Drug delivery technologies incorporating extracellular matrix and plant extract targeted for management of chronic wounds.
\r\n\tEven though video surveillance systems have been part an integral part of the public and security sectors for decades, there is a significant interest in them outside of those industries. This interest is largely due to increased crime rates and security threats all around the globe, which are driving a continuous growth of the video surveillance market. According to a recent report, the video surveillance market was valued at $29.98 billion in 2016 and is expected to reach a value of $72.19 billion by 2022. This market potential is also propelled by recent advances in Artificial Intelligence and Computer Vision research fields—boosting the intelligence, scalability, and accuracy of intelligent video surveillance solutions.
\r\n\r\n\tThe book's goal is to provide a game-changing and cross-disciplinary forum that brings together experts from academia, industry, and government to advance the frontiers of theories, methods, systems, and applications.
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Since then, he has been working on several research topics regarding artificial intelligence and computer vision. Dr. Mazzeo joined the Italian National Research Council of Italy (CNR) as a researcher\nin 2002. He is currently involved in projects for algorithms for video object tracking, face detection and recognition, facial expression recognition, deep neural networks, and machine learning. He has authored and co-authored 100 publications, including more than fifteen papers published in international journals and book chapters. He has also co-authored five national and international patents. Dr. Mazzeo acts as a reviewer for several international journals and for some book publishers. He has been regularly invited to take part in the scientific committees of national and international conferences.",institutionString:"Italian National Research Council",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"9",title:"Computer and Information Science",slug:"computer-and-information-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"444315",firstName:"Karla",lastName:"Skuliber",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/444315/images/20013_n.jpg",email:"karla@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"8725",title:"Visual Object Tracking with Deep Neural Networks",subtitle:null,isOpenForSubmission:!1,hash:"e0ba384ed4b4e61f042d5147c97ab168",slug:"visual-object-tracking-with-deep-neural-networks",bookSignature:"Pier Luigi Mazzeo, Srinivasan Ramakrishnan and Paolo Spagnolo",coverURL:"https://cdn.intechopen.com/books/images_new/8725.jpg",editedByType:"Edited by",editors:[{id:"17191",title:"Dr.",name:"Pier Luigi",surname:"Mazzeo",slug:"pier-luigi-mazzeo",fullName:"Pier Luigi Mazzeo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10390",title:"Deep Learning Applications",subtitle:null,isOpenForSubmission:!1,hash:"5cc6cd7972551be6cfc4d3c87bf8fb5c",slug:"deep-learning-applications",bookSignature:"Pier Luigi Mazzeo and Paolo Spagnolo",coverURL:"https://cdn.intechopen.com/books/images_new/10390.jpg",editedByType:"Edited by",editors:[{id:"17191",title:"Dr.",name:"Pier Luigi",surname:"Mazzeo",slug:"pier-luigi-mazzeo",fullName:"Pier Luigi Mazzeo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],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:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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:"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:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.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"}}]},chapter:{item:{type:"chapter",id:"77212",title:"Polymeric Biomaterials for Wound Healing Incorporating Plant Extracts and Extracellular Matrix Components",doi:"10.5772/intechopen.98556",slug:"polymeric-biomaterials-for-wound-healing-incorporating-plant-extracts-and-extracellular-matrix-compo",body:'Biomaterials are polymers that are compatible with the body system introduced into the body to correct an anomaly or used for therapeutic purposes. These materials are broadly divided into three classes – synthetic polymers (usually hydrophobic), natural polymers and inorganic polymers [1]. These polymeric materials have found usefulness in various aspects of medicine such as tissue engineering [1], drug delivery [2], gene therapies [3], wound healing etc. Wounds occur when an intact body organ or tissue is compromised. The body immediately sets off several processes to ensure healing. The successful completion of this healing process is dependent on several factors such as immune cells, infection at the wound site, external factors such as drugs and underlying conditions like diabetes, and hypoxia. Wounds can either be classed as acute where the healing period is between 8 to 12 weeks and chronic where healing is delayed beyond 12 weeks [4] as in vascular ulcers, diabetic foot ulcers and pressure ulcers [5].
Wound healing involves four sequential but partially overlapping processes of hemostasis, inflammation, proliferation, and remodeling [3, 5]. Ideally, with proper wound care such as regular cleaning, debridement and change of dressing, the healing process should proceed uninterrupted to completion. However, due to underlying conditions, poor nutrition, possible contamination of wound site and sometimes overactive immune responses, conventional therapy is introduced to control and ensure complete healing. Wound management also involve primary close by suturing, plastering or use of adhesives at first presentation to ensure proper healing [6]. The major objectives of wound care are to prevent infection, ensure proper wound closure and reduce scar formation [7].
Conventional treatment of wounds some of which have been alluded to above include drug therapies for pain, prevention or treatment of infections and wound cleaning. Bandages and closure systems are commonly used to create an enabling environment for healing. Polymeric biomaterials, synthetic or natural are an improvement on conventional wound therapy. These polymeric materials are constructed to ensure moisture and warmth is retained at the wound site while also sealing the wound from infectious agents [4]. Some of the materials are naturally occurring such as hyaluronan, chitosan, alginates. Others include hydrocolloids, polycaprolactone (PCL), polylactide-co-glycolide (PLGA), polyethylene glycol (PEG), polyurethane (PU) etc. A major advantage of biomaterials in wound care is their biocompatibility at the site of application [1]. These materials are also biodegradable; a quality that is particularly needed when the aim is to deliver medication to a wound site. This ensures that the biomaterial will degrade after drug delivery and so does not require surgical removal. Biomaterials are constructed to promote or stimulate the processes of wound healing. For instance, hydrogels can be used to hydrate the wound and serve as barrier to pathogens; curcumin, zinc nanoparticles and antibacterial can also be incorporated to stimulate the healing process [7, 8]. Polyethylene glycol when combined with polymyxin B or alginate has antibacterial activity and promotes wound regeneration respectively [9]. Biomaterials also act as scaffolds for incorporation of growth factors and as skin substitutes using hyaluronan and collagen to mimic the extracellular matrix (ECM) [9].
