> Cordeiro et al. [85] presented a modularity-based dynamic community detection algorithm. The algorithm is a modification of the original Louvain method where dynamically added and removed nodes and edges only affect their related communities. In each iteration, the algorithm remains unchanged in all the communities that were not affected by modifications to the network. By reusing community structure obtained by previous iterations, the local modularity optimization step operates in smaller networks where only affected communities are disbanded to their origin. The stability of communities is also an improvement over the original algorithm (Figure 10). Given that only parts of the network change during iterations, the nondeterminism of the algorithm will have a reduced effect on the community assignment. Most node-community assignments remain unchanged between snapshots, providing better com-

> Figure 10. Example of Cordeiro et al. [85] dynamic Louvain for the addition of a cross-community edge (1–4). Top figures show the lower-level network. At the bottom, are shown, the corresponding upper-level network with aggregated

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For the cases when a large number of edges, representing interactions, arrive continuously, in some cases at high or very high rates, and are superposed over much larger networks, streaming graph algorithms should be preferred to perform community detection. In streaming scenarios, the ability to perform the deletion of edges in community detection algorithms is important. In short, as discussed in Section 3.3, this will dictate if the method of analysis is to be performed over the sliding window of edges, and therefore edges are deleted from the tail end of the sliding window, or over a landmark window, in case there is no possibility to delete or forget old edges. Several methods were proposed for dynamic community discovery in graph streams. Wang et al. [86] motivated by the variability of the underlying social behavior of individuals over different graph regions modeled the problem according to the so-termed local heterogeneity, where a local weighted-edge-based pattern (LWEP) summary is efficiently maintained and used afterward to cluster the graph stream and perform dynamic community detection in weighted graph streams. Taking an almost linear time, Raghavan et al. [87] investigated a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities. By analyzing the problem of real-time community detection in large networks and having by baseline the algorithm proposed by Raghavan et al. [87] with

munity stability than its counterparts.

6.2.2. Streaming networks

communities.

#### 6.2.1. Slowly evolving networks

When moving from static community detection to dynamic community detection, often, static techniques are used to detect communities in evolving network. The Louvain algorithm by Blondel et al. [75] is no exception, and it is still one of the fastest ways to perform community detection on evolving networks by considering individual static snapshots. Frequently employed in dynamic network community detection scenarios by performing individual runs of the algorithm in snapshots of the network, this approach is computationally inefficient and does not allow the tracking of communities in a fine-grained way between static snapshots. The community detection work referenced in the Fortunato [72] survey was later complemented by an incremental community detection algorithm based on modularity and was proposed by Shang et al. [76]. The algorithm applies the principles of events in the life of communities (growth, contraction, merging, splitting, birth and death) as defined by Palla et al. [77] and, in each one of the iterations, calculates the modularity gain of affected communities. This allows to detect and track communities over time in incremental networks. This algorithm only considers the addition of new edges and relies on the original two-step approach used in community detection for static communities. The QCA [78], presented as a fast and adaptive algorithm, provides efficient identification of the community structure of dynamic social networks by allowing the addition and removal of nodes and edges dynamically. The algorithm starts with the initial communities calculated via the Louvain method, and then it applies the adaptive node community changes by considering each node as an autonomous agent demonstrating flocking behavior toward their preferable neighboring groups [79]. The AFOCS [80] community detection algorithm for dynamic networks shares the same principles of QCA being only modified in order to allow the possibility of detection of overlapping communities. A detailed comparison between QCA and AFOCS was presented by Nguyen et al. [80]. Label propagation techniques and specifically speaker-listener label propagation (SLPA) were used in community detection over large networks. LabelRank [81] and GANXiSw [81, 82] used the SLPA technique to perform static network community detection while LabelRankT [83] was designed to handle dynamic networks. Being designed for overlapping community detection, all of the previous algorithms also work in a non-overlapping mode, with satisfactory performance for low overlapping density networks [84].

Evolving Networks and Social Network Analysis Methods and Techniques http://dx.doi.org/10.5772/intechopen.79041 123

Figure 10. Example of Cordeiro et al. [85] dynamic Louvain for the addition of a cross-community edge (1–4). Top figures show the lower-level network. At the bottom, are shown, the corresponding upper-level network with aggregated communities.

