A short presentation about the differences between 2D and 3D culture models, why researchers are moving toward 3D models in anticancer drug screening, the methods used in doing so and a recent case study of 3D tumour model being used for drug screening.
Mechanical signals inhibit growth of a grafted tumor in vivo proof of conceptRemy BROSSEL
We apply the principles of physical oncology (or mechanobiology) in vivo to show the effect of a “constraint field” on tumor growth.
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152885
Interrogating differences in expression of targeted gene sets to predict brea...Enrique Moreno Gonzalez
Genomics provides opportunities to develop precise tests for diagnostics, therapy selection and monitoring. From analyses of our studies and those of published results, 32 candidate genes were identified, whose expression appears related to clinical outcome of breast cancer. Expression of these genes was validated by qPCR and correlated with clinical follow-up to identify a gene subset for development of a prognostic test.
Mycobacterium ulcerans infection (Buruli ulcer) is a neglected but treatable skin disease endemic in over
30 countries. M. ulcerans is an environmental mycobacteria with an elusive mode of transmission to humans. Ecological and Molecular epidemiological studies to identify reservoirs and transmission vectors are important
for source tracking infections especially during outbreaks and elucidating transmission routes.
Mechanical signals inhibit growth of a grafted tumor in vivo proof of conceptRemy BROSSEL
We apply the principles of physical oncology (or mechanobiology) in vivo to show the effect of a “constraint field” on tumor growth.
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152885
Interrogating differences in expression of targeted gene sets to predict brea...Enrique Moreno Gonzalez
Genomics provides opportunities to develop precise tests for diagnostics, therapy selection and monitoring. From analyses of our studies and those of published results, 32 candidate genes were identified, whose expression appears related to clinical outcome of breast cancer. Expression of these genes was validated by qPCR and correlated with clinical follow-up to identify a gene subset for development of a prognostic test.
Mycobacterium ulcerans infection (Buruli ulcer) is a neglected but treatable skin disease endemic in over
30 countries. M. ulcerans is an environmental mycobacteria with an elusive mode of transmission to humans. Ecological and Molecular epidemiological studies to identify reservoirs and transmission vectors are important
for source tracking infections especially during outbreaks and elucidating transmission routes.
Advances in Childhood Cancer: Big Data & Immunotherapeutics
Friday, 31st August, 2018
Venue: Australian National Maritime Museum, Darling Harbour, Sydney
KCA warmly invites you to this symposium featuring the latest developments in big data analytics, bioinformatics and, immunotherapeutic targeting.
Registration is FREE.
Seating is limited*. To register please email your full name, preferred email address, and name of your primary institution to KCAadmin@ccia.org.au
For more details please contact Dr Michael Evtushenko MEvtushenko@ccia.unsw.edu.au
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms.This technique gives an accuracy of 98%.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many
biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon
cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies
in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in
their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms
and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the
matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix
Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification
accuracies are then compared for these algorithms.This technique gives an accuracy of 98%
How is machine learning significant to computational pathology in the pharmac...Pubrica
• Plentiful amassing of advanced histopathological pictures has prompted the expanded interest for their examination; for example, PC supported determination utilizing AI procedures.
• In this blog, Pubrica explains the applications of machine learning in digital pathology field using Biostatistics Services.
Continue Reading: https://bit.ly/37Vp6co
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
Why Pubrica?
When you order our services, Plagiarism free|onTime|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
A CLASSIFICATION MODEL ON TUMOR CANCER DISEASE BASED MUTUAL INFORMATION AND F...Kiogyf
A CLASSIFICATION MODEL ON TUMOR CANCER DISEASE BASED MUTUAL INFORMATION AND FIREFLY ALGORITHM
ABSTRACT
Cancer is a globally recognized cause of death. A proper cancer analysis demands the classification of several types of tumor. Investigations into microarray gene expressions seem to be a successful platform for revising genetic diseases. Although the standard machine learning (ML) approaches have been efficient in the realization of significant genes and in the classification of new types of cancer cases, their medical and logical application has faced several drawbacks such as DNA microarray data analysis limitation, which includes an incredible number of features and the relatively small size of an instance. To achieve a reasonable and efficient DNA microarray dataset information, there is a need to extend the level of interpretability and forecast approach while maintaining a great level of precision. In this work, a novel way of cancer classification based on based gene expression profiles is presented. This method is a combination of both Firefly algorithm and Mutual Information Method. First, the features are used to select the features before using the Firefly algorithm for feature reduction. Finally, the Support Vector Machine is used to classify cancer into types. The performance of the proposed system was evaluated by using it to classify datasets from colon cancer; the results of the evaluation were compared with some recent approaches.
