**5.1.5 Inefficient tax system**

Poor or mistaken tax policies have created obstacles to the sustainable management of forests in Cameroon [and DRC] (Sikod et al., 1996). In an effort to resolve the issue of market failure, the government of Cameroon under the 1994 forest policy embarked on reforming the forestry taxation system, output targets, regulation, private incentives and macroeconomic management, using policy instruments such as royalty12 for the forest area, felling tax, exit duty on logs, and factory taxes.13 These policy interventions have instead created controversies which have contributed to more deforestation in some areas. For example, the lack of concrete modalities for handling tax revenues and rents from royalties

12 Royalties are "rents" not "taxes," but are placed under tax regulations to ensure that concessionaires pay for the right to access the resource.

13 Exit duty on sawn products and entry taxes on logs taken into factories.

notably:

game...

scientific benefits:

information

Not only are services provided by the forests in the Congo Basin not traded in markets but charges paid by timber concessionaires in the region are relatively low compared to the market value of the resource (Fomété, 2001). These low charges are encouraging foreign timber companies especially from countries with rapidly growing economies like China to aggressively seek concessions here. In fact, the Chinese company Vicwood-Thanry which began exploring Cameroon's forest in 1997 now owns 12% of foreign-owned concessions in Cameroon (Topa et al., 2009). There is also the lack of incentive for long term management due to logging competition among the many concessionaires. High profits due to low charges are also potential drivers of corruption that has handicapped strict enforcement of

Poor or mistaken tax policies have created obstacles to the sustainable management of forests in Cameroon [and DRC] (Sikod et al., 1996). In an effort to resolve the issue of market failure, the government of Cameroon under the 1994 forest policy embarked on reforming the forestry taxation system, output targets, regulation, private incentives and macroeconomic management, using policy instruments such as royalty12 for the forest area, felling tax, exit duty on logs, and factory taxes.13 These policy interventions have instead created controversies which have contributed to more deforestation in some areas. For example, the lack of concrete modalities for handling tax revenues and rents from royalties

12 Royalties are "rents" not "taxes," but are placed under tax regulations to ensure that concessionaires

13 Exit duty on sawn products and entry taxes on logs taken into factories.

The forest provides basic resources,

The forest provides esthetic, cultural and

**Regulatory functions Productive functions**

**Physical support functions Informational functions**

Table 2. Categories of Environmental Services Provided by Forests

The forest provides support to economic

activities and human well-being by:

The forest provides the space and re-

quired substrates for:

concession terms (Sieböck, 2002).

pay for the right to access the resource.

**5.1.5 Inefficient tax system**

Source: Lescuyer et al., 2009

Waste

Another problem with the forest taxation system of these countries is lack of transparency. There is little or no flow of information between the different stakeholders. Most forest inhabitants in both countries are illiterate. The few who can barely read and write find it difficult to interpret policy documents. This is a huge limitation which, coupled with the lack of adequate information to estimate their expected revenue, causes inhabitants to fall prey to the greedy loggers and government official imposters. An even more serious problem with the forestry taxation system of these countries is that though it claims to be a decentralized system, both governments still place strong emphasis on centralized decisionmaking, ownership and control of forest resources. In Cameroon for example, the creation of community forests under the 1995 forestry law was aimed at giving complete autonomy for managing the forests to the communities themselves. However, communities are still unable to fully control the management process as local, national and regional government representatives sit in management committees and impose government directives on community leaders (Assembe, 2006; Mbatu, 2010). This has caused the local population not to cooperate with the government in managing the forests. These centralized decision-making policies have been partly responsible for failures to protect forests and for its extensive loss and degradation in Cameroon. The decentralization of forest management in Cameroon was meant to act as a model for the Central African region but in many ways has fallen short of the desired prototype. Though not a prototype of an ideal decentralized management system, the model has been adopted by some countries in the region including DRC which adopted a new forestry law in 2002 and passed a new constitution in 2006 (Government of DRC, 2006) in a move aimed at empowering communities to take control over the management of local forests. Although the 2006 constitution "stipulates that taxes are to be collected by decentralized autonomous local governments (Government of DRC, 2006), all forests-related taxes are [still] been paid directly to the national Treasury" (Klaver, 2009).


*2.3. Mechanomutable Asphalt Materials for the Guidance of Autonomous Vehicles*

According to the levels established by the US National Highways Traffic Safety Administration (NHTSA), there are five levels, ranging from zero (no automation) to four (full self-driving automation) for classifying the autonomy of vehicles [58]. Due to the fact that the majority of the new conventional vehicles have one or more specific control functions that are automated, it can be assumed that Level 1 and Level 2 are now being implemented in real life. Ongoing research by the major vehicle manufacturers is being carried out with the aim of reaching Level 3, where the drivers can concede full control of the safety-critical functions of the vehicle under certain traffic or environmental conditions. Finally, some researchers [2,59,60] consider that to fully achieve Level 3 and Level 4, that is, full self-driving automation, it will be essential to incorporate the necessary infrastructure.

If the benefits of assigning a multifunctional character to the road are also considered, the use of mechanomutable asphalt materials can be extended to encoding the road [2] (Figure 6). Encoding the road entails assigning, alongside it, a type of language that is easily read by magnetic field sensors, with the aim of processing this signal so that it can be converted into specific functions for the vehicles and the rest of the traffic signals in the road infrastructure. Therefore, studies [2] in this field have addressed this issue by evaluating the performance of MAMs to allow for encoded roads, with the aim of establishing design methodologies and guidelines for their real-life implementation.

Figure 6 shows the potential areas of application, such as tunnels (where the GPS signal can be weak) and intersections (where the interaction can also involve traffic signals, pedestrians and personal mobility vehicles).

**Figure 6.** Schema of the applications of the encoded roads.

In addition, it is important to note that, with the emerging concept of smart cities, the mentioned benefits of MAMs would greatly benefit and be used in harmony with already existing smart systems. These other systems include smart sensors (both in the road and on vehicles and traffic signals), artificial intelligence (based on machine learning), image processing, big data and computer vision, to develop better traffic management systems and manage the road network in real time [61–66] (Figure 7).

**Figure 7.** Schema of a novel traffic management system using encoded roads.


**8.1 Biochemical function and analysis**

Unequivocal evidence for functional specialization can be drawn from studies of enzyme kinetics. By measuring the substrate affinity and catalytic rates of enzymes, for example, it is possible to quantitatively measure differences in performance between duplicate genes. Biochemical approaches are very labor intensive and only readily applicable to certain classes of genes, but the evidence they provide is direct and readily interpreted.

In a study highlighting the potential importance of the EAC model, Des Marais and Rauscher (2008) used enzyme affinity assays to demonstrate the enzymatic function of paralogous anthocyanin biosynthetic pathway genes in morning glories. Enzyme kinetics were compared across different species that differed by a duplication of a specific enzyme, with the unduplicated ortholog acting as a proxy for the ancestral function.

A recent innovative approach to studying gene function used directed (in lab) evolution to try and encourage a derived gene to revert to an hypothesized ancestral function (Bershtein & Tawfik, 2008). The authors studied the rate of 'reversion' and how this rate varied when various degrees of selective pressure (selecting for the ancestral function, the current function, or both) were applied. By studying the transitional states the gene underwent as its function shifted, the authors found evidence that best fit the subfunctionalization model of duplicate gene evolution.