Injuries or wounds are currently treated via autografting or allografting. However, due to organ rejection by the immune system in some cases and lack of donors, the use of scaffolds has become increasingly popular. These scaffolds used in tissue repair are expected to be biocompatible, biodegradable, easily sterilizable and structurally desirable [10]. They can be cell or drug loaded to enhance healing; however, the constituent materials of the scaffolds can also have innate tissue repair properties. Depending on the desired properties, scaffolds are fabricated using synthetic or natural polymers which come with their unique characteristics.
Some synthetic polymers like polyurethane are used in the fabrication of semi-permeable dressings because of its permeability to moisture and vapor while acting as barrier to bacteria [4]. Fibrous scaffolds made with Poly(lactide-co-glycolide) polymers have been employed in the regeneration of bone tissues, they are also formulated as injectable in situ scaffolds [10]. Polyethylene glycol (PEG) polymers are used as carriers for growth factors i.e., EGF for targeted delivery to the wound site [11] and electrospun scaffolds of polycaprolactone (PCL), a biocompatible and bioresorbable polymer mimics the extracellular matrix (ECM) and therefore suitable for the treatment of acute and chronic wounds [4]. Polyvinyl alcohol and eudragit polymers are also useful additions in tissue engineering.
Natural polymers employed in wound healing include collagen, gelatin, chitosan, and hyaluronic acid. Chitosan is used in burns and wound healing because of its biocompatibility, tissue repair ability and lack of side effects [12, 13]. It serves as a carrier for heavy molecules such as proteins, antigens, and peptides. Ahmad et al. [14] investigated the wound healing properties of mupirocin-loaded chitosan-based hydrogel membrane. The study showed promising reports of good wound healing potentials with controlled release and no skin irritation. Conventional treatment with topical mupirocin ointment requires multiple applications and is less acceptable because of complaints associated with soiling of patient wears. Similarly, an investigative study of high molecular weight chitosan in wound healing showed exceptionally good re-epithelialization and fast wound closure compared to fucidin-ointment treated wounds [13]. Collagen and gelatin nanofibrous scaffolds are fabricated for wound healing and cartilaginous tissue regeneration respectively [15, 16] and nanofibrous scaffolds of hyaluronic acid mimics the ECM essential in controlling cellular function [2, 17].
Extracellular Matrix (ECM) is a structural scaffold that organizes cell adhesion and migration it also controls cellular growth, metabolism, and differentiation signals. It is composed of a wide variety of dynamic macromolecules and their regulatory factors which provide structural aid and physical protection [18]. Novel research has dynamically changed our understanding of the role of the extracellular matrix in tissue regeneration. The extracellular matrix is thought to provide passive structural support for cells however it has now been discovered that the individual or fragmented Extracellular matrix can send signals vital for cell processes during wound healing through integrin reactions coupled with growth factor activation [19]. Studies have shown that the Extracellular Matrix plays an active role in chronic wound healing. In a study by Baek et al. [20], the extracellular matrix was fabricated as a porous sheet matrix derived from human adipose tissue. Its aim was to act not just as a scaffold but a tool to enhance the overall process of wound healing through its components. Application of the extra cellular matrix sheet dressing showed enhanced wound healing rate compared to the control which was foam wound dressing [20]. The extracellular matrix is a broad molecule network made up of protein glycosaminoglycan and glycoconjugate, elastin and collagen. The extracellular matrix is a non-vascular structure that controls a vast number of cellular functions. The extracellular matrix is a complex structural network and undergoes constant restructuring of its network through matrix degrading enzymes [21]. The extra cellular matrix is composed of multiple matrix proteins that make up its main part. Proteins provide structural support to cells and tissues. The proteins that make up the extracellular matrix can be structural or non-structural depending on their roles and responsibilities [22]. In a study by Hui et al. [23], growth factor re-enforced extracellular matrix was prepared, and the wound healing properties were evaluated using a mouse model. It reflected that the extra cellular matrix promotes wound healing in the early stage of adipocyte recruitment. Rapid re-epithelization, enhanced granulation, tissue growth and supported angiogenesis were also observed. Growth factor re-enforced extracellular matrix was used to treat the wounds and total wound healing was observed on day seven of wound healing [23]. To accelerate healing processes and decrease the complication occurrence various agents, growth factors, natural and synthetic antioxidants (coenzyme Q10-CoQ10), are applied. Amajuoyi et al. incorporated natural ECM matrix co-enzyme Q10 and keratin in electrospun keratin/Co Enzyme Q10/Poly vinyl alcohol nanofibrous scaffold [24]. This potential dressing for infected wounds was effective in preventing the proliferation of microorganism. Encapsulation of CoQ10 in nanoliposomes has also been shown to enhance CoQ10 activity by accelerating wound healing process after tooth extraction [24, 25]. A reduction in inflammatory reaction and increase in collagen deposition following surgical procedure, were previously obtained in animals when CoQ10 was applied in a form of ointment resulting. The expression of IL-1β, TNF-α, NF-κB and HO-1, cytokines involved in inflammation and oxidative tissue damage, were significantly suppressed by CoQ10 application for 3 days following surgical procedure [25]. The ECM was shown to be more stimulated to facilitate wound healing when formulated with biomaterials. Table 1 show in details the Drug delivery technologies incorporating Extracellular matrix and Plant extract targeted for management of chronic wounds.
