These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
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This collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\n
To celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\n
Initially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\n
These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\n
This collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\n
To celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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This book is a part of a four volume collection (covering material aspects, physical effects, characterization and modeling, and applications) and focuses on the characterization of ferroelectric materials, including structural, electrical and multiphysic aspects, as well as innovative techniques for modeling and predicting the performance of these devices using phenomenological approaches and nonlinear methods. 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After working as a post-doctoral fellow in the Center for Intelligent Material Systems and Structures (CIMSS) in Virginia Tech, Blacksburg, VA, USA in 2009, Dr. Lallart has been hired as an Associate Professor in the Laboratoire de Génie Electrique et Ferroélectricité. 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1. Introduction
First of all, what is multivariate data analysis and why is it useful in waste management?
Methods dealing with only one variable are called univariate methods. Methods dealing with more than one variable at once are called multivariate methods. Using univariate methods natural systems cannot be described satisfactorily. Nature is multivariate. That means that any particular phenomenon studied in detail usually depends on several factors. For example, the weather depends on the variables: wind, air pressure, temperature, dew point and seasonal variations. If these factors are collected every day a multivariate data matrix is generated. For interpretation of such data sets multivariate data analysis is useful. Multivariate data analysis can be used to process information in a meaningful fashion. These methods can afford hidden data structures. On the one hand the elements of measurements often do not contribute to the relevant property and on the other hand hidden phenomena are unwittingly recorded. Multivariate data analysis allows us to handle huge data sets in order to discover such hidden data structures which contributes to a better understanding and easier interpretation. There are many multivariate data analysis techniques available. It depends on the question to be answered which method to choose.
Due to the requirement of representative sampling number of samples and analyses in waste management lead to huge data sets to obtain reliable results. In many cases extensive data sets are generated by the analytical method itself. Spectroscopic or chromatographic methods for instance provide more than 1000 data points for one sample. Evaluation tools can be developed to support interpretation of such analytical methods for practical applications. For specific questions and problems different evaluation tools are necessary. Calculation and interpretation are carried out by the provided evaluation tool.
In this study an overview of multivariate data analysis methods and their application in waste management research and practice is given.
2. Multivariate data analysis in waste management
The main objectives of multivariate data analysis are exploratory data analysis, classification and parameter prediction. Many different multivariate data analysis methods exist in literature. Thus the following list is not exhaustive however subdivided into the mentioned superior categories. It only concentrates on the methods applied in waste management.
Table 1 gives an overview of the existing literature in waste management on multivariate data analysis applied by several authors. It can be summarised that PCA and PLS1 are the most popular multivariate data analysis methods applied in waste management. Details are given in the following sections 2.1 and 2.2. Due to easy traceability of the parameters investigated in the different papers parameter descriptions have been taken as they were mentioned in the original.
In practice there are many software packages available which include different multivariate data analysis methods. Some software tools are: SPSS (www.spss.com\\de\\statistics), Canoco (www.canoco.com), The Unscrambler (www.camo.com) and the Free Software R-project (www.cran.r-project.org).
Pattern recognition
Calibration
Method
PCA
FA
CCA
CA
DA
SIMCA
MLR
PLS1
PLS2
PSR
Chapter
2.1.1
2.1.2
2.1.3
2.1.3
2.2.1
2.2.2
Compost science
[1-23]
[24]
[25]
[1, 4, 22, 24-31]
[3, 9]
[8, 12]
[29, 32, 33]
[2, 6, 8, 19, 21, 23, 34-47]
[8, 21, 48]
[49]
Municipal solid waste
[50-55]
[56]
[17, 53, 57, 58]
Landfill research
[59-72]
[65]
[73, 74]
[72, 75]
[66, 71, 76, 77]
[78]
[79, 80]
[17, 61, 62, 66, 71, 78]
Logistics
[81]
[82]
[82]
[83, 84]
Table 1.
Literature review of different multivariate data analysis methods applied in waste management; PCA – Principal Component Analysis, FA – Factor Analysis, CA – Cluster Analysis, CCA – Canonical Correspondence Analysis, DA – Discriminant Analysis, SIMCA – Soft Independent Modelling of Class Analogy, MLR – Multiple Linear Regression, PLS-R – Partial Least Squares Regression, PSR – Penalised Signal Regression
2.1. Pattern recognition
2.1.1. Exploratory data analysis
Principal Componant Analysis (PCA)
PCA is mathematically defined as an orthogonal linear transformation that arranges the data to a new coordinate system in that the greatest variance by any projection of the data takes place along the first coordinate (called the first principal component), the second greatest variance along the second coordinate, and so on. Theoretically the PCA is the optimum transformation for a given data set in least square terms. That means PCA is used for dimensionality reduction of variables in a data set by retaining those characteristics of the data set that contribute most to its variance. The transformation to the new coordinate system is described by scores (T), loadings (P) and errors (E). In matrix terms, this can be written as X = T * P + E. Fig. 1 illustrates the mathematical transformation using PCA. The matrices can be displayed graphically. The scores matrix illustrates the data structure and the loading matrix displays the influence of the different variables on the data structure.
Figure 1.
Principle of the PCA (according to Esbensen [85])
PCA displays hidden structures of huge data sets. PCA is applied in different fields of waste management to find out the relevant parameters of a large parameter set. So we can see which properties of a sample are significant and important to answer a particular question. Due to the results obtained time and money can be saved in further research activities.
Many applications can be found in compost science. Zbytniewski and Buszewski [1] applied PCA to reveal the significant parameters and possible groupings of chemical parameters, absorption band ratios and NMR data. Campitelli and Ceppi [3] investigated the quality of different composts and vermicomposts. The collected data were evaluated by means of PCA to extract the significant differences between the two compost types. Gil et al. [4] used PCA to show effects of cattle manure compost applied on different soils. Termorshuizen et al. [13] carried out a PCA based on disease suppression data determined by bioassays in different compost/peat mixtures and pure composts. PCA was applied by Planquart et al. [10] to examine the interactions between nutrients and trace metals in colza (Brassica napus) when sewage sludge compost was applied to soils. LaMontagne et al. [7] applied PCA on terminal restriction fragment length polymorphisms (TRFLP) patterns of different composts to reveal their characteristics with respect to microbial communities. Malley et al. [8] recorded near infrared spectra from cattle manure during composting. The collected spectral data were evaluated by PCA to show the relationships among samples and changes due to stockpiling and composting. Hansson et al. [6] observed the anaerobic treatment of municipal solid waste by using on-line near infrared spectroscopy. For spectral data interpretation PCA was carried out. Albrecht et al. [2] also performed a PCA for near infrared (NIR) spectra evaluation from an ongoing composting process. Smidt et al. [12] used PCA to show differences in spectral characteristics of different waste materials. Lillhonga et al. [23] used PCA to observe spectral characteristics of different composting processes. Vergnoux et al. [21] applied a PCA on NIR spectra as well as on physico-chemical and biochemical parameters to derive regularities from the data. Nicolas et al. [9] used PCA to evaluate data from an electronic nose. The correlations between the sensor of an electronic nose and chemical substances were determined by Romain et al. [11] using PCA. PCA was applied to observations of a composting process by means of analytical electrofocusing. The electrofocusing profiles were evaluated by Grigatti et al. [5]. PCA was also used by Biasioli et al. [19] to evaluate odour emissions and biofilter efficiency in composting plants using proton transfer reaction-mass spectrometry. Bianchi et al. [18] also used PCA to reduce the complex data set and to analyse the pattern of organic compounds emitted from a composting plant, a municipal solid waste landfill and ambient air. The effect of 14 different soil amendments on compost quality were evaluated using a PCA by Tognetti et al. [20]. Smidt et al. [16] applied PCA to illustrate the influence of input materials and composting operation on humification of organic matter. Böhm et al. [14] and Smidt et al. [15, 17] used PCA to illustrate spectral differences caused by different materials such as biowaste, manure, leftovers, straw and sewage sludge.
PCA was also applied to illustrate the alteration of municipal solid waste during the biological degradation process reaching stability limits for landfilling as well as to demonstrate similarities and differences of reactor and old landfills based on thermal data [53, 66]. Scaglia and Adani [52] focused on municipal solid waste treatment. They used PCA to create a stability index for quantifying the aerobic reactivity of municipal solid waste. Abouelwafa et al. [54, 55] investigated the degradation of sludge from the effluent of a vegetable oil processing plant mixed with household waste from landfill. Abouelwafa et al. [54] applied PCA on various parameters measured during composting (e.g. pH, electrical conductivity, moisture, C/N, NH4/NO3, ash, decomposition in percent, level of polyphenols, lignin, cellulose, hemicellulose, humic acid) to find the main parameters in the decomposition and restructuring phase [54]. Abouelwafa et al. [55] extracted fulvic acids from the samples mentioned above and extended the data set used for PCA by a series of absorption band ratios resulting from of FTIR spectra.
PCA has also been used in landfill research. Mikhailov et al. [62] applied PCA for monitoring data from different landfills. They included parameters such as depth, ash content, volumetric weight, humidity, amounts of refuse in summer and winter as well as the topsoil depth of landfill sections, sewage sludge lenses and the existence of a protection system. Kylefors [61] investigated data of leachate composition using PCA. The idea was to reduce the analytical monitoring program for further investigations. Durmusoglu and Yilmaz [60] used PCA to extract the significant independent variables of the collected data of raw and pre-treated leachate. A comparable work was done by De Rosa et al. [59]. They also investigated the leachate composition of an old waste dump connected to the groundwater. Olivero-Verbel et al. [63] investigated the relationships between physico-chemical parameters and the toxicity of leachates from a municipal solid waste landfill. PCA was used to find out which parameters were responsible for their toxicity. Jean and Fruget [72] used PCA to compare landfill leachates according to their toxicity and physico-chemical parameters. Ecke et al. [71] showed an example for PCA application in landfill monitoring of data from landfill test cells, leachate and gas data. Smidt et al. [64] investigated landfill materials by means of mid infrared spectroscopy, thermal analysis and PCA. They used PCA to support data interpretation. Van Praagh et al. [70] investigated the potential impacts on leachate emissions using pretreated and untreated refuse-derived material as a cover layer on the top of a municipal solid waste landfill. To interpret leachate characteristics they used PCA. Tintner and Klug [69] used PCA to illustrate how vegetation can indicate landfill cover features. Diener et al. [67] investigated the long-term stability of steel slags used as cover construction of a municipal solid waste landfill by means of a PCA. Smidt et al. [17] used PCA to display spectral characteristics of different landfill types.
