Basic parameters of the tested fabric
\r\n\tThe development of the interpersonal model and the Kleinian school in the second half of the last century allowed the emergence of an original understanding of the unconscious mind. Within the intersubjective paradigm, the psychoanalytic situation is conceptualized as an interpersonal field to which both the analyst and the patient contribute substantially. We have shown elsewhere how the failure to give a full account of such an intersubjective dimension in both psychoanalytic theory and practice amounts to a core liability in contemporary psychoanalytic discourse.
\r\n\r\n\tThe present book will focus on a few areas where the insufficient development of our discipline is currently apparent: five wounds that mark the body of the psychoanalytic enterprise.
\r\n\r\n\tNew contributions are particularly needed in the following areas: Current conceptualization of the unconscious mind is mechanistic and not suited to incorporate the full network of interpersonal exchanges which unfolds in the analytic room; Furthermore, the development of interpersonal psychoanalysis and the theory of the object relations warrants a greater appreciation of the impact of extratranference relations (e.g., couple, family, peers) on the patient's inner life both within and without the psychoanalytic situation.
\r\n\r\n\tAn integration of theories and models from other psychological paradigms is clearly in order here; the book will also focus on Barangers’ theory of the bi-personal field that makes traditional unipersonal models of the psychoanalytic process untenable. Also, it will help in the understanding of the reciprocal interactions of the two partners in the psychoanalytic dyad in most psychoanalytic institutes the training format relies naively on models from the academic or the professional domains. This fosters rigidity, conformism, and a hierarchical organizational style in the institutional life; e) all over the long span of his creative life Freud showed consistent interest in the application of psychoanalysis to literature, the arts, religion, and politics. Contemporary psychoanalysis is getting more and shyer and is pressed at the margins of social and political debate. The psychoanalytic theory includes unique lore of knowledge about the conscious and unconscious mind. Without it, a comprehensive understanding of human reality will stay out of the reach of contemporary culture.
",isbn:"978-1-80356-882-9",printIsbn:"978-1-80356-881-2",pdfIsbn:"978-1-80356-883-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"c6a104ee38fec8d9ba8aa139a33003ce",bookSignature:"Dr. Paolo Azzone",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11591.jpg",keywords:"Unconscious, Repression, Conformism, Intersubjective Paradigm, Interpersonal Psychoanalysis, Object Relation Theory, Couple Therapy, Family Therapy, Psychoanalytic Process, Transference Interpretation, Resistance, Controtransference",numberOfDownloads:12,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 31st 2022",dateEndSecondStepPublish:"June 17th 2022",dateEndThirdStepPublish:"August 16th 2022",dateEndFourthStepPublish:"November 4th 2022",dateEndFifthStepPublish:"January 3rd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Paolo Azzone, M.D., is a psychiatrist and a psychoanalyst with over 20 years of experience in mental health topics. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2270",title:"Fourier Transform",subtitle:"Materials Analysis",isOpenForSubmission:!1,hash:"5e094b066da527193e878e160b4772af",slug:"fourier-transform-materials-analysis",bookSignature:"Salih Mohammed Salih",coverURL:"https://cdn.intechopen.com/books/images_new/2270.jpg",editedByType:"Edited by",editors:[{id:"111691",title:"Dr.Ing.",name:"Salih",surname:"Salih",slug:"salih-salih",fullName:"Salih Salih"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"117",title:"Artificial Neural Networks",subtitle:"Methodological Advances and Biomedical Applications",isOpenForSubmission:!1,hash:null,slug:"artificial-neural-networks-methodological-advances-and-biomedical-applications",bookSignature:"Kenji Suzuki",coverURL:"https://cdn.intechopen.com/books/images_new/117.jpg",editedByType:"Edited by",editors:[{id:"3095",title:"Prof.",name:"Kenji",surname:"Suzuki",slug:"kenji-suzuki",fullName:"Kenji Suzuki"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"872",title:"Organic Pollutants Ten Years After the Stockholm Convention",subtitle:"Environmental and Analytical Update",isOpenForSubmission:!1,hash:"f01dc7077e1d23f3d8f5454985cafa0a",slug:"organic-pollutants-ten-years-after-the-stockholm-convention-environmental-and-analytical-update",bookSignature:"Tomasz Puzyn and Aleksandra Mostrag-Szlichtyng",coverURL:"https://cdn.intechopen.com/books/images_new/872.jpg",editedByType:"Edited by",editors:[{id:"84887",title:"Dr.",name:"Tomasz",surname:"Puzyn",slug:"tomasz-puzyn",fullName:"Tomasz Puzyn"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"49017",title:"Weaving Complex Patterns — From Weaving Looms to Weaving Machines",doi:"10.5772/61091",slug:"weaving-complex-patterns-from-weaving-looms-to-weaving-machines",body:'The term textile is usually associated with a cloth that is produced on a loom or weaving machine as a fabric manufactured by interlacing warp and weft threads. Weaving is one of the oldest crafts and has a long historical development. Studying the history of the production of fabrics is based on archaeological finds, pictorial representations, frescoes, stone monuments, archival documents, etc. One of the valuable records is the Bible, which mentions the intricate network of Solomon\'s Temple. In Greek myths, Arachne, the weaver is mentioned who was turned into a spider by Athena; Homer describes decorative veils of the Temple of Athena. The beginnings of fabric production date back far into the past and cannot be precisely defined. The oldest sites as evidence of fabric production are certainly records and drawings in China and Egypt, carved in stone or clay pottery, which date back to 12,000 BC. It is assumed that the first interlacing of warp and weft occurred much earlier in making dwellings from brushwood. To create a roof area and better insulation of the space, mud and leaves were used, which were coiled and intertwined with each other in two directions (warp and weft directions). Thus, a solid and long-lasting roof structure was made. After that the manufacture of baskets, bags, and other household and transport articles developed. This interweaving of brushwood was probably the forerunner in the development of fabric making. By hand twisting twigs, leaves, and animal hair, an initially coarse, and afterward a finer thread was made from which fabric was manufactured by interlacing [1]. Despite coarse weaving, these fabrics had a definite advantage when worn compared to fur, especially in the summer. The preserved drawings show that fur was the first to have been used to cover the body. Fabric has gradually assumed the function of the fur and was primarily used to protect the body from bad weather. Sensitivity of textile products, and the tendency to decay, represents a particular difficulty in assessing the beginnings of making fabrics so that drawings of looms carved in stone or clay are more reliable and older evidence of the beginnings of the craft of weaving [2].
Weaving has always represented an extremely complex operation that requires not only a certain knowledge of the technological process, but also a way of creating patterns on the fabric. The production of yarns from different fibers and its transformation into a fabric is one of the oldest human crafts and technologies. Various historical textiles and materials, as an invaluable part of cultural material heritage, testify to social background and time of origin [3]. Throughout history more complex fabrics, especially silk fabrics with gold or silver threads, represented a status symbol of high society and were of great value. Most often they were used for garments, church vestments, and furniture decoration. Old simple fabrics without or with smaller patterns made from wool, linen, cotton, or similar fibers, are also greatly appreciated and carefully preserved [4].
The manufacture of closely woven silk fabric with multicolored Jacquard patterns always required a weaver with certain technological knowledge, skill of transferring a pattern on the fabric, and sense of matching colors in the pattern. Woven fabric is formed by interlacing warp and weft threads according to a weave and pattern in order to produce a compact fabric with an appropriate design. In addition, the appearance of the fabric depends on the density of warp and weft threads, composition and fineness of yarn, thread tension, loom type, and the weaver (precision, patience, skill, knowledge of weaves, matching of density and color, etc.), especially when it comes to hand weaving.
