Logistics of the SAM operating mechanisms and principles.
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\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
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Regardless, whether we are young or old, man or woman, American or Japanese, it is an integral part of what we do and who we are.
\r\n\r\n\tThe book will focus on the following aspects:
\r\n\r\n\t1) Human Cycle Response cycle and sex education.
\r\n\t2) Human sexual disorders in males and females.
\r\n\t3) Psychological aspects of the human sexual response cycle and its disorders.
\r\n\t4) The therapeutic aspects.
\r\n\tThe human sexual response cycle and human sexual behavior are interrelated. How this inter-relationship and its association to normal sexual health need to be delineated. In a world torn between sex and sexually transmitted disease, clear-cut scientific information in the form of a monograph is required to educate.
\r\n\r\n\tHuman sexuality, gender identity, and sexuo-erotic orientation play great roles in human health and disease. Sex education is the need of the hour and a reflection will be timely.
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Although this chapter is dedicated to the arena of acoustic microscopy, this topic in and of itself also entails far more details than can be covered here. For pedagogical purposes, the material has been condensed to introduce the basic concepts and definitions for this unique form of microscopy.
\nMedical ultrasound (or ultrasonography) is a common diagnostic imaging technique used to noninvasively visualize subcutaneous body structures including tendons, muscles, joints, vessels, and internal organs for possible pathology or lesions; its most common use is in obstetrics to image the developing fetus. In physics, the term “ultrasound” applies to all sound waves with a frequency above the audible range of human hearing, approximately 20,000 Hz. The frequencies used in conventional diagnostic ultrasound—including for obstetrics—are typically 2–10 MHz [1, 2].
\nComparison between a typical multielement, linear array transducer using in standard medical ultrasound, (a) and a single element confocal transducer on the SAM (b). Note the former device is handheld, while the SAM’s transducer (arrow) is mounted above the specimen stage and is approximately the size of a standard pen cap. An illustration contrasting the differences between the two transducer types is included (c).
The most common type of 2D image by using conventional ultrasound is a B-Mode (brightness mode) image which is produced from the acoustic impedance (Z) of the object being scanned by the system [1, 3, 4]. The speed of sound varies as it travels through different materials and is dependent on the acoustic impedance of the material. However, the sonographic instrument assumes that the acoustic velocity (C) is constant. An effect of this assumption is that in a real body with nonuniform tissues, the beam becomes somewhat defocused and image resolution is reduced. Typical acoustic velocity in degassed/deionized water is at 25 °C = 1497 m/s.
\nThe transducers used in conventional ultrasound imaging usually consist of a handheld device with multiple elements (Figure 1a)—and sometimes a servomotor—allowing simultaneous radiofrequencies which are then merged into a real-time image.
\nThe sound from the transducer is focused either by the shape of the transducer, a lens in front of the transducer, or a complex set of control pulses originating from the ultrasound scanner (beamforming). This focusing produces an arc-shaped sound wave from the face of the transducer. The wave travels into the body and comes into focus at a desired depth.
\nThe return of the sound wave to the transducer results in the same process as sending the sound wave, except in reverse. The returned sound wave vibrates the transducer which converts the vibrations into electrical pulses that travel to the ultrasonic scanner where they are processed and transformed into a digital image.
\nTo produce an image, the ultrasound scanner must determine two factors from each received echo:\n
The difference in the time it takes for the echo to be received from the time when the sound was transmitted.
The intensity of the echo.
Once the ultrasonic scanner determines these two factors, it can locate which pixel in the image to light up and to what intensity.
\nTransforming the received signal into a digital image may be explained by using an analogy involving a long, column-like, multilayered box. Picture placing a transducer at the top of the box and then sending pulses down the length of the box; listen for any return echoes. When an echo is heard, how long it took for the echo to return is noted; the longer the wait, the deeper the layer (1, 2, 3, etc.). The strength of the echo determines the brightness setting for that layer (white for a strong echo, black for a weak echo, and varying shades of gray for everything in between). When all the echoes are recorded for the box, a grayscale image can be produced for it.
\nTo generate an acquired 2D ultrasound image, the ultrasonic beam is swept across the area being examined. A transducer may sweep either mechanically by rotating or swinging it manually; a 1D phased array transducer may also be used to sweep the beam electronically. The received data is processed and used to construct the image. The acquired image is then a 2D representation of the slice into the body.
\n3D images can be generated by acquiring a series of adjacent 2D images. Commonly, a specialized probe that mechanically scans a conventional 2D image transducer is used. However, since the mechanical scanning is slow, it is difficult to make 3D images of moving tissues. Recently, 2D phased array transducers that can sweep the beam in 3D have been developed. These can image faster and can even be used to make live 3D images of a beating heart [5].
\nUltrasound also applies Doppler sonography in order to study physiological processes such as blood flow and other organ functions [1, 2]. The different detected speeds are represented in color for ease of interpretation. To image and differentiate tissues and organs—in particular healthy and affected—the damaged tissue often shows up in a unique color (i.e., flash of different color in a blood vessel leak). Colors can alternatively represent differences in amplitudes between any received echoes. Doppler ultrasound is generally applied in conventional ultrasound, but not in acoustic microscopy; it will not be discussed in detail here.
\nThe scanning acoustic microscope (SAM) has been employed since the 1970s—mostly in materials sciences and engineering—typically to study failure analyses and nondestructively assess structural properties. In the past two decades, it has been similarly utilized in biology and medicine to examine the structures and functions of cells and tissues [18]. For SAM, the frequencies applied to image materials are usually greater than 50 MHz; this provides a significant dynamic range ratio between the lowest and highest values used in ultrasonic.
\nIn contrast to the aforementioned conventional ultrasound system, the acoustic microscope often employs a smaller transducer—similar in size to a pencil eraser—mounted on the body of the microscope (Figure 1b). The SAM’s transducer is generally a single-element piezoelectric confocal unit, producing a signal similar to its conventional counterpart.
\nGiven such high frequencies for imaging, there is a trade-off between penetration depth of the signal through an object and spatial resolution. With higher frequency ultrasound, there is an increased attenuation coefficient, resulting in it being more readily absorbed by the material it is scanning. This is analogous with light microscopes: Increasing the magnification enhances a particular area, but reduces the field of view of the imaged specimen; likewise, with SAM, increasing the frequency increases spatial resolution, but reduces the depth that the specimen can be imaged. Figure 1c illustrates the differences between high- and low-frequency ultrasound imaging, comparing the benefits and limitations between them.
\nA typical SAM transducer used in imaging biological specimens is approximately 50–60 MHz. Its parameters are the following:\n
15 μm scanning step size in both the transverse and horizontal directions.
Axial resolution (Rax) equals 24 μm.
Lateral resolution (Rlat) equals 37 μm.
Depth of field (DOF) equals 223 μm.
The f–number is 1.5.
A routine scan is sampled at 300 mega samples/second.
Note: All of these parameters can be manually adjusted to accommodate a specific scan of the desired object.
\nThe axial resolution depends on the dimensions of the pressure pulse, which is related to the transducer bandwidth (BW) and system electronics:\n | \n
The depth of field (DOF) is calculated by:\n | \n
Due to a small f/number (which provides a tight beamwidth), the DOF is also limited. | \n
Lateral resolution is the resolution orthogonal to the propagation direction of the ultrasound wave. | \n
Acoustic impedance (Z): The impedance determines the amplitude of the reflected and transmitted waves at the fluid–tissue interface. Complex scattering properties of tissues are due to acoustic impedance interfaces in microstructure of tissues:\n | \n
Logistics of the SAM operating mechanisms and principles.
Rax is the resolution in the direction of propagation and is determined by the length of the ultrasound pulse propagating in the tissue; Rlat is the resolution orthogonal to the propagation direction of the ultrasound wave. The ultrasound signal encountering the specimen has three possibilities: scatter where the signal travels in directions orthogonal to the transducer, transmission where the signal passes through the specimen, or reflection where the signal is received back to the transducer for processing into an image. Essentially, it is this impedance mismatch between the signal propagating through water and encountering the specimen which results in the processed image. Table 1 provides further comprehensive details of the SAM’s operating mechanisms, including axial and lateral resolutions, DOF, and acoustic impedance.
\nThe advantages of using SAM over conventional light and electron microscopes include being able to image materials—including cells and tissues—without performing any preparations which could potentially kill or alter them. This provides a more accurate representation of the materials’ natural properties [18]. In biological imaging, it can also provide evidence as to any degree(s) of differentiation which the cells undergo without chemically affecting their properties [6–9]. In some SAM systems, there is also the ability to scan across three axes, allowing for a three-dimensional composite image of the desired specimen to be produced. This is covered in greater detail later in this chapter.
\nThe SAM’s main components are similar to those found on typical light microscopes. In place of the magnifying lens, there is often a single-element confocal transducer attached to stepper motors which control the transducer’s movement in three dimensions: horizontal, transverse, and vertical. For the stage, there is also a small tank filled with fluid—usually degassed/deionized water—in the specimen being imaged as immersed (Figure 2). Just as with any conventional ultrasound, when imaging with the SAM, all materials being imaged must be submerged in a fluid medium or have coupling gel between the transducer and the area being scanned as ultrasound energy scatters when exposed to air.
\nClose up of the SAM transducer immersed in a tank of degassed and deionized water, above the material to be imaged.
In addition to the stage, other key features of the SAM include the following: a broadband high-frequency pulser where the excitation signal originates; a diode expander which reduces the baseline noise before the signal travels into the transducer where it is converted into ultrasonic waves. Signals received by the transducer go into a diode limiter which blocks some of the high-amplitude excitation signal from the receiving electronics. The received signal is generally amplified by a set of attenuators and 32 dB fixed-gain amplifiers. Finally, the signal is captured by a high-speed 8-bit digitizing board.