Pablos et al. [68] used a PCA to evaluate toxicity bioassays for biological characterisation of hazardous wastes.
Other publications focus on the process monitoring of municipal solid waste incineration residues. Ecke [50] performed PCA on leaching parameters from municipal solid waste incineration fly ash to get an overview of the mobility of metals under certain conditions. Mostbauer et al. [51] carried out PCA to observe the long-term behaviour of municipal solid waste incineration (MSWI) residues.
In the field of waste management logistics PCA is rarely applied. Dahlén et al [81] used PCA to display the impact of waste costs on a weight basis in a specific municipality.
Factor Analysis (FA)
FA is related to PCA but differs in its mathematical conception [86]. FA is also used to describe the variability of observed variables in terms of fewer variables called factors. That means factor analysis is a tool which reveals unobservable underlying features of a specific phenomenon by previous visible observations. The observed variables are modelled as linear combinations of the factors plus "error" terms. The information about interdependencies can be used to reduce the number of variables in a data set.
In waste management practice PCA is preferentially used. Differences between factor analysis and PCA are found to be small [86]. Srivastava and Ramanathan [65] investigated the groundwater quality of a landfill site in India by means of FA. They explained the observed relationship in simple terms expressed as factors. Bustamante et al. [24] used FA to identify the principal variables associated to the composting of agro-industrial wastes. Lin et al. [82] used FA for selecting the best food waste recycling method.
Canonical Correspondence Analysis (CCA)
CCA is a multivariate method to explain the relationships between biological communities and their environment [87]. The method is designed to extract environmental gradients from ecological data sets. By means of the gradients an ordination diagram describing and visualising the diverse habitat preferences of taxa is calculated.
CCA is sometimes used in waste management if, for example, microbial communities or vegetation surveys are analysed. CCA was applied by Franke-Whittle et al. [25] and El-Sheikh et al. [73]. Franke-Whittle et al. [25] applied CCA to illustrate the similarities in microbial communities of three different composting processes. El-Sheikh et al. [73] investigated the ten-year primary succession on a newly created landfill at a lagoon of the Mediterranean Sea. Vegetation surveys where the basis for CCA. Kim et al. [74] applied CCA to investigate the vegetation and the soil of a not properly maintained landfill to suggest restoration alternatives by comparing the vegetation of the landfill to the nearby forests.
2.1.2. Unsupervised pattern recognition
Cluster analysis (CA)
Clustering is the classification of objects into groups called clusters. Objects from the same cluster are more similar to one another than objects from different clusters. The difference of clusters is based on measured distances without any unit. Cluster analysis can be illustrated graphically in a dendrogram as shown in Fig. 2. The samples 2, 3 and 5 are clustered due to the high degree of similarity as well as the samples 1 and 4. The two clusters show little similarity.
Figure 2.
Example of a cluster analysis visualised by a dendrogram
CA was applied in compost science by Zybtniewskie and Buszewski [1]. They applied CA to conventional compost parameters and NMR data to find out the grouping depending on the composting time. He et al. [56] used a hierarchical cluster analysis to show the similarities and differences of UV-Vis and fluorescence spectra of water extractable organic matter, originating from municipal solid waste that had been subjected to different composting times. A hierarchical cluster analysis was also used by He et al. [22] to investigate water-extractable organic matter during cattle manure composting. Gil et al. [4] displayed dendrograms to illustrate the similarities or differences by application of cattle manure compost to different soils. Bustamante et al. [24] studied physico-chemical, chemical and microbiological parameters of different composts. The evaluation of the composts was conducted by a hierarchical cluster analysis [24].
Lin et al. [82] applied a CA for the selection of optimal recycling methods for food waste.
A stepwise cluster analysis (SCA) was used to describe the nonlinear relationships among state variables and microbial activities of composts by Sun et al. [29]. Sun et al. [30] developed a genetic algorithm aided stepwise cluster analysis (GASCA) to describe the relationships between selected state variables and the C/N ratio in food waste composting.
Furthermore CA has often been used to evaluate microbiological data, especially in compost science [25-28, 31]. Innerebner et al. [26] and Ros et al. [27, 28] used CA to identify related samples and similar groups of microorganisms. Franke-Whittle et al. [25] used CA to show the similarities of Denaturing Gradient Gel Electrophoresis (DGGE) data of three different compost types with proceeding compost maturity. Xiao et al. [31] used a hierarchical cluster analysis of DGGE data to estimate the succession of bacterial communities during the active composting process.
Tesar et al. [75] applied CA to spectral data to illustrate the effect of in-situ aeration of a landfill. Jean and Fruget [72] used CA to compare landfill leachates on the basis of their toxicity and physico-chemical parameters.
2.1.3. Supervised pattern recognition
All supervised methods are classifications. Classification can be considered as a predictive method where the response is a category variable. Different classification methods exist. There are types of “hard” and “soft” modelling. Hard modelling means that a non-relocatable line between the defined groups exists. One object can only belong to one group. Soft modelling allows an overlapping of the defined classes. An object can belong to both groups [88]. With regard to waste management practice two different classification methods are described in detail.
Discriminant analysis (DA)
DA is a classification method of hard modelling. Campitelli and Ceppi [3] carried out a DA to distinguish between compost and vermicompost on the basis of parameters such as total organic carbon (TOC), germination index (GI), pH, total nitrogen (TN), and water soluble carbon (WSC). Nicolas et al. [9] performed a DA to classify data of an electric nose according to defined exceeded levels of odour. Ecke et al. [71] investigated samples from three different landfill sites by the biochemical methane potential and used DA for data evaluation. Huber-Humer et al. [77] applied DA to determine methane oxidation efficiency of different materials based on chemical and physical variables. Smidt et al. [66, 76] used DA to differentiate the infrared spectral [76] and thermal patterns [66] of municipal solid waste incinerator (MSWI) bottom ash before and after CO2 uptake. A DA on the CO2 ion current recorded during combustion was applied to illustrate the effect of CO2 treatment of MSWI bottom ash [66]. DA was also used to illustrate the spectral characteristics of leachate from landfill simulation reactors under aerobic and anaerobic conditions [17].
Soft independent modelling of class analogy (SIMCA)
SIMCA is a special method of soft modelling recommended by Wold in the 1970s [88]. Objects can belong to one of the defined class, to both classes or to none. Whether SIMCA can be applied on the data set depends on the question to be answered. According to Brereton [88] it is often legitimate in chemistry that an object belongs to more than one class For example a compound may have an ester and an alkene group which are both reflected by an infrared spectrum. Thus they fit in both classes. In natural science it is allowed in most cases for an object to be in line with more than one class simultaneously.
Contrarily in other cases an object can belong only to one class and the application of SIMCA is inappropriate. Brereton [88] gives a good example where the concept of SIMCA is not applicable: A banknote is either forged or not. In many cases there is only one true answer. For such problems SIMCA is not the adequate method.
In compost science Malley et al. [8] and Smidt et al. [12] carried out a SIMCA. Malley et al. [8] classified different decomposition stages of manures by means of near infrared spectroscopy and SIMCA. Smidt et al. [12] carried out a SIMCA to classify different waste materials such as biowaste compost, mechanically-biologically pretreated waste and landfill materials based on their spectroscopic pattern. Smidt et al. [78] used the SIMCA model developed by Smidt et al. [12] to identify different landfill types such as reactor landfill and industrial landfill samples.
2.2. Calibration
2.2.1. Multiple Linear Regression (MLR)
MLR is directed at modelling the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data. Every value of the independent variable X is associated with a value of the dependent variable Y, with explanatory or predictive purposes. A direct correlation between Y and X-matrix is performed.
In waste management MLR was applied by Chikae et al. [32] to predict the germination index which was adopted as a marker for compost maturity. Thirty-two parameters of 159 samples were measured. MLR was carried out to reduce this huge parameter set to some significant parameters. Lawrence and Boutwell [79] used MLR for predicting the stratigraphy of landfill sites using an electromagnetic method. Moreno-Santini et al. [80] applied MLR to determine arsenic and lead levels in the hair of residents in a municipality constructed on a former landfill.
Noori et al. [84] compared two different statistical methods (artificial neural networks and MLR based on a PCA) to predict the solid waste generation in Tehran. Cheng et al. [83] used MLR to predict the factors associated with medical waste generation at hospitals. Sun et al. [29] used MLR to predict mesophilic and thermopilic bacteria in food waste composts. Suehara and Yano [33] applied MLR to predict conventional compost parameters by NIR spectral data.
2.2.2. Partial Least Squares Regression (PLS-R)
PLS-R is used to find out the fundamental relations between two matrices. PLS-R is a bilinear modelling method. The main idea behind it is to calculate the principal components of the X and the Y matrix separately (external correlation) and to develop a regression model between the scores of the principal components (inner correlation). The concept of PLS-R is demonstrated in Fig. 3.
PLS1 is often used to predict time consuming or expensive parameters using an alternative analytical method. Modern analytical tools such as spectroscopic, chromatographic and thermo analytical methods generate data with inherent information on different parameters. With the development of an evaluated prediction model conventional analytical methods can be replaced by easier and/ or faster handling and robust methods.
Figure 3.