It is assumed that wool is the oldest fiber that was used for making fabric. However, the oldest preserved samples are not from wool, although records mention wool processing. Egyptians wrapped mummies mainly in linen, which did not decompose, and in addition, their religion did not allow taking animal fibers into the tomb; this is the main reason why wool fabrics are not the oldest preserved samples. Over time, certain skills and weaving techniques preserved in secrecy in narrower communities were developed. Increasingly more valuable and more beautiful fabrics recognizable by regions and communities of origin began to appear. The value of a fabric was assessed according to the raw materials’ complexity of manufacturing and pattern size. The fabric was evaluated according to the raw material (silk), fineness, density, and the complexity of manufacturing, had mostly a high price, and in some regions it was used as currency. Valuable preserved patterns belonged to the higher class, for vestments and appropriate clothing for various ceremonies.
Sensitivity of textile products and tendency to decompose represent a particular difficulty in assessing the beginnings of fabric manufacturing as well as manufacturing complexity. The oldest preserved samples of linen fabrics were taken from Egyptian mummies and date back to 5000 BC, when woven fabrics of certain dimensions were also woven. Most of the preserved fabrics with complex patterns were woven from natural silk. China had a centuries-old monopoly in the production and processing of natural silk. However, this monopoly was threatened as early as in the 12th century by the Italian city of Luca, which was the most famous producer of silk in the West. In the 13th century, the Silk Guild of Paris produced famous green silk fabrics woven with motifs of birds, lilies, and vine leaves. After that, the German city of Regensburg was known for its half-silk fabrics and Venice for samples of brocade with metallic golden threads. The following are some familiar names of fabrics woven on handlooms and their description [5].
At the beginning of weaving longitudinal threads (warp threads) were hung next to each other on a bar with hanging weights forming one set of threads which were then interlaced with horizontally arranged threads (weft threads). Wefts were inserted, beaten up from bottom to top. This way a fabric of specific structure, softness, breathability, and comfort was produced.
In the old records weaving frames are described. The warp was taut during weaving, previously dimensioned fabrics were woven and later they were joined laterally and transformed into simple articles of clothing.
The form of the first loom changed through history and it cannot be said for certain whether the first loom was vertical or horizontal (Fig. 1). Vertical looms take up less space and are more suitable for weaving outside dwellings, but their height required prolonged abnormal body posture and standing work, especially in the beginnings of weaving when the warp was hung to weights and the weft was inserted from top to bottom. This made weft beating up more difficult. No higher warp and weft densities could be achieved, warp tension was irregular, and each weft was inserted by interlacing the warp with fingers, until the formation of the shed was invented. Vertical looms were developed mostly for weaving loosely woven fabrics in the warp direction, but with very high strength. The assumption is that the tapestries, rugs and carpets were mainly woven on vertical looms with larger widths where the colored weft conceals the loosely woven warp and creates a pattern. By tightening the warp on both sides of the frames, thereby eliminating the weights for tightening the warp, it is possible to weave from bottom to top, which made weft beating up easier and achieving a higher weft density. Additional bars and strings enabled to form two sheds, specifically for weaving in plain weave; this increased the fabric production and quality of these looms. Vertical looms are more suitable than horizontal ones for weaving larger patterns, according to the picture, which can be easily placed under the warp (no shafts that prevent placing the picture below the warp as on horizontal looms) as well as drawing pictures or drawings on the warp before weaving. This has resulted in unique items of immeasurable values. Despite slower work, and thus lower productivity in relation to the horizontal loom, vertical looms were probably more numerous at the beginning of weaving. Because of their simpler construction, vertical looms allowed achieving the utilization of the whole warp whereby it is possible to weave fabrics with selvedges on all four sides to make one article of clothing without cutting. This fact is confirmed by the preserved samples of fabrics (e.g., oldest woollen fabric woven in Europe with the edges on all four sides found in Bosnia and Herzegovina 1983; fabric age dates back to 3550-3800) [6]. It can be interpreted from the loom drawings carved in stone or clay that they are more reliable and older evidence of the beginnings of the craft of weaving and weaving process than the preserved samples of fabrics [7].
Warping on the loom in the form of a frame to produce a fabric with selvedges on all four sides; a) vertical loom, b) horizontal loom
Horizontal looms are more suitable for weaving in dwellings; weavers wove in a sitting or squatting position. With the introduction of shafts and treadles weaving on the horizontal loom became easier because of the use of the feet (to press treadles) and hands (weft beating). Horizontal looms experienced a noticeable development as weaving the most complex patterns. Their productivity rose, with the introduction of the shuttle with a cop, especially in case of simpler patterns. Their development was in several directions, e.g., with regard to raw materials, weaving processes, and pattern complexity. Over time, the increasing weavers’ skills became more apparent in interlacing warp and weft threads creating more complex weaves, using different raw materials, finer yarn counts, and greater thread densities. In weaving greater lengths and widths of fabrics were achieved, and fabrics were later cut and sewn into more complex items of clothing. Larger and more complex patterns requiring an excellent skill and knowledge of weavers were created. The yarn was dyed with natural dyestuffs and woven, or less frequently, the fabric was dyed after weaving. Woollen fabrics were sometimes filled in order to produce warmer and softer articles of clothing. At the same time looms were adapted for weaving more complex patterns and lightweight fabrics. In this regard, horizontal looms were at an advantage: the fabric had regular and uniform dimensions across the width; it was possible to create a shed by pressing the treadles with a variety of connecting shafts and treadles, which made it easier to insert the weft; easier and faster weaving; better quality fabrics; and more possibilities of pattern design. Horizontal looms became increasingly complicated when making larger patterns and larger dimensions with extreme fineness and density of threads, which required a complex and longer warp and weft preparation. For larger fabric widths looms with two pairs of treadles and with two weavers working in pairs were constructed (Fig. 2). Sometimes, weavers were also artists or worked with them closely in weaving large patterns. Art paintings, which corresponded to proportions of drawings and colors, were adapted and woven. This required long training of weavers for weaving such patterns and reading the records of the pattern, but also artistic talent in matching colors according to the model, and skill in using different weaving techniques and knowledge of weaves that were the most suitable for the development of certain patterns and designs. Horizontal looms were specialized in weaving specific raw materials (for lightweight silk fabrics, heavy woollen fabrics, etc.), in the technique of weaving (velvet, terry, damask, lampas, etc.), in the complexity of a pattern (with extra floating wefts (lance), with brocaded wefts, with metal (gold-plated or silver-plated wefts etc.). The complexity of making patterns begins with the use of fiber (e.g., natural silk, linen, wool, cotton), yarn count (from the finest silk threads to coarse wool and metallic threads), and warp and weft density amounting to 150 threads/cm.
On the first looms, the warp had a relatively small length which sufficed for making an article of clothing. That the skill of weaving existed in the Stone Age is witnessed by findings of bone needles and dressmaking equipment made of stone. One of the more valuable artifacts are stone weights for the warp, which originate from Troy 2500 years BC, confirming the use of looms at that time [8].