\nThe principal operating units relative to the SAM’s overall layout. In addition to the stage, other key features of the SAM include the following: a broadband high-frequency pulser where the excitation signal originates; a diode expander which reduces the baseline noise before the signal travels into the transducer where it is converted into ultrasonic waves. Signals received by the transducer go into a diode limiter which blocks some of the high-amplitude excitation signal from the receiving electronics. The received signal is generally amplified by a set of attenuators and 32-dB fixed-gain amplifiers. Finally, the signal is captured by a high-speed 8-bit digitizing board. The SAM signals are then synchronized to reduce any signal to signal jitter and improve the efficiency of signal averaging.
A custom-built circuit takes the 10 MHz synchronized output from the digitizing board and divides it down to a 9.76 kHz square wave that triggers both the pulser and the digitizing board. The SAM signals are synchronized to reduce any signal to signal jitter and improve the efficiency of signal averaging. A basic illustration of the SAM signal transmission and reception is shown in Figure 3: the principal operating units relative to the SAM’s overall layout.
\nBecause the desired object for imaging is often submerged in an imaging tank (often no larger than 20 cm3), the object itself should be no greater than 1.0 cm2 in area (both the object’s size and its scanning areas can be increased as long as the operator knows and recalls the exact region being examined is) and no thicker than approximately 5.0 mm, less than the focal length of the transducer. Further, it must not be composed partly or completely of material that is miscible in water after being submerged for approximately 2–3 h. Ideally, the object should have greater density than the water, but with most biological materials, this is not the case. Therefore, a method of mounting the object either with push pins or embedding the base of it deep into mounting clay at the base of the immersion tank would be suitable. The area being imaged must be fully exposed to the transducer. Examples of mounting the specimens are illustrated in Figure 4 with respect to their positions to the SAM transducer. The tank with the specimen must be placed on the stage below the transducer with enough fluid in the tank so that it is approximately 1–2 mm below the rim.
\nTwo examples of mounting specimens in the immersion tank for imaging. The first involves using mounting pins through the object (away from the imaging area) into a softer backing, usually an adhesive sponge (a); the second involves embedding the specimen into mounting clay at the base of the tank, exposing the top surface of the object over the transducer (b).
The SAM requires turning on both the computer and each hardware component responsible for powering the scope. These include the power supplies for both the pulser-receiver and the digitizer. Additionally, the digitizer and pulser-receiver units must be manually powered on.
\nBecause of the single-element configuration of the transducer—typically lacking a servomotor which allows the transducer to fluctuate—its movement is dependent on a stepper motor system. Therefore, initialization of the stepper motor driver card is imperative; this card connects to and controls the stepper motor driver.
\nAfter powering up the microscope and its accompanying computer, the operator must check the computer’s appropriate site for its devices and interfaces, scroll down, and select the driver card. When the prompt screen appears, “Initialize” is selected to engage its software features.
\nThe operator will then select an icon or tab labeled “Move”; a screen with three-dimensional axes will appear. The x-axis moves the transducer along the longitudinal direction; the y-axis moves the transducer along the transverse direction; the z-axis moves the transducer along the vertical direction. Each axis is programmed to move a specific step size (typically between 1.25 and 2.5 μm for all three axes); therefore, inputting the number of desired steps multiplies the distance of the desired step size by that number. For example: inputting 20 steps and selecting once along an axis set to 2.5 μm will move the transducer 50 μm. When operating the stepper motor, selecting “In” on the Move screen will move the transducer toward the stepper motors for each of the 3 selections, while “Out” moves it away from the motors. Figure 5 provides a screenshot of the SAM software display for the X, Y, Z stepper motors.
\nIllustration of a typical screenshot display for the software used to operate the SAM. The gray area displays the logistics of the transducer, the specimen being scanned, and any comments about the experiment. The 3D axes x, y, and z are labeled horizontal, transverse, and vertical, respectively. The blue bars in each of the axes’ fields indicate how much of each scan has been completed. The bar labeled “Pusher” refers to the compressor used for elastography.
Once the transducer is positioned over the prepared tank, it is slowly lowered in using the “Move” feature to control the stepper motor along the z-axis. It is recommended that once the transducer is touching the fluid surface to descend the transducer at a rate of 10–20 steps per “Move” selection. While the transducer is immersed in the fluid, but close to the surface, its bottom face is inspected under the fluid for the presence air bubbles. All air bubbles must be removed as they could scatter the ultrasound energy. Removal can often be accomplished using a cotton swab or a plastic twist tie that has a slight kink at the bottom to gently remove the bubbles off of the bottom face of the transducer;
Oscilloscope images showing the background signal of the SAM in water (a) and upon encountering the specimen (b). The RF signal is generated by the impedance (Z) mismatch between the ultrasound energy in water and the specimen.
Top-down view illustrating the scanning pattern made by the SAM transducer. Upon scanning in the transverse direction, the transducer shifts over in the horizontal direction and the scanning process repeats itself. After the desired area is scanned, the transducer shifts in the vertical direction in order to scan multiple layers of the specimens as desired.
Once all the configurations are in place, the start key can be selected; the scope will start scanning the desired area of the specimen. A typical scan using the parameters listed above will take approximately 20–30 min. It is recommended that the SAM computer not be used during the scanning session as this often will disrupt the scan and may potentially shut down the system, which will require reconfiguring the SAM set-up. The transducer will acquire the specimen image first along the transverse line, firing 31 scans into the specimen before moving to the next point which will be 1.25 μm × the number of steps which were entered for the scan. After scanning the desired distance along the transverse axis (usually 1.0 mm), the transducer will shift 2.5 μm × the number of selected steps in the horizontal direction and repeat scanning in the transverse direction, followed by a repeated shifting 2.5 μm × the number of selected steps in the horizontal direction and repeating in the transverse direction. It will form a “zigzag” pattern until it has completed the scan over the desired area of the specimen surface. The transducer will then shift 2.5 μm × the number of steps down in the Z direction and the process will repeat itself; Figure 7 illustrates this scanning pattern.
\nAfter the scan is complete, both the dataset and logistics on the scan will be produced (an example of the latter is shown in Table 2). These are generally saved under preselected LabVIEW files.
\n31 | \nNumber of averages | \n
256 | \nNumber of points per trace | \n
923 | \nOffset from the trigger | \n
6 | \nNumber of 2.50 μm pulses between horizontal sampling points (X) | \n
12 | \nNumber of 1.25 μm pulses between transverse sampling points (Y) | \n
48 | \nNumber of 2.50 μm pulses between vertical sampling points (Z) | \n
10 | \nNumber of 1.5875 μm pulses between compressor sampling points | \n
70 | \nNumber of sites along horizontal axis (X) | \n
70 | \nNumber of sites along transverse axis (Y) | \n
7 | \nNumber of sites along vertical axis (Z) | \n
1–5 | \nNumber of sites along compressor | \n
0.50 | \nVoltage scale (V) | \n
14 | \nInitial setting for transmission attenuator | \n
14 | \nInitial setting for reception attenuator | \n
0.00 | \nVoltage offset (V) | \n
250 | \nSampling frequency (MHz) | \n
3.000 | \nTrigger level (V) | \n
External | \nTrigger source | \n
Positive | \nTrigger slope | \n
9.76 | \nAvtech repetition rate (kHz) | \n
8 | \nAvtech frequency setting (1–10) | \n
7 | \nAvtech amplitude setting (1–10) | \n
1.555 | \nF–number of the transducer | \n
3.111 | \nFocal length of the transducer (mm) | \n
61.0 | \nCenter of frequency of the transducer (MHz) | \n
30.0 | \nBandwidth of the transducer (MHz) | \n
Example of the settings and operating parameters applied to perform a standard SAM scan.
Upon completing the specimen scan, the RF data is first translated into an acoustic envelope for each scan point. The raw acquired signal initially includes its inverse image; this is removed by eliminating all frequencies below zero frequency. An inverse Fourier transform is applied to convert the data from a frequency domain into a time domain. Figure 8 is the resulting acoustic envelope of a processed signal; in this case, a threshold is set approximately 20 dB above the background noise so that the acoustic energy encountering the specimen will be displayed as the acoustic envelope.
\nRepresentation of a typical acoustic envelope developed from the SAM scan of a specimen and derived from the raw RF scan. The raw scan initially includes an inverse image below zero frequency; this is eliminated by removing any frequency below zero. An inverse Fourier transform is performed converting the scan from a frequently domain to one of time. A threshold is set 20–30 dB above the water signal to establish point at which the acoustic signal encounters the specimen. By examining the envelope, one can see at what time the signal attenuates, allowing the calculation of the specimen’s thickness.
The signals are then merged to form the image in B-Mode—called a B-Scan—which is an amalgamation of the intensities of the echoes received from the transducer. The two acoustic parameters determining the image quality are absorption which indicates how ultrasonic energy was converted into heat in the specimen, and scattering, specifically backscatter. Scattering means that an ultrasonic wave when striking an obstacle radiates part of its energy orthogonally to the specimen. Quantifying the degree of backscatter from small particles or perturbations in tissues and other materials allows differentiations in the degrees of pixel intensity in the B-Scan images; such scatter properties include the average scatterer (energy scattered) diameter and average scatterer concentration [19].
\nTemplate 1 illustrates a program to acquire the image and converge the images using a program (such as in MATLAB) to assist in processing the acquired images from the SAM scans.
\nB-Scan merge processing in either the vertical/horizontal plane or the vertical/transverse plane. On the left are unmerged images. On the right is the resulting merged image.
The “ABC’s” of acoustics. Defining the raw ultrasonic signals and how they are converged to form images. The A-Scan (or A-Line) is the RF signal from a single point (x, y) in the scan. After they are converted into acoustic envelopes and merged into a vertical cross section, they form a B-Scan. The C-Scan is the collected data from a specified depth over the scanned area (a horizontal cross-section).
