scholarly journals CRISPR Cas/Exosome Based Diagnostics: Future of Early Cancer Detection

2021 ◽  
Author(s):  
P.P. Mubthasima ◽  
Kaumudi Pande ◽  
Rajalakshmi Prakash ◽  
Anbarasu Kannan
Keyword(s):  
Cancer Cells ◽  
Cancer Detection ◽  
Diagnostic Tool ◽  
Early Cancer ◽  
Early Stage ◽  
Extracellular Vesicle ◽  
Early Cancer Detection ◽  
Diagnostic Tools ◽  
Per Se ◽  
Almost All

Trending and Thriving, CRISPR/Cas has expanded its wings towards diagnostics in recent years. The potential of evading off targeting has not only made CRISPR/Cas an effective therapeutic aid but also an impressive diagnostic tool for various pathological conditions. Exosomes, 30 - 150nm sized extracellular vesicle present and secreted by almost all type of cells in body per se used as an effective diagnostic tool in early cancer detection. Cancer being the leading cause of global morbidity and mortality can be effectively targeted if detected in the early stage, but most of the currently used diagnostic tool fails to do so as they can only detect the cancer in the later stage. This can be overcome by the use of combo of the two fore mentioned diagnostic aids, CRISPR/Cas alongside exosomes, which can bridge the gap compensating the cons. This chapter focus on two plausible use of CRISPR/Cas, one being the combinatorial aid of CRISPR/Cas and Exosome, the two substantial diagnostic tools for successfully combating cancer and other, the use of CRISPR in detecting and targeting cancer exosomes, since they are released in a significant quantity in early stage by the cancer cells.

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

2021 ◽  
Author(s):  
Lin Huang ◽  
Kun Qian
Keyword(s):  
Machine Learning ◽  
Lung Adenocarcinoma ◽  
Cancer Detection ◽  
High Performance ◽  
Large Scale ◽  
Early Cancer ◽  
Early Stage ◽  
Early Cancer Detection ◽  
Ionization Mass ◽  
Efficient Test

Abstract Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 second using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.

scholarly journals A mathematical model of ctDNA shedding predicts tumor detection size

2020 ◽  
Author(s):  
Stefano Avanzini ◽  
David M. Kurtz ◽  
Jacob J. Chabon ◽  
Everett J. Moding ◽  
Sharon Seiko Hori ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Cancer Detection ◽  
Lung Tumor ◽  
Early Cancer ◽  
Early Stage ◽  
A Priori ◽  
Circulating Tumor Dna ◽  
Early Cancer Detection ◽  
Tumor Evolution ◽  
Early Stage Lung Cancer

AbstractEarly cancer detection aims to find tumors before they progress to an incurable stage. We developed a stochastic mathematical model of tumor evolution and circulating tumor DNA (ctDNA) shedding to determine the potential and the limitations of cancer early detection tests. We inferred normalized ctDNA shedding rates from 176 early stage lung cancer subjects and calculated that a 15 mL blood sample contains on average 1.7 genome equivalents of ctDNA for lung tumors with a volume of 1 cm3. For annual screening, the model predicts median detection sizes between 3.8 and 6.6 cm3 corresponding to lead times between 310 and 450 days compared to current lung tumor sizes at diagnosis. For monthly cancer relapse testing based on 20 a priori known mutations, the model predicts a median detection size of 0.26 cm3 corresponding to a lead time of 150 days. This mechanistic framework can help to optimize early cancer detection approaches.

scholarly journals A signal-amplifiable biochip quantifies extracellular vesicle-associated RNAs for early cancer detection

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Jiaming Hu ◽  
Yan Sheng ◽  
Kwang Joo Kwak ◽  
Junfeng Shi ◽  
Bohao Yu ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Early Cancer ◽  
Extracellular Vesicle ◽  
Early Cancer Detection

scholarly journals Small-area data on socioeconomic status and immigrant groups for evaluating equity of early cancer detection and care

2021 ◽  
pp. 1-6
Author(s):  
Ulf Strömberg ◽  
Brandon L. Parkes ◽  
Amir Baigi ◽  
Carl Bonander ◽  
Anders Holmén ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Cancer Detection ◽  
Small Area ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
Area Data

Dying To Be Men: Masculinity and Early Cancer Detection Among Nigerian Men

2021 ◽  
pp. 0272684X2110049
Author(s):  
Darlingtina Esiaka ◽  
Candidus Nwakasi ◽  
Kelsey Brodie ◽  
Aaron Philip ◽  
Kalu Ogba
Keyword(s):  
Public Health ◽  
Cancer Detection ◽  
Attachment Styles ◽  
Early Cancer ◽  
Sub Saharan Africa ◽  
Early Cancer Detection ◽  
Anxious Attachment ◽  
Incidence And Mortality ◽  
Nigerian Men ◽  
Sub Saharan

Cancer incidence and mortality in Nigeria are increasing at an alarming rate, especially among Nigerian men. Despite the numerous public health campaigns and education on the importance of early cancer detection in Nigeria, there exist high rate of fatal/advanced stage cancer diagnoses among Nigerian men, even among affluent Nigerian men. However, there is limited information on patterns of cancer screening and psychosocial predictors of early cancer detection behaviors among Nigerian men. In this cross-sectional study, we examined demographic and psychosocial factors influencing early cancer detection behaviors among Nigerian men. Participants (N = 143; Mage = 44.73) responded to survey assessing: masculinity, attachment styles, current and future cancer detection behaviors, and sociodemographic characteristics. We found that among the participants studied, education, masculinity and anxious attachment were significantly associated with current cancer detection behaviors. Additionally, education and anxious attachment were significantly associated with future cancer detection behaviors. Our finding is best served for clinicians and public health professionals, especially those in the field of oncology in Sub-Saharan Africa. Also, the study may be used as a groundwork for future research and health intervention programs targeting men in Sub-Saharan Africa.

Knowledge of Testicular Cancer Risk and Need for Self-Examination in College Students: A Call for Equal Time for Men in Teaching of Early Cancer Detection Techniques

PEDIATRICS ◽  
1984 ◽  
Vol 74 (6) ◽  
pp. 1093-1096
Author(s):  
John M. Goldenring ◽  
Elizabeth Purtell
Keyword(s):  
Testicular Cancer ◽  
Cancer Detection ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
Men And Women ◽  
Equal Time ◽  
Detection Techniques ◽  
The Past ◽  
Knowledge And Practice ◽  
Self Examination

College athletes were surveyed about their knowledge and practice of early cancer detection techniques. Males were almost completely unaware of their risk for testicular cancer (87%). Only 9.6% had been taught testicular self-examination and only half of these by their physician. Six percent actually examined themselves regularly. In comparison, more than 60% of women had been taught breast self-examination (75% by a physician) and about one third were doing regular examinations. More than 90% of the young men and women had been seen by physicians for a physical examination within the past 3 years. Physicians need to begin educating males about testicular cancer and its early detection.

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