Horizontal loom for larger fabric widths
The first looms for making larger patterned fabrics with larger repeats that were predecessors to the Jacquard loom had a very complex construction (Fig. 3a). Before making a Jacquard loom with programming cards, making patterns with a great weave unit in combination of different weaves required a very complex weaving preparation and weaving itself [9]. A stencil plate (Fig. 3b) was prepared according to a painting, which was usually an artist’s work of art. A copy of the painting and sometimes the original work was divided into squares that were 1 cm wide and 1 cm long.
a) Weaving loom for making old complex patterns before the Jacquard weaving loom, b) picture prepared for weaving with number of effects in the picture
First, it was necessary to determine the density of ends/1 cm and to calculate the total number of warp threads (density of ends/1 cm multiplied by the fabric width to be woven with the addition of threads for selvedges). According to the painting, the number of effects on the fabric pattern, which were distinguished by color and / or weave, were determined. One thread system was single-colored (or hidden) and another multicolored (creating the fabric face). In such patterns, the warp is usually single-colored, and the weft is in different colors, which creates a pattern on the fabric face. In this way, the preparation of single-colored warp is simpler and faster. According to this weaving technique, the warp threads were hidden within the fabric by beating up the weft, whereas the multicolored weft created versatile effects on the fabric. In order to hide the warp threads on the fabric face, the warp threads should be loosely warped, tauter, stronger, twisted, and preferably thinner than the weft threads. This relationship is mostly based on experience, now known as double weft, triple weft, or multiweft fabrics. In multiwarp fabrics the weft is invisible, the warp threads create a pattern, and they are finer and denser than the weft threads. Warp threads were dyed before warping, so warp preparation and warping lasted longer, but weaving was faster because the weft was mostly single-colored. A group of cords for drawing warp threads for the corresponding effect is assigned to each effect on the picture or the fabric. One of the weavers pulled the cords (weaving with two weavers) according to the corresponding effect over the whole warp width. The other weaver had to watch out for the effect in the direction of the fabric width and insert the corresponding weft color. In order to insert the warp thread over the whole fabric width, the cords for each effect in the fabric fell had to be drawn. The weft color corresponding to the effect was inserted in that fabric segment where this effect was drawn according to the pattern and the program for raising the cords for this effect depended on the weave. After the weft thread had been inserted over the whole fabric width for all effects being woven at that moment, the weft threads were beaten up, and the process was repeated for the next weft insertion. The pattern was woven face downward for easier weft insertion, and a mirror was used to control the fabric pattern. The number of picks/cm depended on weft thickness and beat-up force. The thinner the weft thread and the stronger the beat up force of the weft, the more beautiful the pattern, with more distinct and accurate pattern contours created, but weaving time was longer or more weft threads had to be inserted per 1 cm. Similarly, the density of warp threads affected the quality and appearance of the fabric pattern. The greater the warp density and the finer the warp threads, the more distinct were the contours and the fabric pattern was more beautiful and more faithful. Fabric patterns made by hand weaving large patterns from natural silk, especially in combination with gold-plated and silver-plated threads (brocades) are of exceptional value [10].
The first weaving loom on which punched cards raised and lowered warp threads according to a program was invented by Joseph Marie Jacquard [9] in 1805 (Fig. 4). This loom raised warp threads with the help of a program card. This eliminated pulling cords according to effects. One cord was pulled attached to the prism and moved the card for one weft thread. By pressing the treadle the needles would come into contact with the card. The card is read for each weft and usage of hooks and the harness raises warp threads (punched hole in the card) or remains in the lower shed (unpatched hole in the card) over the whole width.
Jacquard loom (1805)
A punched hole in the card means that the needle passes through it and pulls the hook because it is engaged with the knife and will raise all the cords fastened to this hook and the heddles hung on the corresponding cords, and thus the threads drawn into the corresponding heddles. If there is no hole in the card, the needle remains on the card surface and does not pull the hook, it is not in contact with the knife and will not pull it, as cords or heddles and warp threads will remain in the lower shed position. One or more weft threads are inserted by colors and fineness, depending on fabric effects in this fabric segment. The technique of weft insertion by effects is the same as on the previous loom without punched card, only the shed is inactive until all weft threads are inserted as required by the pattern. This weaving technique using punched card (Jacquard punched card loom) achieves higher production, enables easier operation, and machine operation is built for one weaver and there are fewer possible faults [11].
Some inventors attempted to build a mechanically driven machine back in the eighteenth century. Cartwright applied steam power to drive the loom. Mechanical machines increasingly dominated in weaving mills of that time. Industrial production began in the second half of the eighteenth century. In 1894, Northrop invented a pirn changer, a mechanical device that provides automatic supply of weft in looms by changing the contents of the shuttle without stopping the loom. Large-patterned fabrics became possible because of weft pattern forming (two shuttle boxes on each side of the machine or 3 multicolored weft threads) and the use of the Jacquard power loom (Fig. 5).
Mechanical Jacquard shuttle loom with weave design
The production increased a number of times; the machines were improved dramatically and new manufacturers appeared. Weft pattern forming was made possible by adding four shuttle boxes on each side of the machine, which allowed up to 7 multicolored weft threads. Automatic shuttle looms were improved and used for nearly one century and played a predominant role in the historical development of weaving. Shuttle-less weaving machines appeared in the mid-twentieth century (Fig. 6) [12].
The advantage of shuttle-less weaving machines is their high productivity, higher product quality, and easier and more comfortable machine operation. On shuttle-less weaving machines, weft threads are inserted into the shed only from one side; they are unwound from the supply package outside the machine which causes problems for making strong and quality selvedges on the fabric. These machines run at high speeds because the weft inserter picks up each weft individually and it is lighter than the shuttle. On shuttle looms, the shuttle inserts the weft on the weft cop; they are slow and cannot achieve the speed of shuttle-less looms. The basic construction of shuttle-less looms does not differ significantly from the construction of shuttle looms; the only exception is the weft inserter. All devices on the loom can be classified into four basic groups: warp-unwinding devices, shed forming devices, weft inserters, and fabric winding devices.
The looms are divided into the following according to shed formation: cam loom, dobby loom, and Jacquard loom, and according to weft insertion: projectile, rapier (rigid and flexible), air jet, and water jet looms. Besides single-phase weft insertion (one weaving cycle is one weft thread), there are multiphase weaving looms. The weaving machine being developed in this direction is serial shed weaving machine which inserts 4 weft threads at each moment over the fabric width, and its productivity amounts to 6.000 m of weft/min. The other devices are weft patterning devices, warp and weft stop motions, weft accumulators, control devices, etc. [13].
Electronic Jacquard shuttle-less weaving machine
Electronic Jacquard shuttle-less weaving machines have several advantages over looms with weave design. Electronic Jacquard allows better and more productive weaving. Using CAD/CAM systems in weaving shortens the pattern preparation for weaving by several times. The CAD (Computer Aided Design) system processes fabric design parameters and adapts the pattern (figure) for weaving. The CAM (Computer Aided Manufacturing) system for weaving allows controlling the production process of the loom and contains the data required for the process of weaving. CIM (Computer Integrated Manufacturing) for the process control of the whole weaving mill allows complete overview of the production, Internet contact with each loom allowing remote control, service, correction, and repair of the program.
The latest developments in Jacquard weaving are harness-less looms (Fig. 7). The upper part of the Jacquard loom (Jacquard) is “lowered” directly above the bottom machine performing the weaving process. In this way, the machine height has been considerably reduced and no harness is necessary that has to be replaced after a certain time period. These weaving machines have certain drawbacks compared with Jacquard looms with harness, but their development goes on indicating a new, more cost-effective way of weaving fabrics with large patterns.
Electronic Jacquard loom without harness (Grosse’s Unished Jacquard)
Despite the rapid development of the textile industry in the twentieth century, hand weaving has remained in handcraft industry, even in factories for manufacturing carpets having high value and quality. Today, Mexican and Persian carpets made on vertical hand looms are famous for their long life and beauty throughout the world.
The silk fabric, which will be analyzed here as an example, was woven on the Jacquard loom (Table 1, Fig. 8). The ground warp is black and has an even density across the fabric width. The ground warp (black) and the ground weft (black) are woven in plan weave across the whole fabric surface, regardless of whether it is the ground fabric or the pattern. Various weaves and color combinations of the warp and weft compose fabric effects. The weaving machine is equipped with two warp beams: one warp beam is intended for the ground warp, and the other warp beam is intended for the pattern warp.