### **8.2 Expression profiling and reconstruction**

Expression profiles (mined from expression assays like high-throughput sequencing) can provide immediate evidence of functional differentiation between duplicated genes (based on divergent, non-overlapping expression behaviour). For example, Yasukawa et al. (2010) use RNA in-situ and reporter analysis to determine precise expression localization and timing of duplicates in *Drosophila*. Rajashekar (2007) used microarray expression profiles to analyze the similarity of duplicates in the hydrophobin gene family in a fungus, *Paxillus involutus*.

By augmenting comparisons of gene expression profiles with reconstructed estimates of parental gene expression (via ancestral character estimation projected onto phylogenetic trees, see section 7), it is further possible to estimate how each gene progeny specialized from its parent following duplication. Case examples are the studies by Doxey et al. (2007) and Zou et al. (2009), both mentioned previously. Zou et al. (2009) reconstructed the expression behaviour in stress response genes in Arabidopsis, and with the additional information made available in the estimates of ancestral behaviour, the authors were able to infer patterns of subfunctionalization and neofunctionalization leading to the expression behaviour in extant duplicates. Karanth et al. (2009) reconstructed the ancestral tissue expression patterns of fatty acid binding proteins in zebrafish, revealing an apparent neofunctionalization event followed by subfunctionalization in a subsequent duplication.

### **8.3 Comparing with a non-duplicated ortholog**

50 Gene Duplication

The most important techniques for studying functional specialization focus on different aspects of gene function, but are all generally associated with the task of distinguishing the roles and fates of duplicate genes. Figure 3 provides a diagram summarizing the various

Unequivocal evidence for functional specialization can be drawn from studies of enzyme kinetics. By measuring the substrate affinity and catalytic rates of enzymes, for example, it is possible to quantitatively measure differences in performance between duplicate genes. Biochemical approaches are very labor intensive and only readily applicable to certain classes of genes, but the evidence they provide is direct and readily

In a study highlighting the potential importance of the EAC model, Des Marais and Rauscher (2008) used enzyme affinity assays to demonstrate the enzymatic function of paralogous anthocyanin biosynthetic pathway genes in morning glories. Enzyme kinetics were compared across different species that differed by a duplication of a specific enzyme, with the unduplicated ortholog acting as a proxy for the ancestral

A recent innovative approach to studying gene function used directed (in lab) evolution to try and encourage a derived gene to revert to an hypothesized ancestral function (Bershtein & Tawfik, 2008). The authors studied the rate of 'reversion' and how this rate varied when various degrees of selective pressure (selecting for the ancestral function, the current function, or both) were applied. By studying the transitional states the gene underwent as its function shifted, the authors found evidence that best fit the subfunctionalization model of

Expression profiles (mined from expression assays like high-throughput sequencing) can provide immediate evidence of functional differentiation between duplicated genes (based on divergent, non-overlapping expression behaviour). For example, Yasukawa et al. (2010) use RNA in-situ and reporter analysis to determine precise expression localization and timing of duplicates in *Drosophila*. Rajashekar (2007) used microarray expression profiles to analyze the similarity of duplicates in the hydrophobin gene family

By augmenting comparisons of gene expression profiles with reconstructed estimates of parental gene expression (via ancestral character estimation projected onto phylogenetic trees, see section 7), it is further possible to estimate how each gene progeny specialized from its parent following duplication. Case examples are the studies by Doxey et al. (2007) and Zou et al. (2009), both mentioned previously. Zou et al. (2009) reconstructed the expression behaviour in stress response genes in Arabidopsis, and with the additional information made available in the estimates of ancestral behaviour, the authors were able to infer patterns of subfunctionalization and neofunctionalization leading to the expression behaviour in extant duplicates. Karanth et al. (2009) reconstructed the ancestral tissue expression patterns of fatty acid binding proteins in zebrafish, revealing

**8. Making a case for duplicate specialization**

**8.1 Biochemical function and analysis**

interpreted.

function.

duplicate gene evolution.

in a fungus, *Paxillus involutus*.

**8.2 Expression profiling and reconstruction**

aspects of gene function that are amenable to these techniques.

As discussed earlier, one effective technique for estimating the function of the ancestor of a pair of duplicate genes is to refer to a related species where the locus is unduplicated. In this case, the assumption is that the orthologous gene is behaving in the related genome as the parental gene was behaving prior to the duplication event. This point of reference makes it possible to distinguish between models of duplicate retention, lending to support towards subfunctionalization versus neofunctionalization, for example.

In a study of zebrafish-specific WGD-produced duplicates, Kassahn et al. (2009) use unduplicated mouse orthologs as a reference, despite the considerable distance separating these two organisms. Multiple gene properties were compared between paralogs and their mouse ortholog, including sequence, structure, and expression information. The authors found support for neofunctionalization in a number of duplicates, and that regulatory changes were far more common than changes to gene products.

In a study of human genes, Panchin et al. (2010) chose to use distantly related gene family members as proxies for ancestors of recent paralogs. They demonstrated that, in many cases, the recent duplicates are evolving asymmetrically, with one duplicate accumulating sequence mutations much faster than its sibling.

Semon and Wolfe (2008) conducted a study comparing the fate of WGD duplicates in *X.laevis*, an allopolyploid, to *X. tropicalis*, a related species that did not undergo any WGD. Expression patterns were compared across 11 tissue types, and related losses of tissue breadth to possible subfunctionalization. In addition to this, the authors also compared the fate of duplicated genes produced through two different large-scale duplication mechanisms by comparing *X.laevis* to zebrafish, a species with a well studied WGD that did not stem from allopolyploidy. They find that duplicates retained in the *X.laevis* duplication were also frequently retained in duplicate in zebrafish, suggesting common influences on the duplicability of these gene varieties.

Another example of a well-studied allopolyploid, cotton, has been discussed in previous sections (Chaudhary et al., 2009; Flagel et al., 2008; Flagel & Wendel, 2010). One unique observation made possible in this system is the phenomenon of transgressive segregation, where the expression profiles of homeologous genes eventually evolve to resemble neither of the parental strains, suggesting a unique adaptation to the presence of two essentially complete genomes within a single cell (Flagel & Wendel, 2010).

### **8.4 Comparing gene product properties**

While not as easily assayed as gene expression, the transcribed content of genes (i.e. proteins) can also suggest the gain and loss of functions. As a simple example, the rate of protein sequence evolution can be compared between duplicates by comparing their respective rates of synonymous and non-synonymous mutation. While not necessarily illustrative of the nature of the difference, this method can provide evidence for asymmetrical selection, suggesting one duplicate is acquiring amino acid altering mutations faster than the other (Ganko et al., 2007). Working from a list of 15 of the most asymmetrically diverged WGD-derived protein sequences in *S. cerevisiae*, Turunen et al. (2009) noted substantial indels in addition to changes in important catalytic residues and

Detection and Analysis of Functional Specialization in Duplicated Genes 53

active/cofactor binding sites. A literature search seemed to support that many of these

Other aspects of gene function, such as the position and number of introns or methylation sites, have also been used to characterize divergence between duplicated genes. For example, Xiong et al. (2010) include intron position in a study of the expansion of the ABC transporter gene family in the ciliate *Tetrahymena thermophila*. In addition to comparing the expression profiles constructed by clustering gene expression data, the authors also compare intron positions to group the family members into functional subcategories (Xiong et al., 2010). A similar study has examined differential splicing forms in duplicate genes of

Yang et al. (2006) compared the "DNA-binding with one finger" (DOF) gene family across three plant species – rice, *Arabidopsis thaliana*, and poplar. Their multifaceted approach to describing gene function included an analysis of protein motif gain/loss and changes to methylation patterns. Combined with information about gene regulation drawn from microarrays, PCR and massively parallel signature sequencing, the authors compared the