Keywords: Feature Selection, Firefly Algorithm, Cancer Disease, Mutual Information
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
The culture of cells in two dimensions does not reproduce the histological characteristics of a tissue for informative or useful study. Growing cells as three-dimensional (3D) models more analogous to their existence in vivo may be more clinically relevant. Discuss the potential of using three dimensional cell cultures for anti-cancer drug screening.
Advances in Childhood Cancer: Big Data & Immunotherapeutics
Friday, 31st August, 2018
Venue: Australian National Maritime Museum, Darling Harbour, Sydney
KCA warmly invites you to this symposium featuring the latest developments in big data analytics, bioinformatics and, immunotherapeutic targeting.
Registration is FREE.
Seating is limited*. To register please email your full name, preferred email address, and name of your primary institution to KCAadmin@ccia.org.au
For more details please contact Dr Michael Evtushenko MEvtushenko@ccia.unsw.edu.au
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms.This technique gives an accuracy of 98%.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many
biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon
cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies
in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in
their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms
and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the
matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix
Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification
accuracies are then compared for these algorithms.This technique gives an accuracy of 98%
How is machine learning significant to computational pathology in the pharmac...Pubrica
• Plentiful amassing of advanced histopathological pictures has prompted the expanded interest for their examination; for example, PC supported determination utilizing AI procedures.
• In this blog, Pubrica explains the applications of machine learning in digital pathology field using Biostatistics Services.
Continue Reading: https://bit.ly/37Vp6co
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
Why Pubrica?
When you order our services, Plagiarism free|onTime|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
A CLASSIFICATION MODEL ON TUMOR CANCER DISEASE BASED MUTUAL INFORMATION AND F...Kiogyf
A CLASSIFICATION MODEL ON TUMOR CANCER DISEASE BASED MUTUAL INFORMATION AND FIREFLY ALGORITHM
ABSTRACT
Cancer is a globally recognized cause of death. A proper cancer analysis demands the classification of several types of tumor. Investigations into microarray gene expressions seem to be a successful platform for revising genetic diseases. Although the standard machine learning (ML) approaches have been efficient in the realization of significant genes and in the classification of new types of cancer cases, their medical and logical application has faced several drawbacks such as DNA microarray data analysis limitation, which includes an incredible number of features and the relatively small size of an instance. To achieve a reasonable and efficient DNA microarray dataset information, there is a need to extend the level of interpretability and forecast approach while maintaining a great level of precision. In this work, a novel way of cancer classification based on based gene expression profiles is presented. This method is a combination of both Firefly algorithm and Mutual Information Method. First, the features are used to select the features before using the Firefly algorithm for feature reduction. Finally, the Support Vector Machine is used to classify cancer into types. The performance of the proposed system was evaluated by using it to classify datasets from colon cancer; the results of the evaluation were compared with some recent approaches.
Keywords: Feature Selection, Firefly Algorithm, Cancer Disease, Mutual Information
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
The culture of cells in two dimensions does not reproduce the histological characteristics of a tissue for informative or useful study. Growing cells as three-dimensional (3D) models more analogous to their existence in vivo may be more clinically relevant. Discuss the potential of using three dimensional cell cultures for anti-cancer drug screening.
Genes and Tissue Culture Technology Assignment (G6)Rohini Krishnan
The culture of cells in two dimensions does not reproduce the histological characteristics of a tissue for informative or useful study. Growing cells as three-dimensional (3D) models more analogous to their existence in vivo may be more clinically relevant.