Table 2 provides an example of scanning logistics. It includes the number of sampling points per scan, the distances traveled by the transducer in all three dimensions, and the parameters set both manually and automatically regarding the scan. Figure 9 shows the resultant B-Scan after acquiring and merging the scanned data. Each of the bright ovoid areas shown in the image represents scatter, and each is approximately 20–40 μm in diameter; this is within size range of many structural cells and white blood cells, but not red blood cells which are generally <10 μm in diameter. At 50–60 MHz, SAM can typically image a collective group of cells and tissues, similar to the resolution of histology images seen at 20× magnification. Figure 10 shows comparative images of histology specimens taken with standard light microscopy (Figure 10a and b) and their SAM B-Scan counterparts (Figure 10c and d). The light microscope images were from a standard H & E stain imaged at 20×, showing the differences in surface characteristics between two biological tissues. The SAM B-Scan images were imaged using a 60 MHz transducer of similar biological tissues.
\nDevelopment of higher-frequency transducers with accommodating digitizing boards have resulted in an acoustic resolutions of subcellular components, in particular noninvasive examination of cells undergoing apoptosis [3, 7].
\nThe C-Scan is the scan along one selected horizontal-transverse plane of the specimen [4]; this is usually the surface of the specimen. It is similar to the B-Scan, but the merged data is orthogonal to those in a typical B-Scan. To acquire the C-Scan, the operator will enter a series of commands into a program such as the MATLAB function shown below after merging the acquired data from the scan (following acquisition of the B-Scan). This command will fit the planar surface and remove any tilt to the image, thus correcting for any irregularities stemming from incorrect mounting of the specimen.
\nMany of the commands contained in this chapter are custom written MATLAB functions (and some custom subfunctions within those functions). SAM operators will need the custom functions as well as the data files and MATLAB will need to be aware of the folder where the custom functions are. Template 2 is an example of MATLAB commands for generating C-Scan images
\nFigure 11 illustrates the three basic scanning modes in SAM as the “ABC’s” of acoustics.
\nThe C-Scan both profiles and quantifies any surface irregularities of the scanned specimen. This is done by fitting the planar image of the specimen, then subtracting everything above and below that plane. All images at the plane will appear as green; those above it will be orange-red and those below as blue. A typical C-Scan image may appear as Figure 12. The resulting image and root-mean-square (RMS) value will be the C-Scan profile. The smaller the number, the less surface variations there are on the specimen.
\nComparative images of histology specimens taken with standard light microscopy (a and b) and their SAM B-Scan counterparts (c and d). The light microscope images were from a standard H&E stain imaged at 20×, showing the differences in surface characteristics between two biological tissues. The SAM B-Scan images were imaged using a 60 MHz transducer of similar biological tissues. Note the differences in the surface characteristics of light microscope images and compare them with the B-Scans: The keratinized surface shown in (b) (arrow) shows up as a bright band in (d) (arrow). Scale bars equal 100 μm.
SAM images of C-Scans taken of two tissue specimens contrasting surface characteristics between them. One image shows the area to be essentially void of any irregularities as evidenced by its uniform color pattern (a). The second image displays significant levels of surface irregularities as the high variations in colors demonstrate. The scale boxes represent 10,000 μm2. The color scale shows the degrees of variance in the surfaces, relative to their height above or below the set planar surface: The greater the yellow-green in appearance, the closer the specimen is to the planar surface and more uniform in structure. The x- and y-axes represent the number of steps in the scan along the horizontal and transverse directions; approximately 1.0 mm × 1.0 mm.
The 3D images are composites of the 2D B-Scans produced. They are generated by simply compiling and merging the acquisition data multiple times. Provided that the parameters listed under the SAM set-up procedures have been maintained, extra steps can now be taken to produce an image of the complete region that the transducer scanned.\n Template 3 is exemplifies what can be generated using MATLAB to assist in processing the acquired images from the SAM scans, following acquisition of the B-Scans, C-Scans, and RMS profiles.
An example of a complete 3D composite produced with SAM (1.0 mm3 scan) is shown in Figure 13.
\n3D rendering of a 1.0 mm3 SAM scan of a tissue specimen. The apical surface faces forward and is rendered in blue to signify the threshold detected by the SAM transducer. Any signal detected above the threshold is rendered in grayscale: The greater the backscatter properties of the ultrasound at the point of detection, the brighter the image at that point. Any signal below the threshold is not registered.
Ultrasound is also used for elastography, which is a relatively new imaging modality that maps the elastic properties of soft tissue [10, 11, 20]; this modality emerged in the last two decades. Elastography is useful in medical diagnoses as it can discern healthy from unhealthy tissue for specific organs/growths. For example, cancerous tumors will often be harder than the surrounding tissue, and diseased livers are stiffer than healthy ones [10–13].
\nThere are many ultrasound elastography techniques [13]. The most prominent include quasistatic elastography/strain imaging, shear wave elasticity imaging (SWEI), supersonic shear imaging (SSI), acoustic radiation force impulse imaging (ARFI), and transient elastography. The steadily growing clinical use of ultrasound elastography is a result of the implementation of technology in clinical ultrasound machines [20]. Currently, an increase of activities in the field of elastography is observed demonstrating successful application of the technology in various areas of medical diagnostics and treatment monitoring.
\nThe SAM compressor positioned within the imaging tank. The transducer is seen on the upper right as it is being moved over the compressor.
An example of a SAM compressor is shown in Figures 14 and 15, by itself and in use with the transducer, respectively. The compressor has a flat aluminum plate with a slit in the center which is approximately 20 mm in length and 2 mm in width. After each scan volume is completed, the plate will move down a designated distance, compressing the specimen as the scan is repeated by the SAM. A full 3D rendering of the scanned specimen at each compression step can then be produced to determine the degree of strain at specific regions of the specimen. Here, the SAM is potentially advantageous over a conventional mechanical compressor in determining what properties in the specimen contribute to the greatest or least changes in overall strain behavior [14, 15]. In this latter case, they can be analyzed through a process called speckle tracking which is not covered in this chapter.
\nThe SAM set up for performing compression studies. The compressor is positioned over the specimen in the tank, while the transducer is located over the slit in the compressor.
The specimen set-up for the compressor is essentially the same as described earlier with the addition of positioning the compressor plate over the object. After mounting the specimen in the immersion tank, the operator will place the tank on the stage, add enough water to barely cover over the specimen, and slowly lower the compressor toward the tank. When the compressor plate is approximately 1.0 cm from contacting the tank, the tank is raised manually so that the compressor plate fits directly inside and over the specimen. The compressor plate can then be lowered using the “Move” program with one hand while manually lowering the tank down to the stage with the other, moved in step sizes ranging from 20 to 30. Once the tank is back on the stage, the operator continues lowering the plate until it comes in contact with the specimen, without pressing it. Once this is done, the controller axis under the “Move” program is zeroed-out. The operator can then begin moving the transducer into place so that it is directly over the compressor.
The transducer is moved in the vertical direction toward the compressor plate until a strong reflection signal is seen in the oscilloscope. At this point, the transducer should be positioned over the metallic portion of the plate—preferably to the right of the compression plate’s slit. The transducer is moved in the horizontal direction toward the slit until the acquisition signal diminishes, followed by several more steps until the signal completely dissipates. The transducer will now be positioned over the compression plate slit. Care must be taken not to run the transducer into the pusher axis. There is not much room beyond the side of the slit closest to the pusher axis. The axis controller is zeroed out, and the operator slowly (in increment steps of 10–20) continues moving the transducer in the horizontal direction until the reflection signal off the compressor plate returns. Once it returns, the horizontal distance moved should read approximately 2.0 mm (the width of the slit). The transducer is moved in the horizontal direction back into the slit until it is positioned 1.0 mm in the center of the slit (the signal reading on the oscilloscope should be minimal). The transducer is now moved in the vertical direction until the acquisition signal appears again; this will be the signal reflecting off of the specimen. The transducer is moved away from the specimen again until the signal is just visible on the right side of the scope. The operator finally zeroes out the axis controller, minimizes the “Move” program, and closes out the acquisition program. The operator now follows the instructions listed under “Image Acquisition” to scan the specimen. Under “Sampling Sites,” the same standards are kept as listed under the basic operations, except for the compressor.
\nTransverse: 70 (1050 μm); horizontal: 70 (1050 μm); vertical: 7 (960.00 μm); pusher (compressor): 1 (15.875 μm). Note that for the pusher, the number of pusher steps desired for the compression scan is selected. A typical selection ranges from 3 to 10, depending on the thickness of the specimen.
\nAfter entering the desired information, the operator starts the scan. Also note, because the compressor is now employed in the scan, the scanning program time will be multiplied by the number of steps entered under the pusher. For instance, entering 3 steps will triple the scan time listed under “Basic Scans.”
\nOne process for producing movies resulting from the compression sequences is derived from the MATLAB files in Template 4.
\nThe computer must have Windows Media Player—or its equivalent—in order to access the movies. Open Media Player and select the saved files, and set to continuous run, if preferred (usually, a film involving 6-step compressions runs approximately 0.5 s). The resulting images will combine to form both a movie displaying the result of the compression steps and the degrees of stiffness/compliance—based on the strain maps generated during the compressions—in the properties of the specimen being tested. The SAM can also display which stages within the specimen (if it is heterogeneous) are the greatest/least changes in the compressions. In this latter case, the operator can better correlate what specific properties within the tested specimen contribute to the observed changes.
\nThe use of apparent integrated backscatter (AIB) to quantify image surfaces further allows additional objective methods to assess the conditions of the specimen. AIB is a measure of the frequency-averaged (integrated) backscattered power contained in some portion of a backscattered ultrasonic signal.
\nChanges in the material’s composition will change the impedance mismatch, resulting in different backscatter characteristics. From a materials perspective, the SAM has been shown to analyze changes in material compositions, interstitial fractures, or densities within the surface and/or subsurface regions of the scanned object. From a biological perspective, we can quantify these changes and correlate them to the development of the cellular changes and production of any cellular products which result from the differentiation and evolution of the cells [5, 16].
\nThe MATLAB commands for applying AIB to the image surfaces are exemplified in Template 5.