Principles of PLS-R (according to Esbensen [85])
Many authors have developed such prediction models in compost science. Zvomuya et al. [44] predicted phosphorus availability in soils, amended with composted and non-composted cattle manure by means of cumulative phosphorus analysis. Fujiwara and Murakami [35] applied near infrared spectroscopy to estimate available nitrogen in poultry manure compost. Huang et al. [36] also used near infrared spectroscopy to estimate pH, electric conductivity, volatile solids, TOC, total N, the C:N ratio and the total phosphorus content. Furthermore they determined nutrient contents such as K, Ca, Mg, Fe and Zn of animal manure compost using near infrared spectroscopy and PLS1 [37]. Malley et al. [8] developed prediction models for total C, organic C, total N, C:N ratio, K, S and P by means of near infrared spectroscopy and PLS1. Morimoto et al. [43] carried out carbon quantification of green grass tissue using near infrared spectroscopy. Hansson et al. [6] predicted the concentration of propionate in an anaerobic process by near infrared spectra. Albrecht et al. [2] developed calibration models between spectral data and C, N, C:N ratio and composting time. Michel et al. [42] predicted chemical and biological properties of composts such as organic C (Corg), total N, C:N ratio, age, microbial biomass (Cmic), Cmic:Corg, basal respiration, enzymatic activity and plant suppression using near infrared spectroscopy. Ludwig et al. [39] also used near infrared spectroscopy to predict pH, electric conductivity, P, K, NO3- and NH4+ and phytotoxicity. Ko et al. [38] predicted heavy metal contents of Cr, As, Cd, Cu, Zn and Pb by means of near infrared spectroscopy and PLS1. They hypothesised that heavy metals are detectable by NIR when they are complexed with organic matter. Capriel et al. [34] found out that mid infrared spectroscopy is a rapid method to estimate the effect of nitrogen and relevant parameters such as total C, total N, the C:N ratio and the pH of biowaste compost. Meissl et al. [40] used PLS1 and the mid infrared region to predict humic acid contents in biowaste composts. Furthermore they determined humic acid contents by near infrared spectroscopy [41]. Sharma et al. [47] developed prediction models for conventional compost parameters, especially ammonia, pH, conductivity, dry matter, nitrogen and ash using NIR and Vis-NIR spectroscopy. Lillhonga et al. [23] used PLS-R for compost parameter prediction based on NIR spectra. They developed models for the parameters: time, pH, temperature, NH3/NH4+, energy (calorific value) and moisture content. Galvez-Sola et al. [45] used PLS1 to predict different compost quality parameters such as pH, electric conductivity, total organic matter, total organic carbon, total N, C/N ratio as well as nutrients contents (N, P, K) and potentially pollutant element concentrations (Fe, Cu, Mn and Zn) from near infrared spectra. Vergnoux et al. [21] applied a PLS1 to predict physico-chemical and biochemical parameters from NIR spectra. Physico-chemical parameters comprised age, organic carbon, organic nitrogen, C/N, total N, fulvic acids (FA), humic acids (HA) and HA/FA. The soluble fraction, lignin and biological maturity index were summarised as biochemical parameters. Mikhailov et al. [62] used PLS1 to predict maturity and stability based on conventionally measured data. Kylefors [61] developed prediction models for leachate concentrations of specific organic substances in leachate by means of conventional leachate analysis and PLS1. Biasioli et al. [19] used PLS1 to predict odour concentrations in composting plants by proton transfer reaction-mass spectrometry (PTR-MS). Mohajer et al. [46] used a PLS1 to generate a model to predict the microbial oxygen uptake in sludge based on different physical compost parameters.
Böhm et al. [57] used PLS1 to predict the respiration activity (RA4) based on FT-IR spectra of mechanically-biologically pretreated (MBT) waste. The potential of thermal data of MBT waste was shown by Smidt et al. [53]. They applied PLS1 to predict the calorific value, total organic carbon (TOC) and respiration activity (RA4). Smidt et al. [17] also developed a prediction model for the calorific value based on spectral data. Biasioli et al. [58] used PLS1 to predict odour concentration from MSW composting plants based on PTR-MS.
Ecke et al. [71] performed detoxification of hexavalent chromium to less toxic trivalent chromium in industrial waste and applied a PLS model to identify the relevant factors. Smidt et al. [78] predicted the biological oxygen demand and the dissolved organic carbon (DOC) of old landfill materials from spectral data. They also used PLS-R to predict the total organic carbon and total nitrogen based on thermal data [78]. Furthermore PLS-R was used to predict respiration activity (RA4) from MS data of old landfill materials [66]. Smidt et al. [17] developed a prediction model for the DOC and the TOC from spectral data of landfill materials.
PLS2 is a variant of the PLS-R method where several Y-variables are modelled simultaneously. An advantage of this method is to find possible correlations or co-linearity between the Y-variables.
Malley et al. [8] developed prediction models for pH, total N, nitrate and nitrite, total C, organic C, C:N ratio, P, available P, S, K and Na by means of near infrared spectroscopy and PLS2. Suehara et al. [48] used PLS2 for simultaneous measurement of carbon and nitrogen content of composts using near infrared spectroscopy. Vergnoux et al. [21] applied PLS2 to predict physico-chemical (moisture, temperature, pH, NH4-N) and biochemical parameters (hemicellulose and cellulose) from NIR spectra.
Penalised signal regression (PSR)
This special regression method is described in Galvez-Sola et al. [49]. Galves Sola et al. [49] used this method to predict the phosphorus content in composts.
3. Selected examples from literature using multivariate data analysis in waste management
In the following chapter four selected examples using multivariate data analysis in waste management are described in detail. To illustrate the application of principal component analysis (PCA) the study by Mikhailov et al. [62] is presented. He carried out multivariate data analysis for the ecological assessment of landfills. The second example illustrates the application of partial least squares regression (PLS-R). Michel et al. [42] applied PLS-R to predict conventional parameters by spectroscopic data. Ros et al. [27] applied a cluster analysis to data of polymerase chain reaction coupled with denaturing gradient gel electrophoresis (PCR-DGGE) to observe the long-term effects of compost amendment on soil microbial activity. A soft independent model of class analogy (SIMCA) was applied by Malley et al. [8]. They used SIMCA to classify different composts according to their spectroscopic characteristic.
3.1. Principal component analysis (PCA)
3.1.1. Objective of the study
The objective of the study by Mikhailov et al. [62] was to evaluate the stability of landfills based on many conventional parameters such as ash content, temperature, volume weight, pH, humidity and depth. They supposed that a multivariate approach could provide a more efficient data interpretation. Therefore they compared conventional and multivariate data analysis methods.
3.1.2. Method of evaluation and results
In a first step Mikhailov et al. [62] collected conventional data to describe landfill stability. They investigated 3 different landfills in Russia, one illegal dump, an old poorly-run dump and a modern well-run landfill. They focused on geodesic surveys to obtain the overall object properties such as size, volume and different layers. Furthermore they investigated the physical and chemical properties of the samples collected in different depths of the landfill. The physical and chemical properties include ash content, humidity, and acidity. Using the conventional collected data they carried out a PCA for each landfill site. They included the ash content, temperature, volume weight, pH, humidity and depth. The PCA for the two landfills in Bezenchuk and Kinel are presented in the study [62]. Based on the data pool Mikhailov et al. [62] could identify two important sources of waste around Bezenchuk, a poultry farm and a granary. In addition to regular domestic refuse, the agricultural and industrial wastes were disposed illegally in this dump. Kinel on the other hand is a modern, well operated landfill, in which both domestic and industrial wastes are disposed. These assumptions were confirmed by chemometric investigations based on PCA. The mentioned PCAs show clustering of the different classes. The results of the PCA of the third investigated landfill are not shown in their study. Otradny was shown to be a poorly maintained landfill. Clear separation of layers by means of the scores plot was not possible. They found out that the information by the landfill manager and the results obtained did not correspond.
3.1.3. Conclusion
Mikhailov et al. [62] concluded that multivariate data analysis is an appropriate tool for ecological monitoring. They pointed out that chemometric methods provide the possibility to explore the structure of waste disposal by identification of specific areas.
3.2. Partial Least Square Regression (PLS1)
3.2.1. Objective of the study
The verification of compost quality has to be monitored consistently. However this is time-consuming and laborious. Due to the fact that NIR is a simple, accurate and fast technique used for routine analysis Michel et al. [42] hypothesised that NIR could be used for parameter prediction. The objective of the study was to use NIR spectroscopy to determine chemical and biological properties.
3.2.2. Method of evaluation and results
The first step was to define compost quality. Michel et al. [42] defined compost quality by C and N contents, suppression of pathogens, stability/ maturity and biological parameters, especially organic carbon (Corg), total N (Nt), C:N ratio, age, microbial biomass (Cmic), Cmic:Corg, basal respiration, enzymatic activity and suppression of plant disease. Spectroscopic data from 98 composts samples as well as the mentioned conventional parameters were collected. Fundamental relations between two matrices can be found by means of PLS1. Michel et al. [42] applied a PLS1 to express conventional parameters by spectral data. They designed for each conventional parameter a PLS1. Table 2 summarises the collected data and results obtained by Michel et al. [42]. The standard error of cross-validation (SECV) and the coefficient of determination (r2) indicate the quality of prediction. The SECV provides information on the prediction error, r2 demonstrates the quality of correlation. Composting age and basal respiration show the highest r2. The specific enzymatic activity and the suppressive effect show the lowest r2. It should be emphasised that biological tests that are carried out with the original wet compost are more susceptible to interferences due to the heterogeneity of the material. Michel et al. [42] concluded that especially compost age and basal respiration are clearly reflected by the NIR spectrum and feature the best results. By contrast, the specific enzyme activity and suppressive effects show the worst prediction results. The assigned correlations are illustrated in the paper [42].
n
Mean
Range
Outliers removed
SECV
r2
Age [d]
98
183.6
82.0 - 268.0
6
16.7
0.82
Corg content [%]
97
26.0
16.4 - 41.5
5
2.32
0.77
Nt content [%]
97
1.4
1.0 - 2.1
4
0.11
0.67
C:N ratio
97
18.2
12.2 - 29.1
4
1.51
0.71
Cmic [μg g-1]
98
4986
774 - 8587
5
954
0.68
Cmic:Corg [mgCmicgCorg-1]
97
18.6
4.0 - 29.4
4
4.00
0.63
Basal respiration [μg C g-1 d-1]
47
574.8
252.0 - 966.0
2
49.2
0.88
qCO2 [μgCO2-C mg Cmic-1 d-1]
47
9.7
4.2 - 17.1
1
1.98
0.83
Hydrolysis of fluorescein diacetate (FDA-HR) [μg g-1h-1]
98
517.9
256.0 - 879.0
5
74.7
0.75
Specific enzyme activity [μgFDA mgCmic-1h-1]
98
118.7
48.6 - 370.9
6
48.6
0.49
Suppression 5‰ (rating) [%]
98
57.3
8.0 - 101.0
2
19.3
0.71
Suppression 5‰ (fresh weight) [%]
98
59.1
14.0 - 103.0
3
18.7
0.47
Table 2.
Excerpt of table 1 and 2 by Michel et al. [42], SECV = standard error of cross-validation, r2 = the coefficient of determination
3.2.3. Conclusion
Michel et al. [42] concluded that NIR spectroscopy was a capable method to predict various chemical and biological parameters using PLS regression. They believe NIR spectroscopy to be capable of monitoring compost quality.