When the information is available, the protein-protein interaction partners of duplicates can also be compared to study duplicate specialization. Nielsen et al. (2010) compared a set of residues in the tail ends of tubulin genes in fruit flies, noting divergence in these regions which may reflect changes in protein-protein interaction partners. Studying the applicability of models like subfunctionalization and neofunctionalization at the level of gene networks has helped integrate duplicate specialization into a broader systems biology context (Gibson

Studies of the evolution of duplicate genes are pushing the field towards more exacting standards and definitions for gene function. Since the rate and extent of duplicate gene specialization is dependent on so many factors, and since novel functions can emerge in so many different ways, integrative approaches will be of paramount importance to understanding this key aspect of genomic evolution. Future studies can benefit in particular from the inclusion of data from gene families as a whole, as this additional information helps both with estimating ancestral gene functions and with evaluating the breadth of

While empirical evidence of differential catalytic function remains the gold standard for proving functional specialization of duplicated genes, high-throughput studies exploiting the vast quantities of minable expression data provide a cheap and effective means for studying functional specialization at the level of whole gene families. Genomes with annotations beyond expression profiles (such as gene-by-gene interaction profiles and essentiality data) should be helpful for determining the extent to which functional changes

Arnaiz, O., Gout, J. F., Betermier, M., Bouhouche, K., Cohen, J., Duret, L., Kapusta, A.,

Meyer, E. & Sperling, L. (2010). Gene expression in a paleopolyploid: a

relative applicability of various duplicate retention models to the DOF family.

highly modified duplicates had acquired novel functions.

& Goldberg, 2009; MacCarthy & Bergman, 2007).

function previously covered by related genes.

at the regulatory level actually impact phenotype.

*Drosophila* (Zhan et al., 2011).

**9. Conclusion**

**10. References**

Fig. 3. Gene properties that can be examined for evidence of functional specialization. The top set (orange) are approaches that check for differences in gene regulation; expression levels reflect measurements of transcription in tissues in response to a series of stresses (e.g. as obtained from microarrays). The bottom set (blue) are aspects of the gene product that may differ between duplicates. Sequence logos may be generated using the WebLogo software (Crooks et al., 2004).

active/cofactor binding sites. A literature search seemed to support that many of these highly modified duplicates had acquired novel functions.

Other aspects of gene function, such as the position and number of introns or methylation sites, have also been used to characterize divergence between duplicated genes. For example, Xiong et al. (2010) include intron position in a study of the expansion of the ABC transporter gene family in the ciliate *Tetrahymena thermophila*. In addition to comparing the expression profiles constructed by clustering gene expression data, the authors also compare intron positions to group the family members into functional subcategories (Xiong et al., 2010). A similar study has examined differential splicing forms in duplicate genes of *Drosophila* (Zhan et al., 2011).

Yang et al. (2006) compared the "DNA-binding with one finger" (DOF) gene family across three plant species – rice, *Arabidopsis thaliana*, and poplar. Their multifaceted approach to describing gene function included an analysis of protein motif gain/loss and changes to methylation patterns. Combined with information about gene regulation drawn from microarrays, PCR and massively parallel signature sequencing, the authors compared the relative applicability of various duplicate retention models to the DOF family.

When the information is available, the protein-protein interaction partners of duplicates can also be compared to study duplicate specialization. Nielsen et al. (2010) compared a set of residues in the tail ends of tubulin genes in fruit flies, noting divergence in these regions which may reflect changes in protein-protein interaction partners. Studying the applicability of models like subfunctionalization and neofunctionalization at the level of gene networks has helped integrate duplicate specialization into a broader systems biology context (Gibson & Goldberg, 2009; MacCarthy & Bergman, 2007).


*4.3. Pulse-Chase and Immunoprecipitation*

BHK cells lines stably expressing CFTR variants were starved for 30 min in methionine-free α-modified Eagle's medium or minimal essential medium and then pulsed for 30 min in the same medium supplemented with 100 µCi/mL [35S] methionine. After chasing for 0, 0.5, 1, 1.5, 2, and 3 h in a-modified Eagle's medium with 8% (*v*/*v*) fetal bovine serum and 1mM non-radioactive methionine, cells were lysed in 1 mL of Radioimmunoprecipitation assay (RIPA) buffer [1% (*w*/*v*) deoxycholic acid, 1% (*v*/*v*) Triton X-100, 0.1% (*w*/*v*) Sodium dodecyl sulfate (SDS), 50 mM Tris, pH 7.4, and 150 mM NaCl]. The immunoprecipitation (IP) was carried out using the anti-CFTR 596 antibody in independent experiments and Protein G–agarose or Protein A–Sepharose beads. Immunoprecipitated proteins were

eluted from the beads with sample buffer for 1h at room temperature and then electrophoretically separated on 7% (*w*/*v*) polyacrylamide gels. Gels were pre-fixed in methanol/acetic acid (30:10, *v*/*v*), washed in water and, for fluorography, soaked in 1M sodium salicylate for 60 min. After drying at 80 ◦C for 2 h, gels were exposed to X-ray films and further analyzed and quantified by densitometry.


2.1 Construction of thermosyphon heat pipe

Thermosyphon is a vessel closed at both ends and attached with a small charging pipe placed at one of the ends. The air in the vessel is evacuated creating a vacuum, then charged with working fluid through the charging pipe. The pipe is usually divided into the following sections:

The materials for the manufacturing of thermosyphon are carefully selected to ensure its effective performance. Other considerations are the type and the quantity of working fluid to be charged into the pipe.

#### 2.2 Operation of thermosyphon heat pipe

The working principles of thermosyphon are similar to that of the wick heat pipe, but differ in the process of the return of the condensed liquid in the condenser due to the absence of wick structure. For proper operation of thermosyphon, the condenser is placed at the top of the evaporator so that the condensed liquid will return to the evaporator by gravity. Figures 4 and 5 show a schematic diagram and a model of a typical thermosyphon (constructed in the University of Birmingham, UK) with heat supplied by coil of wire and heat rejected to the flowing water in the water jacket provided on the condenser section [17]. However, in some operation set ups, the heat can be supplied by hot water surrounding the evaporator of the pipe.

#### 2.2.1 Operation limits of heat pipe

Heat pipe (with or without wick materials) operates within certain limits which are shown in Figure 6. For the heat pipe to operate, the maximum capillary pumping pressure must be greater than the total pressure drop; thus:

$$
\Delta P\_{c,\max} \ge \Delta P\_l + \Delta P\_v + \Delta P\_\mathcal{g} \tag{1}
$$

2.3 Advantages of thermosyphon over wick heat pipe

ii. More compactness

Limitation of heat pipe for heat transport.

3D view of a typical thermosyphon pipe.

Thermosyphon Heat Pipe Technology

DOI: http://dx.doi.org/10.5772/intechopen.85410

Figure 6.

11

Figure 5.

iv. Cost-effectiveness

iii. High durability and reliability

vi. Simplicity in construction

on the overall thermal resistance Rth, given by:

advantages over wick heat pipe, some of which are listed below:

v. Less weight due to the absence of wick materials

2.4 Measurement of the performance of thermosyphon

Apart from the general advantages of heat pipe, thermosyphon has other

i. Relative low-temperature difference between the heat source and heat sink

The performance of thermosyphon under different conditions is evaluated based

Rth ¼ Tae � Tac Qin

(2)

The pressure drop is the sum of the following:

ΔPl = Pressure drop necessary for the liquid to return from the condenser to the evaporator.

ΔPv = Pressure drop necessary for the vapour to rise from the evaporator to the condenser.