Drug delivery Technology incorporating biomaterials | Plant extract(s) | Extracellular matrix component | Pharmacological action | Ref. |
---|---|---|---|---|
a. Alkyl acrylate polymer | . | Therapeutic properties of green and fermented rooibos extract loaded hydrogels have been established in vivo, with the best wound healing indices shown by the hydrogels containing fermented rooibos extract. This is possibly a result of a shorter inflammatory phase resulting in quicker wound closure and reduced fibrosis. | [26] | |
b. Hyaluronic acid and chitosan | Angiogenic promoting growth factor vascular endothelial growth factor | The hydrogels possessed both antibacterial and angiogenic, suggesting it might have potential as a wound healing therapeutic. The hydrogels that have incorporated hyaluronan have been shown to promote blood clotting and possess antibacterial properties | [27] | |
a. Polycaprolactone (PCL) for skin tissue engineering | — | PCL/ | [28] | |
b. Chitosan nanoparticles and electrospun scaffolds | — | Novel chondrogenic growth factors (Nell-1) | Nell-1 specifically promotes inducing human bone mesemchymal cells | [29] |
a. Epidermal/dermal substitute | Fibroblast | Apligraf® neonatal dermal fibroblasts grown in a matrix that consists of bovine-derived type I collagen with layers of human neonatal epidermal keratinocytes on top that have been exposed to air to promote stratification in order to mimic the stratum corneum hence facilitating chronic wound healing. | [30] | |
b. Allogenic dermal substitutes | Neonatal fibroblasts | TransCyte™ a collagen-coated nylon matrix with an outer silicon film seeded with human neonatal fibroblasts, has been used for both partial and full-thickness burn wounds. Dermagraft™, used both for burns and chronic wounds, consists of a bioresorbable polyglactin scaffold containing human neonatal fibroblasts | [31] | |
a. Silver Nanoparticles | — | Cassia auriculata L.-mediated silver nanoparticles were effective on both incision and excision wound models in Wistar albino rats exhibiting better performance in wound healing process rather than the extract and Povidone Iodine ointment. | [32] | |
b. Dual growth factor-releasing nanoparticle-in-nanofiber system | Vascular endothelial growth factor | Normal full thickness rat skin wound models demonstrated that nanofiber/nanoparticle scaffolds significantly accelerated the wound healing process by promoting angiogenesis, increasing re-epithelialization and controlling granulation tissue formation. | [33] | |
c. Liposomal nanocarriers | Curcumin | — | The antibacterial activity of the Curcumin-liposomal formulation was found to be like silver sulfadiazine cream 1% regarding the inhibition of the bacterial growth. At low dose of curcumin nano-liposomal formulation efficiently improved injuries and infections of burn wounds | [34] |
Drug delivery technologies incorporating extracellular matrix and plant extract targeted for management of chronic wounds.
GAG is a lengthy linear polysaccharide chain. It is a sulphated di-saccharide formed by uronic acid and N-acetyl- glucosamine or N-acetyl -galactosamine. GAG in partnership with proteoglycans control the wound healing process, GAG is involved in the remodeling phase as it supports capillary growth, fibronectin, and collagen formation at the site of the injury so that vascular density of the wound can be restored. GAG also participates in cell to cell and cell to matrix interactions cell proliferation migration and cytokine and growth factor signaling associated with wound healing. GAG chain reflects an impressive structural diversity because of the dynamic biosynthesis that is tightly controlled in biological systems allowing modified GAG to particularly interact with various ligands in a controlled and timely manner [35]. In a study by Amaral et al. [35], Collagen-GAG scaffolds were fabricated with platelet rich protein infused in the pores of its scaffold. The composite scaffold containing collagen, GAG and platelet rich protein was observed to release key growth factors such as, TGFβ, FGF, VEGF and PDGF for vascular regeneration for 14 days. Growth factors released were enough to enhance the proliferation of major cells involved in wound healing. It also increased the angiogenic and vascularization abilities which are key indices for progress in wound healing, conclusively indicating promising results as therapy for wound healing [36].
Collagen is the most common protein in the body. It is highly populated in the extracellular matrix of the connective tissue like the tendon, cartilage, and skin. It is the most abundant structured protein found in the extra cellular matrix. It gives tensile strength and takes part in adhesion and migration. In the extra cellular matrix collagen is aligned as fibrils to allow for support of the structural framework of the tissues. Collagen type I is in all tissues, tendon, and skin. Collagen type II is found in the cartilage and cornea. Collagen type III is found in the walls of blood vessels [18, 19]. In a study by Lei et al. [37], Collagen hydrogel was fabricated for wound dressing. It was shown to enhance the rate and quality of wound healing. It also improved the tensile strength of regenerated tissue and skin at the wound site. In the study the effect of collagen hydrogel dressing on chronic wound healing and capillary regeneration was explored in diabetic Sprague Dawley rat models. Rats treated at the wound site with collagen hydrogel showed faster healing with smaller wound areas by days seven and fourteen compared to the untreated rats [37]. In another study by Morteza et al. [38] bacterial cellulose/collagen hydrogel as wound dressing was compared to collagenase ointment and the control was an untreated wound. Bacterial Cellulose Collagen hydrogel showed better regeneration and tissue repair when applied at the wound site than the collagenase ointment or control. The study concluded that Bacterial Cellulose/Collagen hydrogel serves as a promising biologically active hydrogel dressing for skin regeneration [38, 39].
It is found in the extra cellular matrix spaces of tissues and is responsible for the flexibility and distensibility of tissues. Elastin is responsible for the dermis stretching ability along with fibrillin and fibulin. The study by Kawabata et al. [40], highlighted cutaneous ulcers treated with silk elastin-based hydrogels. It was shown that silks elastin enhanced rapid wound healing in chronic ulcers of diabetic mice. Silk elastin hydrogels showed enhanced epithelialization rate compared to conventional hydrogels in chronic ulcer models. Indicating that elastin hydrogel is a promising material for accelerating the healing of chronic ulcers [40].