3.3. Cluster analysis (CA)
3.3.1. Objective of the study
The objective of the study by Ros et al. [27] was to find out the long-term effects of composts on soil microbial communities. Different types of compost were applied over a period of 12 years. DNA was extracted by Ros et al. [27] from differently treated soils. The microbial community was described by polymerase chain reaction coupled with denaturing gradient gel electrophoresis (PCR-DGGE). They used multivariate data analysis to show the differences or similarities of microbial communities using DGGE data.
3.3.2. Method of evaluation and results
A polymerase chain reaction coupled with denaturing gradient gel electrophoresis (PCR-DGGE) was performed to characterize the microbial community. In Fig. 4 a DGGE fingerprint is shown. For the interpretation of such fingerprints statistical tools are necessary. DGGE data were converted into a binary system for cluster analysis (Fig. 4). As mentioned above, cluster analysis visualises the similarity between the samples in a dendrogram.
Ros et al. [27] show the cluster analysis of the DGGE profiles of 16S rDNA from the whole bacterial community. The cluster analysis illustrates the segregation of two soil groups. The clusters are caused by two different amendments. One cluster comprises the soil with compost and nitrogen application, the second cluster represents the soil with amendment of different composts (compost + nitrogen as mineral fertiliser).
Figure 4.
DGGE fingerprint and an example of a binary DGGE data matrix
3.3.3. Conclusion
Ros et al. [27] concluded that the differences between soils with compost with additional nitrogen fertiliser, and the second cluster comprising compost, control and mineral fertiliser soils are stronger than the influence of the different compost types. Furthermore they hypothesised that a certain microbial community inherent to the different composts is irrelevant after 12 years of compost application. Based on the cluster analyses of the PCR-DGGE data, they concluded that the combined application of compost and nitrogen affected soil properties regarding microbial communities much more.
3.4. Soft independent modelling of class analogy (SIMCA)
3.4.1. Objective of the study
Malley et al. [8] used a portable near infrared (NIR) spectrometer to investigate changes of biogenic waste materials during composting. The idea of this study was to observe the composting process continuously in an easy and inexpensive way using NIR spectroscopy.
3.4.2. Method of evaluation and results
First of all many spectra were collected by Malley et al. [8]. The interpretation of spectral data requires experience in spectral interpretation. To provide rapid interpretation of the measured infrared spectra Malley et al. [8] applied the classification method SIMCA. The SIMCA model allows the assignment of a new sample to a defined class. A SIMCA model is always based on the PCAs of the various defined classes. Malley et al. [8] defined 3 different classes: raw manure (M), stockpiled manure (S) and manure compost (C). In the study 2 years of composting were observed (2000 and 2001). Figure 2 by Malley et al. [8] shows the scores plot of the PCA based on the spectral data of the three different classes in the year 2001. The PCA demonstrates a clear grouping of the 3 classes manure, stockpiled manure and manure compost.
Malley et al. [8] illustrated the results of the SIMCA by means of a Coomans plot. In figure 3 by Malley et al. [8] they show the Coomans plot for the investigations of 2001. The vertical and horizontal lines in the Coomans plot mark the 5 % level of significance. That means that 95 % of the samples that truly belong to this group are found within the line. Due to the fact that compost lies on the opposite side of the vertical line from the raw and stockpiled samples Malley et al. [8] concluded that compost is significantly different from the other two classes. The groups of raw manure and stockpiled manure are overlapping. Thus Malley et al. [8] concluded that they did not differ significantly. Nevertheless some raw samples were different. With these results Malley et al. [8] demonstrated that spectroscopic data and multivariate data analysis, especially SIMCA provides a sensitive analysis to differentiate between the products of stockpiles and compost.
3.4.3. Conclusion
Malley et al. [8] concluded that NIR spectroscopy and the multivariate data analysis method SIMCA can be a rapid, inexpensive method for assessing a composting process.
4. Critical discussion of multivariate statistical methods
In fact there are some statistical restrictions, which cannot be solved easily. The simple situation starts with the general linear model. This model usually has a character variable y depending on one or more predictor variables x1, x2, …, xk:
In case of cross-classified two-way analysis of variance (equal subclass numbers):
yijk = µ + ai + bj+ wij + eijk, (i = 1,, a; j = 1, …, b; k = 1, …, n) (1)
µ is the general mean, ai are the main effects of factor A, bj are the main effects of factor B, wij are the interactions between Ai and Bj, eijk are the random error terms.
yj is the j-th value of y depending on the j-th values x1j, … xkj ;
ej are error terms with E(ej) = 0, var(ej) = σ² (for all j), cov(ej\', ej) = 0 for j\'≠j
The simple case assumes a linear dependency. The statistical parameters (the model coefficients) of the model can be estimated, y can be estimated for given values x1, … xk. Assuming that the ej are normally distributed, confidence intervals can be calculated for each model coefficient and finally tests of hypotheses about the model coefficients can be performed. By this procedure each variable can be tested whether its influence on the variable y is significantly different from 0 or not. The type I and type II error can be stated. Furthermore optimal designs for the experiments and surveys can be calculated [89]. Several assumptions are typically made regarding the distribution of the populations and regarding homoscedasticity. Furthermore the problem of extreme values and outliers respectively is critical, especially in environmental measurements. Increasing the number of regressors and factors respectively also increases the error terms.
For some univariate models robust and powerful alternatives regarding the distribution assumptions and regarding homoscedasticity [90-92] already exist. In the case of cross classification there is still no satisfying, powerful alternative. Many multiple regressors methods (multiple regression models, logistic regression models, discriminant analysis, cross classification models) need independent variables.
In chemometrics some of these problems are highly relevant. Usually the number of regressor variables exceeds the number of samples, which excludes most of the common oligovariate models. Many of the regressor variables are highly collinear. Due to these reasons dimension reduction methods are used such as correspondence analysis or factor analysis. The new factors in the latter are strictly independent from one another and can therefore be used in conventional models. There are several possibilities to extract these factors, like Principal Components or Maximum Likelihood. A possibility to model discrete variables is the classification by means of cluster analysis. These clusters can be tested later by contingency tables. Both steps (factor analysis and cluster analysis) lead to descriptive variables of the data set. Just as all descriptive methods in statistics they do not serve as tests against hypothesis of pure chance. There is no risk assessment of the results. Testing of the new descriptive variables implies the understanding of these new variables. By loading the original variables onto the new variables sometimes the interpretation can be done easily. Then models with these variables can be established (PCR or PLS-R) with several quality parameters (e.g. correlation coefficient). A test of significance for the cross-validated r² was performed by Wakeling and Morris [93]. In this paper critical values of r² occurring just by chance alone are tabulated for one to three dimensional models at a significance level of 5 % based on Monte Carlo simulations. A comparable method was used by Stahle and Wold [94] to develop a polynomial approximation of the test statistic for the two-class problem and the number of objects, the number of variables, the percentage variance explained by the first component in X and the percentage of missing values.
cvd/sd =√PRESS/RSS(3)
cvd: cross-validated deviances
sd: standard deviation
PRESS: prediction error sum of squares
RSS: residual sum of squares
Unfortunately the definition of hypothesis regarding the regression coefficients still refers to the new components and provides no results regarding the original variables. There is no statistical possibility to prove whether the extraction method is optimal. Other methods of dimension reduction are already in use (e.g. Boosting, Random forest). Robust alternatives for PLS-R are also available [95].
As long as there are no satisfying testing routines, the results of the presented multivariate methods have to be interpreted very carefully. There is an inherent risk of over-interpretation, especially when using descriptive methods such as PCA or cluster analysis. There is no definition of the error probability of the results. That means whatever interpretation of the picture is done, it could be just pure coincidence and there is no information about the risk. The only possibility to overcome these problems would be to analyse a large number of samples and in case of regression models to validate these models.
5. Summary
In waste management research and practice often huge data sets for statistical evaluation are required to verify the findings. This request concerns both the natural scientific and the logistic field of waste management. Huge data sets can be generated on the one hand by vast numbers of investigated parameters and samples and on the other hand by modern analytical methods such as spectroscopic, chromatographic methods or thermal analysis.
Multivariate data analysis can help to explore data structures of the investigated samples. Another advantage is that the results can be displayed graphically. Furthermore, validated models can serve as adequate evaluation tools for practical application. Different software types are offered to develop such evaluation tools.
In this study the most important multivariate data analysis methods applied in waste management were described in detail and documented by a literature review. It could be demonstrated that Principal Component Analysis (PCA) and Partial Least Square Regression (PLS-R) are the most applied methods in waste management. PCA was used to find hidden data structures, groupings and interrelationships of data. In most cases PLS-R was applied to predict parameters using new analytical instruments that allow faster and cheaper analyses.
In general it can be stated that multivariate data analysis was successfully applied in all experiments. Several authors compared different multivariate methods to determine which one provided the best results. Depending on the data set and the question to be answered the appropriate method must be identified.