ΔPg = Pressure due to gravity whose value depends on the angle of inclination of the pipe.

If condition in Eq. (1) is not met (capillary limit), then the wick materials will dry out and the pipe will not operate. Detailed discussions on the heat pipe limits (shown in Figure 6) are available in heat pipe books, which can be referred.

Figure 4. Dimensions of a typical thermosyphon with water manifold [17].

The materials for the manufacturing of thermosyphon are carefully selected to ensure its effective performance. Other considerations are the type and the quantity

The working principles of thermosyphon are similar to that of the wick heat pipe, but differ in the process of the return of the condensed liquid in the condenser due to the absence of wick structure. For proper operation of thermosyphon, the condenser is placed at the top of the evaporator so that the condensed liquid will return to the evaporator by gravity. Figures 4 and 5 show a schematic diagram and a model of a typical thermosyphon (constructed in the University of Birmingham, UK) with heat supplied by coil of wire and heat rejected to the flowing water in the water jacket provided on the condenser section [17]. However, in some operation set ups, the heat

Heat pipe (with or without wick materials) operates within certain limits which

ΔPl = Pressure drop necessary for the liquid to return from the condenser to the

ΔPv = Pressure drop necessary for the vapour to rise from the evaporator to the

ΔPg = Pressure due to gravity whose value depends on the angle of inclination of

If condition in Eq. (1) is not met (capillary limit), then the wick materials will dry out and the pipe will not operate. Detailed discussions on the heat pipe limits (shown in Figure 6) are available in heat pipe books, which can be referred.

ΔPc,max ≥ ΔPl þ ΔPv þ ΔPg (1)

are shown in Figure 6. For the heat pipe to operate, the maximum capillary pumping pressure must be greater than the total pressure drop; thus:

can be supplied by hot water surrounding the evaporator of the pipe.

The pressure drop is the sum of the following:

Dimensions of a typical thermosyphon with water manifold [17].

of working fluid to be charged into the pipe.

Recent Advances in Heat Pipes

2.2 Operation of thermosyphon heat pipe

2.2.1 Operation limits of heat pipe

evaporator.

condenser.

the pipe.

Figure 4.

10

Figure 5. 3D view of a typical thermosyphon pipe.


*3.1. Polymers*

Polymer materials are composed of chemical compounds typically formed from carbon, hydrogen, oxygen, and nitrogen. These monomer structures are repeated and bound with itself to create a longer molecular chain [35]. Polymers have become the most popular choice for 3D bioprinting in biomedical applications as it is often inexpensive, biocompatible, biodegradable, and can easily be manipulated with regard to its mechanical, chemical, and biological properties [36,37]. The polymer's manipulative property is particularly important in 3D printing because the printability of a material is dependent on its viscosity [37]. The ink being printed should be stiff enough to support subsequent layers, however if it becomes too viscous, it may lead to blockage of the printing nozzle [37]. Blockage is further avoided by printing the material in its pre-polymerized form. However, a limitation to using synthetic polymers is that the printing process for synthetic polymers induces high temperatures in which cells and growth factors cannot survive or remain active. Thus, cells or biologically active components are incorporated post-extrusion [37].

Polymeric materials can be printed in various forms as well, including powder, filament, and sheet form [38]. The polymer category encompasses a wide variety of materials that can range from being soft to hard, or synthetic to natural, however, the most commonly used polymers in craniofacial tissue engineering include PCL, PEEK, PLA, poly(lactic-co-glycolic acid) (PLGA), and chitosan [2,39,40]; the choice of polymer will depend on the goal of the researcher. For example, PLA or PCL are popular choices for drug delivery purposes, while alginates and gelatin are more popular choices for cell encapsulation [37,38]. The wide variety in polymeric materials makes them highly versatile, and thus may be highly useful in dental tissue regeneration when being combined with the superior spatial resolution provided by 3D printing.


**1. Introduction of industry 4.0 technologies**

Industry 4.0, often known as smart manufacturing, is built on information technology (IT)-driven industrial processes [1]. It combines smart factories and products with the Internet of Things [2, 3], with the goal of providing real-time information on production, machines, and component flow, and integrating this data to assist managers in making decisions, monitoring performance, and tracking parts and products.

#### **1.1 Industry 4.0 technologies**

#### *1.1.1 Big data analytics*

Big data analytics (BDA) is the collecting of real-time data, the use of analytical tools, and the use of computer algorithms to derive relevant insights and patterns for better decision-making using data, text, audio, video [4]. Big Data 6Vs framework describe Volume, a very large amount of data; Velocity, the data are generated very quickly and must be processed in a very short time; Variety, a large number of structured and unstructured data types are processed; Value, the goal is to generate significant value for the organisation; Veracity, reliability of the processed data; and Variability, flexibility to adapt to new data formats by collecting, storing, and processing them. BDA aids in the achievement of long-term corporate success and competitive advantage [5].

#### *1.1.2 Internet of things*

Internet of Things (IoT) allows devices to communicate with one another without the need for human involvement [6]. The Internet of Things is based on a network of devices, each of which has its own unique identity to the computer system to which it is attached. IoT-controlled industrial systems are intelligent because they can accurately and efficiently operate all linked equipment from afar [7]. For example, big data has been utilised to enhance product development [8], SC demand forecasts [9], and green production strategies [8, 10].

#### *1.1.3 Block chain technology*

Blockchain is a distributed data structure—a distributed ledger—in which the data is shared on a peer-to-peer network. The network members and nodes communicate and validate the data following a predefined protocol without a central authority. Distributed ledgers can be either decentralised, giving equal rights to all users or centralised, providing specific users with special rights.

#### *1.1.4 Artificial intelligence*

Artificial intelligence (AI) is a field in computer science encompassing the development of systems capable of performing tasks that normally necessitate human intelligence. The science of making machines do things that would require intelligence if done by men. Three main functions are sensing and interacting, learning from the data and decision making.

#### *1.1.5 Autonomous robotic vehicle*

Autonomous vehicles as more environmentally friendly automobiles are capable of sensing its navigation without human input, thus the costs, emission level and working time can be reduced dramatically. They can be used not only for long distances, but also for operational level of the supply chain.

#### *1.1.6 Additive manufacturing (3D printing)*

Additive manufacturing (AM) begins with the creation of a three-dimensional digital model using computer-aided design software, followed by the formation of the finished object using a 3D printer [11]. AM helps to Industry 4.0 goals by generating personalised items in small batches and producing complex and lightweight designs with great precision [3].

## *1.1.7 Cloud manufacturing*

Cloud manufacturing is a method of providing a shared network of manufacturing resources and capabilities through the internet by establishing a virtual and global environment. The logic of cloud manufacturing is service-based, which means providers and consumers interact to sell and acquire services such as product design, simulation, production, and assembly [12].


**1.2 Derivatives**

For derivatives based Minimization methods, calculation of the derivatives of the energy is performed with respect to the different variables i.e. Cartesian or internal coordinates, as the case may be. These derivatives can be generated using either analytical or numerical procedures but derivatives obtained through analytical procedure are more preferred because these can be generated more readily and these are more exact. Although, if derivatives generated by only numerical procedure is available then one should use a non-derivative Minimization procedure as it is more efficient [3].

Although, in some situations it is always preferable to use derivatives generated though numerical procedure. By following way these can be generated: suppose there is a small alteration (*δx*i) in one of the coordinates *x*i and the energy calculation is performed for this new alteration the by dividing the alteration in energy (*δE*) by the alteration in coordinate (*δE / δx*j), the derivative *∂E/∂x*i is obtained. This rigorously yields the derivative at the mid-point between the two points *x*i and *x*i + *δx*i. A more correct value of the derivative at the point *x*i; could also be acquired (at the price of a further energy calculation) by assessing the energy at two points, *x*i + *δx*i and *x*i – *δx*i. The derivative is then obtained by dividing the variation with in the energies by 2*δx*i.