Fibronectins exist in two different forms, firstly as plasma that migrates the blood, secondly as cellular protein created by fibroblast. Fibronectin is aligned into a network of fibrils. It is created in the form of a disulphide-bonded dimer that can be broken down. Fibronectin is involved in the development and response to injury. It plays an important role in enhancing and modulating cell functions in the extracellular matrix [18, 19, 40]. In a study by Norris et al. [41] an Acoustic fabrication of Collagen -Fibronectin composite gels were carried out to accelerate microtissue regeneration. The ultrasound-based fabrication altered the collagen fiber structure and arrangement this led to improvement in its bioactivity. The study investigated how the synergistic effect of collagen and fibronectin coupled with the ultrasound effect altered the protein alignment and bioactivity of composite hydrogels. Results from the investigation showed that the fibronectin can be redistributed within three-dimensional hydrogels under the influence of ultrasound to produce composite hydrogels which lead to the improvement of microtissue regeneration. Conclusively ultrasound waves can lead to protein realignment and fibronectin rearrangement which can enhance wound healing. This is a promising and novel tool and provides a less invasive treatment for chronic wounds [12].
Extra cellular matrix also plays an indirect role in the modulation of extra cellular protease production and activation it also modifies growth factor availability and activity for wound healing [42]. In a study by Riis et al. 2020, adipose derived stem cells which have the ability to deposit extracellular matrix are being investigated for novel treatment of chronic wound and enhancement of wound healing. The extracellular matrix eventually forms a scaffold which is composed of collagen I and III and fibronectin, all of which are essential for progress in wound healing processes [13]. PLA-based electrospun fibers loaded with hyaluronic acid-valsartan hydrogels have been shown to be stable and possess proven diabetic wound healing property. This was as a result of the known biomimetic effect of the fibers and increased re-epithelization facilitated by the hydrogels containing angiotensin inhibitors which is facilitated by the presence of hyaluronic acid as the ECM components [43].
Biomaterials such as biomimetic polymers have been utilized as carrier systems for plant extracts utilized in management of chronic wounds. The problems of resistance and environmental degradation associated with irrational use of orthodox medicines have increased interests in natural and safer alternatives when managing chronic of wounds. Chah et al. [44] evaluated the antibacterial and wound healing activities of methanolic extracts of
Curcumin is a known natural polyphenolic compound which is gotten from the rhizome of the natural plant
Utilization of medicinal plants with known wound healing activities such as
Wound healing is a complex and dynamic process of restoring cellular structures and tissue layers in damaged tissues as closely as possible to its normal state. Plant extracts and human extra cellular matrices that have been seen to possess wound healing activities have the capability of facilitating re-epithelization and tissue regeneration which accelerates the wound healing process. Utilization of appropriate biomaterials as carrier systems can enhance the activity of the plant extracts in hastening the inflammatory, proliferative and the remodeling phases of chronic wounds without the inherent problem of antibiotic resistance and hypersensitivity to the very few medications available. Increased utilization of folkloric plant extracts with proven wound healing activities will ensure an increased option and platform for management of Chronic wounds. There still exits inherent challenges in the use of extracellular matrix loaded biomaterials, cellular and extra cellular treatments options which can enable delivery of multiple molecules at the wound site without degradation is required. The cost of these technologies should also be affordable to encourage scale up.
The authors have no conflict of interest.
Convolutional Neural Networks (CNNs) are specially designed to handle data that consists of multiple arrays/matrixes such as an image composed of three matrixes in RGB channels [1]. The key idea behind CNNs is the convolution operation, which is to use multiple small kernels/filters to extract local features by sliding over the same input. Each kernel can output a feature map and all the feature maps are concatenated together, this is also known as a convolutional layer and it is the core component in a CNN. Note that these concatenated maps can be further processed by the next layer. To reduce the computational cost, the pooling operation such as maximum pooling is usually applied on these feature maps. A typical CNN is usually structured as a series of layers, including multiple convolutional layers and a few of fully connected layers. For example, the famous LeNet [2] consists of two convolutional layers and three fully connected layers, and the pooling operation is used after each convolutional layer.
In addition to building a neural network, a loss function is essential to measure the model performance. Therefore, the process of training a CNN model is transformed into an optimization problem, which normally seeks to minimize the value of the loss function over the training data. Specifically, a gradient-descent based algorithm is usually adopted to iteratively optimize the parameters in a CNN.
Figure 1 shows the high-level abstraction of CNNs in this survey. Specifically, we firstly introduce two types of convolution operations in Section 2. Then four methods are summarized for constructing convolutional layers in CNNs in Section 3. In Section 4, we group the current CNN architectures into three types: encoder, encoder-decoder and GANs. Next, we discuss two main types of loss functions in Section 5. In Section 6, we give the advanced applications based on the three types of CNN structures. Finally in Section 7, we conclude this research and give future trends.
High-level abstraction of convolutional neural networks in this survey.
The main reason why CNNs are so successful on a variety of problems is that kernels (also known as filters) with fixed numbers of parameters are adopted to handle spacial data such as images. In particular the weight sharing mechanism can help reduce the number of parameters for low computational cost while remaining the spacial invariance properties. In general, there are mainly two types of convolution operations, including basic convolution and transposed convolution.