\n\t\t\t\t | \n\t\t\tGround warp (black) | \n\t\t\t92 | \n\t\t
Pattern warp (in 7 colors) | \n\t\t\t50 | \n\t\t|
\n\t\t\t\t | \n\t\t\tGround weft (black) | \n\t\t\t35 | \n\t\t
Pattern warp (blue and red) | \n\t\t\t36 | \n\t\t|
\n\t\t\t\t | \n\t\t\tGround warp (black) | \n\t\t\t44 | \n\t\t
Pattern warp (in 7 colors) | \n\t\t\t94 | \n\t\t|
\n\t\t\t\t | \n\t\t\tGround weft (black) | \n\t\t\t82 | \n\t\t
Pattern warp (blue and red) | \n\t\t\t242 | \n\t\t
Basic parameters of the tested fabric
Fabric pattern with a multicolored warp (ground: black, pattern: cream-colored, pale gray, gray, dark gray, yellow, brown, and dark brown) and multicolored wefts (ground: black, extra floating weft: blue and red) 1-4 effects created by the pattern
Weave design for 1x1 cm fabric (pronounced square on the fabric pattern,
The first effect or ground fabric is created by the warp (black) and weft (black) in plain weave; a) weave unit; b) cross section in the weft direction
The third effect is created by different colors of warp threads; a) cream-colored, b) pale gray, c) gray, d) dark gray, d) yellow, f) brown, g) dark brown; they interlace with the ground in 5-end warp satin; on the back side, ground warp (black) and ground weft (black) interlace in plain weave
The third effect is created by the red and blue weft threads which interlace with pattern warp threads in 5-end weft satin; on the back side, the ground warp (black) and ground weft (black) interlace in plain weave, in the middle section of the fabric the pattern warp threads interlace with the ground warp threads in 8-end warp satin; a) 5-end weft satin, b) 8-end weft satin; c) plain weave, d) cross section with blue weft on the fabric face, e) cross section with red warp thread on the fabric face
The fourth effect is created by different colors of the warp threads: a) cream-colored, b) pale gray, c) gray, d) dark gray, e) yellow, f) brown, g) dark brown interlace with the ground weft in 8-end warp satin; on the fabric face, the ground warp (black) and ground weft (black) interlace in plain weave
Marked effects on the fabric are only those created by different weaves. Effects by the colors of warp and weft threads are not specifically identified, but they are visible on the fabric and in the weave development. In addition to the ground weft, two extra floating wefts (lance) (red and blue) creating the pattern with the pattern warp run in the weft direction. The pattern warp and lance weft threads are on the back side of the fabric and do not interlace in the ground fabric part. The fabric was woven on the Jacquard weaving machine, and the pattern does not include the weave unit. The weave is drawn on the appropriate graph paper which corresponds to the ratio of warp-to-weft density. Since the weave unit is relatively large, the total number of warp and weft threads is not covered by the analysis of drawing the weave. That is why the weave of only one fabric part has been drawn, covering all the effects. It is important that in the transition from one into another effect there is opposing thread (warp or weft) float. In that case, a certain displacement of interlacing points in favor opposing thread float is allowed which was done in the figure representing the weave development (Fig. 9).
The looms, which were used to weave pattern fabrics before Jacquard weaving, had to be adjusted for this kind of interlacement which required a specific skill and knowledge of the weaver. In transition areas of effects the weave is disrupted, meaning that the weft interlacing point is applied in one effect opposite the warp interlacing point in the other effect and vice versa in order to point out the boundary between two effects. In hand weaving, the weaver had to pull weft threads past the shed in transition areas of effects in order to create opposing thread float. Pulling the weft thread through in these areas was difficult and resulted in faults in the weave, breaks of warp threads and longer weaving. This problem disappeared when Jacquard looms were introduced because cards were punched according to the prepared weaving pattern where opposing thread float had been drawn. In this way, the shed was opened for each warp with opposing thread float in areas between effects, resulting in a great increase of productivity and fabric quality. Punching cards for each warp thread allowed forming one shed for all effects and weft colors to be inserted into the corresponding shed. Thus, it was unnecessary that the other weaver raised warp threads according to the effects because the card was punched for all warp threads over the whole fabric width or all effects across the fabric width at this moment.
Each effect is explained in detail below:
The first effect is the ground part of the fabric, where the ground warp (black) and the ground weft (black) are interlaced in plain weave (Fig. 10).
The pattern warp in different colors (cream-colored, pale gray, gray, dark gray, yellow, brown, and dark brown) and the ground weft makes the second effect (black). The weave on the fabric face (pattern warp and ground weft) is 5-end warp satin (A 4/1 with a leap to the right), and plain weave on the backside (ground black warp and ground black weft) (Fig. 11).
The third effect is made by the lance weft (black or red) and the pattern warp in different colors (cream-colored, pale gray, gray, dark gray, yellow, brown, or dark brown) which interlace in weft 5-end satin (A ¼ with a leap 3 to the right), and the weft is visible on the fabric face. In one fabric segment the red weft and the blue weft interchange, and the logic rule of their interchange in the pattern is not visible. In the second effect, the ground weft and the pattern warp in different colors interlace in 8-end warp satin (A 7/1 with a leap 3 to the right) which is the middle layer of the fabric, while the ground warp and the ground weft interlace in plain weave on the backside of the fabric (Fig. 12).
The fourth effect is made by the pattern warps in different colors (cream-colored, pale gray, gray, dark gray, yellow, brown, and dark brown) and the ground weft (black) in 8-end warp satin (A 7/1 with a leap 3 to the right) (Fig. 13).
The historical development of weaving dates back before the Neolithic period. Interweaving brushwood in building dwellings is the forerunner of interlacing warp and weft threads in fabric making. At first, a coarse textile fabric with low-density simple weaves was made; by increasing yarn fineness and improving weaving procedures, the fabric became finer and more comfortable. Gradually, weaving skills and techniques kept in secrecy in the narrower community were developed. Larger and more complex patterns were introduced. Weaving looms changed throughout history and were adapted to types of fabrics woven on the loom. The manufacture of fabrics with larger and more complex patterns, before Jacquard looms had been constructed, was performed on more complex designed Jacquard looms operated by two weavers according to a drawing or a picture. Productivity was very low regardless of the skill and training of weavers. Yarn preparation (spinning, dyeing, warping, drawing in) required an operator\'s knowledge and skills. With the advent of the Jacquard loom in the early nineteenth century, productivity rose several times as well as the quality of weaving; one weaver operated the loom.
The principle of the shed formation using a card with punched and unpunched holes did not change for decades. Only the Jacquard gauge changed and the possibility of weaving with several differently interlacing warp threads increased. With the advent of shuttle weft insertion power looms productivity increased several times. Machine operation was simpler, because of the automatic pirn change in the shuttle, one weaver could operate several looms, and the fabric became better and cheaper. The electronic Jacquard loom with CAD/CAM weaving system increased the pattern preparation for weaving as well as the possibility of making larger patterns or greater weave units. Moreover, the consumption of cards was eliminated as well as trial weaving, and greater faults in weaving were not possible.
