Understanding the Human Machine - A Primer for Bioengineering - Max E. Valentinuzzi

ings. Under sterile surgical conditions, these authors (Wilke, Neef, Caimi et al., 1999) implanted a pressure transducer in the nucleus pulposus of a normal L4-L5 disc of a male volunteer. Pressure was recorded with a telemetry system during a period of approximately 24 hours for various lying positions; sitting positions in a chair, in an arm-chair, and on a pezziball (ergonomic sitting ball); during sneezing, laughing, walking, jogging, stair climbing, load lifting, and others. The following values were measured: lying prone, 0.1 MPa; lying laterally, 0.12 MPa; relaxed standing, 0.5 MPa; standing flexed forward, 1.1 MPa; sitting unsupported, 0.46 MPa; lifting a 20 kg weight with round flexed back, 2.3 MPa; with flexed knees, 1.7 MPa. Recall that 1 Pa = 1Newton/m2; 1000 mmHg = 133,300 Pa = 0.133 MPa, where M stands for mega). They found a good correlation with most of Nachemson’s data during many exercises, cautiously concluding that the intradiscal pressure during sitting may be less than that in erect standing, that muscle activity increases pressure, that constantly changing position is important to promote flow of fluid (nutrition) to the disc, and that many of the physiotherapy methods studied are valid, but a number of them should be reevaluated.

Human locomotion and human gait biomechanics is a subject well covered in the literature and of utmost relevance (Medved, 2001). In this field, the importance of signals and measurements is well recognized. Engineering solutions, systems and procedures facilitate a more objective evaluation and a better understanding of the locomotor function, which is yet to be fully understood. It is possible to acquire new and better insight into the mechanism of action and function of the neuro-musculo-skeletal system, both in normal and pathological conditions, which from the engineering point of view, is one of the most complex automatic control systems. Special attention must be paid to kinematic variables, searching for answers as for example the question of which variables have to be measured, and why these can be determined via biomechanical modelling of the human body, thereby enabling quantitative characterization of locomotion by treating the body as a complex multisegmental mechanical system. Limbs, trunk, neck, and head are divided in segments with angular movements developing specific torques.

Data collected through the U.S. Department of Labor's Annual Survey of Occupational Injuries and Illnesses demonstrate the high morbidity of

work-related injuries and disorders. Estimates involving days away from work, approximately 34% of the total, result simply from overexertion or repetitive motion. The true cost of work-related overexertion injuries and disorders in the United States is not known. Conservative estimates of annual expenditures, based on workers compensation payments (indemnity and medical services) and other direct costs, range between $13 to 20 billion. The total cost to society is believed to be substantially higher due to various indirect costs (e.g., lost productivity, costs of hiring and training replacement workers, overtime, administrative costs, and miscellaneous transfer payments) that are not included in the conservative estimates. The total annual societal cost has been estimated to be as high as $100 billion.

Epidemiological and laboratory-based research methods have both been used to evaluate the significance of various risk factors associated with work-related musculoskeletal disorders. These studies are designed to look for significant associations between exposure to ergonomic risk factors such as force, repetition, and/or posture. The latter closely relates to excessive tension in muscles, ligaments, tendons and even bone structures. There is strong evidence also of a causal relationship between lowback pain and whole-body vibration, and between segmental vibration and hand-arm vibration. However, there is an inherent difficulty in the determination of the most adequate variables. Biomechanical models and laboratory studies do not replace epidemiological studies, but these approaches provide important complementary information in the quest toward understanding the complex process of how exposures to ergonomic risk factors result in physiological responses that may ultimately lead to work-related injuries and illnesses.

3.4. Signals Produced by Biomaterials

Whenever an external technological piece is introduced in the biosystem, in close contact with it, the problem of compatibility/rejection shows up. The new material does not have to react neither chemically nor biologically nor the tissue cells must attack the first one in any way.

The biomaterials, which currently are utilized in the majority of tissue engineering approaches, function to provide mechanical support and/or

deliver cells to a desired anatomic site. However, they are not designed to specifically interact with desired cell populations and guide the resultant structure and function of engineered or regenerated tissues. Biomaterials are also designed to provide several different types of information to cells in their environment and, thus, mimic many functions of the native extracellular matrices found in tissues. This aspect involves the design and synthesis of new polymers, the development of new processing approaches for existing polymers, and extensive physical and biological characterization of the resulting tissue engineering scaffolds.