Application of Microarray Technology and softcomputing in cancer BiologyCSCJournals
DNA microarray technology has emerged as a boon to the scientific community in understanding the growth and development of life as well as in widening their knowledge in exploring the genetic causes of anomalies occurring in the working of the human body. microarray technology makes biologists be capable of monitoring expression of thousands of genes in a single experiment on a small chip. Extracting useful knowledge and info from these microarray has attracted the attention of many biologists and computer scientists. Knowledge engineering has revolutionalized the way in which the medical data is being looked at. Soft computing is a branch of computer science capable of analyzing complex medical data. Advances in the area of microarray –based expression analysis have led to the promise of cancer diagnosis using new molecular based approaches. Many studies and methodologies have come up which analyszes the gene espression data by using the techniques in data mining such as feature selection, classification, clustering etc. emboiding the soft computing methods for more accuracy. This review is an attempt to look at the recent advances in cancer research with DNA microarray technology , data mining and soft computing techniques.
Development of cancer therapeutics is often carried out in 2D cultures prior to testing on animal model. In comparison to 2D cultures, discuss the potential of using 3D in vitro models for drug efficiency testing.
Cancer remains one of the most formidable challenges in modern medicine, posing significant health burdens globally. In recent years, extensive research efforts have aimed at understanding the intricate mechanisms underlying cancer development, progression, and treatment. This review provides a comprehensive overview of the latest advancements in cancer research across various domains. The elucidation of genetic and molecular alterations driving oncogenesis has revolutionized our understanding of cancer biology. Key discoveries in genomics, transcriptomics, and proteomics have unveiled the heterogeneous nature of tumors, paving the way for personalized treatment approaches. Moreover, advancements in high-throughput sequencing technologies have facilitated the identification of novel cancer biomarkers with diagnostic, prognostic, and therapeutic implications. The tumor microenvironment (TME) has emerged as a critical determinant of cancer progression and therapy response. Research focusing on the dynamic interactions between cancer cells, immune cells, and stromal components within the TME has led to the development of immunotherapeutic strategies, including immune checkpoint inhibitors and adoptive cell therapies, which have demonstrated remarkable efficacy in various cancer types. In addition to targeted therapies and immunotherapies, the advent of precision medicine has transformed cancer treatment paradigms. Molecular profiling of tumors enables clinicians to match patients with specific targeted therapies, optimizing therapeutic outcomes while minimizing adverse effects. Furthermore, the integration of artificial intelligence and machine learning algorithms in cancer research has facilitated the prediction of treatment responses and identification of novel therapeutic targets.
Top downloaded article in academia 2020 - International Journal of Computatio...ijcsity
International Journal of Computational Science and Information Technology (IJCSITY) focuses on Complex systems, information and computation using mathematics and engineering techniques. This is an open access peer-reviewed journal will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area of Computation theory and applications. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of advanced Computation and its applications.
Revolutionizing Cancer Research Immunohistochemistry and Digital Slide Scanne...ihc-prs
In the ever-evolving landscape of cancer research, two key players have joined forces to enhance our understanding of the disease – immunohistochemistry (IHC) and digital slide scanners. This dynamic duo is reshaping the way researchers visualize and analyze cancer tissues, offering unprecedented insights into the molecular intricacies of tumors.
PREDICTION OF BREAST CANCER USING DATA MINING TECHNIQUESIAEME Publication
Women who have improved from breast cancer (BC) constantly panic about setback. The way that they have persevered through the meticulous treatment makes repeat their biggest fear. However, with current spreads in technology, early repeat prediction can enable patients to get treatment prior. The accessibility of broad information and propelled techniques make precise and fast prediction possible. This examination expects to think about the exactness of a couple of existing information mining calculations in predicting BC repeat. It inserts a particle swarm optimization as highlight choice into ANN classifier. An objective of increasing the accuracy level of the prediction model.