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Tintner",authors:[{id:"20376",title:"Dr.",name:"Katharina",middleName:null,surname:"Böhm",fullName:"Katharina Böhm",slug:"katharina-bohm",email:"katharina.boehm@boku.ac.at",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Natural Resources and Life Sciences",institutionURL:null,country:{name:"Austria"}}},{id:"22840",title:"Dr.",name:"Ena",middleName:null,surname:"Smidt",fullName:"Ena Smidt",slug:"ena-smidt",email:"ena.smidt@boku.ac.at",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Institute of Wood Science and Technology",institutionURL:null,country:{name:"India"}}},{id:"30197",title:"MSc.",name:"Johannes",middleName:null,surname:"Tintner",fullName:"Johannes Tintner",slug:"johannes-tintner",email:"johannes.tintner@boku.ac.at",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Institute of Wood Science and Technology",institutionURL:null,country:{name:"India"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Multivariate data analysis in waste management",level:"1"},{id:"sec_2_2",title:"2.1. Pattern recognition",level:"2"},{id:"sec_2_3",title:"2.1.1. Exploratory data analysis",level:"3"},{id:"sec_3_3",title:"2.1.2. Unsupervised pattern recognition ",level:"3"},{id:"sec_4_3",title:"2.1.3. Supervised pattern recognition",level:"3"},{id:"sec_6_2",title:"2.2. Calibration",level:"2"},{id:"sec_6_3",title:"2.2.1. Multiple Linear Regression (MLR)",level:"3"},{id:"sec_7_3",title:"2.2.2. Partial Least Squares Regression (PLS-R)",level:"3"},{id:"sec_10",title:"3. Selected examples from literature using multivariate data analysis in waste management",level:"1"},{id:"sec_10_2",title:"3.1. Principal component analysis (PCA)",level:"2"},{id:"sec_10_3",title:"3.1.1. Objective of the study",level:"3"},{id:"sec_11_3",title:"3.1.2. Method of evaluation and results",level:"3"},{id:"sec_12_3",title:"3.1.3. Conclusion",level:"3"},{id:"sec_14_2",title:"3.2. Partial Least Square Regression (PLS1)",level:"2"},{id:"sec_14_3",title:"3.2.1. Objective of the study",level:"3"},{id:"sec_15_3",title:"Table 2.",level:"3"},{id:"sec_16_3",title:"3.2.3. Conclusion",level:"3"},{id:"sec_18_2",title:"3.3. Cluster analysis (CA)",level:"2"},{id:"sec_18_3",title:"3.3.1. Objective of the study",level:"3"},{id:"sec_19_3",title:"3.3.2. Method of evaluation and results",level:"3"},{id:"sec_20_3",title:"3.3.3. Conclusion",level:"3"},{id:"sec_22_2",title:"3.4. Soft independent modelling of class analogy (SIMCA)",level:"2"},{id:"sec_22_3",title:"3.4.1. Objective of the study",level:"3"},{id:"sec_23_3",title:"3.4.2. Method of evaluation and results",level:"3"},{id:"sec_24_3",title:"3.4.3. Conclusion",level:"3"},{id:"sec_27",title:"4. Critical discussion of multivariate statistical methods",level:"1"},{id:"sec_28",title:"5. 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Chemometr. 6189198'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"K. Böhm",address:null,affiliation:'
Institute of Waste Management, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences, Vienna, Austria
Institute of Wood Science and Technology, Department of Material Sciences and Process Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
Institute of Wood Science and Technology, Department of Material Sciences and Process Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
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1. Introduction
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The popularization of general anesthesia by William Morton in the 1840’s and the concept of antisepsis introduced by Joseph Lister would lead to a paradigm shift and the emergence of modern surgery [1, 2]. The mastery of surgery was no longer associated with speed or flamboyance, but instead focused on meticulous dissection, careful handling of tissues, hemostasis, and correct approximation of tissue planes to promote adequate healing. Among operative specialties, this transition from “art” to “science” of surgery was most profound in the neurosurgical field, enabling rapid advances to occur.
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Early in the evolution of modern surgery, the issue of hemostatic control became prominent, as the heavily vascularized central nervous system and its propensity to bleed resulted in limitations of procedures and posed significant challenges [3, 4, 5]. Surgical ligation was utilized sparingly for fear of vessel rupture or vascular occlusion that may compromise entire vascular distributions. The instruments and techniques in neurosurgical armamentarium therefore relied predominately on application of pressure with gauze to combat bleeding [3, 6]. As a result, a search and incorporation of novel alternatives in hemostatic techniques would effectively lead to the development of modern neurosurgery as represented by Horsley’s use of bone wax and other pioneering hemostatic maneuvers [7, 8] and the introduction of electrosurgery by Cushing and Bovie in the 1920’s [9, 10]. Later in the revolutionary era of neurosurgery, biosurgical materials were introduced [6, 11].
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2. Neurosurgical biosurgery
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In its broadest sense, the term biosurgery relates to the utilization of biomaterials that are defined as systemically and pharmacologically inert substances designed for implementation within or incorporation with living systems [12, 13, 14]. In the context of the current chapter, biosurgical materials (BSMs) are defined as biomaterials that are intended as adjuncts in attaining surgical hemostasis [15, 16]. The gradual development of hemostatic techniques has greatly impacted not only the field of neurosurgery, but all of surgery. The application of biosurgical agents first developed in the neurosurgical theater proved immensely valuable across virtually all surgical applications.
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One of the earliest neurosurgical applications of biosurgical hemostats involved the control of bleeding in inaccessible areas with difficult tissue topography and in situations where use of electrocautery, sutures, or clips may simply not be feasible [4, 13, 17]. This chapter will review the categories, mechanism of action, efficacy, advantages, disadvantages, and complications of the various biological materials currently available for hemostasis in neurosurgery.
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3. Classification
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The abundance of biosurgical materials available for use requires a system of categorization. These agents can be divided into specific categories based on their mechanism of action, including passive or active hemostatics, flowable agents, and sealants [18, 19, 20]. Passive or mechanical agents act through contact with the site of bleeding to promote platelet aggregation [21, 22]. They form a matrix type network at the site of bleeding, thereby activating the coagulation pathway to provide a platform for platelet aggregation and clot formation. At the same time, these agents will be ineffective if used on patients with known coagulopathies due to factor deficiencies or platelet dysfunction. These products include gelatins, collagens, cellulose, and polysaccharide spheres. They require no special storage, minimal or no preparation, and are relatively inexpensive [23, 24].
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Active hemostatic agents act biologically and directly participate in the coagulation cascade to stimulate fibrinogen at the site of bleeding to produce a fibrin clot [21, 25]. These agents primarily include the different forms of thrombin, and are useful in patients with coagulopathies or platelet dysfunction [18, 26]. However, they rely on the presence of fibrinogen in the patient’s blood to be effective. In general, they control bleeding more effectively than passive agents, are more costly, and are prepared/available in various forms and formulations.
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Flowable hemostatic agents consist of various combinations of active and passive components within a single application [20, 27]. This category includes products that work by providing a physical barrier to blood flow while actively converting fibrinogen in blood into fibrin at the bleeding site [26, 28]. Finally, sealants work by the formation of a barrier impervious to flow [24, 29]. There are several types of sealants currently available for use. Our subsequent discussion will focus on each of the various types of biosurgicals utilized, with emphasis on neurosurgical applications.
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4. Passive hemostatic agents
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4.1 Microfibrillar collagen
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This material contains 1-μm microcrystals of purified bovine dermal collagen available as flour-like or sheet-like format [30, 31]. The microcrystalline collagenous network provides surface for platelets to aggregate while coagulation factors are released [30, 31]. The effectiveness of microfibrillar materials may be decreased in cases of severe thrombocytopenia (<10,000 mL) [32]. The material should be kept dry prior to use because moisture may decrease its activity and the hydrophilic nature of product results in adherence to surgical gloves and possible mis-application. Consequently, the material is best handled with sterile forceps. As with other biologic hemostats, optimally the smallest amount required to arrest bleeding should be utilized, although this may not be precisely known in every situation.
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Of importance, microfibrillar collagen is considered a foreign substance and can therefore serve as a nidus for infection and/or foreign body reaction [33]. The small particles of the flour-like material are useful for arresting bleeding from cancellous bone. In this setting, it has demonstrated superior efficacy when compared with other agents, such as thrombin alone or thrombin combined with gelfoam [7]. It also does not seem to interfere with bone healing in contrast to oxidized cellulose or bone wax [34]. It is recommended to firmly pack product into bone surface followed by direct pressure for 5–10 minutes. In terms of clinical application considerations, it should not be used in areas where it may exert pressure on adjacent structures because of fluid absorption and expansion. Also, excessive expansion along a dural sinus may lead to occlusion after bone flap replacement. Although collagen is relatively less antigenic and only results in minor inflammation there remains the very small risk of allergic reactions [35, 36]. Finally, it can also lead to infection, abscess, pseudo-abscess or granuloma formation [37, 38, 39].
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4.2 Oxidized regenerated cellulose
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This type of biomaterial was developed in the 1940’s to help facilitate hemostasis [40, 41]. It is available as pads, strips or powder [40, 42, 43]. It can absorb seven to ten times its own weight [44]. This ubiquitous hemostatic agent is one of the most frequently used. Upon contact with blood, the material reacts to form a reddish black gelatinous mass containing hematin (accounts for the color change) [45]. The oxidation of cellulose results in a product of low pH with resultant bacteriostatic properties [46, 47]. Despite the antimicrobial properties the rates of infection do appear to correlate with the amount of retained product [48]. Consequently, though often left in place in surgical beds, excess amounts should be removed prior to wound closure. Of note, the addition of saline or thrombin to oxidized regenerated cellulose may decrease its effectiveness in addition to inactivating thrombin as a result of the acidic environment [49]. It may also interfere with bone healing and may cause blood vessel compression [49]. Finally, there are reports of excessive postoperative swelling of this type of biomaterial [50].
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4.3 Absorbable gelatin sponge
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Also introduced in the 1940’s this type of hemostatic material consists of water-insoluble sponges prepared from purified porcine skin gelatin [11]. It provides hemostasis by absorbing up to 45x its weight in fluid, thus restricting blood flow and providing stable matrix for clot formation [51]. Gelfoam with gentle pressure can tamponade and treat most dural sinus bleeding without occlusion of the sinus. Although hemostatically beneficial, this capacity to expand physically can lead to compression of neural (and vascular) structures [52]. Absorbable gelatin material is considered relatively nonreactive, however there have been case reports of giant-cell granuloma formation at the implantation site [53, 54]. Although generally non-antigenic, this type of biosurgical material is considered a foreign body and can serve as a nidus for infection [55].
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4.4 Polysaccharide hemospheres
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This is a relatively new category of biosurgicals, derived from vegetable starch containing no animal or human components [56, 57, 58]. It is available in powder form with a bellows-type applicator [56, 59]. The material requires no mixing and is available for immediate use. It produces a hydrophilic effect to dehydrate blood and concentrate solid components to increase barrier formation [59, 60]. It poses little risk to patients since it lacks any human or animal components and should not be used in closed spaces because of physical expansion / swelling.
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5. Active hemostatic agents
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5.1 Thrombin (bovine, pooled human plasma thrombin, and recombinant)
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There are three forms of thrombin products differentiated based on type of plasma used to provide concentrated thrombin to rapidly convert fibrinogen to fibrin clot [61, 62, 63]. This class of biosurgicals should be used in cases of mild to moderate bleeding, mainly because such products tend to be easily washed off in the setting of brisk arterial bleeding or surgical irrigation [64]. In addition, thrombin-based hemostatic agents may be less effective in situations of severe fibrinogen deficiency [15]. Finally, thrombin products should not be allowed to enter the vascular system as intravascular thrombosis can occur [65].
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Antibody formation represents a risk with the use of bovine thrombin, leading to coagulopathy and even death in rare cases [66, 67]. In fact, there is a “Black Box” warning associated with this complication. The use of bovine thrombin is contraindicated if the patient is allergic or has known sensitives to materials of bovine origin [23, 68]. On the other hand, pooled human plasma carries a potential risk of viral or prion disease transmission since multiple units of blood are required to manufacture each lot of product [69, 70].
