Engineers' new microfluidic device could help with drug development with 3-D cell growth

MIT engineers have built a device that gives them an unprecedented view of three-dimensional cell growth and migration, including the formation of blood vessels and the spread of tumor cells.
The microfluidic device, imprinted on a square inch of plastic, could be used to evaluate the potential side effects of drugs in development, or to test the effectiveness of cancer drugs in individual patients.
Roger Kamm, MIT professor of biological and mechanical engineering, and his colleagues reported their observations of angiogenesis -- the process by which blood vessels are formed -- in the Oct. 31 online issue of the journal Lab on a Chip.
Microfluidic devices have been widely used in recent years to study cells, but most only allow for the study of cells growing on a flat (two-dimensional) surface, or else lack the ability to observe and control cell behavior. With the new device, researchers can observe cells in real time as they grow in a three-dimensional collagen scaffold under precisely controlled chemical or physical conditions.
Observing angiogenesis and other types of cell growth in three dimensions is critical because that is how such growth normally occurs, said Kamm.
Working with researchers around MIT, Kamm has studied growth patterns of many types of cells, including liver cells, stem cells and neurons. He has also used the device to investigate the pressure buildup that causes glaucoma.
The device allows researchers to gain new insight into cell growth patterns. For example, the researchers observed that one type of breast cancer cell tends to migrate in a uniform mass and induces new capillaries to sprout aggressively toward the original tumor, while a type of brain cancer cell breaks from the primary tumor and migrates individually but does not promote capillary formation.
The system is configured so that researchers can manipulate and study mechanical and biochemical factors that influence cell growth and migration, including stiffness of the gel scaffold, concentration of growth factors and other chemicals, and pressure gradients.
Two or three channels imprinted onto the plastic square contain either a normal cell growth medium or a chemical under study, such as growth factor. Cells growing in the scaffold between the channels are bathed in chemicals from the channels, and the effect of the chemicals can be evaluated based on various measures of cell function.
Kamm and his colleagues first described their microfluidic device in a January 2007 paper in Lab on a Chip. Vernella Vickerman, a graduate student in chemical engineering, and Seok Chung, a postdoctoral fellow in biological engineering, played critical roles in developing the device, Kamm said.
The research was funded by Draper Laboratory

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MIT research helps convert brain signals into action

Lakshminarayan Srinivasan (S.M., Ph.D. 2006) is part of a team that develops standardizing math equations to allow neural prostheses to work better. He is currently a medical student in the Harvard-MIT Division of Health Sciences and Technology and a postdoctoral researcher at the Center for Nervous System Repair at Massachusetts General Hospital.
sponsored by careerbd Brain the most wonderful thing in the space . their are many belivers that if sometime we can discover our brain we may live life diffrent
MIT researchers have developed a new algorithm to help create prosthetic devices that convert brain signals into action in patients who have been paralyzed or had limbs amputated.

The technique, described in a paper published as the cover article in the October edition of the Journal of Neurophysiology, unifies seemingly disparate approaches taken by experimental groups that prototype these neural prosthetic devices in animals or humans.

"The work represents an important advance in our understanding of how to construct algorithms in neural prosthetic devices for people who cannot move to act or speak," said Lakshminarayan "Ram" Srinivasan (S.M., Ph.D. 2006), lead author of the paper.

Srinivasan, currently a postdoctoral researcher at the Center for Nervous System Repair at Massachusetts General Hospital and a medical student in the Harvard-MIT Division of Health Sciences and Technology (HST), began working on the algorithm while a graduate student in MIT's Department of Electrical Engineering and Computer Science (EECS).

Trauma and disease can lead to paralysis or amputation, reducing the ability to move or talk despite the capacity to think and form intentions. In spinal cord injuries, strokes, and diseases such as amyotrophic lateral sclerosis (Lou Gehrig's disease), the neurons that carry commands from the brain to muscle can be injured. In amputation, both nerves and muscle are lost.

Neural prosthetic devices represent an engineer's approach to treating paralysis and amputation. Here, electronics are used to monitor the neural signals that reflect an individual's intentions for the prosthesis or computer they are trying to use. Algorithms form the link between neural signals that are recorded and the user's intentions that are decoded to drive the prosthetic device.

Over the past decade, efforts at prototyping these devices have divided along various boundaries related to brain regions, recording modalities, and applications. The MIT technique provides a common framework that underlies all these various efforts.

The research uses a method called graphical models that has been widely applied to problems in computer science like speech-to-text or automated video analysis. The graphical models used by the MIT team are diagrams composed of circles and arrows that represent how neural activity results from a person's intentions for the prosthetic device they are using.

The diagrams represent the mathematical relationship between the person's intentions and the neural manifestation of that intention, whether the intention is measured by an electoencephalography (EEG), intracranial electrode arrays or optical imaging. These signals could come from a number of brain regions, including cortical or subcortical structures.

Until now, researchers working on brain prosthetics have used different algorithms depending on what method they were using to measure brain activity. The new model is applicable no matter what measurement technique is used, according to Srinivasan. "We don't need to reinvent a new paradigm for each modality or brain region," he said.

Srinivasan is quick to underscore that many challenges remain in designing neural prosthetic algorithms before they are available for people to use. While the algorithm is unifying, it is not universal: the algorithm consolidates multiple avenues of development in prostheses, but it isn't the final and only approach these researchers expect to see in the years to come. Moreover, energy efficiency and robustness are key considerations for any portable, implantible bio-electronic device.

Through a better quantitative understanding of how the brain normally controls movement and the mechanisms of disease, he hopes these devices could one day allow for a level of dexterity depicted in movies, such as actor Will Smith's mechanical arm in the movie, "I, Robot."

The gap between existing prototypes and that final goal is wide. Translating an algorithm into a fully functioning clinical device will require a great deal of work, but also represents an intriguing road of scientific and engineering development for the years to come.

Other authors on the paper are Uri Eden (Ph.D. 2005), assistant professor in mathematics and statistics at Boston University; Sanjoy Mitter, professor in EECS and MIT's Engineering Systems Division; and Emery Brown, professor in brain and cognitive sciences, HST, and anesthesia and critical care at Massachusetts General Hospital. The cover image for the October issue of Journal of Neurophysiology that depicts this research was designed by Rene Chen (B.S. 2007) and Eric Pesanelli.

This work was sponsored by the National Institutes of Health and the National Science Foundation.