**4. MONITOR digital health ecosystem**

The first case of COVID-19 in Mexico was reported on February 27, 2020 [108, 109]. Fully aware of the benefits of DH, the CSF had begun to work on the design of a robust response plan in January 2020. On March 13, 2020, the Carso COVID Protocol was launched, including a comprehensive set of actions and recommendations to prevent and contain COVID-19 in the workplace. As part of implementation of the Protocol, the CSF launched the MONITOR digital health ecosystem (MDHE), aimed at monitoring the wellbeing of CG employees and their families.1

1 Annex 1 provides a detailed description of the recommendations and actions in the Carso COVID-19 protocol.

*The MONITOR Ecosystem: A Digital Health Intervention for the Early Detection, Control… DOI: http://dx.doi.org/10.5772/intechopen.95260*

MONITOR was initially designed based on WHO and CDC guidelines, and its recommendations are in compliance with current regulations of Mexico's Ministry of Health. Furthermore, MONITOR has been continuously updated with the latest available scientific evidence.

The MDHE comprises three interconnected platforms operating concurrently: a mobile phone application and a web portal for employees and their families to register and report symptoms on a daily basis, the Integrated Measurement for Early Detection (MIDO)-COVID Platform to assess NCDs and COVID-19 serological status among employees at worksites, and the Health Intelligence Platform with robust analytics to support decision making. Importantly, the MDHE is compliant with Mexico's regulatory standards in terms of confidentiality, security and privacy.

## **4.1 Prevention and containment of COVID-19 in the workplace**

MONITOR enables the implementation of a COVID-19 prevention and containment strategy according to the following stepwise process:

	- a.No risk (no symptoms): the employee is encouraged to continue notifying on a daily basis.
	- b.Mild risk: the employee is encouraged to increase the number of notifications to twice a day and to continue monitoring.
	- c.Moderate risk: the employee is encouraged to increase notifications to twice a day and if necessary, to call a dedicated call medical center operating 24/7/365.
	- d.Severe risk: the employee is encouraged to increase notifications to twice a day and to call a medical call center. In addition, an alert is triggered to the human resources (HR) department of the employee's company, to signal a potential complication.
	- a.The employee receives a pulse oximeter for self-monitoring during the 14-day isolation period. The employee is required to notify symptoms via MONITOR.


represents mean S.E.M (n=6). \**p*<0.05, \*\*\**p*<0.001, statistically different from control group. ### *p*<0.001, ## *p*<0.01 statistically different from scopolamine-treated group.

neuroprotective potential of Sch B on the basis of its antioxidant property.

**4. Behavioral evidences**

**4.1 Passive avoidance task (PAT)**

**Control 0 THA 10 25 50**

\*\*\*

**0**

**100**

**Latency time (sec)**

**200**

**300**

**Scopolamine 1mg/kg**

**### ###**

**A B**

**## ###**

The acquisition and retention of a spatial navigation task is examined using a Morris Water Maze (Kumar, Seghal et al. 2006). The hippocampal formation plays an important role in memory and learning. The Morris Water Maze (MWM) is a test of spatial learning for rodents that relies on distal cues to navigate from started locations around the perimeter of an open swimming arena to locate a submerged escape platform. Spatial learning is assessed across repeated trials and reference memory is determined by preference for the platform area when the platform is absent (Vorhees and Williams 2006).

The memory enhancing potential of Sch B was also observed in the spatial memory task where Sch B treatment significantly decreased the escape latency and the result was comparable to that of THA. In the probe trail the time spent in the target quardrant was significantly improved the Sch B (Giridharan, Thandavarayan et al. 2011).

Schisandrin B, a Lignan from *Schisandra chinensis* Prevents

**Control**

**Control**


**Polyolefins**

**Chapter 5**

**Provisional chapter**

**Poly(olefin sulfone)s**

**Poly(olefin sulfone)s**

Takeo Sasaki, Khoa Van Le and Yumiko Naka

Additional information is available at the end of the chapter

Takeo Sasaki, Khoa Van Le and Yumiko Naka

DOI: 10.5772/intechopen.69317

In this chapter, we introduce poly(olefin sulfone)s and review the recent progress on the photoinduced depolymerization of poly(olefin sulfone)s as well as their applications. Poly(olefin sulfone)s combined with photobase generators (PBGs) are depolymerized upon irradiation with light. A poly(olefin sulfone) is a 1:1 alternating copolymer of olefin monomer and sulfur dioxide in which the protons on the carbons adjacent to the sulfonyl groups can be readily abstracted by a base. This removal leads to a depolymerization chain reaction, resulting in incorporation of a photobase generating chromophore that can undergo a photoinduced unzipping reaction. During this reaction, the original olefin monomer and sulfur dioxide are regenerated from the primary chain of the poly(olefin sulfone). The photoinduced depolymerization of poly(olefin sulfone)s has been investigated for a wide variety of applications, including stereolithography, printable microcir-

**Keywords:** poly(olefin sulfone)s, depolymerization, photopolymers, dismantlable

Photopolymers can change their physical and/or chemical properties through the absorption of light [1]. Although many studies on photopolymers have been reported, a few studies show photoinduced conversion to monomers under mild conditions. If a portion of the polymer is converted completely into monomers under light irradiation, the average molecular weight decreases, and this process has potential application to sophisticated device processing, including stereolithography without solvents. Such depolymerization under mild conditions is expected to yield recyclable (and, thus, environmentally friendly or "green") polymers. However, most of the depolymerizable polymers reported require X‐ray or electron‐beam irradiation to trigger the depolymerization process [2, 3]. Only a few reports describe use

Additional information is available at the end of the chapter

cuit fabrication, and removable adhesives.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

© 2018 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

http://dx.doi.org/10.5772/intechopen.69317

**Abstract**

adhesives

**1. Introduction**

**Provisional chapter**


**Smart Control of Ship Propulsion System**

Editors

**Marco Altosole Maria Acanfora Flavio Balsamo Bowen Xing**

Basel • Beijing • Wuhan • Barcelona • Belgrade • Novi Sad • Cluj • Manchester

*Editors* Marco Altosole Department of Industrial Engineering University of Naples "Federico II" Naples, Italy

Bowen Xing College of Engineering Science and Technology Shanghai Ocean University Shanghai, China

Maria Acanfora Department of Industrial Engineering University of Naples "Federico II" Naples, Italy

Flavio Balsamo Department of Industrial Engineering University of Naples "Federico II" Naples, Italy

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Journal of Marine Science and Engineering* (ISSN 2077-1312) (available at: https://www.mdpi.com/ journal/jmse/special issues/smart ship propulsion).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

Lastname, A.A.; Lastname, B.B. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-9356-2 (Hbk) ISBN 978-3-0365-9357-9 (PDF) doi.org/10.3390/books978-3-0365-9357-9**

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license.


