The document describes The Online Anatomical Human (OAH), a web-based anatomy education tool that links 2D and 3D anatomy. The OAH allows users to explore 3D pelvic models from segmented cryosection data in detail. It also enables annotation and quizzing functions. A MOOC on Coursera utilizing the OAH had over 7,000 participants worldwide. Ongoing evaluation includes large-scale user studies to assess user experience and performance. The goal is to provide open access to complex 3D anatomy models and knowledge traditionally limited to medical students.
Cyber-Anatomy MedVRTM is the highest quality
visual and interactive software for learning
anatomy in 3D.
Building upon the award winning Cyber-Anatomy Med™ softwareapplication, Cyber-Anatomy Med VR™ enables users to view the human body in stereoscopic 3D, perceiving spatial relationships like never before.
The system is built on advanced engineering
and simulation technologies.
Cyber-Anatomy 3D models are medically accurate and present the latest in technological advancement. Anatomists and medical doctors have worked over the past 5 years to ensure fidelity to human anatomy.
Among huge variety of interesting tech developed at Glympse here is an overview of our client SDK - cross-platform library powering numerous mobile applications in automotive, navigation and messaging areas.
Lec2: Digital Images and Medical Imaging ModalitiesUlaş Bağcı
2017 Spring, UCF Medical Image Computing Course
X-ray?
• Ultrasound?
• ComputedTomography(CT)?
• MagneticResonanceImaging(MRI)?
• PositronEmissionTomography(PET)? • DiffusionWeightedImaging(DWI)?
• DiffusionTensorImaging(DTI)?
• MagneticParticleImaging(MPI)?
• OpticalCoherenceTomography(OCT)?
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...) • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Recent advances in VR and AR technology have enabled interactive graphics applications to support healthcare professionals in training, diagnosis, planning, and treatment. This field has progressed enough to warrant a course that can inspire new ideas within the graphics community. Medical images create virtual human anatomy models, allowing for natural interaction and visualization in healthcare scenarios. VR and AR are conceptually different and suited for different types of problems. VR is immersive and suitable for learning anatomy, surgical skills, and analyzing 3D medical data. AR, on the other hand, overlays helpful information onto the physical environment, making it useful for tasks such as communication with patients and training assistants. However, several challenges, such as nonstandard equipment and disorientation, still limit the widespread use of these technologies. This course covers current advances and challenges in this area, including integrating AI techniques.
About the Speaker: Joaquim Jorge holds the UNESCO Chair of Artificial Intelligence & Extended Reality at the University of Lisboa, Portugal. He joined Eurographics in 1986 and ACM/SIGGRAPH in 1989. He is Editor-in-Chief of the Computers and Graphics Journal, Eurographics Fellow, ACM Distinguished Member, and member of IEEE Computer Society Board of Governors. He organized 50+ conferences, including Eurographics 2016 (IPC CO-Chair), IEEE VR 2020/21/22 as co-(papers)chair, and ACM IUI 2012 (IPC co-chair). He served on 210+ program committees and (co)authored over 360 peer-reviewed publications and five books. His research interests include graphics, virtual reality, and advanced HCI techniques applied to health technologies.
Websites:
https://en.wikipedia.org/wiki/Joaquim_Jorge_(computer_scientist)
Google Scholar:
https://scholar.google.com/citations?user=RgiMdpAAAAAJ&hl=en
D.S. Lopes, D. Medeiros, S.F. Paulo, P.B. Borges, V. Nunes, V. Mascarenhas, M. Veiga, J.A. Jorge, Interaction Techniques for Immersive CT Colonography: A Professional Assessment, In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11071, Pages 629–637, Springer, Cham, 2018. DOI: 10.1007/978-3-030-00934-2_70
M. Sousa, D. Mendes, S. Paulo, N. Matela, J. Jorge, D.S. Lopes, VRRRRoom: Virtual Reality for radiologists in the reading room, Proceedings of the 35th Annual ACM Conference on Human Factors in Computing Systems (CHI 2017), New York: ACM Press, 2017. DOI: 10.1145/3025453.3025566
D.S. Lopes, P.F. Parreira, S.F. Paulo, V. Nunes, P.A. Rego, M.C. Neves, P.S. Rodrigues, J.A. Jorge, On the utility of 3D hand cursors to explore medical volume datasets with a touchless interface, Journal of Biomedical Informatics, 72, Pages 140–149, 2017. DOI: 10.1016/j.jbi.2017.07.009
Cyber-Anatomy MedVRTM is the highest quality
visual and interactive software for learning
anatomy in 3D.