\nThis chapter provides a general overview of acoustic imaging techniques at the microscopic level: its uniqueness as an imaging modality along with its benefits and limitations. It also compares its principal features and differences between conventional ultrasound and light microscopy. Ideally, SAM will evolve to become a standard biomedical tool within laboratory settings—both research and clinical—as with other currently available microscopy systems.
\nBecause SAM requires no preparatory work on the specimens, recent studies have been performed to image cells and tissues under aseptic conditions to effectively monitor their growth and differentiation [9]. Other potential applications include real-time imaging of cells and tissues and even
One possible clinical application has been recently examined: utilizing SAM’s elastography abilities to test for physical properties in the cornea. This has a strong potential for improving current screening methods in patients electing to undergo LASIK surgery [16]. It may also be utilized to screen for other vision disorders, including glaucoma and keratoconus.
\nAnother major arena of clinical study for SAM is in dermatology [6]. Because skin is an exposed organ, it is relatively easy to noninvasively examine its structures and functions, both
Differentiating normal versus affected conditions is a major advantage of SAM, especially in examining changes in elastic properties of cells and tissues. A recent study examined stiffness properties in breast tumors and correlating them to degrees of metastasis [17]. This and the aforementioned work on cell growth/development, cornea analyses for LASIK, and skin imaging clearly shows the potential of acoustic microscopy as a major instrument of detection and analyses in both biomedical research and possibly even in clinical patient care.
\n1. Producing the wavefield plot
\nfid = fopen(‘>filename<’, ‘r’, ‘b’)
\nfread ‘int32’
\n256k 9800 double
\nfigure plot rf
\nFocal zone: set between 100 and 150 and pick area without ultrasound signal denoise (‘>filename w/prefix.data<’)
\n(This averages across 1000 signals and subtracts out lower level signals, eliminating much of the data associated with the horizontal lines.)
\n2. Prefilter “oval filter” used to remove the “side noises”—temporal and spatial frequency filters oval (applies to 2D custom filter on 3D dataset)
\nprefilter2(‘filename(n)’); the n at the end stands for denoise
\n(For this prefilter command, the filter is customized to the selected transducer’s specifications. If a different transducer (with different specifications) is used, the new filter customized to the new transducer is needed.)
\nappend ‘f’ onto the file name
\n(Which will now be suffixed as filename.nf)
\n3. Refile2
\n(Puts the scanned data in order: either xzy and yzx (z-axis depth always in the center)
\n4. Merge6x (or merge6y)
\n(Corrects any limit on the signal magnitude. If the signal is bad, then will ignore the correction. Typical use of a correction magnitude is 0.75. After calculating correlations, this will put out a baseband file (labeled ‘bb’) at the end in a transverse–vertical slice. In a 3D dataset: will compose each of the slices (there are 70, each approx. 15 m thick))
\npos=ithreshold(ab,40,1:300,0);
\n[xn,yn]=size(pos);
\n[x1,y1]=meshgrid(1:yn,1:xn);
\ncoeff=planefit(x1,y1,pos);
\ntilt=coeff(1)*x1+coeff(2)*y1+coeff(3);
\npos2=pos-tilt;
\nfigure;imagescale(pos2);colormap jet;axis image;
\nrms1 = ((sum(sum(pos2.^2)))/(xn*yn)).^0.5
\nrms2 = 1480*rms1/(2*250)
\nfor nf = 1:10
\nbb = merge_load6(‘>Enter Filename Here<’,nf,0,69,0);
\nab = 20*log10(abs(bb));
\nab1 = ab(:,0:69,0:69);
\nmask1 = threshold(ab1,70,20:500,0);
\np1(nf) = max(max(ithreshold(ab1,70,10:500,0)));
\nmask2 = threshold(ab1,60,1:700,1);
\nab2 = mask1.*mask2.*ab1;
\nview3d(ab2,1);
\nset(gca,‘CLim’,[90-60,90]);
\nfr(nf) = getframe(gcf);
\nclf;
\nend
\nbb = merge_load6(‘>Enter Filename Here<’nf,0,44,0);
\nab = 20*log10(abs(bb));
\nab1 = ab(:,2:42,2:42);
\nmask1 = threshold(ab1,70,20:500,0);
\np1(nf) = max(max(ithreshold(ab1,70,10:500,0)));
\nmask2 = threshold(ab1,60,1:700,1);
\nab2 = mask1.*mask2.*ab1;
\nview3d(ab2,1);
\nset(gca,‘CLim’,[90-60,90]);
\nfr(nf) = getframe(gcf);
\nclf;
\nend
\nmovie2avi(10)
\nfigure;
\nbb = merge_load6(‘>Enter Filename Here<’,nf,0,69,0);
\nab = 20*log10(abs(bb));
\nab1 = ab(:,2:68,2:68);
\nmask1 = threshold(ab1,55,200:325,0);
\nmask2 = threshold(ab1,65,1:300,1); 30
\nab2 = mask1.*ab1;
\nview3d(ab2,1);
\nset(gca,‘CLim’,[75-60,75]);
\nfr(nf) = getframe(gcf);
\nclf;
\nM(nf) = getframe(gcf);
\nend
\nfr2 = flipdim(fr,2);
\nfr3 = [fr fr2];
\nmovie2avi(fr,’ >Enter Filename Here<,‘compression’,‘none’);
\nmovie2avi(fr3, >Enter Filename Here<’,‘compression’,‘none’);
\nmovie(M,10)
\n/* load merged basebanded signals */
\nbb = merge_load6(‘>Enter Filename Here<’,1,0,69,0);
\n/* convert to signal magnitude in dB */
\nab = 20*log10(abs(bb));
\n/* find axial (time) position of surface from thresholding */
\npos = ithreshold(ab,30,1:300,0);
\n/* calculate apparent integrated backscatter */
\n/* import reference spectrum (sp7) with workspace function */
\nib1 = aibs(bb,sp7,pos,76,108:146);
\n/* subtract amplifier difference between tissue and reference */
\nib1 = ib1 - 27;
\n/* calculate mean aibs over entire surface */
\nibm = mean(ib1(:));
\n/* load merged basebanded signals */
\nbb = merge_load6(‘>Enter Filename Here<’,1,0,69,0);
\n/* convert to signal magnitude in dB */
\nab = 20*log10(abs(bb));
\nBioprocesses, which are consisted of a series of enzymatically catalyzed biochemical reactions in all living things, are necessary for survival. They have a high potential in terms of material synthesis, which has recently been performed by chemical techniques [1]. Furthermore, the advancement of heterologous production systems and genetic engineering techniques has resulted in pioneering initiatives to manufacture usable biomaterials [2]. These advancements also enabled the successful generation of primary and secondary metabolic pathway products in physiologically and genetically well-defined hosts, such as
NRPs are secondary metabolites that are synthesized outside of the ribosomal machinery and have a variety of properties such as cytostatics, immunosuppressants or anticancer agents, antibiotics, pigments, siderophores, toxins [3, 5, 6]. NRPs are typically produced by marine microorganisms and invertebrates, as well as soil-inhabiting microorganisms [5, 7, 8]. The majority of natural products produced by sponges, bryozoans, mollusks, and tunicates are members of the NRP or mixed polyketide–NRP families. Several of NRPs are being used in the development of new medicines for inflammatory, cancer, neurological diseases, and infectious disease nowadays [7].
Non-ribosomal peptide synthetases (NRPSs) enzymes are poly-functional mega-synthases that biosynthesize NRPs [7, 9, 10]. NRPSs, multi-modular enzyme or enzyme complexes from common bacteria, less common eukarya, and rare archaea, are capable of producing a wide range of natural pharmaceutical products (Bacitracin, antibiotics for skin infections; Bleomycin antitumor; Cyclosporin, antifungal, and immunosuppressive drugs; Daptomycin, antibiotics) [5, 7, 11]. NRPSs use both proteinogenic and nonproteinogenic amino acids (not encoded by DNA) as building blocks for the growing peptide chain [1, 7, 11, 12]. They catalyze multiple biosynthetic processes, each of which is responsible for particular one amino acid elongation on the growing peptide chain [10]. This chapter looks at the structure, function, and synthesis of NRPs, as well as producer microorganisms and their various applications.
NRPSs are responsible for nonribosomal peptide (NRP) synthesis. These are large multi-enzyme complexes that are modularly organized and serve as biosynthetic machinery and templates [5, 11, 12, 13, 14]. For example, a single NRPS of 1.6 MDa synthesizes the Cyclosporine A (7). In fungal systems, such as in the case of cyclosporine A (7), a single NRPS synthesizes peptides, whereas bacteria frequently use numerous NRPSs with genes grouped in an operon. NRPSs have a modular structure [14, 15].
In a genome mining research of 2699 genomes, Wang et al. found that more than half of the NRPS enzymes were non-modular NRPS enzymes [16]. Nonmodular NRPS enzymes can be found in siderophore biosynthesis pathways, such as EntE and VibH in enterobactin and VibE in vibriobactin, or as a standalone peptidyl carrier protein, such as BlmI from the bleomycin gene cluster. NRPS enzymes are found frequently in bacteria, less frequently in eukaryotes, and infrequently in archaea. Actinobacteria, Cyanobacteria, Firmicutes, and Proteobacteria were the phyla with the greatest number of these enzymes in the bacterial domain. There was a correlation between genome size and the number of NRPS clusters [5, 17].
A module is a part of the NRPS polypeptide chain that is in charge of integrating one amino acid into the final product. Modules can further be separated into domains (Figure 1), which represent enzyme units that catalyze distinct steps of NRP synthesis. On the protein level, domains are defined by distinctive, greatly conserved order of patterns known as “core motifs.” In certain instances, biochemical and structural data were used to confirm the involvement of greatly conserved residues in domain function (Table 1) [14].