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5.2 Flowable agents
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These products represent a combination of absorbable passive and active hemostatic components [71, 72]. One of the flowables currently available is a combination of bovine gelatin particles and pooled human thrombin [73, 74], while the other consist of absorbable porcine gelatin particles combined with stand-alone thrombin [27, 75]. In order to become effective, the flowables require direct contact with blood as fibrinogen source [76]. Reconstitution is required, with these products having a paste-like consistency and the ability to “remain in place” compared to liquid thrombin [43]. Flowables are applied with a syringe-like applicator and require 2–3 minutes of preparation time [28, 77]. Direct injection into emissary veins or venous sinuses should be avoided to decrease the risk of dural venous sinus thrombosis and post-operative venous stroke.
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6. Sealants
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6.1 Fibrin sealants/glue
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This group of agents consists of concentrated fibrinogen and thrombin. They increase the rate of blood clot formation by providing higher concentrations of both fibrinogen and thrombin [78]. There are three available types: (a) Pooled human plasma [79, 80]; (b) Individual human plasma, bovine collagen, and bovine thrombin [81]; and (c) Pooled human plasma and equine collagen [82].
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Sealants may be used in coagulopathic patients with insufficient fibrinogen [64, 78, 83]. These agents can also be used in heparinized patients since they do not rely on host factors for hemostasis [84, 85, 86]. Typical indications are hemostasis during cardiopulmonary bypass, splenic injuries, and a number of less commonly utilized general surgical applications [23]. However, they are widely used as hemostatic adjuncts and sealants during neurosurgical procedures, including the prevention of cerebrospinal fluid (CSF) leaks [87, 88].
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6.2 Polyethylene glycol polymers
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There are three different product types in this class of biosurgicals [89, 90]. They tend to be most efficacious when used on a relatively dry field to allow sufficient time for polymerization [91]. One type of polyethylene glycol polymers (PGPs) consists of a combination of 2 polyethylene glycol (PEG) polymers that cross-link to each other and contact tissue following application [92]. In effect, the PGP-based network acts as a sealant to tissue fluids as well as barrier to cell ingrowth and adhesion formation [92, 93].
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Another type of PGP material consists of a combination of PEG polymer, trilysine amine, and blue dye [94, 95]. This particular component mix produces a hydrogel able to help with dural closure because of its ability to form a watertight seal [96]. A modified derivative using a reduced molecular weight PEG component can be used in sutured dural repair during spinal surgery [96, 97]. The built-in blue dye is used to provide accurate placement of sealant [98, 99]. Some concerns about this particular material being associated with cases of postoperative spinal cord compression have been voiced [94, 100, 101, 102]. As such, specific non-expanding formulations exist for usage in the spinal canal [103].
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The third class of PEG polymer compound consists of a combination with human serum albumin. This substance is biodegradable and fairly well studied in terms of safety and effectiveness [104]. It provides a strong barrier, as evidenced by the FDA approval for use on visceral pleura to close air leaks of >2 mm during pulmonary surgeries [105]. There may also be associated economic benefits of using this type of biologic sealant [106].
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7. Neurosurgical applications
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It is important to realize that biosurgical agents are adjuncts to hemostasis when standard methods like direct pressure, suturing, or cautery are impractical or ineffective [107, 108]. Good knowledge of the mechanism of action of different available biosurgical hemostats is critical, with multiple considerations including the patient’s anticoagulation status, the rate of bleeding, the presence of thrombocytopenia, fibrinogen level assessment, and many other factors. The choice of a specific biosurgical product should be dependent on the type of surgery, site of bleeding, other anatomic considerations, cost, and preference of the operating neurosurgeon [3, 16, 32, 109, 110].
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The continuous oozing encountered from dilated varicose intraspinal veins and bone during spinal surgery can be effectively managed with topical hemostats [111, 112]. With that said, such materials should not be left in contact with intra or extradural nerve roots due to possibility of granuloma formation [39, 53, 54, 113]. There have been reports of paraplegia from use of oxidized cellulose during thoracotomy from passage of material through the intervertebral foramen resulting in spinal cord compression [114, 115]. Therefore, it is recommended to use only the minimum required amount and any excess material should be removed once adequate hemostasis is attained.
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Biosurgical materials are increasingly applied during spinal cord surgery to help with hemostasis since opportunities for electrocautery use are limited in this setting. Bipolar cautery, although more focused than monopolar cautery, can also allow dissipation of heat from the tips inducing thermal injury to vascular and neural structures. Fibrin glues are commonly used as hemostatic agents in neurosurgical procedures, including the management of epidural, cortical, and dural sinus bleeding [116].
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During surgical resection of brain tumors, from craniotomy to extradural hemostasis following dural closure, one will find that these agents are generally used throughout the procedure. For example, oxidized regenerated cellulose is widely used during ablation of lesion(s) and at the end to prevent and abate any bleeding in the remaining cavity. However, excess agent should not be left along the surgical cavity. A single layer of oxidized regenerated cellulose should be sufficient for hemostasis without significant risk. Evaluations of the efficacy and safety of polysaccharide hemospheres reported no adverse events after use in brain surgery. There have been several reports of signal anomalies on post-operative imaging mimicking residual tumor or early recurrence, or even abscess when oxidized regenerated cellulose or gelatin sponges are left in the operative field. A pediatric case series of 3 patients who underwent intracerebral surgery with use of microfibrillar collagen reported that all required second surgery for new or recurrent seizures [38]. An MRI of preoperatively suspected tumor recurrence or abscess subsequently confirmed to be microfibrillar collagen-centric necrotizing granuloma surrounded with macrophages and eosinophils. One must remain aware of the above considerations and remove any local hemostatic agent prior to dural closure.
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Bleeding during surgery on the pituitary via a transsphenoidal approach may significantly impede visualization while not being conducive of the use of electrocautery [117]. The use of oxidized cellulose and Floseal (Baxter, Deerfield, IL) can be useful in this situation. Mild persistent oozing from brain tissue following excision can be controlled with application of oxidized regenerated cellulose followed by its removal from the remaining cavity prior to closure. Defects of the skull base in some cases require filling of the defect with bone graft, followed by suture and/or grafting of the dura reinforced with fibrin sealant. A minimally invasive treatment of spontaneous supratentorial intracerebral hemorrhage was also described using Floseal. Floseal was placed in 31 patients without evidence of vascular anomalies or coagulopathy following evacuation of hematoma from a 3 cm craniotomy. Hemostasis was achieved in all but 1 patient who required re-exploration [118].
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A multicenter, prospective randomized study with 237 patients undergoing elective cranial surgery demonstrated PEG hydrogel (DuraSeal, Integra LifeSciences, Princeton, NJ) similarly safe when used with common dural sealing techniques (eg, sutures, autologous grafts, gelatin or collagen sponges, fibrin glues) or when used as dural closure augmentation in cranial surgery [99]. The incidences of neurosurgical complications, surgical site infections and CSF leaks were similar between treatment groups using PEG hydrogel and the control group using standard dural sealing techniques. DuralSeal was also found to be statistically significantly superior to fibrin sealant at preventing CSF leaks following posterior fossa craniotomy or craniectomy [119]. However, the special formulation of DuraSeal Exact should be used in areas where expansion can lead to neurologic compromise – such as the spinal canal [120]. This is because neurologic compromise after expansion has been described in the literature [101, 121]. This again emphasizes mindfulness to use to least amount of material to achieve closure and/or hemostasis while precluding migration or expansion of any excess materials.
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8. Synthesis
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Hemostasis in neurosurgery – more so that many other surgical disciplines – is challenged by the closed space environment of the brain, spinal cord, and other critical structures [3, 122, 123]. Unlike other areas in which the “bulk” or space-occupying characteristics of biosurgicals may represent a potential benefit as they expand, this is less desirable in neurosurgical applications. In the closed environment of the skull, brain, or the spinal cord – even if bone is removed to help minimize the effect of swelling from edema – a relatively small amount of compression, especially in critical areas, like the brain stem, can have devastating consequences. As discussed in previous sections of this chapter, such compressive complications, if left untreated can result in irreversible neurologic damage [94, 100, 101, 102], with resultant “Black Box” warnings clearly outlining biomaterial-specific restrictions.
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Of growing concern in all aspects of surgery is the increasing utilization of anticoagulants and anti-platelet therapies – and often various combinations of both [124, 125]. While the indications for such therapies are outside of the scope of this review, it is clear that more and more patients are being prescribed agents belonging to these broad medication classes. This is of special significance in the elderly population, where anticoagulants and antiplatelet drugs are used for stroke reduction (e.g., in non-valvular atrial fibrillation); various prophylaxis indications (e.g., orthopedic surgery); and of most concern, being used without any clear indications [126]. However, many clinical factors that prompt physicians to initiate antiplatelet or anticoagulant use also increase the neurosurgical risk associated with even minor traumatic events (e.g., falls and minor head injuries). Under such circumstances, even minor neurological injuries can quickly evolve into bleeding-related catastrophes when patients are anticoagulated [127, 128, 129].
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Neurosurgical interventions in the setting of traumatic (or even spontaneous) subdural, epidural, and intraventricular hemorrhage, when confounded by drugs that inherently interfere with hemostasis can compound the difficulties and risks of an already complex clinical scenario [130, 131]. The overall challenge can be magnified even further as acute reversal agents tend to be expensive, may not always be available, reliable/effective, and could result in thrombotic complications in high-risk scenarios such as multi-trauma or massive transfusion [131, 132, 133, 134]. Furthermore, reversal algorithms and guidelines, while helpful do not always definitively address the acute problem once significant bleeding starts, and regardless of the mechanism and defect in the clotting cascade, a consumptive process may be triggered that might require a multi-faceted approach to effectively and timely manage the associated coagulopathy [134, 135, 136, 137]. Similar to other surgical scenarios, such as major trauma and cardiothoracic surgery, at times the best way of managing bleeding is via a timely and aggressive operative intervention. The above concepts are critical in the setting of neurosurgical applications of biosurgical hemostats in that it must be recognized that “bleeding” is a complex problem and often requires a combination of tools to control. Such basic tools require an understanding of the primary question – why is this patient bleeding?
Mechanical bleeding – Direct blood vessel injury that requires sutures, clips, and/or physical closure. No amount of blood products, biosurgicals, or other hemostatic agents can substitute for definitive surgical hemostasis [138].