A variety of types of information may be conveyed to cells from the biomaterial. The specific mechanism of cell adhesion to a material (e.g., specific ligand-cell receptor bonds) may be designed into a material to promote a specific pattern of gene expression in the adherent cells. Mechanical signals may be conveyed to the adherent cells via the biomaterial, and the specific receptors used to convey the mechanical signal may be varied to tune the cellular response to the mechanical signal. In addition, specific combinations or sequences of growth factors may be locally released from the biomaterials to affect local cell populations, and drive various processes such as migration or proliferation. Both biodegradable polymers and ceramic systems are also being developed to control local tissue formation with biomaterials.

There is a tremendous medical need for new tissues and organs for people suffering from a variety of diseases and accidents. New ways are being studied to grow, or engineer (thus, this has come to be called tissue engineering) new tissues and organs using biomaterials that serve to guide new tissue formation following placement in the body. There are a variety of approaches to achieve this goal such as transplanting cells, delivering proteins which make cells already in the body form new tissues, using gene therapy to grow tissues, and using the biomaterial by itself to drive regeneration. A common theme in all of these approaches is combining basic studies on the mechanisms by which cells interact with materials and synthesizing new polymers that mimic natural materials in the body. These include bone, cartilage, smooth muscle, skeletal muscle, liver and other soft tissues such as intestinal tissue, salivary glands, and pancreas.

The biomaterials field is certainly dynamic and diverse. Medical device manufacturers benefit from the availability of biocompatible materials — particularly biocompatible coatings and implant materials— but may in fact find their devices competing with entirely new treatment modalities. A piece of equipment that mimics organ function may eventually give way to a complete synthetic organ, and a hip implant could be rendered unnecessary thanks to a newfound ability to regenerate the patient's natural bone. Such developments are still in the future, but medical device manufacturers should start considering how they can compete with or complement these emerging technologies.

For example, researchers at the Massachusetts Institute of Technology and New York University reported in the June 6, 2000 issue of the Proceedings of the National Academy of Sciences (PNAS) that they have made a biomaterial that supports living nerve cells. This peptide-based scaffold, on which neurons grow fibers to communicate with each other and establish functional synapses, may be the long-sought ideal medium for growing replacement nerve cells for victims of spinal cord injuries and other forms of nerve damage.

New biological materials based on the tiny protein linkages called peptides will become increasingly important in developing approaches for a wide range of innovative medical technologies, including controlled drug release, new scaffolds for cell-based therapies, tissue engineering and biomineralization. These peptide-based biomaterials can be designed at the molecular level. Although parts of animal cells such as collagen can be extracted as a basis for growing cells, such animal-derived materials may carry and pass on viruses to the attached growing cells. In contrast, if the peptide-based material is not extracted from animal cells, the latter problem does not exist; however, unlike other synthetic materials, these peptides are still completely biological. The peptides are composed of amino acids, which are the building blocks of all proteins. The peptides do not evoke an immune response or inflammation in living animals, and they can be used for a variety of applications. They can be individually tailored to grow virtually every type of cell in the body.

While a successful scaffold has to be cell-friendly, it has to be particularly hospitable to grow nerve cells. The brain seems to keep firm control on the reproduction of its cells. Neurons grown on the wrong substrate

die. After an initial period of development at the very beginning of life, very few new neurons are produced by the adult central nervous system. Researchers have found that peptides can self-assemble into non-protein- like structures such as fibers, tubules, sheets and thin layers. They can be made responsive to changes in acidity, mechanical forces, temperature, pressure, electrical and magnetic fields and light. They are stable at temperatures up to 350 degrees Celsius and can be produced up to a ton at a time at affordable cost. They can be programmed to biodegrade.

3.5. Cellular Signals

Mechanical signals regulate the development of a variety of tissues (e.g., blood vessels, bone, cartilage) in our bodies, and may play a role in various diseases as well. We hypothesize that mechanical signals will be critical to the development of engineered tissues as well, and will be required to achieve full function. At the most basic level, studies of how externally applied mechanical signals are sensed by cells and are transferred intracellularly (mechanotransduction) are being performed with muscle and bone cells. The role of focal contacts, both as pathways for mechanical signal propagation, and as targets of the mechanical signal, are being studied. There is also the possibility that the cytoskeletal assembly is directly regulated by external mechanical signals. In other words, the cytoskeleton would be able to translate mechanical signals into changes in biochemical signaling pathways. At the tissue level, the response of three-dimensional engineered tissues to external mechanical loading is being delineated in a variety of in vitro model systems. Altogether, these studies should define mechanisms by which external mechanical signals regulate gene expression and lead to strategies to exploit these mechanisms in the context tissue engineering and regeneration. Obviously, this subject strongly intermixes with tissue engineering.