Catalyzed reactions by various NRPS-domains [
A1 L(TS)YxEL A2 LKAGxAYL(VL)P(LI)D A3 LAYxxYTSG(ST)TGxPKG A4 FDxS A5 NxYGPTE A6 GELxJGx(VL)ARGYL A7 Y(RK)TGDL A8 GRxPxQVKIRGxRIELGEIE A9 LPxYM(IV)P A10 NGK(VL)DR | T LGG(DH)SL |
C1 SxAQxR(LM)(WY)xL C2 RHExLRTxF C3 MHHxISDG(WV)S C4 YxD(FY)AVW C5 (IV)GxFVNT(QL)(CA)xR C6 HN)QD(YD)PFE C7 RDxSRNPL | Te GxSxG |
E1 PIQxWF E2 HHxISDG(WV)S E3 DxLLxAxG E4 EGHGRE E5 RTVGWFTxxYP(YV)PFE E6 PxxGxGYG E7 FNYLG(QR) | Cy1 FPL(TS)xxQxAYxxGR Cy2 RHx(IM)L(PAL)x(ND)GxQ C3 D(NLI)xDxxS Cy3 LPxxPxLPLxxxP Cy4 (TS)(PA)3x(LAF)6x(IVT)LxxW Cy5 (GA)DFTxLxLL Cy6 PVVFTSxL Cy7 (ST)(QR)TPQVx(LI)D13xWD |
Ox1 KYxYxSxGxxY(PG)VQ Ox2 GxxxG(LV)xxGxYYY(HD)P Ox3 IxxxYG | M1 VL(DE)xGxGxG M2 NELSxYRYxAV M3 VExSxARQxGxLD |
R1 V(L)(L)TG(A)TG(F)(L)GxxLL R2 Vx(L)(L)VR(A) R3 GPL(G)x(P)x(L)GL R4 V(Y)PYxYLxx(P)NVxxT R5 GYxxSKW(A)(A)E R6 R(P)G R7 YxxxxG(LF)LxxP |
NPRS-domains’ core-motifs [14].
There are three domains in a module. These are 1) the adenylation (A) domain, 2) the peptidyl carrier protein (PCP) or thiolation (T) domain, and 3) the condensation (C) domain, all of which are responsible for the synthesis of NRPs. A module may include additional tailoring or altering domains incorporating epimerization (E), methylation and oxidation domains or a heterocyclization (Cy) domain in place of a C-domain. Finally, most NRPS termination modules have a TE-domain, which is in charge of releasing linear, cyclic, or branching cyclic peptides [5, 9, 10, 11, 15, 18, 19, 20, 21].
The order of the modules is frequently aligned with the sequences of the resulting peptides. NRP synthesis begins at the N-terminus and ends at the C-terminus, yielding peptides that are typically 3–15 amino acids long. The released peptides contain amino acids, that is, imino acids or ornithine and their structures are linear, cyclic-macrocyclic, branched-cyclic, branched-macrocyclic, dimers or trimers of identical structural elements [5].
The A-domain is responsible for the first step in biosynthesis, which involves recognizing and activating the amino acid substrate via adenylation with Mg-ATP, resulting in an aminoacyl adenylated intermediate. Around 550 amino acids make up domain A. It has 10 amino acid residues that serve as NRPS enzyme “codons” and are essential for substrate specificity. The D and L forms of the 20 amino acids used in ribosomal protein synthesis, as well as non-proteinogenic amino acids like imino acids, ornithine, and hydroxy acids like β-butyric acids and α-aminoadipic, are substrates recognized by the A-domain. The PCP-domain, which consists of about 80 amino acids and covalently attaches the activated amino acid to their cofactor 4′-phosphopantetheine (PP) arm via a thioester bond, completes the second step. And also, the active substrate and elongation intermediates are transferred to the C-domain via this domain. In the last step, C-domain, which contains approximately 450 amino acids, catalyzes the formation of peptide bonds between the carboxyl group of the incipiently synthesized peptide and the amino acid transported by the side module [5, 22]. Furthermore, this domain allows the expanding chain to be translocated to the next module. Following this step, the linear intermediate peptide is liberated in bacteria via internal cyclization or hydrolysis with the help of the Thioesterase (TE) domain. On the other hand, it appears less commonly in fungi’s NRPSs. Fungi use a variety of ways to release chains. The first is a thioesterase NADP(H)-dependent reductase domain (R), which catalyzes NADPH reduction to create an aldehyde and the second is a terminal C domain, which catalyzes release by forming intermolecular or intramolecular amide bonds. By N-, C-, and O-methylation, halogenation, acylation, hydroxylation, glycosylation, or heterocyclic ring formation, the primary product of this synthesis can be changed post-synthetically to reach its mature form by additional tailoring enzymes that are not part of the NRPS. The structural diversity of NRPs is formed in part by these enzymes and their reactions [5].
Because of their extensive multidomain organization, NRPS genes are easier to identify using recent genome mining technologies, and they are also relatively easy to detect. Secondary metabolites production genes are frequently found in bacterial and fungal gene clusters. The clusters’ core is thought to be NRPS genes. Nevertheless, they are linked to genes involved in building blocks synthesis, product ornamentation, self-resistance, and peptide export. For the purpose of analyzing and in silico exploration of NRPS pathways, advanced genome sequencing techniques have made genome mining methodologies available, which are assisted by a variety of bioinformatics tools, such as AntiSMASH, PRISM, and SMURF [23].
Nowadays, known NRP structures are divided into various categories, each with its own structural characteristics. Lipocyclopeptides with varied linkage patterns, such as fengycin, iturin, surfactin, and head-to-tail-cyclized peptides of varying ring sizes, such as cyclosporine, gramicidin S, maybe the largest group. There are also a lot of linear peptide configurations. They include tripeptides (such as sevadicin and bialaphos) as well as 20-mer peptides (e.g., alamethicin, peptaibols). The current highest size limit for NRPs is syringopeptin 25A, which has 25 amino acids (syringopeptin 25A). Tailoring enzymes modify the structure of some NRPs. The most structurally complicated molecules are probably bleomycins, ergopeptides, glycopeptide antibiotics, and β-lactams [23].
Figure 2 shows some NRPs with diverse structures and a wide spectrum of activities. ACV-tripeptide (6), for example, is a precursor to antibiotics of the penicillin and cephalosporin families. Gramicidin S (4), tyrocidine A (1), and vancomycin (5) are three other antibiotic-active substances. Cyclosporin A (7), an immunosuppressive drug, is used in the post-transplantation care of patients. Cancer is treated with cytostatic agents, such as bleomycin A2 (8) and epothilone (9). Enterobactin (10), bacillibactin (11), mixochelin A (12), yersiniabactin (13), and vibriobactin (14) are examples of iron chelating agents. These compounds, known as siderophores, are created in iron-deficient environments to provide bacteria with an iron source. Figure 2 also depicts the structures of pigments like indigodin (15), toxins like thaxtomin A (17), and peptides with uncertain functions like anabaenopeptilide 90-A (18) [14].
Some NRPs with structural diversity [
NRPs have a number of structural characteristics that distinguish them from ribosomal peptides. For example, non-proteinogenic amino acids, such as ornithine in 1, 2, and 4, hydroxyphenyl or dihydroxyphenyl-glycine in 5 and (4R)-4-[(E)-2-butenyl]-4-methyl-L. -threonine (Bmt) in 7, are included. Furthermore, the structures are frequently macrocyclic (1), branched macrocyclic (2), or dimers of two (4) or trimers of three (10, 11) structural components. Smaller heterocyclic rings, such as thiazole in 9, thiazoline in 13, or oxazoline in 14, are common structural properties of nonribosomal peptides. In addition, fatty acids (3), glycosylations (5), N-methylations (7), and N-formylations (18) may also be present, as well as the addition of propionate units (8) or acetate [14].
NRPs are typically produced by marine microorganisms, soil-inhabiting microorganisms, including
Novel peptide products’ biological functions are strictly associated with their chemical structure, which is constrained by a peptide sequence that ensures specific interaction with a specific molecular target. Chemical alterations, such as the incorporation of fatty acid chains, D-amino acids, glycosylated amino acids, and heterocyclic rings, as well as cyclization or oxidative cross-linking of side chains, add a lot to these unique interactions. Bacitracin, fengycin, pristinamycin, surfactin, tyrocidine, and vancomycin are examples of novel peptides with antibacterial and antifungal properties [25].
When the ribosomal code was deciphered in the 1960s, Tatum and coworkers discovered that ribosomes had no effect on cell-based tyrocidine production [23, 26]. The first NRPs agent is tyrocidine, a cyclic decapeptide that is biosynthesized outside of the
Compound | Biosynthetic class of agent | Source | Disease/Molecular target |
---|---|---|---|
Bacitracin | Cyclic peptide | Antibiotic/dephosphorylation of C55-isoprenyl pyrophosphate | |
Bleomycin | Hybrid peptide | Antibiotic/inhibition of DNA synthesis | |
Capreomycin | Cyclicpeptide | Antibiotic/protein synthesis inhibitor | |
Carbapenems | Synthetic thienamycin | Antibacterial (multidrug resistant)/bacterial cell-wall biosynthesis (peptidoglycan;β-lactamase inhibition) | |
Cephalosporin | Antibiotic/Alters bacterial outer membrane | ||
Chlorampheniol | Synthetic;further derivatives: thiamphenicol [c], florfenicol | Antibacterial/inhibition of ribosomal protein synthesis | |
Colistin (Polymyxin E) | — | Antibacterial/binding to lipopolysaccharide (outer membrane), interaction with the cytoplasmic membrane | |
Dalbavancin | Semisynthetic teicoplanin derivative | — | Antibacterial (Gram-positive)/membrane anchoring; disruption of cell membrane and inhibition of bacterial cell wall biosynthesis |
Daptomycin | Lipopeptide | Antibiotic (Gram-positive)/disrupts the cell membrane | |
Gramicidin | Linear pentadecapeptide | Antibiotic/ion-channel formation, increasing the permeability of the membrane | |
Lincomycin | — | Antibacterial (patients allergic to penicillin) inhibition of the ribosomal protein synthesis (50S-subunit, dissociation of peptidyl-tRNA from the ribosome) | |
Monobactams | — | Antibacterial (Gram-negative)/bacterial cell-wall biosynthesis | |
Oritavancin | — | Semi synthetic | Antibiotic/disrupts the cell membrane |
Polymyxin B | Polypeptides | Antibacterial (Gram-negative)/binding to lipopolysaccharide (outer membrane), interaction with cytoplasmic membrane | |
Pristinamycin | Depsipeptide | Antibacterial (Gram-positive)/ribosomal biosynthesis (50S-subunit, peptidyl transfer, and elongation of protein synthesis) | |
Teicoplanin | Glycopeptide | Antibiotic/inhibit cell wall synthesis | |
Telavancin | — | Antibacterial (Gram-positive) disruption of cell membrane and inhibition of bacterial cell-wall biosynthesis | |
Tyrothricin | — | Antibacterial (Gram-positive)/disruption of cell membrane | |
Vancomycin | Glycopeptide | Antibiotic/inhibit cell wall synthesis | |
Virginiamycin | — | Antibacterial/ribosomal biosynthesis (50S-subunit, peptidyl transfer, and elongation of protein synthesis) |
As demonstrated in Table 2, systemic and topical antibacterials are the most often used NRPs-based drugs, accounting for billions of dollars in the chemical and pharmaceutical industry sales. Table 3 lists their other applications, which include anticancer agents, antifungals, animal feed additives, immunosuppressants (cyclosporine), obstetrics (ergometrine), and pain management (ergotamine).