Building upon the award winning Cyber-Anatomy Med™ softwareapplication, Cyber-Anatomy Med VR™ enables users to view the human body in stereoscopic 3D, perceiving spatial relationships like never before.
The system is built on advanced engineering
and simulation technologies.
Cyber-Anatomy 3D models are medically accurate and present the latest in technological advancement. Anatomists and medical doctors have worked over the past 5 years to ensure fidelity to human anatomy.
Among huge variety of interesting tech developed at Glympse here is an overview of our client SDK - cross-platform library powering numerous mobile applications in automotive, navigation and messaging areas.
Lec2: Digital Images and Medical Imaging ModalitiesUlaş Bağcı
2017 Spring, UCF Medical Image Computing Course
X-ray?
• Ultrasound?
• ComputedTomography(CT)?
• MagneticResonanceImaging(MRI)?
• PositronEmissionTomography(PET)? • DiffusionWeightedImaging(DWI)?
• DiffusionTensorImaging(DTI)?
• MagneticParticleImaging(MPI)?
• OpticalCoherenceTomography(OCT)?
Basics of Radiological Image Modalities and their clinical use (MRI, PET, CT, fMRI, DTI, ...) • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Recent advances in VR and AR technology have enabled interactive graphics applications to support healthcare professionals in training, diagnosis, planning, and treatment. This field has progressed enough to warrant a course that can inspire new ideas within the graphics community. Medical images create virtual human anatomy models, allowing for natural interaction and visualization in healthcare scenarios. VR and AR are conceptually different and suited for different types of problems. VR is immersive and suitable for learning anatomy, surgical skills, and analyzing 3D medical data. AR, on the other hand, overlays helpful information onto the physical environment, making it useful for tasks such as communication with patients and training assistants. However, several challenges, such as nonstandard equipment and disorientation, still limit the widespread use of these technologies. This course covers current advances and challenges in this area, including integrating AI techniques.
About the Speaker: Joaquim Jorge holds the UNESCO Chair of Artificial Intelligence & Extended Reality at the University of Lisboa, Portugal. He joined Eurographics in 1986 and ACM/SIGGRAPH in 1989. He is Editor-in-Chief of the Computers and Graphics Journal, Eurographics Fellow, ACM Distinguished Member, and member of IEEE Computer Society Board of Governors. He organized 50+ conferences, including Eurographics 2016 (IPC CO-Chair), IEEE VR 2020/21/22 as co-(papers)chair, and ACM IUI 2012 (IPC co-chair). He served on 210+ program committees and (co)authored over 360 peer-reviewed publications and five books. His research interests include graphics, virtual reality, and advanced HCI techniques applied to health technologies.
Websites:
https://en.wikipedia.org/wiki/Joaquim_Jorge_(computer_scientist)
Google Scholar:
https://scholar.google.com/citations?user=RgiMdpAAAAAJ&hl=en
D.S. Lopes, D. Medeiros, S.F. Paulo, P.B. Borges, V. Nunes, V. Mascarenhas, M. Veiga, J.A. Jorge, Interaction Techniques for Immersive CT Colonography: A Professional Assessment, In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11071, Pages 629–637, Springer, Cham, 2018. DOI: 10.1007/978-3-030-00934-2_70
M. Sousa, D. Mendes, S. Paulo, N. Matela, J. Jorge, D.S. Lopes, VRRRRoom: Virtual Reality for radiologists in the reading room, Proceedings of the 35th Annual ACM Conference on Human Factors in Computing Systems (CHI 2017), New York: ACM Press, 2017. DOI: 10.1145/3025453.3025566
D.S. Lopes, P.F. Parreira, S.F. Paulo, V. Nunes, P.A. Rego, M.C. Neves, P.S. Rodrigues, J.A. Jorge, On the utility of 3D hand cursors to explore medical volume datasets with a touchless interface, Journal of Biomedical Informatics, 72, Pages 140–149, 2017. DOI: 10.1016/j.jbi.2017.07.009
Automatic Spatial Plausibility Checks for Medical Object Recognition Results ...Daniel Sonntag
We present an approach using medical expert knowledge represented in formal ontologies to check the results of automatic medical object recognition algorithms for spatial plausibility. Our system is based on the comprehensive Foundation Model of Anatomy ontology which we extend with spatial relations between a number of anatomical entities. These relations are learned inductively from an annotated corpus of 3D volume data sets. The induction process is split into two parts. First, we generate a quantitative anatomical atlas using fuzzy sets to represent inherent imprecision. From this atlas we then abstract the information further onto a purely symbolic level to generate a generic qualitative model of the spatial relations in human anatomy. In our evaluation we describe how this model can be used to check the results of a state-of-the-art medical object recognition system for 3D CT volume data sets for spatial plausibility. Our results show that the combination of medical domain knowledge in formal ontologies and sub symbolic object recognition yields improved overall recognition precision.