In order to analyze the pattern of the fabric, it is necessary to define the face of the fabric, the warp and weft direction, and the size of the weaving unit if the fabric pattern involves the weave unit. The analysis of the large pattern fabric involves analyzing the fabric construction parameters for each design or the development of each fabric effect. Due to the complexity of making the weave in the pattern, the analysis is often conducted only on a small area of fabric (1×1cm), preferably that it encompasses all designs or analyzes each design separately. The implementation of such an analysis is important because in order to prepare a certain amount of yarn by color, warp length, and the method of winding on one or two warp beams. Besides, it is important to define the density, contraction, and yarn fineness. According to warp thread density, fabric width, and arrangement of colors in the fabric width it is possible to calculate the total number of warp threads as well as the number of threads by colors. According to the weaves per designs, it is necessary to draw in warp threads into the harness according to the weave, design program, and lowering warp threads (manual pulling the hooks before the Jacquard, punching the cards after the Jacquard or pattern preparation using the CAD/CAM weaving system). In order for the fabric pattern to have precise contours (or boundaries) between individual designs, it is necessary to subsequently move the warp and weft interlacing points (to achieve opposing thread float as much as possible) on the edges of the design.
Every year, approximately 338,000 individuals are diagnosed with kidney cancer globally, representing about 2% of all cancers [1]. Renal cell carcinoma (RCC) accounts for approximately 90% of all kidney cancers—affecting an estimated 300,000 people each year [2, 3]. Approximately 30% of kidney cancer patients represent an advanced disease stage at diagnosis, with an average 5-year survival rate of approximately 16% [4, 5].
The management of RCC, regardless of its histological subtype or stage, involves surgical resection of the tumor through either a radical or partial nephrectomy [6]. While surgery is not curative in cases involving metastatic disease, with localized RCC, surgical intervention is considered the optimal standard of care [6, 7].
But despite that, postsurgical recurrence of cancer is a prevalent issue in cases of localized RCC (stage 2 or 3 disease) with a 5-year relapse rate of 30–40% and, as such, surgery is insufficient for long-term disease free survival [8, 9]. Hence, even though the current standard for postoperative care continues to be radiographic surveillance, the need for effective adjuvant therapy for localized high risk for recurrence RCC would be helpful and desired by the surgical community [8, 9, 10].
In view of these findings and the effective treatment of metastatic RCC with Immunotherapy in the 1990s or more recently with targeted therapy, a strong rationale for systemic adjuvant therapy exists in high risk for recurrence patients.
In this chapter, we review different treatment modalities have been used as an adjuvant therapy for nonmetastatic renal cancer postsurgical resection with emphasis on targeted therapy as becoming an option to offer patients.
A critical element in both the testing and effective clinical use of adjuvant therapy involves determining whether there is a high risk of disease recurrence post nephrectomy and accordingly identifying patients that are most likely to benefit from the therapy. As discussed earlier, the determination of recurrence risk is currently nonstandardized in adjuvant therapy testing. Several models and clinical nomograms have been developed to predict the risk of disease recurrence and progression, as well as evaluate additional oncological endpoints [11, 12, 13, 14, 15, 16, 17, 18, 19]. Examples of some validated models include the Cindolo Recurrence Risk Formula, Leibovich scoring system, Karakiewicz scoring system, Kattan nomogram, Mayo Clinic stage, size, grade, and necrosis scoring system (SSIGN), and the University of California Los Angeles Integrated Staging System (UISS) [11, 12, 13, 14, 15, 16, 17, 18, 19] (Tables 1 and 2). These systems usually incorporate information regarding different variables and various prognostic signs and indictors such as tumor size, stage and characteristics, clinical risk factors, and various other pathological features and signs for a relatively robust evaluation [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]. Among these models, the UISS, Kattan and SSIGN nomograms have shown relatively better discriminative accuracy in some comparative studies and hence are most commonly utilized [13, 22, 23].
Characteristics | Points | |
---|---|---|
Tumor | pT1a | 0 |
pT1b | 2 | |
pT2 | 3 | |
pT3-pT4 | 4 | |
Dimension | <10 cm | 0 |
>10 cm | 1 | |
Fuhrman | 1–2 | 0 |
3 | 1 | |
4 | 3 | |
Tumor necrosis | Absent | 0 |
Present | 1 | |
Lymph nodes | pNx/pN0 | 0 |
pN1-pN2 | 2 |
Leibovich prognosis score.
T stage | 1 | 2 | 3 | 4 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Furman grade | 1–2 | 3–4 | 1–4 | 1 | 2–4 | 1–4 | ||||
ECOG PS | 0 | >1 | any | >1 | any | 0 | >1 | 0 | >1 | any |
Risk group | Low | Intermediate | High |
UISS prognosis score.
In terms of a general approximation, recurrence risk can be segregated into three broad categories based on the UISS nomogram: low, intermediate and high risk [18]. These three risk groups are differentiated based on the probability of survival and disease recurrence and patients, in a clinical setting, can be stratified through an independent clinical assessment of UISS components, such as tumor stage, grade, and other pathophysiological characteristics [18, 19]. While the UISS components have not been formally validated as independent recurrence risk prediction models, they are important prognostic indicators for various oncological outcomes and endpoints that are invariably linked with the risk of disease relapse [18, 19]. As such, an evaluation of tumor characteristics—particularly tumor stage—can serve as a rough guide for preliminary differentiation between high, intermediate and low risk categories in the clinical setting [24, 25, 26, 27]. This correlation has been supported by independent studies which have reported higher recurrence free survival (RFS) rates for smaller, T1a-T1b stage tumors and lower RFS rates for larger, T3-T4 stage tumors [24, 25, 26, 27]. Thus, patients with T1a-T2a tumors can be estimated to have lower recurrence risk while those with T3b-T4 tumors can be placed into the high-risk category [24, 25, 26, 27]. Among these varying risk levels, currently only those who present a high risk of disease recurrence can potentially benefit from adjuvant therapy postsurgical resection of the tumor.
The incorporation of biotechnology and an improved understanding of genetic and molecular markers may potentially lead to the next major advancement in improving the predictive accuracy of relapse risk. Recent studies have reported the development of novel gene assays and have further elucidated several new biomarkers [28, 29, 30, 31]. Nonetheless, further investigation, testing and development is required before molecular approaches can be incorporated for clinical application in an efficient and economically viable manner.
Two trials that used adjuvant IFN-α [32, 33] and one study that used adjuvant high-dose IL-2 (82) were negative for any benefit. The latter study was designed and powered to show an improvement in predicted 2-year DFS from 40% for the observation group to 70% for the treatment group. Despite full accrual 30% improvement in 2-year DFS could not be achieved which lead to early study closure.
Combination treatment with IFN-α and IL2 also failed to improve DFS in one trial [34].
The combination of cytokines with 5-fluorouracil (5-FU) also failed to improve DFS in the adjuvant setting [35, 36].
In one randomized adjuvant trial, triple combination therapy using IL-2, IFN-α, and5-FU was associated with significant toxicity which leads to 35% of the patients did not complete the study and also resulted in no benefit in DFS or OS [37].
Autologous irradiated tumor cells mixed with bacillus Calmette-Guérin (BCG) were tested in two randomized trials and did not result in prolonged DFS [38, 39, 40].
Similarly, autologous, tumor-derived heat-shock protein (glycoprotein 96)-peptide complex (HSPPC-96; vitespen) did not result in a statistically significant improvement of DFS [41].
A trial with an autologous renal tumor cell vaccine only reported improved DFS in the vaccine group [42], but the number of patients lost after the randomization step, the imbalance of this loss, and the absence of tabulation of OS led to criticism of the results [43].
This therapy has not been implemented in routine clinical practice.
The occasional response of patients with metastatic RCC to hormonal therapy with medroxyprogestrone acetate (MPA) provided a rationale in trying it in adjuvant sitting.
In a prospective randomized trial of adjuvant MPA after radical nephrectomy, 136 patients received either MPA 500 g (three times a week) for 1 year or observation. With a median follow up of 5 years. There were no significant differences in relapses between the adjuvant group and the observation group (32.7 vs. 33.9%, respectively) [44].