**3. TFPI-2 plasmid expression vector construct**

#### **3.1 TFPI-2 gene sequencing**

162 Hepatocellular Carcinoma – Basic Research

In Situ Hybridization Detection Kit (Boster, Wuhan, China) according to the manufacturer's instructions. Briefly, the sections were hybridized in prehybridization buffer supplemented with 0.1 ug/ml digoxigenin-labeled, 1.2-kb antisenese TFPI-2 probe overnight at 37C and incubated with biotinylated mouse antidigoxigenin antibody (1:1000 dilution), then incubated with biotinylated peroxidase. Staining was developed with DAB. Slides were counterstained with hematoxylin, dehydrated, and mounted. The number of cells stained brown (indicating the presense of TFPI-2 mRNA) were assessed by light microscopy. The hybridization probe replaced with PBS was used as a negative control. Mature placenta

Tissue sections were prepared in the same manner as above. Then the expression of TFPI-2 was determined by incubation with a mouse polyclonal antibody against human TFPI-2 (Santa Cruz, CA), horseradish peroxidase (HRP)-conjugated sheep anti-mouse lgG secondary antibodies (Chinagen, Shenzhen, China), and final detection using the non Biotin-labeled

Fig. 1. TFPI-2 expression in normal hepatic and hepatocarcinoma tissues. Expression of (A) TFPI-2 mRNA in hepatocarcinoma tissue and (B) tumor-adjacent normal hepatic tissue was examined by *in situ* hybridization with a digoxigenin-labeled TFPI-2 probe. Expression of (C) TFPI-2 protein in hepatocarcinoma tissue and (D) tumor-adjacent normal hepatic tissue was examined by immunohistochemical analyses with TFPI-2 antibody. Magnification, x400.

tissue, known to express large amounts of TFPI-2, was used as a positive control.

**2.3 Immunohistochemistry**

The RNA from hepatic tissue of human fetor (Shenzhen People's Hospital) was isolated and full length TFPI-2 cDNA was amplified with RT-PCR kit (TaKaRa). The cloned gene was inserted into plasmid pcDNA2.1 (Chinagen, Shenzhen, China), and sequenced from forward and reverse direction at Shanghai Biotechnology (China), then it was inserted into eukaryotic expression vetor pcDNA3.1, a gift from Dr Tiyuan Li (Central Laboratory, Shenzhen People's Hospital) verified by enzyme digestion and sequencing.

Tissue Factor Pathway Inhibitor-2 Inhibits the Growth and Invasion of

vector pcDNA3.1-TFPI-2 was constructed accurately.

**4. Construct HepG2-TFPI-2 stable cell line**

**4.1 Cell culture and transfection**

construct (HepG2-TFPI-2).

**4.2 RT-PCR**

establishing HepG2-TFPI-2 stable cell line.

ATAGGATCCACATGGACCCGCTCGC-3'

**3.2 Plasmid construct**

sequencing.

Hepatocellular Carcinoma Cells and is Inactivated in Human Hepatocellular Carcinoma 165

A 0.7-kb fragment encoding TFPI-2 cDNA was amplified from normal liver tissue with the primers 5'-GCTTTCTCGGACGCCTTGC-3' and 5'-GAATACGACCCCAAGAAATGAGTGA-3'. PCR product was purified and cloned into the BamHΙ and XhoΙ sites of the pCDNA3.1 expressing vector. The DNA sequence of the recombinant plasmid was confirmed via DNA

The Chinese TFPI-2 gene is 1222bp. Sequencing results showed that the cloned Chinese TFPI-2 gene has three bases different (258, 585 and 884 bp) with that registered in Genbank (Fig. 2). Our sequencing results has been transmitted and accepted by Genbank, accession number is TFPI AY691946. TFPI-2 gene was inserted to eukaryotic expression vetor pcDNA3.1 successfully. The result of nucleotide sequencing confirmed that the recombinant

To explore the functional role of TFPI-2 in HCC, we employed HepG2 cells as a model. Based on RT-PCR, we found that the expression of TFPI-2 mRNA in HepG2 cells was undetected (data not shown), therefore we introduced TFPI-2 into HepG2 cells by

Human hepatoma HepG2 cells were obtained from Cancer Institute, Chinese Academy of Medical Sciences, and cultured in 6% CO2 to 94% air and 96% humidity at 37C in DMEM supplemented with 10% bovine calf serum (Hyclone, Logan, UT), 1.0% glutamine, 100 ug/ml strepotomycin, 100 ug/ml penicillin. The recombinant constructs or pCDNA3.1 vector was transfected into HepG2 cells using Lipofectamine 2000 transfection reagent (Invitrogen) according to the manufacturer's instructions. Selection of transfected cells with 0.8 mg/ml G418 sulfate (Invitrogen) was initiated 48 h after transfection. After a 4 week selection, stable transfectants were expanded and used for the study. The HepG2 cells were divided into three groups: HepG2 parental cells (HepG2-P), HepG2 cells transfected by pCDNA3.1 vector (HepG2-V) and HepG2 cells transfected by TFPI-2

Total RNA was isolated from HepG2 cells using TRIZOL reagent (Invitrogen) following a standard protocol. Using the 2-step RT-PCR kit (TaKaRa), cDNA was synthesized with RNA as the template. PCR amplification of human TFPI-2 and ß-actin was performed with Taq Master Mix (Promega, Madison, WI, USA) with synthesized cDNA. The primer were

and 5'-GGCCTCGAGAAATTGCTTCTTCCGAATTTCC-3', amplicion 700 bp. ß-actin 5'-CTGGCACCACACCTTCTACAATG-3' and 5'-AATGTCACGCACGATTTCCCGC-3'. The PCR condition were: denaturing at 95C for 30 sec, annealing at 52C for 30 sec, and extension at 72C for 40 sec for 32 cycles. After electrophoresis of PCR products, the data were analyzed by Image Master Tatal Laboratory ID software. The level of TFPI-2 mRNA

synthesized by Shanghai Biotechnology (China) as follows: TFPI-2 5'-

was calculated by the ratio of density of TFPI-2 to ß-actin.

Fig. 2. Homology analysis of TFPI-2 gene

#### **3.2 Plasmid construct**

164 Hepatocellular Carcinoma – Basic Research

Fig. 2. Homology analysis of TFPI-2 gene

A 0.7-kb fragment encoding TFPI-2 cDNA was amplified from normal liver tissue with the primers 5'-GCTTTCTCGGACGCCTTGC-3' and 5'-GAATACGACCCCAAGAAATGAGTGA-3'. PCR product was purified and cloned into the BamHΙ and XhoΙ sites of the pCDNA3.1 expressing vector. The DNA sequence of the recombinant plasmid was confirmed via DNA sequencing.

The Chinese TFPI-2 gene is 1222bp. Sequencing results showed that the cloned Chinese TFPI-2 gene has three bases different (258, 585 and 884 bp) with that registered in Genbank (Fig. 2). Our sequencing results has been transmitted and accepted by Genbank, accession number is TFPI AY691946. TFPI-2 gene was inserted to eukaryotic expression vetor pcDNA3.1 successfully. The result of nucleotide sequencing confirmed that the recombinant vector pcDNA3.1-TFPI-2 was constructed accurately.