As shown on the left in Figure 2, convolution operation essentially is a linear model for the local spacial input. Specifically, it only performs the sum of element-wise dot products between the local input and the kernels (usually including a bias), and output a value after an activation function. Each kernel slides overall spacial locations in the input with a fixed step. The result is that we can get an 1-channel feature map. Note that there are generally many kernels in one convolutional layer, and all of the output feature maps are concatenated together, e.g., if the number of kernels used in this convolutional layer is
While the kernel size of
Normally the size of output feature maps generated from the basic convolution is smaller than the input space (i.e., the dimension of input
Similarly, we can still use dilated kernels in transposed convolution. The main reason why we need transposed convolution is that it is the fundamental idea to construct a decoder network, which is used to map a latent space into an output image, such as the decoders in U-Net [6] and GANs. Specifically, the transposed convolution is widely used in tasks such as model visualization [7], image segmentation [6], image classification [8] and image super-resolution [9].
The core components in CNNs are convolutional layers. In the last section, we have demonstrated two types of convolution operations and they are the main idea to construct convolutional layers. In this part, we summarize the main methods in deep learning for building convolutional layers, including basic convolutional layers, convolutional layers with shortcut connection, convolutional layers with mixed kernels and convolutional capsule layers.
Recall that there are normally
where
While there are many variants related to the activation function, the typical ones which are widely adopted are ReLU
Note that after convolution operation, the width and height of the output feature map
where
It is true that deep neural networks normally can learn better representation from the data than shallow neural networks. However, stacking more layers in a CNN can lead to the problems of vanishing or exploding gradients, which make the networks hard to optimize. A simple and effective way to address this problem is to use shortcut connections, which can help directly transform the information from the previous layer to the current layer in a network.
Note that
So far we have demonstrated that we normally use many convolutional kernels with the same size in one convolutional layer such as
where pool(I) denotes the pooling operation such as max-pooling. Therefore, the size of the output feature map is
However, if we directly add different sizes of kernels in one convolutional layer, the computational cost involved will increase sharply. In the inception module [13, 14], a
In general, pooling operation is essential to reduce the size of output feature maps so that we can obtain high-level abstractions from input by stacking multiple convolutional layers in a CNN. However, the cost is that some information in the feature maps has been abandoned such as conducting max-pooling.
In 2017 [15], Hinton et al. proposed an alluring version of convolutional architectures, which is known as capsule networks, followed by the updated versions in 2018 [16] and 2019 [17]. The convolutional capsule layers in capsule networks are very similar to the traditional convolutional layers. The main difference is that each capsule (i.e., an element in convolutional feature maps) has a weight matrix
Although numerous variants of CNN architectures for solving different tasks are proposed from the deep learning community every year, their essential components and over-all structures are very similar. We group the recent classic network structures into three main types, including encoder, encoder-decoder and GANs.
In 1990, LeCun et al. proposed a seminal network called LeNet [2], which help establish the modern CNN structure. Since then, many new methods and compositions are proposed to handle the difficulties encountered in training deep networks for challenging tasks such as objective detection and recognition in computer vision. Some representative works in recent years are AlexNet [18], ZFNet [7], VGGNet [19], GoogleNet [13], ResNet [11], Inception [14]. As mentioned earlier, new methods for constructing convolutional layers in these networks are proposed, e.g., shortcut connection [11] and mixed kernels [14, 20].
In general, the above-mentioned networks can all be regarded as encoders, in which each input such as an image is encoded into a high-level feature representation, as shown on the left in Figure 4. And this encoded representation can be further used for, such as image classification, object detection etc. In some literatures, an encoder is also called as a feature extractor. Specifically, the basic convolutional layers are the main components for constructing an encoder network, by stacking multiple layers, each layer in the network can learn high-level abstractions from previous layers [1]. More formally, an encoder network can be written as
where
In some specific tasks such as image segmentation [20], our goal is to map an input image to a segmented output image rather than an abstraction. An encoder-decoder structure is specifically designed for solving this type of task. There are many possible ways to implement an encoder-decoder structure, and many variants have also been proposed to improve the drawbacks in the last few years. A naive version of encoder-decoder network which was introduced in [20] can be denoted as
where
As shown in the middle in Figure 4, an encoder-decoder network is still one complete network and we can train it with an end-to-end method. Note that there are generally many convolutional layers in each coder network, which results that it can be challenging to train a deep encoder-decoder network directly. Recall that the shortcut connection is often adopted to address the problems in deep CNNs. Naturally, we can add connections between the encoder and the decoder. An influential network based on this idea is U-Net [6], which is widely applied in many challenging domains such as medical image segmentation. The above two equations can also be rewritten as a composition of two functions.
Specifically, in unsupervised learning, an encoder-decoder network is also well known as autoencoder. And there are many variants of autoencoders proposed in recent years, some famous ones including variational autoencoder [21], denoising variational autoencoder [22] and conditional variational autoencoder [23, 24].
Since generative adversarial networks were firstly proposed by Goodfellow et al. [25] in 2014, this type of architectures for playing two-player minimax game has been most extensively studied. Partly because it is an unsupervised learning method and we can obtain a fancy generator network which can help generate fake examples from a latent space (i.e., a vector with some random noise). On the right in Figure 4 shows the basic structure of GANs, in which a generator network can map some input noise into a fake example and make it look as real as possible and a discriminator network always tries to identify the fake sample from its input. By iteratively training the two players, they can both improve their methods. More formally, we can have
where
As shown in Table 1, numerous variants of GANs architectures can be found in the recently published literatures and we broadly summarize these representative networks according to their published time. Note that the fundamental methods behind these architectures are very similar.