Cordeiro et al. [85] presented a modularity-based dynamic community detection algorithm. The algorithm is a modification of the original Louvain method where dynamically added and removed nodes and edges only affect their related communities. In each iteration, the algorithm remains unchanged in all the communities that were not affected by modifications to the network. By reusing community structure obtained by previous iterations, the local modularity optimization step operates in smaller networks where only affected communities are disbanded to their origin. The stability of communities is also an improvement over the original algorithm (Figure 10). Given that only parts of the network change during iterations, the nondeterminism of the algorithm will have a reduced effect on the community assignment. Most node-community assignments remain unchanged between snapshots, providing better community stability than its counterparts.

### 6.2.2. Streaming networks

(Figure 9(c)). At the end of the sweep, one obtains first-level partition. In the second step, communities are replaced by super vertices, and the weight of the edge between the super vertices is the sum of the weights of the edges between the represented communities at the lower level (Figure 9(d)). The two steps of the algorithm are then repeated, yielding new

When discussing methods for finding communities in dynamic networks, the division of methods for slowly evolving networks and streaming networks is consensual [27]. In the

When moving from static community detection to dynamic community detection, often, static techniques are used to detect communities in evolving network. The Louvain algorithm by Blondel et al. [75] is no exception, and it is still one of the fastest ways to perform community detection on evolving networks by considering individual static snapshots. Frequently employed in dynamic network community detection scenarios by performing individual runs of the algorithm in snapshots of the network, this approach is computationally inefficient and does not allow the tracking of communities in a fine-grained way between static snapshots. The community detection work referenced in the Fortunato [72] survey was later complemented by an incremental community detection algorithm based on modularity and was proposed by Shang et al. [76]. The algorithm applies the principles of events in the life of communities (growth, contraction, merging, splitting, birth and death) as defined by Palla et al. [77] and, in each one of the iterations, calculates the modularity gain of affected communities. This allows to detect and track communities over time in incremental networks. This algorithm only considers the addition of new edges and relies on the original two-step approach used in community detection for static communities. The QCA [78], presented as a fast and adaptive algorithm, provides efficient identification of the community structure of dynamic social networks by allowing the addition and removal of nodes and edges dynamically. The algorithm starts with the initial communities calculated via the Louvain method, and then it applies the adaptive node community changes by considering each node as an autonomous agent demonstrating flocking behavior toward their preferable neighboring groups [79]. The AFOCS [80] community detection algorithm for dynamic networks shares the same principles of QCA being only modified in order to allow the possibility of detection of overlapping communities. A detailed comparison between QCA and AFOCS was presented by Nguyen et al. [80]. Label propagation techniques and specifically speaker-listener label propagation (SLPA) were used in community detection over large networks. LabelRank [81] and GANXiSw [81, 82] used the SLPA technique to perform static network community detection while LabelRankT [83] was designed to handle dynamic networks. Being designed for overlapping community detection, all of the previous algorithms also work in a non-overlapping mode, with satisfactory

following section, an algorithm for both scenarios will be presented and analyzed.

hierarchical levels and supergraphs (Figure 9(f)).

122 Social Media and Journalism - Trends, Connections, Implications

6.2. Finding communities in dynamic networks

performance for low overlapping density networks [84].