3.6. Image as a Signal

by Emilce Moler and Max E. Valentinuzzi

What we hear, feel, taste or smell certainly may cause a lasting impression on us, but what we see really catches our inner self.

3.6.1. What is an Image?

The retina acts as the instant film that continuously plays the changing surroundings where we act and move about. Many times, the scenery is somehow, permanently or temporarily, fixed on a physical vehicle (piece of paper or screen) taking the form of a picture, painting, radiography or the like. In this way, examination can be carried out at leisure and supposedly in detail. As opposed to a static view (say, a photograph), a dynamic event (as shown in a movie) is even better. Obviously, seeing the object and/or seeing the phenomenon going on appear as most attractive to the scientist at large and to the biological researcher and clinician in particular. This is the main reason the effort invested in the development of imaging techniques has been enormous, constant and still growing, and the accomplishments have been outstanding and even astonishing, from the already centenary X-rays to the newer systems introduced in the last decades, be it computed assisted tomographies (CAT´s) of different kind, nuclear magnetic resonance (NMR), or ultrasounds.

In simple words, an image is a representation, likeness, or imitation of an object or thing, a vivid or graphic description, something introduced to act in place of something else. An image contains descriptive information about the object it symbolizes. For example, a photograph displays information in a manner that allows the human eye and brain to visualize the subject.

3.6.2. An Image Can Be Converted to a Sequential Time Event

Most images originate in the form of optical light energy; this is the image form that we deal with every day. Optical light energy images are the type we perceive at once with our eyes, having the simultaneity effect, as some kind of on-off phenomenon: the landscape is either perceived as a whole or not, in two dimensions (the third dimension is not real, but per-

ceived as such, that is, it belongs to the integration process carried out at higher centers). However, an optical image may be converted to an electrical sequential signal with a video camera or similar device, so becoming a time event. This conversion changes the representation of the image from an optical light form to a continuously varying electrical signal that can be displayed on an oscilloscope screen. Of course, the signal seen on the scope is not perceived any more as an image, but it does contain the necessary information about it. Furthermore, the analogue image signal can be digitized and turned into discrete data form. Image processing operations can be applied to an image either in the optical, analogue or digital form.

Besides the true images that can be seen and perceived by eye (as photographs, drawings, or paintings), there are also some non-visible physical properties (for instance, temperature, pressure, population density maps, mountainous geographic regions, sea depths, or the like) that may be converted in some special type of image. They are, instead, distributions of measurable physical properties. In other words, an image (as a landscape) is created from a given characteristic distribution.

3.6.3. Mathematical Model of an Image

In order to represent and manipulate images on the computer, we must define appropriate mathematical models where we try to find schemes to allow the discrete representation of these models, with the purpose of obtaining an encoding of the image on the computer.

Since the image is presented as a two-dimensional signal from which an abstract model is to be derived, the conceptual framework of signal theory appears acceptable and useful. It becomes necessary to make an extension of the sampling theorem of Shannon–Whittaker because this theorem was derived assuming that the signal is one-dimenensional. It establishes that if the signal is limited to a frequency band ranging from 0 to Ω cycles per second, it is completely determined by samples taken at uniform intervals at most 1/(2Ω) seconds apart. Thus, we must sample the signal at least twice every full cycle. The sampling rate limit 1/(2Ω) is known as the Nyquist limit, in honor of H. Nyquist, who pointed out to the importance of such value in telegraphy. In other words, the theorem relates the high frequencies in the signal with the sampling rate. Intuitively speaking, the higher the frequencies present in the signal, the

higher the sampling rate must be if we want to have a faithful reconstruction. It is easy to extend the theorem to two-dimensional signals for its application to images.

The term image refers to a two-dimensional light intensity function, denoted by ƒ(x,y) where the value of ƒ at spatial coordinates (x,y) gives the intensity or brightness of the image at that point. As light is a form of energy, ƒ(x,y) must be finite and a non-zero number, that is,

The images people perceive in every day visual activities normally consist of light reflected from objects. The basic nature of ƒ(x,y) may be characterized by two components: illumination and reflectance. They are combined as a product to form ƒ(x,y), that is,

where i(x,y) is the illumination component. It represents the amount of source light incident on the scene. The nature of this component is determined by the light source. The second function r(x,y) is the reflectance component. It represents the amount of light reflected by the objects in the scene.