Name | Year | Summary |
---|---|---|
GANs [25] | 2014 | The original version of GANs, where |
Conditional GANs [26] | 2014 | Labels are included in |
Laplacian Pyramid GANs [27] | 2015 | CNNs with the laplacian pyramid method. |
Deep Convolutional GANs [28] | 2015 | Transposed convolutional layers are used to construct |
Bidirectional GANs [29] | 2016 | An extra encoder was adopted based on the traditional GANs. |
Semi-supervised GANs [30] | 2016 | The |
InfoGANs [31] | 2016 | An extra classifier was added into the GANs. |
Energy-based GANs [32] | 2016 | The |
Auxiliary Classifier GANs [33] | 2017 | An auxiliary classifier was used in the |
Progressive GANs [34] | 2017 | Progressive steps are adopted to explain the networks. |
BigGANs [35] | 2018 | A large GANs with self-attention module and hinge loss. |
Self-attention GANs [36] | 2019 | The self-attention mechanism is proposed to build |
Label-noise Robust GANs [37] | 2019 | A noise transition model is included in |
AutoGANs [38] | 2019 | The neural architecture search algorithm is used to obtain |
Your Local GANs [39] | 2020 | A new local sparse attention layer was proposed. |
MSG-GANs [40] | 2020 | There are connections from |
Representative architectures of GANs in recent years.
Before introducing the loss functions, we need to understand that the ultimate goal to train a neural network
where
Note that there are numerous variants of loss functions used in the deep learning literature. However, the fundamental theories behind them are very similar. We group them into two categories, namely Divergence Loss Functions and Margin Loss Functions. And we also introduce six typical and classic loss functions that are commonly used for training neural networks.
Divergence loss functions denote a family of loss functions based on computing the divergences between the predicted results and true labels, mainly including Kullback-Leibler Divergence, Log Loss, Mean Squared Error.
Before introducing the Kullback–Leibler divergence, we need to understand that the fundamental goal of deep learning is to learn a data distribution
where
Specifically,
where
Log loss is widely used in the current deep neural networks due to its simplicity and power. The binary log loss function is defined as
where
When the learning task is multi-class classification, each sample label is normally encoded with the one-hot-encoding format, which can be denoted as
where
We may wonder why the log loss is a reasonable choice. Informally, let
And our goal is to minimize the divergence between
Probably the mean squared error is one of the most familiar loss functions as it is really like the least square loss function. It directly calculates the difference between the predicted result and the true label, which is denoted as
One example which can help us deeply understand the mean squared error is that minimize the mean squared loss of a linear regression model is equivalent to maximum likelihood. In other words, this is a method to optimize the parameters of our model so that the distribution learned by our model is most probable under the observed training data. Therefore, the fundamental goal is still the same as above, which is to make the model distribution and the data distribution as close as possible.
Margin loss functions represent a family of margin maximizing loss functions. The typical functions include Hinge Loss, Contrastive Loss and Triplet Loss. Unlike the divergence loss functions, margin loss functions calculate the relative distances between outputs and they are more flexible in terms of training data.
Hinge loss is well known to train Support Vector Machine classifiers. Specifically, there are two main types of hinge losses. The first type is for each sample with only one correct label, it is denoted as
where
However, in real tasks such as attribute classification, each samples can have multiple correct labels. e.g., a photo posted on Facebook may include a set of hashtags. Therefore, the second type for multiple labels is
where
Contrastive loss is specially designed for measuring the similarity of a pair of training samples. Considering two pairs of samples
where
where
Triplet loss looks similar to the contrastive loss, but it is a measure of the difference between the matched pair and the unmatched pair. Considering three samples
Note that minimize the loss function is equivalent to minimizing the distances of matched pairs and maximizing the distances of unmatched pairs.
One of the most exciting areas in deep learning is that we can apply neural networks to a numerous number of applications that cannot be solved well or be handled by the traditional machine learning method. In this section, we summarize the typical advances that CNNs has achieved based on the three types of CNN structures.
A basic task in machine learning is classification, which is the problem of identifying to which of a list of labels a new sample belongs, such as the well-known CIFAR-10 dataset, in which there are 10 categories of images and the goal is to train a model for correctly classifying an unseen image based on observing the training dataset. In particular, CNNs have made many breakthroughs on large scale image datasets such as the ImageNet challenge [18]. As mentioned in Section 4.1, the classic encoders such as AlexNet [18], ZFNet [7], VGGNet [19], GoogleNet [13], ResNet [11], Inception [14] are regarded as the milestones in the past few years. The successes of these encoders are all based on supervised learning, which means that manual labelling is essential for the dataset such as the ImageNet dataset [42]. Specifically, a labeled dataset is normally divided into training and test dataset (may also include a validation dataset), and our goal is to achieve good performance on the test dataset after training a neural network with the training dataset, and the pre-trained model can be further used for classifying new images that are from the same data distribution space.
Classification can also be treated as a fundamental problem in machine learning, the successes of these encoders on image classification also help establish the foundation for many other applications. Specifically, we can utilize an encoder to extract high-level representation from the low-level input image, and the obtained representation can be further used for many other applications.
In addition to image classification, object detection is also very important in computer vision. Image classification gives us the answer to what a given image is, and object detection is about telling us the specific positions of objects in an image. Specifically, the goal is to train an encoder to output a suitable bounding box and associated class probabilities for each object in a given image. Two typical methods are widely used in the current computer vision, including YOLO [43] and SSD [44]. The core idea of YOLO is that object detection is treated as an regression problem, which means that each image is divided into multiple grids and each grid cell outputs a pre-defined number of bounding boxes, the corresponding confidence for each box and class probabilities [43]. Since the first version of YOLO was proposed, the updated versions have also been proposed. SSD is a more simple method, which utilizes a set of default boxes with different aspect ratios, and each box outputs the shape offsets and the class confidences [44].