6.2.1. Slowly evolving networks

For the cases when a large number of edges, representing interactions, arrive continuously, in some cases at high or very high rates, and are superposed over much larger networks, streaming graph algorithms should be preferred to perform community detection. In streaming scenarios, the ability to perform the deletion of edges in community detection algorithms is important. In short, as discussed in Section 3.3, this will dictate if the method of analysis is to be performed over the sliding window of edges, and therefore edges are deleted from the tail end of the sliding window, or over a landmark window, in case there is no possibility to delete or forget old edges. Several methods were proposed for dynamic community discovery in graph streams. Wang et al. [86] motivated by the variability of the underlying social behavior of individuals over different graph regions modeled the problem according to the so-termed local heterogeneity, where a local weighted-edge-based pattern (LWEP) summary is efficiently maintained and used afterward to cluster the graph stream and perform dynamic community detection in weighted graph streams. Taking an almost linear time, Raghavan et al. [87] investigated a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities. By analyzing the problem of real-time community detection in large networks and having by baseline the algorithm proposed by Raghavan et al. [87] with linear time O mð Þ on a network with m edge-label propagation, or "epidemic" community detection, Leung et al. [88] proposed a method with near-linear time community detection in graphs. Leung et al. identified the characteristics and drawbacks of the base [87] algorithm and extended it by incorporating different heuristics to facilitate reliable and multifunctional realtime community detection. Yun et al. [89] proposed two efficient streaming memory-limited clustering algorithms for community detection based on spectral methods. Yun and Proutière [90] proposed community detection via random and adaptive sampling. Sariyuce et al. [91] proposed SONIC, a find-and-merge type of overlapping community detection algorithm that can efficiently handle streaming updates. Recently, Hollocou et al. [92] proposed SCoDA, a linear streaming algorithm, for community detection in very large networks.

Corman [98] in which the evolution of the network is unrolled, and each step is represented as a layer. A complex network with a larger number of links may prevent users from recognizing salient structural patterns. To overcome this common problem with visualization, two widely known link reduction algorithms, namely minimum spanning trees (MSTs) and pathfinder networks (PFNETs), were analyzed and compared by Chen and Morris [99]. Bender-deMoll and McFarland [100] propose a framework for visualizing social networks and their dynamics and presented a tool that enables debate and reflection on the quality of visualizations used in empirical research. With the focus on the evolution of communities over time, Falkowski et al. [101] proposed two approaches to analyze the evolution of two different types of online communities on the level of subgroups. This analysis was conducted by observing changes in the interaction behavior of the members of the communities. Chen [102] devised a generic approach for detecting and visualizing emerging trends and transient patterns in scientific literature. Other recent work of interest is presented by Beck et al. [103, 104] and the visualization of evolving graphs with multiple visual metaphors of Burch [105]. The combination of

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dynamic network visualization with graph sampling techniques is often used [106].

This chapter provided an overview of the methods and techniques for modeling, analyzing, measuring and visualizing evolving social network analysis. In the past, static techniques were adapted to dynamic networks with relative success, but nowadays, with the advent of social media, scale and velocity of most of those static techniques reveal weaknesses that only can be addressed by methods and techniques designed for dealing with evolving data. After presenting two areas of direct applicability of evolving network analysis such as criminological research and research on journalism, the ways on how dynamic networks can be represented and modeled according to their timescale, windowing strategies and methods of analysis were discussed. These theoretical aspects were then used to present elementary network measures, link analysis methods, community detection methods and visualization techniques. It is clear that in recent years this area of research will continue to have significant development in the future, several problems are still unsolved and many of them can be significantly improved. The areas of applicability of evolving networks and social network analysis are also broader, with many of the abovementioned techniques moving from wellsucceeded areas like world wide web, communication, telecommunication and mobile networks, to newer areas like social network recommendations, news and blog analysis and social network event detection. Specifically in the area of social network event detection, the detection of unusual patterns, anomalies or changes in trends in the social streams can lead to valuable information, which can be used timely in many real-word scenarios [107]. Cordeiro [108] addressed the monitoring and tracking of the dynamics of social network communities with the objective to unveil real-world events, whereas Cordeiro [109] was devoted to the problem of mining the twitter stream to unravel events, interactions and communities in real time. Future trends of social network analysis will continue to be driven by future trends and characteristics of the network data, such as the size of data, which is incredibly getting large,

8. Conclusion and future trends

### 7. Visualization of evolving networks

The visualization of networks is known to be challenging, and this task gains additional complexity when moving from static to evolving networks. In this section an overview of the methods and techniques is presented, currently used for the visualization of evolving networks.