Agent | Origin | Properties and area of application |
---|---|---|
Actinomycin D (Dactinomycin) | Antitumor/DNA intercalator, inhibition of transcription | |
Bialaphos | Herbicide/tripeptide prodrug, inhibitor of glutamine synthetase | |
Bleomycin A2, B2 | Antitumor/metal-dependent oxidative cleavage of DNA in presence of molecular oxygen | |
Capreomycin | Antituberculous/ inhibition of the ribosomal protein synthesis (16S and 23S-rRNA) | |
Carfilzomib | Synthetic derivative of epoxomycin ( | Anticancer/proteasome inhibitor |
Caspofungin | Antifungal (candidiasis, aspergillosis) fungal cell-wall integrity ((1-3)-β-D-glucan synthase) | |
Cyclosporine A | Immunosuppressant/cyclophilin binding, inhibition of IL-2 expression (inhibition of T-cell activation) | |
Emodepside | Anthelmintic/Slo-1 receptor (K+ channel) | |
Enduracidin (Enramycin) | Antibacterial, food additive/inhibition of MurG (essential for cell-wall biosynthesis in Gram positive bacteria), inhibition of the transglycosylation step of peptidoglycan biosynthesis | |
Enniatins (fusafungine) | Antibacterial (topical), antifungal, anti-inflammatory/ ionophore (NH4+) membrane depolarization | |
Ergometrine (ergonovine) | Obstetrics/interaction with a-adrenergic, dopaminergic and serotonin receptors | |
Ergotamine | Migraine vasoconstrictive (5-HT1B receptor, but also dopamine and noradrenaline receptors) | |
Romidepsin | Antitumor/histone deacetylase inhibitor (inducing apoptosis) | |
Trabectedin | Bacterial symbiont of | Antitumor (antiproliferative, treatment of soft tissue sarcoma) DNA binder, blocks binding of transcription factors |
Marketed-NRPs agents [23].
In the medical field, NRP-based marketed drugs, such as Cyclosporin A and Bleomycin A2, have high selling prices. The cost of these medicines is $107 for 25 mg of Cyclosporine A (98% purity) obtained from
The 70% discovery of NRPs with antibacterial, antiviral, cytostatic, immunosuppressive, antimalarial, antiparasitic, animal growth promoters, and natural insecticides activity is mostly attributed to marine organisms [13]. NRPs obtained from marine organisms (sponges, tunicates, and their associated phyla, such as Acidobacteria, Actinobacteria, Bacteriodetes, Chloroflexi, Cyanobacteria, Nitrospira, Planctomycetes, Poribacteria, Proteobacteria, Verrucomicrobia, and Archaea) have excellent binding properties, low off-target toxicity, and high stability and these properties make them a promising molecule for the development of new therapeutics pharmacologically active in many clinical searches. Table 4 shows the chemical structure and source of various NRPs isolated from marine sponges and tunicates.
NRPs agents | Chemical class | Origin | Target |
---|---|---|---|
Miraziridine A | Linear pentapeptide | Cancer/inhibit protease cathepsin B | |
Haligramides A-B | Cyclic hexapeptides | Cancer/A-549 (lung), HCT-15 (colon), SF-539 (CNS), SNB-19 (CNS) | |
Prepatellamide A | Cyclic peptide | Cancer/P388 murine leukemia cell lines | |
Tamandarins A-B | Depsipeptides | Cancer/pancreatic carcinoma BX-PC3, prostatic cancer DU-145, head and neck carcinoma UMSCC10b | |
Microsclerodermins F–I | Cyclic peptides | Cancer/HCT-116 cell line | |
Wainunuamide | Cyclic hexapeptide | Cancer/A2780 ovarian, K562 leukemia cancer cells | |
Leucamide A | Cyclic hexapeptide | Cancer/Tumor cell lines HM02, HepG2, Huh7 | |
Axinellin C | Cyclic octapeptide | Cancer/A2780 ovarian, K562 leukemia cancer cells | |
Milnamide D | Linearpeptide | Cancer/HCT-116 cells | |
Kapakahines E–G | — | Cancer/P388 murine leukemia cells | |
Didmolamides A-B | Cyclic hexapeptides | Cancer Tumor cell lines (A549, HT29 and MEL28) | |
Bistratamides E–J | Cyclic hexapeptides | Cancer/Human colon tumor (HCT-116) cell line | |
Milnamide C | — | Cancer/MDA-MB-435cancer cells | |
Scleritodermin A | Cyclic peptide | Cancer | |
Microcionamids A-B | — | Cancer/Human breast tumor cell lines MCF-7 and SKBR-3 | |
Kendarimide A | Linear peptide | Cancer/KB-C2 cells | |
Phakellistatin 14 | Cyclo heptapeptide | Cancer/Murine lymphocytic leukemia P388 cell line | |
Polytheonamides A-B | Polypeptides | Cancer/P388 murine leukemia cells | |
Neopetrosiamides A-B | Tricyclic peptides | Cancer | |
Seragamides A–F | Depsipeptides | Cancer | |
Theopapuamide | Cyclic depsipeptide | Cancer/CEM-TART, HCT-116 cell lines | |
Azumamide A-E | Cyclo tetrapeptides | Cancer | |
Callyaerin G | Cyclic peptide | Cancer/Mouselymphoma cell line (L5178Y) and HeLa cells | |
Stylopeptide 2 | Cyclo decapeptide | Cancer/BT-549 and HS578T breast cancer cell lines | |
Ciliatamides A-C | Lipopeptides | Cancer/HeLa cells | |
Diazonamides C–E | Macrocyclic peptides | Cancer/Human tumor cell lines (A549, HT29, MDA-MB231) | |
Rolloamide A-B | Cyclic heptapeptides | Cancer | |
Euryjanicin A | Cycloheptapeptide | Cancer | |
Callyaerin A–F and H | Cyclic peptides | Cancer/L5178Y cell line | |
Papuamides E-F | Depsipeptides | Cancer/Brine shrimp | |
Stylissamide X | Octapeptide | Cancer/HeLa cells | |
Gombamide A | Hexapeptide | Cancer/K562 and A549 cell lines | |
Microspinosamide | Cyclic depsipeptide | HIV | |
Neamphamide A | Cyclic depsipeptide | HIV | |
Mirabamides A-D | Cyclic depsipeptide | HIV | |
Homophymine A | Cyclodepsipeptide | HIV/PBMC cell line | |
Celebeside A-C | Depsipeptides | HIV/Colon carcinoma (HCT-116) cells | |
Theopapuamides B–D, Mutremdamide A, Koshikamides C-H | Cyclic depsipeptide | HIV | |
Ceratospongamide | Cyclic heptapeptide | Inflammation | |
Halipeptin A-B | Cyclic depsipeptide | Inflammation | |
Perthamide C-D | Cyclopeptide | Inflammation | |
Solomonamide A- B | Cyclic peptide | Inflammation | |
Stylissatin A | Cyclic peptide | Murine macrophage RAW264.7 | |
Dicynthaurin | — | Antimicrobial | |
Nagahamide A | Depsipeptide | Antibacterial | |
Plicatamide | Octapeptide | Antimicrobial | |
Callipeltins | — | ||
Citronamides A- B | — | ||
Renieramide | Cyclic tripeptide | — | |
Phoriospongins A-B | Depsipeptide | Nematocidal/ |
Agents produced from marine sponges and tunicates which are based on NRPs [7].
In the NCBI database, there are currently about 1.164 distinct non-ribosomal peptides that form over 500 different monomers including both proteinogenic and non-proteinogenic L- and D-amino acids, as well as amines and carboxylic acids. These complex secondary metabolites’ linear, cyclic, branching, or other complicated primary structures are frequently altered to enhance clinical qualities and/or bypass resistance mechanisms. Indeed, the nucleotide sequence modification of a native NRPS gene or mixing modules from multiple NRPSs makes them more efficient with pharmacological properties. Several bioengineering and molecular techniques have been developed during the last few decades to produce modified NRPs with improved physicochemical characteristics and bioactivity [13].
In this chapter, we discussed the significance, synthesis, and application areas of NRPs-based agents, which have received a lot of interest as a new source of pharmaceutical agents. NRPs with unique chemical structures and diverse biological actions, such as antibacterials (penicillin, vancomycin), anticancer compounds (bleomycin), and immunosuppressants (cyclosporine), have been researched as novel compounds for new drug discovery and development throughout the last several decades.
