained slides to examine the relations between visual morphological signal, clinical subtyping, gene expression, and mutation status in breast cancer. We first designed fully automated models for tumor detection and pathology subtype classification, with the results validated in independent cohorts (area under the receiver operating characteristic curve ≥ 0.950). Using only visual information, our models achieved strong predictive performance in estrogen/progesterone/HER2 receptor status, PAM50 status, and TP53 mutation status. We demonstrated that these models learned lymphocyte-specific morphological signals to identify estrogen receptor status. Examination of the PAM50 cohort revealed a subset of PAM50 genes whose expression reflects cancer morphology. This work demonstrates the utility of deep learning-based image models in both clinical and research regimes, through its ability to uncover connections between visual morphology and genetic statuses.

scholarly journals BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1774
Author(s):  
Niyazi Senturk ◽  
Gulten Tuncel ◽  
Berkcan Dogan ◽  
Lamiya Aliyeva ◽  
Mehmet Sait Dundar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Intelligence ◽  
Risk Factors ◽  
Risk Assessment ◽  
Cancer Patients ◽  
Breast Cancers ◽  
Breast Cancer Patients ◽  
Intelligence Models ◽  
Artificial Intelligence Models

Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients’ data were used to train the system using Mamdani’s Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network’s overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations’ risk assessment in breast cancers as well as a unique tool for personalized medicine software.

scholarly journals Estrogen Receptor and Progesterone Receptor Status in Different Types of Breast Cancers

2009 ◽  
Vol 5 (2) ◽  
pp. 26-32
Author(s):  
Geeta Shakya ◽  
S. Malla ◽  
M. Sharma ◽  
R. Panth
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Progesterone Receptor ◽  
Health Research ◽  
Research Council ◽  
Breast Cancers ◽  
Progesterone Receptor Status ◽  
Receptor Status ◽  
Different Types ◽  
Health Research Council

Not uploaded.Key words: Estrogen Receptor; Progesterone Receptor; Breast Cancer and ImunohistochemistryDOI: 10.3126/jnhrc.v5i2.2468Journal of Nepal Health Research Council (JNHRC) Vol. 5, No.2, October 2007 26-32

scholarly journals An Investigation Towards Understanding the Role of CDH1 Gene in Different Types of Breast Cancers

2021 ◽  
Author(s):  
Antara Sengupta ◽  
Raja Banerjee
Keyword(s):  
Breast Cancer ◽  
Regular Function ◽  
Breast Cancers ◽  
Cdh1 Gene ◽  
Root Cause ◽  
Different Types ◽  
Genetic Aberration ◽  
Breast Tissues

Abstract At recent age breast cancer attracts the attention of both the medical and the scientific community for its deadly occurrence throughout the globe as it is considered to be happened due to genetic aberration. Out of several genes expressed, it is found that cadherin 1, type 1 (CDH1) is responsible in several ways to control the metabolic order in human. Very recently it has been shown that deregulation of the function of protein E-cadherin, expressed from CDH1 plays an important role in lobular breast cancer. In order to understand the root cause of this recent claim, we focus on CDH1 gene whether the genetic information translated due to any deviation/alteration/modification in its sequence is related for the occurrence of the several other types of this deadly disease. Towards this end, study of the available genomic sequences of CDH1 gene obtained from the Sanger Database for 79 patients, suffering from various types of breast cancer, clearly emphasizes that alternation/modification in the sequence of the CDH1 gene can be detrimental. This would affect the regular function of the cell which may have a potential role in damaging the different types of breast tissues, causing malfunction and leading to breast cancers.

scholarly journals Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions

2021 ◽  
Vol 11 (1_suppl) ◽  
pp. 23S-29S
Author(s):  
Zamir A. Merali ◽  
Errol Colak ◽  
Jefferson R. Wilson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Spine Surgery ◽  
Human Performance ◽  
Automated Analysis ◽  
Learning Technologies ◽  
Current Status ◽  
Learning Technology ◽  
Spinal Disorders ◽  
Pubmed Database

Study Design: Narrative review. Objectives: We aim to describe current progress in the application of artificial intelligence and machine learning technology to provide automated analysis of imaging in patients with spinal disorders. Methods: A literature search utilizing the PubMed database was performed. Relevant studies from all the evidence levels have been included. Results: Within spine surgery, artificial intelligence and machine learning technologies have achieved near-human performance in narrow image classification tasks on specific datasets in spinal degenerative disease, spinal deformity, spine trauma, and spine oncology. Conclusion: Although substantial challenges remain to be overcome it is clear that artificial intelligence and machine learning technology will influence the practice of spine surgery in the future.

scholarly journals Rational design and development of HDAC inhibitors for breast cancer treatment

2021 ◽  
Vol 27 ◽  
Author(s):  
Deepansh Mody ◽  
Julie Bouckaert ◽  
Savvas N. Savvides ◽  
Vibha Gupta
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Histone Deacetylases ◽  
Rational Design ◽  
Hdac Inhibitors ◽  
New Drugs ◽  
Breast Cancers ◽  
Different Types ◽  
Cancer Types ◽  
Structural Insights

Background: Breast cancer is the most prevalent cancer amongst females across the globe and with over 2 million new cases reported in 2018, it poses huge economic burden to the already dwindling public health. A dearth of therapies in the pipeline to treat triple-negative breast cancers, and acquisition of resistance against existing line of treatments urges the need to strategize novel therapeutics in order to add new drugs to the pipeline. HDAC inhibitors (HDACi) is one such class of small molecule inhibitors that target histone deacetylases to bring about chromosomal remodelling and normalize dysregulated gene expression that marks breast cancer progression. Objective: While four HDACi have been approved by the FDA for treatment of different cancer types, no HDACi is specifically earmarked for clinical management of breast cancer. Owing to the differential HDAC expression pertaining to different types of breast cancers, isoform-selective HDAC inhibitors need to be discovered. Conclusion: This review attempts to set the stage for rational structure-based discovery of isoform-selective HDACi by providing structural insights into different HDACs and their catalytic folds based on their classes and individual landscape. Development of inhibitors in accordance with the differential expression of HDAC isoforms exhibited in breast cancer cells is a promising strategy to rationally design selective and effective inhibitors, adopting a ‘personalized-medicine’ approach.

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