Lab Medicine Pathology Grand Rounds presentation for Thursday Dec. 13th, 2012 in ECHA 2-420 at noon on Technology and the Future of Medicine: The Course and the Reality.
http://unityindiversity.ualberta.ca/ Dr. Kim Solez speaking here Thursday noon August 21, 2014 on "IT and Me: Reflections of a Pathologist, Futurist, Technology Advocate Doctor Guy!"
Role of machine learning in detection, prevention and treatment of cancerArpana Awasthi
Author: Dr. Arpana Chaturvedi (Jagannath International Management School, New Delhi, ac240871@gmail.com)
Artificial Intelligence, Machine Learning and Deep Learning now-a-days started playing its very effective and important role resulting great impact on various domains. These fields have been used in all areas as Data scientists realized that with the strength and power of rapidly growing data. The data shared by people of all ages in almost all social media handlers is of different types and in huge volume. This data consists of various kind of information related to almost all domains. Data analyst knows the power of this data and they introduced various techniques to get fruitful hidden insights from the data to benefit various organizations.
Medical imaging meets psychology of perception: optical illusions!Herbert Klein
PowerPoint about an optical illusion and psychology of perception as applied to medical imaging. It is interactive (as with Keynote). A rotating 3D image of a nuclear medicine bone scan is the key clinical example.
Kim Solez Technology, the Future of Medicine, and the Bridge between Transpla...Kim Solez ,
Dr. Kim Solez presents "Technology, the Future of Medicine, and the Bridge between Transplantation and Regenerative Medicine" at the Alberta Interprofessional Conference 2015 on Sunday March 22nd, 2015 at the University of Alberta in Edmonton, Canada. Copyright (c) 2015, JustMachines, Inc.
Medical Technology will save our minds and bodiesAshley Dibley
What is medical technology?
History of Medical Technology.
Advanced Medical Technology.
Pro's/Con's of Medical Technology
Different Types of Modern Medical Technology
Principles of Virtual Reality In Surgical Training - Review by Sanjoy SanyalSanjoy Sanyal
This PPT was presented in 10th National Medical Dental Conference in Seychelles in February 2007. It has been reproduced on 'as is' basis. It was also presented in faculty and students's symposium in a medical school in Seychelles. Presenter is Dr Sanjoy Sanyal, (then) Associate Professor and Masters candidate in Royal College of Surgeons of Edinburgh / University of Bath
Piero Scaruffi's introduction to the Stanford Multidisciplinary Multimedia Meeting of Arts, Science and Humanities... SMMMASH! - Part 5: Body (Jan 17, 2013)
Automatic Spatial Plausibility Checks for Medical Object Recognition Results ...Daniel Sonntag
We present an approach using medical expert knowledge represented in formal ontologies to check the results of automatic medical object recognition algorithms for spatial plausibility. Our system is based on the comprehensive Foundation Model of Anatomy ontology which we extend with spatial relations between a number of anatomical entities. These relations are learned inductively from an annotated corpus of 3D volume data sets. The induction process is split into two parts. First, we generate a quantitative anatomical atlas using fuzzy sets to represent inherent imprecision. From this atlas we then abstract the information further onto a purely symbolic level to generate a generic qualitative model of the spatial relations in human anatomy. In our evaluation we describe how this model can be used to check the results of a state-of-the-art medical object recognition system for 3D CT volume data sets for spatial plausibility. Our results show that the combination of medical domain knowledge in formal ontologies and sub symbolic object recognition yields improved overall recognition precision.