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'At IntechOpen, the majority of OAPFs are paid by an Author’s institution or funding agency - Institutions (73%) vs. Authors (23%).
\n\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
\n\nHowever, as Open Access becomes a more commonly used publishing option for the dissemination of scientific and scholarly content, in addition to institutions, there are a growing number of funders who allow the use of grants for covering OA publication costs, or have established separate funds for the same purpose.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. 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Foodborne diseases can be prevented and acute diarrhea syndromes, fever and even death from dehydration can be avoided, especially in children under the age of 5 and in immunocompromised people.",book:{id:"5873",slug:"poisoning-from-specific-toxic-agents-to-novel-rapid-and-simplified-techniques-for-analysis",title:"Poisoning",fullTitle:"Poisoning - From Specific Toxic Agents to Novel Rapid and Simplified Techniques for Analysis"},signatures:"Cecilia Hernández-Cortez, Ingrid Palma-Martínez, Luis Uriel\nGonzalez-Avila, Andrea Guerrero-Mandujano, Raúl Colmenero Solís\nand Graciela Castro-Escarpulli",authors:[{id:"204160",title:"Prof.",name:"Graciela",middleName:null,surname:"Castro-Escarpulli",slug:"graciela-castro-escarpulli",fullName:"Graciela Castro-Escarpulli"},{id:"204162",title:"Dr.",name:"Cecilia",middleName:null,surname:"Hernández-Cortez",slug:"cecilia-hernandez-cortez",fullName:"Cecilia Hernández-Cortez"},{id:"204163",title:"MSc.",name:"Ingrid",middleName:null,surname:"Palma-Martinez",slug:"ingrid-palma-martinez",fullName:"Ingrid Palma-Martinez"},{id:"204164",title:"MSc.",name:"Luis Uriel",middleName:null,surname:"González-Avila",slug:"luis-uriel-gonzalez-avila",fullName:"Luis Uriel González-Avila"},{id:"204165",title:"MSc.",name:"Andrea",middleName:null,surname:"Guerrero-Mandujano",slug:"andrea-guerrero-mandujano",fullName:"Andrea Guerrero-Mandujano"}]},{id:"56530",doi:"10.5772/intechopen.69955",title:"Poisoning by Anticoagulant Rodenticides in Humans and Animals: Causes and Consequences",slug:"poisoning-by-anticoagulant-rodenticides-in-humans-and-animals-causes-and-consequences",totalDownloads:1845,totalCrossrefCites:10,totalDimensionsCites:17,abstract:"Anticoagulant rodenticides (ARs) are a keystone of the management of rodent populations in the world. The widespread use of these molecules raises questions on exposure and intoxication risks, which define the safety of these products. Exposures and intoxications can affect humans, domestic animals and wildlife. Consequences are different for each group, from the simple issue of intoxication in humans to public health concern if farm animals are exposed. After a rapid presentation of the mechanism of action and the use of anticoagulant rodenticides, this chapter assesses the prominence of poisoning by anticoagulant rodenticides in humans, domestic animals and wildlife.",book:{id:"5873",slug:"poisoning-from-specific-toxic-agents-to-novel-rapid-and-simplified-techniques-for-analysis",title:"Poisoning",fullTitle:"Poisoning - From Specific Toxic Agents to Novel Rapid and Simplified Techniques for Analysis"},signatures:"Sébastien Lefebvre, Isabelle Fourel, Stéphane Queffélec, Dominique\nVodovar, Bruno Mégarbane, Etienne Benoit, Virginie Siguret and\nVirginie Lattard",authors:[{id:"180156",title:"Dr.",name:"Virginie",middleName:null,surname:"Lattard",slug:"virginie-lattard",fullName:"Virginie Lattard"},{id:"185579",title:"Dr.",name:"Sébastien",middleName:null,surname:"Lefebvre",slug:"sebastien-lefebvre",fullName:"Sébastien Lefebvre"},{id:"185580",title:"Prof.",name:"Etienne",middleName:null,surname:"Benoit",slug:"etienne-benoit",fullName:"Etienne Benoit"},{id:"209023",title:"Dr.",name:"Isabelle",middleName:null,surname:"Fourel",slug:"isabelle-fourel",fullName:"Isabelle Fourel"},{id:"209031",title:"Mr.",name:"Stéphane",middleName:null,surname:"Queffélec",slug:"stephane-queffelec",fullName:"Stéphane Queffélec"},{id:"209032",title:"Dr.",name:"Bruno",middleName:null,surname:"Megarbane",slug:"bruno-megarbane",fullName:"Bruno Megarbane"},{id:"209033",title:"Dr.",name:"Dominique",middleName:null,surname:"Vodovar",slug:"dominique-vodovar",fullName:"Dominique Vodovar"},{id:"209034",title:"Prof.",name:"Virginie",middleName:null,surname:"Siguret",slug:"virginie-siguret",fullName:"Virginie Siguret"}]},{id:"51450",doi:"10.5772/63888",title:"ECMO Biocompatibility: Surface Coatings, Anticoagulation, and Coagulation Monitoring",slug:"ecmo-biocompatibility-surface-coatings-anticoagulation-and-coagulation-monitoring",totalDownloads:4436,totalCrossrefCites:9,totalDimensionsCites:17,abstract:"The interaction between the patient and the ECMO (extracorporeal membrane oxygenation) circuit initiates a significant coagulation and inflammatory response due to the large surface area of foreign material contained within the circuit. This response can be blunted with the appropriate mix of biocompatible materials and anticoagulation therapy. The use of anticoagulants, in turn, requires appropriate laboratory testing to determine whether the patient is appropriately anticoagulated. Physicians must balance the risks of bleeding with the risks of thrombosis; the proper interpretation of these tests is often shrouded in mystery. It is the purpose of this chapter to help demystify the coagulation system, anticoagulants, biocompatible surfaces, and coagulation testing so that ECMO practitioners can make informed decisions about their patients and to spur coordinated efforts for future research to improve our understanding of these complex processes.",book:{id:"5202",slug:"extracorporeal-membrane-oxygenation-advances-in-therapy",title:"Extracorporeal Membrane Oxygenation",fullTitle:"Extracorporeal Membrane Oxygenation - Advances in Therapy"},signatures:"Timothy M. Maul, M Patricia Massicotte and Peter D. Wearden",authors:[{id:"182691",title:"Dr.",name:"Timothy",middleName:"Michael",surname:"Maul",slug:"timothy-maul",fullName:"Timothy Maul"},{id:"187110",title:"Dr.",name:"Peter",middleName:null,surname:"Wearden",slug:"peter-wearden",fullName:"Peter Wearden"},{id:"187112",title:"Dr.",name:"Patti",middleName:null,surname:"Massicotte",slug:"patti-massicotte",fullName:"Patti Massicotte"}]},{id:"39638",doi:"10.5772/51484",title:"The History of Sepsis from Ancient Egypt to the XIX Century",slug:"the-history-of-sepsis-from-ancient-egypt-to-the-xix-century",totalDownloads:10550,totalCrossrefCites:5,totalDimensionsCites:15,abstract:null,book:{id:"2583",slug:"sepsis-an-ongoing-and-significant-challenge",title:"Sepsis",fullTitle:"Sepsis - An Ongoing and Significant Challenge"},signatures:"Johan Sebastián Hernández Botero and María Cristina Florián Pérez",authors:[{id:"141171",title:"Dr.",name:"Johan",middleName:"Sebastian",surname:"Hernandez Botero",slug:"johan-hernandez-botero",fullName:"Johan Hernandez Botero"},{id:"141520",title:"Dr.",name:"Maria Cristina",middleName:null,surname:"Florian Perez",slug:"maria-cristina-florian-perez",fullName:"Maria Cristina Florian Perez"}]}],mostDownloadedChaptersLast30Days:[{id:"56521",title:"Food Poisoning Caused by Bacteria (Food Toxins)",slug:"food-poisoning-caused-by-bacteria-food-toxins-",totalDownloads:5880,totalCrossrefCites:9,totalDimensionsCites:20,abstract:"In the environment, there are polluting substances that can cause adverse reactions in human beings when entering the body through different ways (ingestion, inhalation, injection, or absorption). The main pollutants can be poisons, chemical compounds, toxic gases, and bacterial toxins. These can be found in different places and their effects depend on the dose and exposure time. Furthermore, foodborne diseases (FBDs) can cause disability; these diseases can be caused by toxins produced by bacteria or other toxic substances in the food, which can cause severe diarrhea, toxic shock syndrome, debilitating infections such as meningitis and even death. FBDs are transmitted through food contaminated with pathogenic microorganisms that have multiple factors of virulence, which gives them the ability to cause an infection; some bacterial genres can produce toxins directly in the food, but other genres can produce them once they have colonized the intestine. Among the pathogens involved in FBDs that are also considered to be toxigenic are Salmonella spp., Vibrio parahaemolyticus, Vibrio cholerae, Staphylococcus aureus, Clostridium botulinum, Clostridium perfringens, Bacillus cereus, Listeria monocytogenes. Foodborne diseases can be prevented and acute diarrhea syndromes, fever and even death from dehydration can be avoided, especially in children under the age of 5 and in immunocompromised people.",book:{id:"5873",slug:"poisoning-from-specific-toxic-agents-to-novel-rapid-and-simplified-techniques-for-analysis",title:"Poisoning",fullTitle:"Poisoning - From Specific Toxic Agents to Novel Rapid and Simplified Techniques for Analysis"},signatures:"Cecilia Hernández-Cortez, Ingrid Palma-Martínez, Luis Uriel\nGonzalez-Avila, Andrea Guerrero-Mandujano, Raúl Colmenero Solís\nand Graciela Castro-Escarpulli",authors:[{id:"204160",title:"Prof.",name:"Graciela",middleName:null,surname:"Castro-Escarpulli",slug:"graciela-castro-escarpulli",fullName:"Graciela Castro-Escarpulli"},{id:"204162",title:"Dr.",name:"Cecilia",middleName:null,surname:"Hernández-Cortez",slug:"cecilia-hernandez-cortez",fullName:"Cecilia Hernández-Cortez"},{id:"204163",title:"MSc.",name:"Ingrid",middleName:null,surname:"Palma-Martinez",slug:"ingrid-palma-martinez",fullName:"Ingrid Palma-Martinez"},{id:"204164",title:"MSc.",name:"Luis Uriel",middleName:null,surname:"González-Avila",slug:"luis-uriel-gonzalez-avila",fullName:"Luis Uriel González-Avila"},{id:"204165",title:"MSc.",name:"Andrea",middleName:null,surname:"Guerrero-Mandujano",slug:"andrea-guerrero-mandujano",fullName:"Andrea Guerrero-Mandujano"}]},{id:"64561",title:"Musculoskeletal Injuries: Types and Management Protocols for Emergency