**A Preliminary Study on Metabolome Profiles of Bu**ff**alo Milk and Corresponding Mozzarella Cheese: Safeguarding the Authenticity and Traceability of Protected Status Bu**ff**alo Dairy Products**

**Angela Salzano 1, Gelsomina Manganiello 2, Gianluca Neglia 1,\*, Francesco Vinale 1,3, Donato De Nicola 1, Michael D'Occhio 4 and Giuseppe Campanile 1**

#### Academic Editor: Derek J. McPhee

Received: 12 November 2019; Accepted: 10 January 2020; Published: 12 January 2020

**Abstract:** The aim of this study is to combine advanced GC-MS and metabolite identification in a robust and repeatable technology platform to characterize the metabolome of buffalo milk and mozzarella cheese. The study utilized eleven dairies located in a protected designation of origin (PDO) region and nine dairies located in non-PDO region in Italy. Samples of raw milk (100 mL) and mozzarella cheese (100 g) were obtained from each dairy. A total of 185 metabolites were consistently detected in both milk and mozzarella cheese. The PLS-DA score plots clearly differentiated PDO and non-PDO milk and mozzarella samples. For milk samples, it was possible to divide metabolites into two classes according to region: those with lower concentrations in PDO samples (galactopyranoside, hydroxybuthyric acid, allose, citric acid) and those with lower concentrations in non-PDO samples (talopyranose, pantothenic acid, mannobiose, etc.,). The same was observed for mozzarella samples with the proportion of some metabolites (talopyranose, 2, 3-dihydroxypropyl icosanoate, etc.,) higher in PDO samples while others (tagatose, lactic acid dimer, ribitol, etc.,) higher in non-PDO samples. The findings establish the utility of GC-MS together with mass spectral libraries as a powerful technology platform to determine the authenticity, and create market protection, for "Mozzarella di Bufala Campana."

**Keywords:** metabolome; GC-MS; buffalo; milk; mozzarella; authenticity

#### **1. Introduction**

The authenticity, integrity, and traceability of food products is very important for market protection in global food systems [1–4]. This applies particularly to food that has a market niche and for which consumers pay a high price. The premium price of niche food makes it susceptible to substitution with falsely-labelled, non-authentic food [5]. Examples are substitution in olive oil [6,7] and dairy products [8–10]. Italian mozzarella cheese is made from the milk of Italian Mediterranean water buffalo (*Bubalus bubalis*) and is recognized globally for its exceptional eating qualities. The water buffalo undergo intense selection for efficiency of production and product quality, and today has an important commercial and cultural niche in several regions of Italy. Genuine Italian mozzarella is known as

"Mozzarella di Bufala Campana." It has European Union protected designation of origin (PDO) and protected geographical indication (PGI) status. This identifies genuine mozzarella with four regions in Italy (Apulia, Campania, Lazio, Molise) [11,12]. Attempts are regularly made to place non-authentic mozzarella in premium markets as a substitute for buffalo mozzarella cheese [13–15]. Consumers can be denied a genuine product and confidence in certified farmers is threatened. The potential for product substitution has led to interest in the development of screening technology that ensures the authenticity of buffalo mozzarella and traceability to geographical origin [14,16,17].

The characterization of the metabolome of food is a promising approach for establishing authenticity [18]. The metabolome signature of biological material can be obtained using gas chromatography/mass spectrometry (GC-MS) [19,20], NMR spectroscopy [21,22], and high-resolution magic angle spinning (HRMAS) NMR spectroscopy [23]. Some initial screening of buffalo mozzarella cheese has used NMR [16], HRMAS NMR [16], and GC-MS/LC-MS [14,15,17]. To date, individual studies have looked at the metabolome of either buffalo milk or mozzarella. Substitution can occur at different stages of the supply chain and it is important that authenticity can be established from primary product (milk) through to secondary product (mozzarella). There are also limited studies in dairy on the impacts of breeding, diet, and geographical location on product quality [24]. The present study sought to compare, for the first time, the metabolomes of both unprocessed milk and corresponding mozzarella cheese for buffalo in PDO and non-PDO regions in Italy. Differences in the milk and/or mozzarella metabolomes between buffalo in PDO and non-PDO regions have potential practical application in safeguarding the authenticity and traceability of protected status buffalo dairy products.

#### **2. Results**

No differences were observed in the quality of milk and mozzarella cheese from PDO and non-PDO regions (Tables 1 and 2). A total of 185 metabolites were detected consistently. In particular, 113 compounds were detected in milk, 102 in mozzarella cheese, and 30 compounds out of 185 were found in both matrices. The PLS-DA score plots clearly differentiated PDO and non-PDO milk and mozzarella samples (Figure 1(A1,B1)). The 15 highest scoring variable importance in projection VIP variables (VIP score > 1.5) identified by PLS-DA are shown in Figure 1(A2,B2).

**Table 1.** Average composition of milk samples from different farms located in protected destination of origin (PDO) and non-PDO regions in Italy. Data are expressed as means ± ES.

U.M. Unit measure.

U.M. Unit measure.

**Table 2.** Average mozzarella cheese composition of samples from different farms located in protected designation of origin (PDO) and non-PDO regions in Italy. Data are expressed as means ± ES.

**Figure 1.** Partial least square discriminant analysis (PLS-DA) models to discriminate PDO and non-PDO buffalo milk (**A1**) and mozzarella (**B1**) samples. The explained variance of each component is shown in parentheses on the corresponding axis. (**A2**) and (**B2**) panels show the 15 top-scoring variable importance in projection (VIP) metabolites (VIP-score ≥ 1.5) for milk (A2) and mozzarella (B2) samples. The colored boxes on the right indicate the relative amount of the corresponding metabolite in each group under study.

Differences in metabolite concentrations between PDO and non-PDO raw milk samples allowed the metabolites to be separated into two classes: those with lower (*p* < 0.05) concentrations in PDO milk (galactopyranoside, hydroxybuthyric acid, allose, citric acid) and those with higher (*p* < 0.05) concentrations in PDO milk (talopyranose, pantothenic acid, mannobiose, maltose, phosphate, mannofuranose, dodecanoic acid, lactose, palmitic acid, n-acetyl glutamic acid, *n*-acetyl glucosamine) (Figure 2).

**Figure 2.** Box and Whisker plot of the VIP metabolites in buffalo milk samples. Boxes represent non-PDO (yellow) and PDO (blue) milk samples. The vertical axis reports the log of the gas chromatography mass spectrometry values of the normalized area of each metabolite.

Metabolites could also be separated into two classes for mozzarella cheese samples: those with higher (*p* < 0.05) concentrations in PDO mozzarella (talopyranose, 2, 3-dihydroxypropyl icosanoate, sorbose, 4-pnehyl glutamic acid, oxalic acid, galactose) and those with higher (*p* < 0.05) concentrations in non-PDO mozzarella (tagatose, lactic acid dimer, ribitol, dodecyl thioglycolate, *n*-acetyl glucosamine, valine, diethylene glycol) (Figure 3).

**Figure 3.** Box and Whisker plot of the VIP metabolites in buffalo mozzarella samples. Boxes represent non-PDO (green) and PDO (orange) mozzarella samples. The vertical axis reports the log of the gas chromatography mass spectrometry values of the normalized area of each metabolite.

#### **3. Discussion**

The present study sought to compare the metabolomes of unprocessed milk and corresponding mozzarella cheese for buffalo in PDO and non-PDO regions in Italy. It was found that the milk metabolome differed between buffalo in PDO and non-PDO regions. The mozzarella metabolome also differed between buffalo in PDO and non-PDO regions. This is the first time that some metabolites have been detected in both the metabolome of unprocessed milk and corresponding mozzarella cheese in buffalo. It is also the first time that differences have been found between buffalo in PDO and non-PDO regions in both milk and mozzarella metabolomes. The number of individual farms from PDO (n = 11) and non-PDO (n = 9) regions might be considered a relatively small sample size, although it was comparable to numbers in previous reports that looked at the metabolome [24–26]. Notwithstanding the sample size, the combination of GC-MS and mass spectral libraries (NIST library) proved to be a robust technology platform for determining the metabolomes of buffalo milk and mozzarella. This platform, together with a rigorous analysis of the data, has provided a sound foundation to inform further studies. In particular, a number of notable metabolites identified in unprocessed milk (n = 15) and mozzarella cheese (n = 13) could be used to validate the utility of the metabolome for safeguarding the protected status buffalo dairy products. Candidate metabolites that differentiated PDO from non-PDO milk included several carbohydrates (d-allose, mannofuranose, maltose, and talopyranose). Candidate metabolites that differentiated both milk and mozzarella between PDO and non-PDO origin were talopyranose and *N*-acetyl glucosamine. Talopyranose is the pyranose form of talose and an epimer of glucose. Talose was already reported to be a "differential" marker between bovine milk and goat milk [27]. *N*-acetylglucosamine is a derivative amide of the monosaccharide glucose and a secondary amide between glucosamine and acetic acid. It is significant in several biological systems and a major component of the cell walls of most fungi [28]. Talopyranose is of particular interest as amounts were substantially higher in both milk and mozzarella of PDO origin. A number of saccharides (tagatose, talopyranose, sorbose, galactose) differentiated mozzarella of DPO and non-DPO origin. Two metabolites in mozzarella cheese had markedly different concentrations between PDO and non-PDO samples. These metabolites could not be identified from public mass spectral libraries. However, they require further study as they may emerge as highly valuable in establishing the authenticity and integrity of protected status buffalo mozzarella cheese.