Lab Medicine Pathology Grand Rounds presentation for Thursday Dec. 13th, 2012 in ECHA 2-420 at noon on Technology and the Future of Medicine: The Course and the Reality.
http://unityindiversity.ualberta.ca/ Dr. Kim Solez speaking here Thursday noon August 21, 2014 on "IT and Me: Reflections of a Pathologist, Futurist, Technology Advocate Doctor Guy!"
Role of machine learning in detection, prevention and treatment of cancerArpana Awasthi
Author: Dr. Arpana Chaturvedi (Jagannath International Management School, New Delhi, ac240871@gmail.com)
Artificial Intelligence, Machine Learning and Deep Learning now-a-days started playing its very effective and important role resulting great impact on various domains. These fields have been used in all areas as Data scientists realized that with the strength and power of rapidly growing data. The data shared by people of all ages in almost all social media handlers is of different types and in huge volume. This data consists of various kind of information related to almost all domains. Data analyst knows the power of this data and they introduced various techniques to get fruitful hidden insights from the data to benefit various organizations.
Medical imaging meets psychology of perception: optical illusions!Herbert Klein
PowerPoint about an optical illusion and psychology of perception as applied to medical imaging. It is interactive (as with Keynote). A rotating 3D image of a nuclear medicine bone scan is the key clinical example.
Kim Solez Technology, the Future of Medicine, and the Bridge between Transpla...Kim Solez ,
Dr. Kim Solez presents "Technology, the Future of Medicine, and the Bridge between Transplantation and Regenerative Medicine" at the Alberta Interprofessional Conference 2015 on Sunday March 22nd, 2015 at the University of Alberta in Edmonton, Canada. Copyright (c) 2015, JustMachines, Inc.
Medical Technology will save our minds and bodiesAshley Dibley
What is medical technology?
History of Medical Technology.
Advanced Medical Technology.
Pro's/Con's of Medical Technology
Different Types of Modern Medical Technology
Principles of Virtual Reality In Surgical Training - Review by Sanjoy SanyalSanjoy Sanyal
This PPT was presented in 10th National Medical Dental Conference in Seychelles in February 2007. It has been reproduced on 'as is' basis. It was also presented in faculty and students's symposium in a medical school in Seychelles. Presenter is Dr Sanjoy Sanyal, (then) Associate Professor and Masters candidate in Royal College of Surgeons of Edinburgh / University of Bath
Piero Scaruffi's introduction to the Stanford Multidisciplinary Multimedia Meeting of Arts, Science and Humanities... SMMMASH! - Part 5: Body (Jan 17, 2013)
Similar to The Online Anatomical Human: Web-based Anatomy Education (20)
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
The Online Anatomical Human: Web-based Anatomy Education
1. The Online Anatomical Human:
Web-based Anatomy Education
Noeska Smit1, Cees-Willem Hofstede1, Annelot Kraima2, Daniel
Jansma2, Marco deRuiter2, Elmar Eisemann1, and Anna Vilanova1
1Delft University of Technology, The Netherlands
2Leiden University Medical Center, The Netherlands
3. Anatomy education
• Illustrations:
• Easily accessible
• Idealized ‘average’ anatomy
• Not interactive
3
• Current software:
• Accessibility?
• Idealized ‘average’ anatomy
• Lacking detail
• No link to 2D anatomy as
seen in medical imaging
• Dissections:
• Limited availability
• Real anatomy
• Interactive
16. MOOC Deployment
• Massive Open Online Course (MOOC) on human
anatomy via Coursera:
• Over 7000 participants worldwide
• https://www.coursera.org/learn/anatomy
16
17. Ongoing Evaluation
• Large scale user study
• Survey:
• Statements with Likert scale responses
• Open questions
• Linked to Coursera user profiles
17
User experience
User performance
18. Conclusion
• The Virtual Surgical Pelvis: 3D virtual atlas
• The Online Anatomical Human: web-based application
• Potential for large scale user studies
18
19. Thank you for your attention!
• http://anatomy.tudelft.nl
• Live OAHction
• http://medvis.org
• The blog on all things medical visualization
19