Radiotherapy has been used for symptoms palliation in metastatic RCC like hematuria and painful bone metastasis. Also, long-term PFS has been reported for in a subset of patients following radiotherapy for solitary bone metastases [31].
One prospective, randomized study in 72 patients comparing administration of radiation of the kidney bed, and ipsilateral and contralateral lymph nodes for stages II and III RCC versus observation reported relapse rates of 48% in both groups. Forty-four percent of patients in the radiotherapy arm had significant complications that contributed to the death of 19% of patients [45, 46, 47].
Systemic therapy for mRCC has particularly changed over the last decade with the introduction of targeted therapy and the evolvement of tyrosine kinase inhibitors (TKI) [7, 49, 50, 51, 52, 53]. This development has directly resulted from an improved understanding of the pathogenesis and molecular biology of RCC [49, 50, 51, 52, 53, 54]. TKIs have provided a novel therapeutic approach for better managing the pathology through the inhibition of targets such as the mammalian target of rapamycin (mTOR) pathway and the vascular endothelial growth factor receptor (VEGFR), which consequently help inhibit processes that are critical for cancer progression [7, 49, 50, 51, 52, 53]. Particularly in cases of metastatic RCC, these inhibitors have been effective in increasing the overall survival and response rates than previously used immunotherapy and chemotherapy agents [7, 49, 50, 51, 52, 53].
Seven drugs are now approved for targeted therapy, and several others are being evaluated in clinical trials [50, 51, 52, 53, 55]. At the molecular level, the mechanism of these drugs involves interrupting the molecular signal transduction of various signaling pathways which then ultimately affects pathogenic factors like tumor vascularity, growth and progression [50, 51, 52, 53, 55]. Sunitinib and Pazopanib are currently the accepted standard of care for the management of metastatic RCC and are the most widely used first line agent due to their robust clinical efficacy and established toxicity profile [50, 51, 52, 53, 55]. The current set of therapeutic agents used in targeted therapy exploit the Von Hippel-Lindau (VHL) and hypoxia-inducible factor (HIF) pathway associated with clear cell RCC pathogenesis [56, 57].
Clear Cell RCC (ccRCC) normally entails a biallelic inactivation of the VHL tumor suppressor gene at the 3p25-26 locus. VHL inactivation, which occurs due to factors such as mutation, hyper-methylation, or deletions, results in the formation of defective pVHL protein—ultimately leading to the activation and upregulation of HIF-1α [56, 57]. Activated HIF protein serves as a transcription factor for various pro-tumorigenic target genes such as vascular endothelial growth factor (VEGF), transforming growth factor-α and platelet-derived growth factor (PDGF) that are involved in pathogenic processes like angiogenesis, tumor cell proliferation and cell survival [56, 57]. Apart from this central pathway, the mTOR pathway also intersects with HIF pathway upstream of the VHL gene and hence also plays a critical role in influencing HIF process and function [56, 57]. Thus, inhibiting different targets in this pathway has yielded favorable results in mRCC cases [50, 51, 52, 53, 55, 56, 57]. Given the success of targeted therapy agents in the metastatic setting, recent efforts have been focused into translating this into the adjuvant setting.
The contemporary endeavors to transpose targeted therapy in the adjuvant setting have been inspired by the increased clinical knowledge gained through the development and evaluation of interventions for stage IV disease [9, 10, 58, 59]. There are currently seven multicenter, double-blind, placebo-controlled, randomized adjuvant clinical trials, involving targeted therapy agents [9, 10, 58, 59]. Five of these trials involve tyrosine kinase inhibitors, while one involves an mTOR inhibitor and the other a monoclonal chimeric antibody [9, 10, 58, 59, 60, 61, 62, 63]. So far, four of these trials have been completed including the, ARISER, ASSURE, S-TRAC and PROTECT trials while the other ones are still in progress [60, 61, 62, 63].
This ARISER trial, completed in 2014, evaluated the efficacy of girentuximab [60], a monoclonal antibody to carbonic anhydrase IX (a HIF downstream target gene), in the adjuvant setting for intermediate to high risk for recurrence patients. This multicenter, phase III trial involved 864 patients with resected clear cell tumors, who were randomized to receive either girentuximab or placebo, once a week, for 24 weeks. Girentuximab recipients received a 50 mg dose during the first week followed by a weekly dose of 20 mg for the next 23 weeks. The median disease free survival (DFS) duration for the participants in the intervention arm was 71.4 months (HR: 0.97; 95% CI, 0.79–1.18) while the endpoint was never reached for the placebo group. As such, the study indicated no interventional advantage but it recommended further investigation of adjuvant girentuximab in patients with high levels of CAIX in affected renal tissue.
The ASSURE trial, completed in 2016, was a randomized, double-blind, placebo-controlled, phase 3 clinical trial in which 1943 patients from 226 study centers in North America were assigned to one of three intervention arms—sunitinib, sorafenib or placebo in intermediate to high-risk patients [61]. Sunitinib patients received 50 mg for 54 weeks on a 4 of 6 week cycle; sorafenib recipients received 400 mg twice per day throughout each cycle, and placebo recipients were randomly assigned either the sunitinib placebo or the sorafenib placebo. The interventions were evaluated using DFS as the primary endpoint. Trial results indicated that the median DFS duration was approximately 5.8 years for sunitinib [HR: 1.02; 97.5% CI: 0.85–1.23; P = 0.8038], 6.1 years for sorafenib (HR: 0.97; 97.5% CI: 0.80–1.17; P = 0.7184), and 6.6 years for placebo—hence suggesting no survival benefit from the interventions relative to the placebo. Instead, the results further reported detrimental effects due to the increased toxicity of the treatment despite the dose reductions—suggesting no benefit of the particular TKI in the adjuvant setting. Of note, this trial had a higher number of TKI dose reductions (potentially suggesting suboptimal drug dosing) and more intermediate risk for recurrence patients than other trials.
The S-TRAC study, also completed in 2016, was a prospective, randomized, double-blind, phase 3 clinical trial involving 615 patients from 21 countries [62]. Of the 615 patients who underwent randomization, 309 were assigned to the sunitinib arm and 306 to the placebo arm. These patients were all “high risk of recurrence.” Sunitinib recipients received 50 mg for a year on a 4 of 6 week cycle. The interventions were evaluated by comparing DFS, the primary endpoint of the study, between the two trial arms. The study results indicated that the median DFS duration was 6.8 years (95% CI: 5.8 to not reached) in the sunitinib group and 5.6 years (95% CI: 3.8–6.6) in the placebo group (HR: 0.76; 95% CI: 0.59–0.98; P = 0.03). The adverse effects observed in sunitinib recipients were consistent with its known toxicity profile. As such, the results from this trial support the potential for sunitinib as a treatment option in the adjuvant setting with a DFS advantage. However, overall survival endpoints have not yet been reported.