Diffuse oozing – This includes needle holes, micro-circulation (arterial and venous), that might benefit from biosurgical agents to augment the natural clotting and hemostatic process [138].
Defects in the clotting cascade – either physiologic, such as inherent factor deficiencies (hemophilia), secondary to pathologic conditions (renal failure, acquired Von Willebrand’s disease in aortic stenosis), or iatrogenic from medical therapies such at anti-coagulants and anti-platelet agents. Even the act of surgical incisions and minor tissue trauma can activate various components of the clotting cascade and thus complicate bleeding management [138, 139].
Temperature management – Clotting is a complex enzymatic process that has evolved to be optimal at physiologic temperatures. Hypothermia, either environmental after trauma, perioperative, or therapeutic (for witnessed cardiac arrests, for example) can have potential adverse effects on hemostasis and must be considered in the context of a bleeding patient [108, 138, 139].
Other patient factors and comorbidities can have unpredictable effects on hemostasis, but must be considered in the bleeding neurosurgical patients. Elderly, debilitated, and frail patients might be malnourished and hence have impaired protein stores which contribute to diminished clotting factor reserves – even in the setting of normal clotting tests. Patients might be taking herbal supplements (which might not even be reported in a medical record or medication lists) that have been correlated with bleeding risks [140, 141, 142].
\n\n
Each of the above considerations requires a focused approach and highly specific management strategy. It is important to recognize that good surgical technique must be augmented with a broader understanding of the biologic mechanisms that contribute to the overall disease process and that there is no single tool that is ideal for all circumstances [108, 138]. Conversely, it must be recognized that there is a growing concern that using the wrong or inappropriate therapy for a given clinical scenario can be just as problematic, if not intrinsically ineffective or potentially dangerous. More so than ever in the past, optimal management of the neurosurgical patient (especially one who is bleeding) requires an in-depth and comprehensive understanding of the complex mechanisms of hemostasis pathways and the clotting cascade while also recognizing those variables, such as pharmaceutical therapies, that might adversely impact the normal balance between clotting and bleeding.
\n
In summary, in the setting of neurosurgical bleeding management, several key concepts must be recognized:
Use the right tool for the right job
Sometimes more than one approach is necessary to control bleeding
Different types of bleeding might require different management strategies
Difficult to access areas
Risk for post-operative re-bleeding
Compressive or expanding agents can cause devastating complications
Recognizing the differences between arterial and venous bleeding and how management of each might be different
Understanding the specific reasons why bleeding is occurring and what interventions – such as biosurgical agents – should be central to the overall hemostatic management.
Sometimes the best approach to managing bleeding is preventing it in the first place with strict attention to surgical technique and anatomy. It might sound inherently obvious, but the best way to avoid a dural sinus bleeding is to avoid injury in the first place.
\n\n
\n
\n
9. Conclusions
\n
Uncontrolled or difficult to control bleeding in neurosurgery is a challenging clinical problem and one that is becoming more common with wider use of anti-coagulant and anti-platelet agents in a population that is aging, becoming more frail, has more co-morbidities, and is at increasingly greater risk for neurotrauma. In addition, as more patients are undergoing major surgical interventions and re-interventions for complex neuro-axial pathologies the risks for bleeding complications also increase. Effective management of bleeding and bleeding related morbidity requires a thorough understanding of the mechanisms of bleeding and potential biologic defects in the normal hemostatic process. With such an understanding, management can be more focused and targeted towards the specific problem. Hence, an understanding of the adjuvant therapies, such as the full-spectrum of biosurgical agents that can be used to manage bleeding is imperative in achieving optimal patient outcomes.
\n
\n
Acknowledgments
\n
The authors would like to acknowledge Dr. Roy S. Hwang, for his support and expertise during the preparation of this manuscript.
\n
\n',keywords:"biosurgery, biosurgical hemostat, biosurgical materials, hemostasis, neurosurgery, surgical bleeding",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/75094.pdf",chapterXML:"https://mts.intechopen.com/source/xml/75094.xml",downloadPdfUrl:"/chapter/pdf-download/75094",previewPdfUrl:"/chapter/pdf-preview/75094",totalDownloads:219,totalViews:0,totalCrossrefCites:0,dateSubmitted:"October 12th 2020",dateReviewed:"January 8th 2021",datePrePublished:"February 11th 2021",datePublished:null,dateFinished:"February 4th 2021",readingETA:"0",abstract:"Hemostasis in neurosurgery has evolved significantly over the past few decades. New advances in hemostatic agents, some developed specifically with neurosurgical applications in mind, allowed for more effective control of difficult intraoperative bleeding. These agents vary in the mechanism of action and each may be indicated in different and often highly specific situations. Here we present a review of the most commonly used hemostatic agents, their mechanism of action and their indications. Focus is placed on key aspects and considerations regarding the use biosurgical materials in neurosurgery, with emphasis on clinical appropriateness and patient safety.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/75094",risUrl:"/chapter/ris/75094",signatures:"Jean Claude Petit-Me, Stanislaw P. Stawicki, Michael S. Firstenberg and Evan Marlin",book:{id:"8206",type:"book",title:"Contemporary Applications of Biologic Hemostatic Agents across Surgical Specialties - Volume 1",subtitle:null,fullTitle:"Contemporary Applications of Biologic Hemostatic Agents across Surgical Specialties - Volume 1",slug:null,publishedDate:null,bookSignature:"Dr. Michael S. Firstenberg and Dr. Stanislaw P. Stawicki",coverURL:"https://cdn.intechopen.com/books/images_new/8206.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-78984-441-2",printIsbn:"978-1-78984-440-5",pdfIsbn:null,isAvailableForWebshopOrdering:!0,editors:[{id:"64343",title:"Dr.",name:"Michael S.",middleName:null,surname:"Firstenberg",slug:"michael-s.-firstenberg",fullName:"Michael S. Firstenberg"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Neurosurgical biosurgery",level:"1"},{id:"sec_3",title:"3. Classification",level:"1"},{id:"sec_4",title:"4. Passive hemostatic agents",level:"1"},{id:"sec_4_2",title:"4.1 Microfibrillar collagen",level:"2"},{id:"sec_5_2",title:"4.2 Oxidized regenerated cellulose",level:"2"},{id:"sec_6_2",title:"4.3 Absorbable gelatin sponge",level:"2"},{id:"sec_7_2",title:"4.4 Polysaccharide hemospheres",level:"2"},{id:"sec_9",title:"5. Active hemostatic agents",level:"1"},{id:"sec_9_2",title:"5.1 Thrombin (bovine, pooled human plasma thrombin, and recombinant)",level:"2"},{id:"sec_10_2",title:"5.2 Flowable agents",level:"2"},{id:"sec_12",title:"6. Sealants",level:"1"},{id:"sec_12_2",title:"6.1 Fibrin sealants/glue",level:"2"},{id:"sec_13_2",title:"6.2 Polyethylene glycol polymers",level:"2"},{id:"sec_15",title:"7. Neurosurgical applications",level:"1"},{id:"sec_16",title:"8. Synthesis",level:"1"},{id:"sec_17",title:"9. Conclusions",level:"1"},{id:"sec_18",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'\nHaridas, R.P. and J.A. Mifflin, Researches regarding the Morton ether inhaler at Massachusetts General Hospital, Boston\n. Anesthesia & Analgesia, 2013. 117(5): p. 1230-1235.\n'},{id:"B2",body:'\nFrancoeur, J.R., Joseph Lister: Surgeon Scientist (1827? 1912). Journal of Investigative Surgery, 2000. 13(3): p. 129-132.\n'},{id:"B3",body:'\nChivukula, S., G.M. Weiner, and J.A. 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Korean Journal of Spine, 2013. 10(1): p. 44.\n'},{id:"B95",body:'\nBernardo, L., et al., Does the use of DuraSeal in head and spinal surgeries reduce the risk of cerebrospinal fluid leaks and complications when compared to conventional methods of dura mater closure? Rev Assoc Med Bras, 2012. 58(4): p. 402-403.\n'},{id:"B96",body:'\nFransen, P., Reduction of postoperative pain after lumbar microdiscectomy with DuraSeal Xact Adhesion Barrier and Sealant System. The Spine Journal, 2010. 10(9): p. 751-761.\n'},{id:"B97",body:'\nMo, F., et al., Evaluation of perivascular adhesion formation in New Zealand white rabbits using Oxiplex and DuraSeal Xact adhesion barrier system. SAS journal, 2009. 3(2): p. 68-76.\n'},{id:"B98",body:'\nKim, K.D. and N.M. Wright, Polyethylene glycol hydrogel spinal sealant (DuraSeal Spinal Sealant) as an adjunct to sutured dural repair in the spine: results of a prospective, multicenter, randomized controlled study. Spine, 2011. 36(23): p. 1906-1912.\n'},{id:"B99",body:'\nOsbun, J.W., et al., A multicenter, single-blind, prospective randomized trial to evaluate the safety of a polyethylene glycol hydrogel (Duraseal Dural Sealant System) as a dural sealant in cranial surgery. World neurosurgery, 2012. 78(5): p. 498-504.\n'},{id:"B100",body:'\nThavarajah, D., et al., Postoperative cervical cord compression induced by hydrogel (DuraSeal): a possible complication. Spine, 2010. 35(1): p. E25-E26.\n'},{id:"B101",body:'\nLee, G., C.K. Lee, and M. Bynevelt, DuraSeal-hematoma: concealed hematoma causing spinal cord compression. Spine, 2010. 35(25): p. E1522-E1524.\n'},{id:"B102",body:'\nMulder, M., J. Crosier, and R. Dunn, Cauda equina compression by hydrogel dural sealant after a laminotomy and discectomy: case report. Spine, 2009. 34(4): p. E144-E148.\n'},{id:"B103",body:'\nIntegra_LifeSciences. DuraSeal® Exact Spine Sealant System. 2020 October 10, 2020]; Available from: https://www.integralife.com/duraseal-exact-spine-sealant-system/product/dural-repair-sealants-duraseal-exact-spine-sealant-system.\n'},{id:"B104",body:'\nPark, B.J., et al., Prospective evaluation of biodegradable polymeric sealant for intraoperative air leaks. Journal of cardiothoracic surgery, 2016. 11(1): p. 168.