### 7.1. Challenges of evolving networks' visualization

The dynamics of social networks remain a challenge regarding visualization [93]. Many researchers argue that traditional graph visualization methods have issues when applied to evolving networks. Additionally, the application of conventional node-link methods to largescale networks provides low-quality cluttered insights. The overlap of nodes in these conditions is not appropriated when trying to extract information from the network. Zaidi et al. [94] and Aggarwal and Subbian [27] presented an overview of the different techniques and methods that exist for the analysis and visualization of dynamic networks. It included the discussion of the basic definitions, formal notations and a set of the most important and recent work regarding analysis and the visualization of dynamic networks. While static graph visualizations are often divided into node-link and matrix representations, Beck et al. [95, 96] presented a hierarchical taxonomy of dynamic graph visualization techniques. This survey about the state of the art in visualizing dynamic graphs identified the representation of time as the major distinguishing feature for dynamic graph visualizations. Two major visualization categories were found: in one category, graphs are represented as animated diagrams or in a second one, visualizations are a set of static charts based on a timeline. Similar conceptual dynamic network visualization categories were devised by Moody et al. [97], and the authors divide dynamic network visualizations also called as network movies into static flip books, where the node position remains constant but edges cumulate over time and dynamic movies, where nodes move as a function of changes in relations. The graph animation is often used to lower the cognitive effort required to follow the transition from one visualization to the next, according to Brandes and Corman [98]. To facilitate the simultaneous analysis of state and change, a layered three-dimensional network visualization was proposed by Brandes and Corman [98] in which the evolution of the network is unrolled, and each step is represented as a layer. A complex network with a larger number of links may prevent users from recognizing salient structural patterns. To overcome this common problem with visualization, two widely known link reduction algorithms, namely minimum spanning trees (MSTs) and pathfinder networks (PFNETs), were analyzed and compared by Chen and Morris [99]. Bender-deMoll and McFarland [100] propose a framework for visualizing social networks and their dynamics and presented a tool that enables debate and reflection on the quality of visualizations used in empirical research. With the focus on the evolution of communities over time, Falkowski et al. [101] proposed two approaches to analyze the evolution of two different types of online communities on the level of subgroups. This analysis was conducted by observing changes in the interaction behavior of the members of the communities. Chen [102] devised a generic approach for detecting and visualizing emerging trends and transient patterns in scientific literature. Other recent work of interest is presented by Beck et al. [103, 104] and the visualization of evolving graphs with multiple visual metaphors of Burch [105]. The combination of dynamic network visualization with graph sampling techniques is often used [106].

### 8. Conclusion and future trends

linear time O mð Þ on a network with m edge-label propagation, or "epidemic" community detection, Leung et al. [88] proposed a method with near-linear time community detection in graphs. Leung et al. identified the characteristics and drawbacks of the base [87] algorithm and extended it by incorporating different heuristics to facilitate reliable and multifunctional realtime community detection. Yun et al. [89] proposed two efficient streaming memory-limited clustering algorithms for community detection based on spectral methods. Yun and Proutière [90] proposed community detection via random and adaptive sampling. Sariyuce et al. [91] proposed SONIC, a find-and-merge type of overlapping community detection algorithm that can efficiently handle streaming updates. Recently, Hollocou et al. [92] proposed SCoDA, a

The visualization of networks is known to be challenging, and this task gains additional complexity when moving from static to evolving networks. In this section an overview of the methods and techniques is presented, currently used for the visualization of evolving

The dynamics of social networks remain a challenge regarding visualization [93]. Many researchers argue that traditional graph visualization methods have issues when applied to evolving networks. Additionally, the application of conventional node-link methods to largescale networks provides low-quality cluttered insights. The overlap of nodes in these conditions is not appropriated when trying to extract information from the network. Zaidi et al. [94] and Aggarwal and Subbian [27] presented an overview of the different techniques and methods that exist for the analysis and visualization of dynamic networks. It included the discussion of the basic definitions, formal notations and a set of the most important and recent work regarding analysis and the visualization of dynamic networks. While static graph visualizations are often divided into node-link and matrix representations, Beck et al. [95, 96] presented a hierarchical taxonomy of dynamic graph visualization techniques. This survey about the state of the art in visualizing dynamic graphs identified the representation of time as the major distinguishing feature for dynamic graph visualizations. Two major visualization categories were found: in one category, graphs are represented as animated diagrams or in a second one, visualizations are a set of static charts based on a timeline. Similar conceptual dynamic network visualization categories were devised by Moody et al. [97], and the authors divide dynamic network visualizations also called as network movies into static flip books, where the node position remains constant but edges cumulate over time and dynamic movies, where nodes move as a function of changes in relations. The graph animation is often used to lower the cognitive effort required to follow the transition from one visualization to the next, according to Brandes and Corman [98]. To facilitate the simultaneous analysis of state and change, a layered three-dimensional network visualization was proposed by Brandes and

linear streaming algorithm, for community detection in very large networks.