Care",slug:"musculoskeletal-injuries-types-and-management-protocols-for-emergency-care",totalDownloads:2489,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"These are a common type of human injuries which can result from the damage of muscular or skeletal systems (i.e., bones, muscles, tendons, ligaments, nerves, blood vessels, etc.); they usually occur due to a strenuous and/or repetitive activity and can result into variety of complaints, complications, and deformities causing a big burden on the financial and health system in all societies. They are among the largest category of work-related injuries and are responsible for almost 30% of all worker’s compensation costs worldwide. Injuries to the musculoskeletal system occur in 85% of patients who sustain blunt trauma; they often appear dramatic, but rarely cause an immediate life-threatening situation, although these injuries must be assessed and managed accurately so life or limb are not jeopardized. The doctor must be familiar with the anatomy and the injury site to protect his patients from further disability and prevent complications. Major musculoskeletal trauma such as crushed injuries that can cause release of myoglobin resulting in renal tubular injury (acute kidney injury), or can be associated with internal torso injuries like acute compartment syndrome. soft tissue and skeletal system traumas may not be initially recognized, so continued reassessment and evaluation are necessary to identify all injuries.",book:{id:"6616",slug:"essentials-of-accident-and-emergency-medicine",title:"Essentials of Accident and Emergency Medicine",fullTitle:"Essentials of Accident and Emergency Medicine"},signatures:"Ahmad Subhy Alsheikhly and Mazin Subhy Alsheikhly",authors:[{id:"144628",title:"Prof.",name:"Ahmad Subhy",middleName:"Humadi",surname:"Alsheikhly",slug:"ahmad-subhy-alsheikhly",fullName:"Ahmad Subhy Alsheikhly"}]},{id:"59641",title:"Problem of Burns in Children: Opportunities for Health Improvement",slug:"problem-of-burns-in-children-opportunities-for-health-improvement",totalDownloads:1415,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Burns are one of the most devastating types of trauma in medicine. Children under 5 years of age are a high-risk group to burns. The most common type is thermal burn caused by hot fluids (scald). Most childhood burns occur at home under parental supervision. These are preventable injuries. The chapter presents results of my studies about risk factors of burns in children and possibilities of health improvement. Simple changes in children’s environment and increasing awareness of caregivers can lead to a decrease in the number of this type of injuries. Moreover, the first aid given to burnt children soon after the injury usually is not adequate (no cooling thermal burns and no analgesia). Health improvement can be obtained by reducing the number of burns, the correct first aid given after the injury, and the organization of specialized health-care centers and rehabilitation services for victims of burns.",book:{id:"6616",slug:"essentials-of-accident-and-emergency-medicine",title:"Essentials of Accident and Emergency Medicine",fullTitle:"Essentials of Accident and Emergency Medicine"},signatures:"Agata Maria Kawalec",authors:null},{id:"51795",title:"ECMO Cannulation Techniques",slug:"ecmo-cannulation-techniques",totalDownloads:4333,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"An extracorporeal membrane oxygenation (ECMO) circuit consists of a pump and a membrane oxygenator. This circuit can interface with the human body in a variety of cannulation strategies to provide different forms and levels of support. These various support techniques can be divided into two broad categories: those designed to support the body’s respiratory functions (lungs) and those designed to support the body’s blood circulation (heart). In this chapter we discuss various cannulation techniques used.",book:{id:"5202",slug:"extracorporeal-membrane-oxygenation-advances-in-therapy",title:"Extracorporeal Membrane Oxygenation",fullTitle:"Extracorporeal Membrane Oxygenation - Advances in Therapy"},signatures:"Chand Ramaiah and Ashok Babu",authors:[{id:"183646",title:"Dr.",name:"Chand",middleName:null,surname:"Ramaiah",slug:"chand-ramaiah",fullName:"Chand Ramaiah"},{id:"189073",title:"Dr.",name:"Ashok",middleName:null,surname:"Babu",slug:"ashok-babu",fullName:"Ashok Babu"}]},{id:"27955",title:"Transfusion-Associated Bacterial Sepsis",slug:"transfusion-associated-sepsis",totalDownloads:8276,totalCrossrefCites:1,totalDimensionsCites:2,abstract:null,book:{id:"802",slug:"severe-sepsis-and-septic-shock-understanding-a-serious-killer",title:"Severe Sepsis and Septic Shock",fullTitle:"Severe Sepsis and Septic Shock - Understanding a Serious Killer"},signatures:"Jolanta Korsak",authors:[{id:"72828",title:"Prof.",name:"Jolanta",middleName:null,surname:"Korsak",slug:"jolanta-korsak",fullName:"Jolanta Korsak"}]}],onlineFirstChaptersFilter:{topicId:"177",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517",scope:"Paralleling similar advances in the medical field, astounding advances occurred in Veterinary Medicine and Science in recent decades. These advances have helped foster better support for animal health, more humane animal production, and a better understanding of the physiology of endangered species to improve the assisted reproductive technologies or the pathogenesis of certain diseases, where animals can be used as models for human diseases (like cancer, degenerative diseases or fertility), and even as a guarantee of public health. Bridging Human, Animal, and Environmental health, the holistic and integrative “One Health” concept intimately associates the developments within those fields, projecting its advancements into practice. This book series aims to tackle various animal-related medicine and sciences fields, providing thematic volumes consisting of high-quality significant research directed to researchers and postgraduates. It aims to give us a glimpse into the new accomplishments in the Veterinary Medicine and Science field. 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After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",institutionURL:null,country:{name:"Portugal"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:3,paginationItems:[{id:"19",title:"Animal Science",coverUrl:"https://cdn.intechopen.com/series_topics/covers/19.jpg",isOpenForSubmission:!0,editor:{id:"259298",title:"Dr.",name:"Edward",middleName:null,surname:"Narayan",slug:"edward-narayan",fullName:"Edward Narayan",profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",biography:"Dr. Edward Narayan graduated with Ph.D. degree in Biology from the University of the South Pacific and pioneered non-invasive reproductive and stress endocrinology tools for amphibians - the novel development and validation of non-invasive enzyme immunoassays for the evaluation of reproductive hormonal cycle and stress hormone responses to environmental stressors. \nDr. Narayan leads the Stress Lab (Comparative Physiology and Endocrinology) at the University of Queensland. A dynamic career research platform which is based on the thematic areas of comparative vertebrate physiology, stress endocrinology, reproductive endocrinology, animal health and welfare, and conservation biology. \nEdward has supervised 40 research students and published over 60 peer reviewed research.",institutionString:null,institution:{name:"University of Queensland",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},{id:"20",title:"Animal Nutrition",coverUrl:"https://cdn.intechopen.com/series_topics/covers/20.jpg",isOpenForSubmission:!0,editor:{id:"175967",title:"Dr.",name:"Manuel",middleName:null,surname:"Gonzalez Ronquillo",slug:"manuel-gonzalez-ronquillo",fullName:"Manuel Gonzalez Ronquillo",profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",biography:"Dr. Manuel González Ronquillo obtained his doctorate degree from the University of Zaragoza, Spain, in 2001. He is a research professor at the Faculty of Veterinary Medicine and Animal Husbandry, Autonomous University of the State of Mexico. He is also a level-2 researcher. He received a Fulbright-Garcia Robles fellowship for a postdoctoral stay at the US Dairy Forage Research Center, Madison, Wisconsin, USA in 2008–2009. He received grants from Alianza del Pacifico for a stay at the University of Magallanes, Chile, in 2014, and from Consejo Nacional de Ciencia y Tecnología (CONACyT) to work in the Food and Agriculture Organization’s Animal Production and Health Division (AGA), Rome, Italy, in 2014–2015. He has collaborated with researchers from different countries and published ninety-eight journal articles. He teaches various degree courses in zootechnics, sheep production, and agricultural sciences and natural resources.\n\nDr. Ronquillo’s research focuses on the evaluation of sustainable animal diets (StAnD), using native resources of the region, decreasing carbon footprint, and applying meta-analysis and mathematical models for a better understanding of animal production.",institutionString:null,institution:{name:"Universidad Autónoma del Estado de México",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"28",title:"Animal Reproductive Biology and Technology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/28.jpg",isOpenForSubmission:!0,editor:{id:"177225",title:"Prof.",name:"Rosa Maria Lino Neto",middleName:null,surname:"Pereira",slug:"rosa-maria-lino-neto-pereira",fullName:"Rosa Maria Lino Neto Pereira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9wkQAC/Profile_Picture_1624519982291",biography:"Rosa Maria Lino Neto Pereira (DVM, MsC, PhD and) is currently a researcher at the Genetic Resources and Biotechnology Unit of the National Institute of Agrarian and Veterinarian Research (INIAV, Portugal). 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"95",type:"subseries",title:"Urban Planning and Environmental Management",keywords:"Circular economy, Contingency planning and response to disasters, Ecosystem services, Integrated urban water management, Nature-based solutions, Sustainable urban development, Urban green spaces",scope:"