The multiple levels of representations learned in the multiple layers of CNNs can also be used for solving the task of human-body pose estimation. Specifically, there are mainly two types of approaches, including regression of body joint coordinates and heat-map for each body part. In 2014, a framework called DeepPose [45] was introduced to learn pose estimation by a deep CNN, in which estimating human-body pose is equivalent to regressing the body joint coordinates. There are also some extension works based on this method, such as a process called iterative error feedback [46], which encompasses both the input and output spaces of CNN for enhancing the performance. In 2014, Tompson et al. [47] propose a hybrid architecture which consists of a CNN and a Markov Random Field, in particular the output of the CNN for an input image is a heat-map. Some recent works based on the heat-map method such as [48], in which a multi-context attention mechanism was proposed to incorporate with CNNs.
The operation of image restoration is to recover a damaged or corrupt image for the clean image such as image denoising and super-resolution. Therefore, a natural way to implement this idea is to utilize a pre-trained encoder-decoder network, where the encoder can map a noise image into a high-level representation, and the decoder can transform the representation into an original image. For example, Mao et al. [49] apply a deep convolutional encoder-decoder network for image restoration, in particular the shortcut connection method is adopted between the encoder and decoder, which has been demonstrated in Section 3.2. And the transposed convolution is used for constructing the decoder network, as mentioned in Section 2.2. Similar work in [50] has also been introduced for image restoration, in which a residual method is used in the network (i.e., in Section 3.2).
The task of image segmentation is to map an input image into a segmented output image. The encoder-decoder networks have been developed dramatically in recent years and achieve a significant impact on computer vision. Specifically, there are mainly two types of tasks including semantic segmentation and instance segmentation. In 2015, Long et al. [20] firstly showed that an end-to-end fully CNN can achieve state-of-art in image segmentation tasks. Similar work has also been introduced in [6] in 2015, in which a U-Net architecture is proposed for medical image segmentation, and the main advance in this architecture is that the shortcut connection method is also used between the encoder and decoder network. Since then, a series of papers based on these two methods have been published. In particular nowadays the U-Net based architectures are widely used for the medical image diagnosis.
One of the exciting applications achieved by CNNs is image captioning, which is to describe the content of an input image with natural language. The basic idea is as follows: Firstly, a pre-trained CNN encoder is used to extract some high-level features from an input image. Secondly, these features are typically fed into an recurrent neural network for generating a sentence. For example, Li et al. [51] proposed a fully convolutional localization network for extracting representation from images and the decoder for generating captions is LSTM. Recently, attention mechanism has been widely used for sequence processing and achieved significant improvements such as machine translation, Huang et al. [52] introduce an encoder-decoder framework, where an attention module is used in the encoder and decoder respectively. Specifically, the encoder is a CNN based network.
Note that speech signals exhibit spectral variations and correlations, CNNs are very suitable to reduce them. Therefore, CNNs can also be utilized for the task of speech processing, such as speech recognition. Sainath1 et al. [53] applied deep CNNs for large vocabulary speech tasks. In [54, 55, 56], the CNNs are used for speech recognition. And the fundamental methods are very similar, both of them use the CNNs to extract features from the raw input, and then these features are fed into an decoder for the specific learning tasks.
The most typical application of GANs is to generate fake examples. Recall that there normally are two dependent networks in GANs, including
Generating fake samples can be regarded as data augmentation, which means that these fake data can be further used to train models. Note that deep learning is also well known as a data-driven approach. In particular most of the advances that deep neural networks achieved are based on supervised learning. Specifically, the current successful neural network models usually consist of millions of parameters. And annotated data is essential to optimize these parameters for guaranteeing the model accuracy when conducting supervised learning. However, manually labeling data is time-consuming and expensive, especially in some specific domains such as medicine. Even more severe is that it can be hard to collect enough data due to the privacy concerns. There are numerous works to utilize GANs for enhancing model performance. E.g., in [57], a semi-supervised framework based on GANs is applied to semantic segmentation in order to address the lack of annotations. [58] is a work of utilizing synthetic medical images for enhancing the performance of liver lesion classification.
Despite the successes of GANs, generating high-resolution, diverse samples is still a challenging task. In [35], they introduce the progressive GANs which can generate high-resolution human faces. Another impressive work to generate realistic photographs is BigGANs [36].
Another interesting application derived from GANs is image translation. While there are many specific applications, we summarize them into three categories, including translation of image to image, translation of text to image and translation of image to super-resolution.
Image editing is regarded as a fundamental problem in computer vision. The emergence of GANs has also brought new chances for this task. In the past few years, GANs have been developed for image editing, such as image inpainting and image matting.
In this research, we have conducted a hierarchically-structured survey of the main components in CNNs from the low level to the high level, namely, convolution operations, convolutional layers, architecture design, loss functions. In addition to introducing the recent advances of these aspects in CNNs, we have also discussed the advanced applications based on the three types of architectures including encoder, encoder-decoder and GANs, from which we can see that CNNs have made numerous breakthroughs and achieved state-of-the-art in computer vision, natural language processing and speech recognition, especially these fantastic results based on GANs.
From the above analyses, we can summarize that the current development tendencies in CNNs mainly focus on designing new architectures and loss functions. Because these two aspects are the core parts when applying CNNs into various types of tasks. On the other hand, the fundamental ideas behind these various applications are very similar, as summarized above.
However, there are still many disadvantages in the current deep learning. The first problem is the requirement of large-scale datasets, in particular constructing a labeled dataset is very time-consuming and expensive such as in the medical domain. Therefore, we need to pay much more attention to semi-supervised learning and unsupervised learning. The second disadvantage is the high computational cost related to training deep CNNs, as the current standard CNN structures become deeper and deeper and they usually consists of millions of parameters. The third issue is that applying CNNs into tasks is not an easy job and it usually requires professional skills and experiences, because training a network involves a lot of hyper-parameters to tune, such as the number of kernels in each layer, the size of kernels, the total number of layers, learning rate etc.