7. Visualization of evolving networks

124 Social Media and Journalism - Trends, Connections, Implications

7.1. Challenges of evolving networks' visualization

networks.

This chapter provided an overview of the methods and techniques for modeling, analyzing, measuring and visualizing evolving social network analysis. In the past, static techniques were adapted to dynamic networks with relative success, but nowadays, with the advent of social media, scale and velocity of most of those static techniques reveal weaknesses that only can be addressed by methods and techniques designed for dealing with evolving data. After presenting two areas of direct applicability of evolving network analysis such as criminological research and research on journalism, the ways on how dynamic networks can be represented and modeled according to their timescale, windowing strategies and methods of analysis were discussed. These theoretical aspects were then used to present elementary network measures, link analysis methods, community detection methods and visualization techniques. It is clear that in recent years this area of research will continue to have significant development in the future, several problems are still unsolved and many of them can be significantly improved. The areas of applicability of evolving networks and social network analysis are also broader, with many of the abovementioned techniques moving from wellsucceeded areas like world wide web, communication, telecommunication and mobile networks, to newer areas like social network recommendations, news and blog analysis and social network event detection. Specifically in the area of social network event detection, the detection of unusual patterns, anomalies or changes in trends in the social streams can lead to valuable information, which can be used timely in many real-word scenarios [107]. Cordeiro [108] addressed the monitoring and tracking of the dynamics of social network communities with the objective to unveil real-world events, whereas Cordeiro [109] was devoted to the problem of mining the twitter stream to unravel events, interactions and communities in real time. Future trends of social network analysis will continue to be driven by future trends and characteristics of the network data, such as the size of data, which is incredibly getting large, and changes in space and time. On one side, there is the urge for scalable and efficient social network analysis methods, and on the other side, there is the need for methods to study the dynamics and evolution of social networks, able to deal with future velocity and timescale dimensions of the network data. Stray [110] focused network analysis as a tool to bridge the "research to reporting" gap in journalism, starting with two use cases (Seattle Art World [111] and Hot Wheels [112]) and the recent state-of-the-art network analysis and visualizations applied to the Panama Papers case [113] where graph databases and entity recognition were used to build interactive network maps from structured data and raw documents. Therefore, it is expected that the study of evolving networks will continue to be a significant strand of research in the context of social network analysis in the near future.

[5] Kwak H, Lee C, Park H, Moon S. What is Twitter, A social network or a news media? In Proceedings of the 19th international conference on World wide web (WWW '10). New York, USA: ACM; 2010;1120(2):591-600. DOI: http://dx.doi.org/10.1145/1772690.1772751

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### Acknowledgements

This work was fully financed by the Faculty of Engineering of the Porto University. Rui Portocarrero Sarmento also gratefully acknowledges funding from FCT (Portuguese Foundation for Science and Technology) through a PhD grant (SFRH/BD/119108/2016). The authors also want to thank the reviewers for the constructive reviews provided in the development of this publication.

### Author details

Mário Cordeiro<sup>1</sup> \*, Rui P. Sarmento<sup>1</sup> , Pavel Brazdil<sup>2</sup> and João Gama<sup>2</sup>

\*Address all correspondence to<EMAIL_ADDRESS>

### References

[5] Kwak H, Lee C, Park H, Moon S. What is Twitter, A social network or a news media? In Proceedings of the 19th international conference on World wide web (WWW '10). New York, USA: ACM; 2010;1120(2):591-600. DOI: http://dx.doi.org/10.1145/1772690.1772751

and changes in space and time. On one side, there is the urge for scalable and efficient social network analysis methods, and on the other side, there is the need for methods to study the dynamics and evolution of social networks, able to deal with future velocity and timescale dimensions of the network data. Stray [110] focused network analysis as a tool to bridge the "research to reporting" gap in journalism, starting with two use cases (Seattle Art World [111] and Hot Wheels [112]) and the recent state-of-the-art network analysis and visualizations applied to the Panama Papers case [113] where graph databases and entity recognition were used to build interactive network maps from structured data and raw documents. Therefore, it is expected that the study of evolving networks will continue to be a significant strand of