It is common to call intensity of a monochrome image f at coordinates ƒ(x,y) the gray level l of the image at that point, which lies within the interval,

Such interval [Lmin, Lmax] defines the gray scale. Common practice is to shift this interval numerically to the interval [0, L], where level l, equated

to level 0, is considered black, and level l becomes equal to level L, is considered white in the scale. All intermediate values are shades of gray varying continuously from black to white.

The use of color in image processing is a powerful descriptor that often simplifies object identification from a scene. Besides, the human eye can discern thousands of colours, shades and intensities, as compared to only no more than two-dozen shades of gray. The process of the human brain color perception is a psycho-physiological phenomenon not yet fully understood. Basically, the colors perceived in an object are determined by the nature of the light reflected from the object. If the light is achromatic, i.e., void of color, its only attribute is its intensity. Thus, the term gray

level refers to a scalar measure of intensity that ranges from black to white.

Three basic quantities are used to describe the quality of a chromatic light source: radiance, luminance and brightness. Radiance is the total amount of energy that flows from the light source; it is usually measured in watts (W). Luminance gives a measure of the amount of the energy an observer perceives from a light source; it is measured in lumens (lm). Brightness is a subjective descriptor that it is very difficult to measure; it represents the achromatic notion of intensity and is one of the major factors in describing color sensation.

Owing to the structure of the human eye, all colors are seen as variable combinations of the three so-called primary colors: red (R), green (G) and blue (B). These primary colors can be added to produced the secondary colours of light: magenta (read + blue), cyan (green + blue) and yellow (red + green).

The characteristics generally used to distinguish one color from another are brightness, hue and saturation. As already said, brightness indicates the chromatic notion of intensity, while hue is an attribute associated with the dominant wavelength in a mixture of light waves, that is, it represents the dominant color perceived by an observer. When we call an object red or blue, we actually refer to its hue. Saturation refers to relativity purity or to the amount of white light mixed with the hue. The pure spectrum colors are fully saturated. Hue and saturation taken together are called chromaticity; hence, a colour may be characterized by its chromaticity and brightness.

3.6.4. Image as a Tool in Medicine and Biology

The analysis of different types of images is an invaluable tool in biology research and medicine in these days. These technologies have greatly increased knowledge of normal and diseased anatomy for medical research and are a critical component in diagnosis and treatment planning. In the late 1960s, the medical diagnostic imaging field began to apply digital image processing techniques to X-ray images. Digital biological image analysis techniques were first applied in the 1970s and, with the advent of microprocessor–based personal computers, it increased in popularity in the 1980’s.

Biological and medical uses for digital image processing techniques have expanded through the 1980’s and 1990’s to include Computed Tomography (TC), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and ultrasound imaging applications. These images provide an effective means for non-invasively mapping the anatomy of the subject.

We can apply various techniques for improving the visibility of features that are not evident or clear in the original image, such as contrast balancing and edge sharpening. In some cases, digital image processing techniques provide totally automated systems for specimen analysis, automatic counting and classification of cell structures and other objects meeting prescribed characteristics (as bone, tissue and cell analysis). Analysis, classification, and matching of DNA material through these techniques is a common practice

Medical radiological imaging looks at the internal components of the human body. X-ray imaging and computed tomography techniques make intensive use of digital image processing too. For instance, Digital Subtraction Angiography (to enhance blood vessel) or Computed Tomography (to create images using multiple projections).

3.6.5. Digital Image Processing

No matter what the origin of an image is, it must ultimately exist in a form the digital processor understands: the digital form. So, we must create a representation universe, where we try to find schemes to allow the discrete representation of these models, with the purpose of obtaining an encoding of the image on the computer. We will now specialize those concepts to the case of images. We have three abstraction levels, corresponding, respectively, to continuous models, discrete representations, and symbolic encoding of images.

Within the digital domain, an image is represented by discrete points of numerically defined brightness. By manipulating these brightness values, the digital computer carry out immensely complex programming process that allows us to adapt and modify digital image processing operations quickly.

Digital Image processing has become a significant form of image processing because of continuing improvements in sophisticated semiconductor technologies. This has led to computer hardware performance in-