\n'},{id:"B105",body:'\nFuller, C., Reduction of intraoperative air leaks with Progel in pulmonary resection: a comprehensive review. Journal of cardiothoracic surgery, 2013. 8(1): p. 90.\n'},{id:"B106",body:'\nZaraca, F., et al., Cost-effectiveness analysis of sealant impact in management of moderate intraoperative alveolar air leaks during video-assisted thoracoscopic surgery lobectomy: a multicentre randomised controlled trial. Journal of thoracic disease, 2017. 9(12): p. 5230.\n'},{id:"B107",body:'\nAucar, J.A., V. Punja, and J.A. Asensio, Biosurgicals and Trauma, in Biosurgicals-The Next Frontier in Operative Approaches. 2019, IntechOpen.\n'},{id:"B108",body:'\nFirstenberg, M.S., J.M. Hanna, and S.P. Stawicki, The Role of Biosurgical Hemostatic Sealants in Cardiac Surgery, in Biosurgicals-The Next Frontier in Operative Approaches. 2020, IntechOpen.\n'},{id:"B109",body:'\nFujimoto, Y., et al., Modified hemostatic technique using microfibrillar collagen hemostat in endoscopic endonasal transsphenoidal surgery. Neurologia medico-chirurgica, 2014. 54(8): p. 617-621.\n'},{id:"B110",body:'\nEllegala, D.B., N.F. Maartens, and E.R. Laws Jr, Use of FloSeal hemostatic sealant in transsphenoidal pituitary surgery. Neurosurgery, 2002. 51(2): p. 513-516.\n'},{id:"B111",body:'\nLandi, A., et al., Efficacy, security, and manageability of gelified hemostatic matrix in bleeding control during thoracic and lumbar spine surgery: FloSeal versus Surgiflo. Journal of Neurological Surgery Part A: Central European Neurosurgery, 2016. 77(02): p. 139-143.\n'},{id:"B112",body:'\nGregori, F., et al., Comparative Analysis about Efficacy, Security, and Manageability of Gelified Hemostatic Matrix in Bleeding Control during Thoracic and Lumbar Spine Surgery: Floseal versus Surgiflo. Global Spine Journal, 2015. 5(1_suppl): p. s-0035-1554306-s-0035-1554306.\n'},{id:"B113",body:'\nRenati, S., et al., Granulomatous meningitis secondary to Avitene (microfibrillar collagen). Neurology: Clinical Practice, 2017. 7(5): p. 384-386.\n'},{id:"B114",body:'\nShort, H.D., Paraplegia associated with the use of oxidized cellulose in posterolateral thoracotomy incisions. The Annals of thoracic surgery, 1990. 50(2): p. 288-290.\n'},{id:"B115",body:'\nHenry, M.C., et al., Postoperative paraplegia secondary to the use of oxidized cellulose (Surgicel). Journal of pediatric surgery, 2005. 40(4): p. E9-E11.\n'},{id:"B116",body:'\nSekhar, L.N., et al., The use of fibrin glue to stop venous bleeding in the epidural space, vertebral venous plexus, and anterior cavernous sinus. Operative Neurosurgery, 2007. 61(suppl_3): p. ONS-E51-ONS-E51.\n'},{id:"B117",body:'\nJane Jr, J.A., et al., Pituitary surgery: transsphenoidal approach. Neurosurgery, 2002. 51(2): p. 435-444.\n'},{id:"B118",body:'\nGazzeri R, Galarza M, Neroni M, Alfieri A, Esposito S. Minimal craniotomy and matrix hemostatic sealant for the treatment of spontaneous supratentorial intracerebral hemorrhage. Journal of neurosurgery. 2009 May 1;110(5):939-42.\n'},{id:"B119",body:'\nThan, K.D., C.J. Baird, and A. Olivi, Polyethylene glycol hydrogel dural sealant may reduce incisional cerebrospinal fluid leak after posterior fossa surgery. Operative neurosurgery, 2008. 63(suppl_1): p. ONS182-ONS187.\n'},{id:"B120",body:'\nKim, K.D., et al., Duraseal exact is a safe adjunctive treatment for durotomy in spine: postapproval study. Global spine journal, 2019. 9(3): p. 272-278.\n'},{id:"B121",body:'\nEpstein, N.E., Dural repair with four spinal sealants: focused review of the manufacturers’ inserts and the current literature. The Spine Journal, 2010. 10(12): p. 1065-1068.\n'},{id:"B122",body:'\nQiu, L., et al., Bioadhesives in neurosurgery: a review. Journal of neurosurgery, 2019. 1(aop): p. 1-11.\n'},{id:"B123",body:'\nGrant, G.A., Update on hemostasis: neurosurgery. Surgery, 2007. 142(4): p. S55-S60.\n'},{id:"B124",body:'\nBeshay, J.E., et al., Emergency reversal of anticoagulation and antiplatelet therapies in neurosurgical patients: a review. Journal of neurosurgery, 2010. 112(2): p. 307-318.\n'},{id:"B125",body:'\nPowner, D.J., E.A. Hartwell, and W.K. Hoots, Counteracting the effects of anticoagulants and antiplatelet agents during neurosurgical emergencies. Neurosurgery, 2005. 57(5): p. 823-831.\n'},{id:"B126",body:'\nHon, H., et al., Inappropriate preinjury warfarin use in trauma patients: A call for a safety initiative. Journal of Postgraduate Medicine, 2016. 62(2): p. 73.\n'},{id:"B127",body:'\nStawicki, S.P., et al., Prognostication of traumatic brain injury outcomes in older trauma patients: a novel risk assessment tool based on initial cranial CT findings. International journal of critical illness and injury science, 2017. 7(1): p. 23.\n'},{id:"B128",body:'\nGarber, S.T., W. Sivakumar, and R.H. Schmidt, Neurosurgical complications of direct thrombin inhibitors—catastrophic hemorrhage after mild traumatic brain injury in a patient receiving dabigatran: Case report. Journal of neurosurgery, 2012. 116(5): p. 1093-1096.\n'},{id:"B129",body:'\nWitt, D.M., et al., Effect of warfarin on intracranial hemorrhage incidence and fatal outcomes. Thrombosis research, 2013. 132(6): p. 770-775.\n'},{id:"B130",body:'\nVespa, P.M., D. Hirt, and G.T. Manley, Traumatic Brain Injury, An Issue of Neurosurgery Clinics of North America, E-Book. Vol. 27. 2016: Elsevier Health Sciences.\n'},{id:"B131",body:'\nHawryluk, G., et al., Management of anticoagulation following central nervous system hemorrhage in patients with high thromboembolic risk. Journal of Thrombosis and Haemostasis, 2010. 8(7): p. 1500-1508.\n'},{id:"B132",body:'\nWisler, J.R., et al., Competing priorities in the brain injured patient: Dealing with the unexpected. Brain injury: Pathogenesis, monitoring, recovery and management. Rijeka, Croatia: InTech, 2012: p. 341-54.\n'},{id:"B133",body:'\nStawicki, S.P., et al., Deep venous thrombosis and pulmonary embolism in trauma patients: an overstatement of the problem? The American surgeon, 2005. 71(5): p. 387-391.\n'},{id:"B134",body:'\nBarry, N., et al., An exploratory, hypothesis-generating, meta-analytic study of damage control resuscitation in acute hemorrhagic shock: Examining the behavior of patient morbidity and mortality in the context of plasma-to-packed red blood cell ratios. International Journal of Academic Medicine, 2016. 2(2): p. 159.\n'},{id:"B135",body:'\nKumar, M.A., Coagulopathy associated with traumatic brain injury. Current neurology and neuroscience reports, 2013. 13(11): p. 391.\n'},{id:"B136",body:'\nMaegele, M., Coagulopathy after traumatic brain injury: incidence, pathogenesis, and treatment options. Transfusion, 2013. 53: p. 28S–37S.\n'},{id:"B137",body:'\nGupta, G., et al., Impact of coagulation profile on outcome of head injury. Journal of clinical and diagnostic research: JCDR, 2016. 10(1): p. PC04.\n'},{id:"B138",body:'\nFirstenberg, M.S. and S.P. Stawicki, Introductory Chapter: Biosurgical Adoption as the Foundation of New Operative Approaches and Strategies, in Biosurgicals - The Next Frontier in Operative Approaches - Volume 1, S.P. Stawicki and M.S. Firstenberg, Editors. 2020, IntechOpen.\n'},{id:"B139",body:'\nFirstenberg, M.S. and S.P. Stawicki, The Use of Sealants in Cardiac Surgery. 2017.\n'},{id:"B140",body:'\nNguyen, T. and A. Garg, The potential of increased bleeding rates in geriatrics who takes saw palmetto, an herbal supplement, in concurrence with blood thinners. Journal of the American Medical Directors Association, 2017. 18(3): p. B24-B25.\n'},{id:"B141",body:'\nEvans, D.C., et al., Comorbidity-polypharmacy scoring facilitates outcome prediction in older trauma patients. Journal of the American Geriatrics Society, 2012. 60(8): p. 1465-1470.\n'},{id:"B142",body:'\nBirriel, T.J., et al., Adverse drug reactions in the era of multi-morbidity and polypharmacy. Journal of basic and clinical pharmacy, 2015. 6(4): p. 122.\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Jean Claude Petit-Me",address:null,affiliation:'
Department of Surgery, Level 1 Regional Trauma Center, St. Luke’s University Health Network, USA
'},{corresp:"yes",contributorFullName:"Stanislaw P. Stawicki",address:"stawicki.ace@gmail.com",affiliation:'
Department of Research and Innovation, St. Luke’s University Health Network, USA
'},{corresp:null,contributorFullName:"Michael S. Firstenberg",address:null,affiliation:'
Department of Neurosurgery, St. Luke’s University Health Network, USA
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2. A justified withdrawal of work by the Author, which had already been accepted during or after production but prior to publication. In this situation, a 50% refund will be made. (IntechOpen reserves the right to determine, at its discretion, whether withdrawal is justified and, consequently, whether a refund should be issued).
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3. In those rare instances where IntechOpen declines to publish a book that had been previously accepted, full refunds will be made to the same account or credit card from which the Author made the original payment.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. 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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From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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He worked as a Executive Research & Development @ Cadila Pharmaceuticals Ltd, Ahmedabad. He received DBT-postdoc fellow @ Molecular Biophysics Unit, Indian Institute of Science, Bangalore under the supervision of Prof. P. Balaram, later he moved to NIH-postdoc researcher at Drexel University College of Medicine, Philadelphia, USA, after his return from postdoc joined NITK-Surthakal as a Adhoc faculty at department of chemistry. Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. 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He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. 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He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. 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In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. 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He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. 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She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. 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This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11411,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. 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