Toward Integrated Clinical and Gene Expression Profiles for Breast Cancer Pro...CSCJournals
Breast cancer patients with the same diagnostic and clinical prognostic profile can have markedly different clinical outcome. This difference is possibly caused by the limitation of current breast cancer prognostic indices, which group molecularly distinct patients into similar clinical classes based mainly on morphological of disease. Traditional clinical based prognosis models were discovered contain some restriction to address the heterogeneity of breast cancer. The invention of microarray technology and its ability to simultaneously interrogate thousands genes has changed the paradigm of molecular classification of human cancers as well as it shifted clinical prognosis model to broader prospect. Numerous studies have revealed the potential value of gene expression signatures in examining the risk of disease recurrence. However, currently most of these studies attempted to implement genetic marker based prognostic models to replace the traditional clinical markers, yet neglecting the rich information contain in clinical information. Therefore, this research took an effort to integrate both clinical and microarray data in order to obtain accurate breast cancer prognosis, by taking into account that these data complements each other. This article presents a review of the development of breast cancer prognosis models, concentrating precisely on clinical and gene expression profiles. The literature is reviewed in an explicit machine learning framework, which include the elements of feature selection and classification techniques.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Nucleophilic Addition of carbonyl compounds.pptxSSR02
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Potentials of 3D models in anticancer drug screening
1.
2. In the quest of successfully launching a new drug, drug discovery in the preclinical phase, and
drug development in the clinical phase is crucial. Especially in the field of anti cancer drug
screening, researchers have been heavily involved in producing new drugs that bridge the gap
between experimental research and clinical trials (Meng et al., 2019).
Recent advancement in bioprinting, has thus produced several in vitro models such as 2D and
3D models that are widely used in drug screening. 2D models are traditionally used by
researchers to evaluate effectiveness of anti-cancer drugs towards cancer cell lines.
Additionally, it is to understand the molecular pathways of cell proliferation. 2D models are
also grown in either shake flasks or Petri dishes with growth medium as their source of
nutrients (Breslin & O’Driscoll, 2013).
Meanwhile, in this presentation, 3D models are used by researchers to mimic tumour
microenvironment and visualise the structure of cells (Breslin & O’Driscoll, 2013). This
presentation will be reviewing the differences between these two models, the recent preference
of researchers for 3D models, and case study that highlights the new direction for the future 3D
bioprinted models.
INTRODUCTION
4. 3D models mimic in vivo physiology of organisms, such as the
histological architecture and heterogeneity (Jackson and Lu,
2016).
They mimic native matrixes and cell-cell interaction as well
as the interactions with the extracellular matrix (ECM) (Leong
and Ng, 2014).
Researchers prefer 3D models because 2D models are
unnatural while animal models are expensive and brings
about ethical issues (Gurski et al., 2010).
Cancer cells cultured in 3D models reflects the behaviour of
cancer cells in their native, in vivo environment (Gurski et al.,
2010)
An example would be where cancer cells cultured in 3D models
respond to chemotherapeutic treatments similarly to in vivo
cancer cells.
WHY RESEARCHERS ARE USING 3D MODELS
Source: (Bourré, 2018)
5.
6. Other methods of generating 3D tumour models
Methods of anticancer drug screening from 3D tumour models
•Once the 3D tumour models have been cultured in anticancer drug supplied medium, different analysis
techniques are used to screen for the most efficient drug.
7. 3D bioprinted cancer model to
test anticancer drugs
A new direction in producing 3D bioprinted in vitro metastatic models via
reconstruction of tumor microenvironments
Source:National
Institute of
Biomedical Imaging
and Bioengineering
(NBIB),2019.
A NIBIB-funded research conducted recently
by a team of researchers from University of
Minnesota (UMN), has developed a newly
dynamic and efficient 3D bioprinted tumor
model for anticancer drug screening. The 3D
model was made in a laboratory dish, and
functions in studying the primary site, growth
and spread of cancer tumours in the body.