Future work should focus on deep learning theory as the solid theory for supporting the current neural models is lacking. Unlike other machine learning algorithms such as support vector machines that have obvious mathematical logic, it is usually very hard to totally understand why a deep network can achieve such an excellent performance on a task. Therefore, based on the current developments of deep learning, we give three trends on which we need to work in the future: Neural Topologies such as the graph neural networks, Uncertainty Estimation such as Bayesian neural networks and Privacy Preservation.
This work is supported by China Scholarship Council and Data61 from CSIRO, Australia.
The authors declare no conflict of interest.
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",metaTitle:"Prior Publication Policy",metaDescription:"Prior Publication Policy",metaKeywords:null,canonicalURL:"/page/prior-publication-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"A significant number of working papers, early drafts, and similar work in progress are openly shared online between members of the scientific community. It has become common to announce one’s own research on a personal website or a blog to gather comments and suggestions from other researchers. Such works and online postings are, indeed, published in the sense that they are made publicly available. However, this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'A significant number of working papers, early drafts, and similar work in progress are openly shared online between members of the scientific community. It has become common to announce one’s own research on a personal website or a blog to gather comments and suggestions from other researchers. Such works and online postings are, indeed, published in the sense that they are made publicly available. However, this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
\n\nThe significance of Peer Review cannot be overstated when it comes to defining, in our terms, what constitutes a published scientific work. Peer Review is widely considered to be the cornerstone of modern publishing processes and the key value-adding contribution to a scholarly manuscript that a publisher can make.
\n\nOther than the issue of originality, research misconduct is another major issue that all publishers have to address. IntechOpen’s Retraction & Correction Policy and various publication ethics guidelines identify both redundant publication and (self)plagiarism to fall within the definition of research misconduct, thus constituting grounds for rejection or the issue of a Retraction if the work has already been published.
\n\nIn order to facilitate the tracking of a manuscript’s publishing history and its development from its earliest draft to the manuscript submitted, we encourage Authors to disclose any instances of a manuscript’s prior publication, whether it be through a conference presentation, a newspaper article, a working paper publicly available in a repository or a blog post.
\n\nA note to the Academic Editor containing detailed information about a submitted manuscript’s previous public availability is the preferred means of reporting prior publication. This helps us determine if there are any earlier versions of a manuscript that should be disclosed to our readers or if any of those earlier versions should be cited and listed in a manuscript’s references.
\n\nSome basic information about the editorial treatment of different varieties of prior publication is laid out below:
\n\n1. CONFERENCE PAPERS & PRESENTATIONS
\n\nGiven that conference papers and presentations generally pass through some sort of peer or editorial review, we consider them to be published in the accepted scholarly sense, particularly if they are published as a part of conference proceedings.
\n\nAll submitted manuscripts originating from a previously published conference paper must contain at least 50% of new original content to be accepted for review and considered for publication.
\n\nAuthors are required to report any links their manuscript might have with their earlier conference papers and presentations in a note to the Academic Editor, as well as in the manuscript itself. Additionally, Authors should obtain any necessary permissions from the publisher of their conference paper if copyright transfer occurred during the publishing process. Failure to do so may prevent Us from publishing an otherwise worthy work.
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\n\nNewspaper and magazine articles usually do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense. Articles appearing in newspapers and magazines rarely possess the depth and structure characteristic of scholarly articles.
\n\nSubmitted manuscripts stemming from a previous newspaper or magazine article will be accepted for review and considered for publication. However, Authors are strongly advised to report any such publication in an accompanying note to the External Editor.
\n\nAs with the conference papers and presentations, Authors should obtain any necessary permissions from the newspaper or magazine that published the work, and indicate that they have done so in a note to the External Editor.
\n\n3. GREY LITERATURE
\n\nWhite papers, working papers, technical reports and all other forms of papers which fall within the scope of the ‘Luxembourg definition’ of grey literature do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense.
\n\nAlthough such papers are regularly made publicly available via personal websites and institutional repositories, their general purpose is to gather comments and feedback from Authors’ colleagues in order to further improve a manuscript intended for future publication.
\n\nWhen submitting their work, Authors are required to disclose the existence of any publicly available earlier drafts in a note to the Academic Editor. In cases where earlier drafts of the submitted version of the manuscript are publicly available, any overlap between the versions will generally not be considered an instance of self-plagiarism.
\n\n4. SOCIAL MEDIA, BLOG & MESSAGE BOARD POSTINGS
\n\nWe feel that social media, blogs and message boards are generally used with the same intention as grey literature, to formulate ideas for a manuscript and gather early feedback from like-minded researchers in order to improve a particular piece of work before submitting it for publication. Therefore, we do not consider such internet postings to be publication in the scholarly sense.
\n\nNevertheless, Authors are encouraged to disclose the existence of any internet postings in which they outline and describe their research or posted passages of their manuscripts in a note to the Academic Editor. Please note that we will not strictly enforce this request in the same way that we would instructions we consider to be part of our conditions of acceptance for publication. We understand that it may be difficult to keep track of all one’s internet postings in which the researcher´s current work might be mentioned.
\n\nIn cases where there is any overlap between the Author´s submitted manuscript and related internet postings, we will generally not consider it to be an instance of self-plagiarism. This also holds true for any co-Author as well.
\n\nFor more information on this policy please contact permissions@intechopen.com.
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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Usually, SLE symptoms include high fever, hair loss, mouth ulcers, chest pain, swollen lymph nodes, painful and swollen joints, increased fatigue, and appearance of red rash more often on the face. The exact reason of SLE appearance is not really clear. Detection of catalytic Abs (abzymes) was shown to be the earliest indicator of different AI disease development. Some abzymes are cytotoxic and can play a dangerous negative role in the pathogenesis of AI diseases. SLE is characterized by the appearance of abzymes with several different catalytic functions including hydrolysis of peptides and proteins, DNA, RNA, and oligosaccharides. 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