This work was fully financed by the Faculty of Engineering of the Porto University. Rui Portocarrero Sarmento also gratefully acknowledges funding from FCT (Portuguese Foundation for Science and Technology) through a PhD grant (SFRH/BD/119108/2016). The authors also want to thank the reviewers for the constructive reviews provided in the development of

, Pavel Brazdil<sup>2</sup> and João Gama<sup>2</sup>

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126 Social Media and Journalism - Trends, Connections, Implications

\*, Rui P. Sarmento<sup>1</sup>

1 Faculty of Engineering, University of Porto, Portugal

\*Address all correspondence to<EMAIL_ADDRESS>
Acknowledgements

this publication.

Author details

Mário Cordeiro<sup>1</sup>

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*2.1. The Labeled RFS*

Throughout this article, we adhere to the following notations. The set exponential is denoted as [*h*(·)]*X* = ∏*x*∈*X h*(*x*) while the inner product notation is denoted as *f* , *g* = \$ *f*(*x*)*g*(*x*)*dx*. The generalization of the Kronecker delta is denoted as follows:

$$\delta\_Y(X) = \begin{cases} 1 & X = Y \\ 0 & X \neq Y \end{cases}$$

The set inclusion function is written as follows:

$$\mathbf{1}\_Y(X) = \begin{cases} 1 & \text{\$X \subseteq \$ Y\$} \\ 0 & \text{otherwise} \end{cases}$$

**X** denotes the labeled set of objects, while **x** = (*x*, *l*) denotes a single labeled object, specifically, *x* ∈ X and *l* ∈ L, where X and L are respectively the kinematic state space and the discrete labels space at the current time step. L is a label extraction function, i.e., L(**x**) = *l* and F(**X**) denote sets of finite subsets of **X**. The "+" sign is used to indicate the next time step when applicable.

The Finite-Set Statistics (FISST) integration is defined as follows [54]:

$$f(X)\delta X = \sum\_{i=0}^{\infty} \frac{1}{i!} \int\_{\mathbb{X}^i} f(\{x\_1, \ldots, x\_i\}) d(x\_1, \ldots, x\_i)$$

In multi-object tracking problem, the cardinality of object sets varies when objects enter or leave the surveillance region. As RFS is a random set of points in the sense that the number of points in the set is random and the points themselves are also random and unordered [54], a set of random objects can be naturally characterized as a RFS. Being introduced systematically for the first time in Reference [13], the labeled RFS incorporates the identities of elements into the unlabeled counterpart. Precisely, with the state space X and marks space L, the labeled RFS is a marked simple point process whereas each realization has a distinct label [13,15]. The distinct label property is satisfied when **X** has the same cardinality as its labels L(**X**). Given this, the distinct label indicator can be written as follows [16]:

$$\Delta(\mathbf{X}) = \delta\_{|\mathbf{X}|}(|\mathcal{L}(\mathbf{X})|) \tag{1}$$

The introduction of labeled RFS to the multi-object tracking problem allows direct estimation of trajectories which cannot be done previously with conventional RFS without a separate labeling scheme.


**5. Conclusions**

The present study deals with the in vitro release and mobility of potentially bioactive elements present in semisolid gel-like formulations obtained by mixing sepiolite and palygorskite with a natural spring water. Hydrogels were subjected to in vitro Franz cell tests and the elements released were analyzed by inductively coupled plasma techniques. Then, the element release and mobility were compared with in vitro biocompatibility tests of the very same formulation. The results demonstrated that, unlike other formulations, the potential therapeutic activity of nanoclay/spring water hydrogels should be studied in depth and characterized.