Thus, tackling the recurring problems in which
previous 2D models, could not replicate the
conditions and outcomes of tumor growth in
the human body. The 3D bioprinting
technology used in this project originated from
UMN lab through Michael McAlpine. Through
this research, 3D printed biochemical
capsules were combined with 3D bioprinted
tumor cells. Through 3D bioprinting
technology, melanoma cancer cells,lung cancer
cells, normal cells, and blood-vessel like
structures, are able to be precisely located in
the laboratory dish based on their individual
functions (Nibib.nih.gov, 2019).
8. Chemicals that guide cancer cell migration
or the growth of blood vessels, are as well
packed in the cores of hydrogels. They are
also encapsulated within an outer shell made
of gold nanorods. A time controlled release
of the capsules are activated by laser
light, which then creates a chemical
gradient that ultimately guides targeted cell
growth. Thus, these features provides a 4D
control over both space and time. “The
cells and capsules are precisely printed in
biologically relevant sites and the
chemical depots propel movement upon a
triggered release. This is a dynamic 3D
tissue engineering system giving the user
control over the diffusion process at some
later point after the printing process.”
emphasized McAlpine (Nibib.nih.gov, 2019). Figure above shows the schematics of the 3D bioprinted in vitro tumor
model, which demonstrates the integration of tumor cells, the blood-
vessel like structures, and chemical gradients
3D bioprinted cancer model to
test anticancer drugs
A new direction in producing 3D bioprinted in vitro metastatic models via
reconstruction of tumor microenvironments
Source:
Nibib.nih.
gov,
2019).
10. 3D cell culture and anticancer drug testing | Cherry Biotech 2019 Cherry Biotech. viewed 8 May 2019, <https://www.cherrybiotech.com/scientific-
note/organs-on-chip/3d-cell-culture-and-anticancer-drug-testing>.
Bing He, Guomin Chen, Yi Zeng, 2016: Three-dimensional cell culture models for investigating human viruses, Virologica Sinica, 31, 363-379.,
viewed 08 May 2019, <https://www.virosin.org/article/doi/10.1007/s12250-016-3889-z#bpampaloni2007>
Bourré, L., 2018. Facilitating Drug Discovery with 3D In Vitro Oncology Models. [online] Blog.crownbio.com. Available at:
https://blog.crownbio.com/in-vitro-3d-organoids-spheroids-oncology [Accessed 3 May 2019].
Breslin, S & O’Driscoll, L 2013, "Three-dimensional cell culture: the missing link in drug discovery", Drug Discovery Today, vol. 18, no. 5-6, pp.
240-249. viewed 1 May 2019, <https://www.ncbi.nlm.nih.gov/pubmed/23073387>.
Duval, K., Grover, H., Han, L. H., Mou, Y., Pegoraro, A. F., Fredberg, J., & Chen, Z., 2017. “Modeling Physiological Events in 2D vs. 3D Cell
Culture.”, Physiology (Bethesda, Md.), 32(4), 266–277., viewed 08 May 2019,
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5545611/?report=classic>
Gurski, L.A., Petrelli, N.J., Jia, X. and Farach-Carson, M.C., 2010. 3D matrices for anti-cancer drug testing and development. Oncology Issues,
25(1), pp.20-25.
Huang, L, Holtzinger, A, Jagan, I, BeGora, M, Lohse, I, Ngai, N, Mutuswamy LB, Crawford, HC, Arrowsmith, C, Kalloger, SE, Renouf, DJ,
Connor, AA, Clearly, S, Schaeffer, DF, Roehrl, M, Tsao MS, Gallinger, S, Keller, G & Muthuswamy, SK 2015, “Ductal pancreatic cancer
modeling and drug screening using human pluripotent stem cell– and patient-derived tumor organoids.” Nature Medicine, vol. 21, no. 11, pp.
1364–1371, viewed 08 May 2019, <https://www.nature.com/articles/nm.3973>
REFERENCES
11. Ivascu, A & Kubbies, M 2006, “Rapid Generation of Single-Tumor Spheroids for High-Throughput Cell Function and Toxicity Analysis”, SLAS
Discovery, vol. 11, no. 8, pp. 922-932, viewed 08 May 2019, <https://journals.sagepub.com/doi/abs/10.1177/1087057106292763>
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