The PROTECT study, completed recently in 2017, was a phase 3 randomized clinical trial that evaluated the efficacy of adjuvant pazopanib as compared to placebo in preventing RCC recurrence in intermediate to high-risk patients [63]. The trial enrolled 1538 participants and the majority of the pazopanib recipients received a revised dosage of 600 mg, daily for a year, following a dose reduction from 800 mg which caused severe side effects. The interventions were evaluated by comparing DFS as the primary endpoint measure between the two trial arms. The study did not meet its primary endpoint and indicated no significant benefit of pazopanib-600 mg in prolonging DFS as compared to placebo (HR: 0.86; 95% CI, 0.70–1.06; P = 0.165). However, a subgroup analysis of pazopanib-800 mg recipients indicated a 31% decline in DFS (HR, 0.69; 95% CI, 0.51–0.94;
The differing outcomes that have been indicated in the current set of completed trials may be accounted for by the distinct sample groups, dose regimens, risk assessment criteria and trial methods [60, 61, 62, 63]. This collectively represents a fundamental limitation that underscores all current adjuvant clinical trials. First, the patient inclusion criteria characteristically differ, in multiple ways, across all adjuvant trials [60, 61, 62, 63]. For example, in the S-TRAC trial, the selected sample exclusively included patients with late-stage, loco-regional, clear-cell RCC while other trials such as the ASSURE, ARTISER and PROTECT trials used a less restricted criteria and included patients with stage 1 or stage 2 tumors and non-clear-cell histologies [60, 61, 62, 63]. In addition, another major cause of heterogeneity lies in the risk assessment and stratification criteria as the scoring system used in the current set of adjuvant trials are not standardized, and hence this invariably contributes to a varied assessment of recurrence risk [60, 61, 62, 63]. With respect to the conflicting sunitinib trials (S-TRAC vs. ASSURE), additional sources of variation that might have led to inconsistent outcomes include varying dose regimens, specifically with respect to the midtrial dose reductions for sunitinib, as well as differing trial criteria for establishing disease status and assessing primary end point status [61, 62, 64].
The development of therapy that targets oncogenic signaling pathways has advanced the treatment landscape for patients with advanced renal cell carcinoma. While nonspecific immunotherapy with IL-2 and IFN-α was the former mainstay in the management of metastatic disease, there was a shift away from it with the advent of targeted therapy which yielded relatively better response rates [32, 33, 34, 48, 49, 50, 51, 52, 53, 54, 65, 66, 67, 68]. However, over the last couple of years, cancer immunotherapy has been revisited and, as a result, targeted immunomodulatory therapy, involving novel immunomodulating agents, has been reincorporated in combination therapy regimes for mRCC management—hence allowing for an induced immuonologic effect in addition to the inhibitory effect on tumor biology and microenvironment [69, 70]. This has been inspired in part by disease resistance that is progressively manifesting itself against standard targeted therapy in the landscape of metastatic disease management [69, 70].
Given that multiple mechanisms are employed by tumors to evade and suppress the immune system, research toward better understanding those mechanisms of immunomodulation has been critical in informing the therapeutic landscape [69, 71]. Particularly, an improved understanding of the factors regulating the antitumor immune response has led to the development a novel form of cancer immunotherapy involving checkpoint inhibitors and other immune therapies such as T-cell agonists, adoptive T-cell therapies and novel vaccines which are being evaluated across different trials for metastatic RCC [69, 71].
Immune checkpoints serve a critical protective function of preventing immune response against host cells through a series of complex interactions [71, 72, 73]. However, investigation into the pathogenic mechanisms of RCC revealed that cancer cells can induce similar interactions with host checkpoint receptors and can hence suppress the human immune response [71, 72, 73]. Immune checkpoint inhibitors counter these molecular mechanisms through which tumor cells evade immune recognition [71, 72, 73].
Programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) are currently the most well understood inhibitory checkpoint receptors [71, 72, 73]. The PD-1/PD-L1 axis involves an inhibitory interaction between a T-cell inhibitory ligand PD-L1, expressed on tumor cell surface, and a PD-1 receptor on the lymphocyte [71, 72, 73]. Hence, mimicking this interaction ultimately allows tumor cells to evade the adaptive immune response through suppression of T-cell function. The CTLA-4 pathway is similarly exploited by tumor cells [71, 72, 73]. During an adaptive immune response, immune activation occurs through an interaction between the T-cell receptor (TCR) and the antigen-presenting cell (APC) along with the co-stimulation of CD28 on the T cell [71, 72, 73]. This activation is negatively regulated by an inhibitory interaction between CTLA-4 and its ligands—CD80 or CD86 [71, 72, 73]. Thus, the targeted inhibition of these checkpoint receptors through targeted antibodies, in both the pathways mentioned above, could allow for T-cell activation and effective immune function [71, 72, 73].
The first checkpoint inhibitor which demonstrated a survival benefit in patients with metastatic RCC was nivolumab—an anti-PD1 monoclonal antibody [74]. The inhibitor, which received FDA approval in 2015 based on the results from a trial evaluating nivolumab versus everolimus, is effective in yielding positive response rates when used for treatment of advanced RCC in patients who have undergone prior anti-angiogenic therapy [74]. Apart from nivolumab, multiple other checkpoint inhibitors are being currently evaluated in different trials against advanced RCC [71, 72, 73].
Given their recent development, many immune checkpoint inhibitors are still being evaluated for their efficacy and toxicity against metastatic RCC, and hence investigation of these inhibitors in the adjuvant setting has been limited. Currently, there are a few ongoing clinical trials that are evaluating different checkpoint inhibitors in both the adjuvant setting as well as the neo-adjuvant (presurgery) setting (Table 3) [75, 76, 77, 78].
Adjuvant clinical trials in RCC using immune therapies | |||||
---|---|---|---|---|---|
Authors | Intervention | Patient population | Design | No. of patients | Outcome |
Pizzocarro et al. [32] | IFN-α2b vs. placebo | Robson stages II and III (T3aN0M0 and T3bN0M0 or T2/3N1-3M0) | Multicenter, randomized, controlled trial | 247 | 5-year OS: 0.665 (control) vs. 0.660 (treatment) (HR 1.040; 95% CI, 0.671–1.613, P ¼ _0.861) 5-year DFS: 0.671 (control) vs. 0.567 (treatment) (HR 1.412; 95% CI, 0.927–2.149, P ¼ _0.107) |
Passalacqua et al. [34] | IL-2 and IFN-α _v observation | pT1, T2, T3 a-b-c; pN0-pN3,M0M | Multicenter, randomized, controlled trial | 310 | 5-year DFS: 0.73 (control) vs. 0.73 (treatment) 10-year DFS: 0.60 (control) vs. 0.73 (treatment) (HR 0.84; 95% CI, 0.54–1.33, P ¼ _0.47) |
Clark et al. [48] | IL-2 vs. observation | T3b-4 or N1-3 (LA) or M1 | Multicenter, randomized, controlled trial | 69 total; 44 LA, 25M1 disease | 2-year DFS: 48% (control in LA patients) vs. 53% (treatment in LA patients) (P ¼ _0.73) 2-year OS: 77% (control in LA patients) vs. 86% |
Messing et al. [33] | IFN-α-NL vs. observation | pT3–4a and/or node-positive | Multicenter, randomized, controlled trial | 283 | At 10.4 years median follow-up: Median survival: 7.4 years (control) vs. 5.1 years (treatment) (P ¼ _0.09). DFS: 3.0 years (control) vs. 2.2 years (treatment) (P ¼ _0.33) |
Atzpodien et al. [35] | IL-2 and IFN-α2a and intravenous 5 vs. fluorouracil | pT3b/c pN0 or pT4pN0), pN, complete resection of tumor relapse or solitary metastasis (R0) | Multicenter, randomized, controlled trial | 203 | At median follow-up of 4.3 years: 2-year OS: 91% (control) vs. 81% (treatment) 5-year OS: 76% (control) vs. 58% (treatment) 8-year OS: 66% (control) vs. 58% (treatment) (P ¼ _0.0278) 2-year DFS: 62% (control) vs. 54% (treatment) 5-year DFS: 49% (control) vs. 42% (treatment) 8-year DFS: 49% (control) vs. 39% (treatment) (P ¼ _0.2398) |
Aitchison et al. [36] | IL-2 and IFN-α2a and intravenous 5-fluorouracil | T3b-c,T4 or any pT and pN1 or pN2 or positive microscopic margins or microscopic vascular invasion | Multicenter, randomized, controlled trial | 309 | 3-year DFS: 50% (control) vs. 60% (treatment) (HR 0.87; 95% CI, 0.63–1.20) 5-year OS: 60% (control) vs. 68% (treatment) (HR 0.91; 95% CI, 0.60–1.38) |
Galligioni et al. [39] | Autologous irradiated tumor cells and BCG vs. observation | Stages I, II, and III | Prospective, randomized, controlled trial | 120 | At 61 months median follow-up: 5-year OS: 78% (control) vs. 69% (treatment) 5-year DFS: 72% (control) vs. 63% (treatment) |
Adler et al. [40] | Autologous irradiated tumor cells & BCG & hormone vs. hormone | All stages | Prospective, randomized, controlled trial | 43 | Trend for prolongation of DFS for stage I, II, and III (P o.1) |
Wood et al. [41] | Autologous, tumor-derived heat-shock protein (glycoprotein 96)-peptide complex (HSPPC-96; vitespen) vs. observation | cT1b–T4 N0 M0, or cTanyN1-2M0Multicenter | Multicenter, randomized, controlled trial | 819 | At 1.9 years median follow-up: recurrence: 39.8% (control) vs. 37.7% (treatment) (HR 0.923; 95% CI, 0.729–1.169, P ¼ _0.506) OS not mature |
Jocham et al. [42] | Autologous renal tumor cells (Reniale) | pT2–3b pN0–3 | Multicenter, randomized, controlled trial | 558 | At 5-year follow-up: DFS: 67.8% (control) vs. 77.4% (treatment) (P ¼ _0.0204). At 70-month follow-up: DFS: 59.3% (control) vs. 72% (treatment). HR for tumor progression: 1.58 (95% CI 1.05–2.37) and 1.59 (1.07–2.36) (P ¼ _0.0204) |
Adjuvant clinical trials in RCC using immune therapies.