Clay/spring water hydrogels are "living formulations" since their ingredients constantly interact with each other, changing the properties of the system. For instance, the presence of an element in high concentration does not mean it would be released in high amounts. Moreover, the high release of bioactive elements is not a *sine qua non* to obtain maximum therapeutic effect. In fact, the ALIG30@20 hydrogel, with lower elemental mobility, not only proved to be biocompatible, but to exert potential proliferative effects over fibroblast cultures. According to the present in vitro release studies, it is possible to state that the ratios of the elements released play a significant role in the final therapeutic activity of the formulation. Moreover, the importance of formulative studies is again highlighted, since it is the optimal combination of the correct ingredients that makes a formulation effective.

As a general conclusion, the present study demonstrates that synergistic effects can be achieved from the formulation of the liquid phase in a semisolid system, in which elemental composition of the solid phase and structure of the system will determine elements' mobility and, ultimately, the therapeutic effects.

**Author Contributions:** Data curation, F.G.-V.; Funding acquisition, P.C., G.S. and C.V.; Methodology, F.G.-V., R.S.-E. and G.S.; Supervision, C.V.; Writing—original draft, F.G.-V.; Writing—review & editing, A.B.-S., P.C., L.C. and C.V All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Ministerio de Ciencia e Innovación, CGL2016–80833-R; Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía, P18-RT-3786 and Ministerio de Educación, Cultura y Deporte, who awarded a predoctoral grant (FPU15/01577).

**Acknowledgments:** This project was supported by the Spanish research group CTS-946. Technical support was provided by the CIC (Centro de Instrumentación Científica, University of Granada) and the IACT (Instituto Andaluz de Ciencias de la Tierra, CSIS-UGR). Special thanks to TOLSA group (Madrid), who kindly gifted clay minerals samples, and Alicún de las Torres thermal station, who provided spring water samples for the study.

**Conflicts of Interest:** The authors declare no conflict of interest.


**8. Diagnosis of peritoneal fibrosis**

### **9. Conventional PD fluids**

First generation Fluids: contain 35–40 mM lactate buffer with an acidic pH of 5.5. The low pH will be aggravating the detrimental effects of the high lactate on peritoneal mesothelium. During heat sterilization and storage, more of (GDPs) (e.g., formaldehyde, acetaldehyde, glyoxal, methylglyoxal, 5-hydroxymethylfurfural (5-HMF), 3-Deoxyglucosone (3-DG) and 3,4-dideoxyglucosone3-ene (3,4-DGE)) are formed, leading to membrane damage (shown in **Figure 7**).

Second generation Fluids: The buffers (such as lactate ± bicarbonate) are in separate chamber and are kept in very low pH to prevent formation of GDPs. Prior to use, they are mixed to a pH of 7–7.5 and are administered. They are detailed below.

Icodextrin: is an osmotic agent (Mol.wt-16,800 Da) derived from the starch, used in long night dwell with very low GDP, since they are absorbed into the

#### **Figure 7.**

*Potential beneficial effects of newer peritoneal dialysis solutions [16]. (Courtesy: Garcia nature reviews 2012). Abbreviations: RRF—residual renal function; LV—left ventricle; and AGE—advanced glycosylated end products).*

circulation, with no sodium sieving. It has an UF capacity comparable to 4.25% dextrose fluid. Despite this, acidic PD fluid has been associated with increased local and systemic inflammation with increased permeability and IL-6. Even though this reaction is reaction, long term exposure may irreversibly change peritoneal morphology. The ISPD guidelines recommends use of icodextrin in high transporters for better volume control.

Amino acid solutions: A bag of 1.1% 21 amino acid PD fluid used in one exchange a day provide 22gm of amino acids (2/3rd essential & 1/3rd nonessential) which is 25% of daily requirement and generate an UF equal to 2 L 1.5% dextrose. It has an acidic pH of 6.6 with very low GDPs and low VEGF. For optimized nutrition of malnourished patients and to prevent increased serum nitrogen levels and metabolic acidosis, they should be applied at a ratio of 1:4 with glucose-containing PD fluids [17]. Nutrineal in one exchange with icodextrin (Extraneal) and Physioneal (Baxter) for other exchange as a regimen (NEPP regimen) has shown to preserve mesothelial integrity but with increased VEGF.