IFN, interferon; IL, interleukin; NL, neutral lymphoblastoid; LA, locally advanced; BCG, bacillus Calmette-Guérin; CI, confidence interval; LA, locally advanced; HR, hazard ratio; M, metastatic; OS, overall survival; DFS, disease-free survival.
The
Trial name. | Trial ID | Intervention | Sample size | Inclusion criteria (histology; stage/grade) | Primary endpoint measure | Completion date |
---|---|---|---|---|---|---|
ARISER | NCT00087022 | Girentuximab | 864 | DFS,OS | 2014 | |
ASSURE | NCT00326898 | Sorafenib or Sunitinib | 1943 | DFS | 2016 | |
S-TRAC | NCT00375674 | Sunitinib | 615 | DFS | 2016 | |
PROTECT | NCT01235962 | Pazopanib | 1500 | DFS | 2017 |
RCC adjuvant clinical trials that have been completed.
Trial name. | Trial ID | Intervention | Sample size | Inclusion criteria (histology; stage/grade) | Primary endpoint measure | Estimated completion date |
---|---|---|---|---|---|---|
SORCE | NCT00492258 | Sorafenib | 1420 | DFS | 2019 | |
ATLAS | NCT01599754 | Axitinib | 592 | DFS | 2019 | |
EVEREST | NCT01120249 | Everolimus | 1218 | DFS | 2021 |
Current set of adjuvant clinical trials that are still in progress.
Trial name | Trial ID | Intervention | Estimated enrollment | Primary endpoint measure | Start date | Completion date |
---|---|---|---|---|---|---|
PROSPER | NCT03055013 | Nivolumab (pre-Nx) | 766 | DFS | February 2, 2017 | 2022 |
KEYNOTE-564 | NCT03142334 | Pembrolizumab | 950 | DFS | June 9, 2017 | 2022 |
CheckMate 914 | NCT03138512 | Nivolumab, ipilimumab | 800 | DFS | July 3, 2017 | 2023 |
IMmotion010 | NCT03024996 | Atezolizumab | 664 | DFS | January 3, 2017 | 2024 |
Ongoing adjuvant and neo-adjuvant clinical trials.
The European Association of Urology Renal Cell Cancer Guidelines Panel, which includes patient representatives and clinicians, considered a number of different scenarios to determine what would be required from S-TRAC to change practice. The decision on practice change was taken in the context of the data from ASSURE. Results showed that only 1 out of 15 (6%) of the panel would change their standard of care when considering the DFS and OS closest to S-TRAC (DFS: HR 0.75,
Recently, on November 2017, the FDA approved the use sunitinib for the adjuvant treatment of adult patients at high risk of recurrent renal cell carcinoma following nephrectomy. The approval was based on (S-TRAC) trail.
Targeted therapy has become the current mainstay in the management of metastatic RCC and its success with advanced stage disease has been the driving force behind the increasing number of targeted therapy trials in the adjuvant setting. The emergence of immune checkpoint inhibitors in the last couple of years has further led to important advances in our understanding and management of mRCC. However, many ongoing trials are yet to be completed in both cases and there is ample potential for further investigation—especially with respect to combinational therapy regimes. This includes the combination of TKIs with immune therapies (e.g., NCT01513187: Pazopanib with Interferon Alfa 2-A), combination of TKIs with chemotherapeutics (e.g., NCT00556049: Sunitinib with Gemcitabine), and the combination of anti-VEGF antibodies and mTOR inhibitors (e.g., NCT01399918: bevacizumab and everolimus). All of these treatments may be of interest for future adjuvant trials in RCC if they are found to be effective in stage IV disease. However, they may have more side effects, making them less suitable in particular for adjuvant treatment. Nonetheless, the current information, which has resulted from all the progress in the field, remains incongruent. While the current set of completed adjuvant clinical trials have provided negative or conflicting results (ARISER, PROTECT, S-TRAC vs. ASSURE), there are additional large-scale trials that are still in progress. The existing trial design has several limitations, the key one being the overall lack of standardization seen across various assessment criteria. Future directions include incorporating a genetic recurrence score to evaluate risk of relapse in patients, developing an adequate and an objectively standardized adjuvant trial design, identifying novel biomarkers and evaluating novel drug targets.
That based on results from current trials, the “high risk for recurrence” RCC patient population (T3-T4, grade 3-4) may benefit from adjuvant sunitinib providing DFS advantage but pending OS results. Patients, in this category, interested in adjuvant therapy would benefit from a discussion with an oncologist regarding the potential benefits and risks of adjuvant treatment post kidney cancer surgery. Overall, the landscape of adjuvant treatment in nonmetastatic high-risk RCC is expected to expand and to further develop in the coming years.
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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:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{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:'"Politechnica" University Timişoara',institution:null},{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:{name:"Tecnalia",country:{name:"Spain"}}},{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:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{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:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{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:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{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"}}}]}},subseries:{item:{id:"87",type:"subseries",title:"Economics",keywords:"Globalization, Economic Integration, Growth and Development, International Trade, Environmental Development, Developed Countries, Developing Countries, Technical Innovation, Knowledge Management, Political Economy Analysis, Banking and Financial Markets",scope:"
\r\n\tThe topic on Economics is designed to disseminate knowledge around broad global economic issues. Original submissions will be accepted in English for applied and theoretical articles, case studies and reviews about the specific challenges and opportunities faced by the economies and markets around the world. The authors are encouraged to apply rigorous economic analysis with significant policy implications for developed and developing countries. Examples of subjects of interest will include, but are not limited to globalization, economic integration, growth and development, international trade, environmental development, country specific comparative analysis, technical innovation and knowledge management, political economy analysis, and banking and financial markets.
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Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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