Irrespective of PDO or non-PDO origin, the milk aqueous fraction was rich in short-chain saturated carboxylic acids (carbonic acid, acetic acid, propanoic acid, butanoic acid, octanoic acid, decanoic acid) and long-chain saturated carboxylic acids (lauric acid, palmitic acid, stearic acid). Free amino acids in milk included serine, threonine, and valine. Compared with milk, mozzarella cheese contained lower amounts of some short-chain saturated carboxylic acids (acetic, propanoic, nonanoic acid) and only two long-chain saturated carboxylic acids were found in mozzarella (palmitic acid, stearic acid). Mozzarella was, however, richer in free amino acids (serine, leucine, isoleucine, alanine, proline, valine, norvaline). Some of the metabolites found in mozzarella samples in the present study (lactic acid dimer, valine, oxalic acid, and galactose) were also identified in a study that looked at the metabolomic and microbiological differences of Italian mozzarella cheese produced with buffalo or cow milk [29].

Processing alters milk constituents and their concentrations. For example, milk monosaccharide levels change in response to heat treatment [30] and storage [31]. The composition of the starter, which can be influenced by the environment, management and cheese-making technologies, also affects cheese characteristics by altering the milk quality. [32–34]. In PDO mozzarella, there may have been a synergistic effect between different lactic acid bacteria and yeast species, which ferment residual galactose and lactose and thereby increase lactic acid production. The processed cheese industry uses citrate or phosphate salts to sequester Ca2+ from residual colloidal calcium phosphate. This solubilizes caseins which can then emulsify fat globules. The acidity of whey at drainage, and the rate of acid development, are important parameters that determine the mineral content, acidity, and quality of cheese [35]. Milk of PDO origin had higher phosphate content and lower citric acid compared to non-PDO milk. Differences in milk salt composition most likely contributed to the differences in metabolites between PDO and non-PDO mozzarella cheese. Differences in packaging could also modify the metabolomic profile of mozzarella cheese. The higher diethylene glycol concentration in samples of non-PDO mozzarella compared to PDO was attributed to packaging [36].

Climatic conditions influence the rate of uptake of metabolites by plants [37,38]. Also, lower temperatures and frequent rainfall can affect the drying process of forages; essentially, reducing sugars, mineral salts, and soluble nitrogenous substances, while increasing fermentation [39]. Rainfall is usually less frequent in the PDO regions in the present study compared with the non-PDO regions, and the higher concentrations of some sugars (maltose, lactose, mannobiose, talopyranose) in milk of PDO origin may have been due to differences in climatic conditions. The climate and soils of a region directly and indirectly impact the biochemical and biophysical properties of food products. For example, the isotopic and elemental composition of milk is closely related to geographical origin and this carries over to the properties of cheese [39]. To have PDO certification, at least 70% of the dry matter of fodder, or 40% of the dry matter of the ration, must come from the PDO region. The use of local fodder helps to maintain the strict relationship between product and region for protected status of buffalo mozzarella cheese. There are close relationships between the environment, rumen microbiome, and animal metabolome. While the major families of microbes in the rumen are broadly similar across diverse landscapes [40], the relative populations of different microbes change according to local conditions of climatic, soil, feed, and management [40–42]. The amount of crude protein, neutral-detergent fiber, and acid detergent lignin are higher in cultivars from the PDO region [43]. It can be assumed that this would have influenced the rumen microbial populations and ruminal metabolome, which could have contributed, at least in part, to differences in the milk and mozzarella metabolomes between PDO and non-PDO regions.

In conclusion, a robust GC-MS and mass spectral library technology platform was used to identify for the first time the metabolome of unprocessed milk and corresponding mozzarella cheese in buffalo. Differences in both the milk and mozzarella metabolomes between buffaloes in PDO and non-PDO regions were also shown for the first time. A number of candidate metabolites in milk and mozzarella were identified that will be important in validation studies that aim to develop practical protocols to distinguish between PDO and non-PDO buffalo milk and mozzarella. Talopyranose was a particularly notable candidate metabolite as it differed substantially between PDO and non-PDO buffalo, for both

milk and mozzarella. The development of quality assurance and certification protocols for milk and cheese will help to ensure the authenticity and traceability of primary (milk) and secondary (mozzarella) protected status buffalo dairy products. This is necessary to ensure that the investment in breeding, feeding, and management of buffalo in PDO regions is safeguarded.

#### **4. Materials and Methods**

#### *4.1. Sample Collection*

The study utilized 20 commercial buffalo dairies. Eleven dairies were located in a protected designation of origin (PDO, Campania, Italy) region and nine dairies were in non-PDO regions in Italy (). All dairies had a processing facility that produced mozzarella cheese exclusively from their own milk. Milk and mozzarella cheese quality was assessed the day of the sampling and the average results from PDO and non-PDO areas are reported in Tables 1 and 2. Pooled samples of raw milk (100 mL) and mozzarella cheese (100 g) were obtained from each dairy. The samples underwent similar processing and were obtained approximately 2 h after preparation. Mozzarella samples were immersed in mozzarella whey and stored at 25 ◦C until analysis.

#### *4.2. Metabolomics Analysis*

#### 4.2.1. Metabolite Extraction and Derivatization

Metabolite extraction, purification, and derivatization were carried out using the MetaboPrep GC kit according to the manufacturer's instructions (Theoreo srl, Montecorvino Pugliano [SA], Italy).

From each mozzarella sample 10 ± 1 mg was transferred to an Eppendorf microcentrifuge tube containing the extraction solution. The samples were then centrifuged at 800× *g* for 30 min, before putting the samples in an ultrasonic bath at 30 ◦C for 30 min. The samples were then centrifuged for 5 min at 10,000× *g* at 4 ◦C. From the supernatant, 200 μL was removed and transferred to an Eppendorf microcentrifuge tube containing a purification mixture, and then vortexed at 800× *g* for 30 sec. The sample was again centrifuged at 10,000× *g* (at 4 ◦C). Finally, 175 μL supernatant was transferred into a 2-mL glass autosampler vial and freeze-dried overnight.

To facilitate derivatization, "50 μL of pyridine/methoxamine (1/1 *v*:*v*) were added to the sample and centrifuged at 800× *g* (25 ◦C) for 90 min. Then 25 μL of the second derivatization mixture containing *N*,*O*-*bis*(trimethylsilyl)trifluoroacetamide (BSTFA) and trimethylchlorosilane (TMCS) were added and vortexed at 800× *g* (25 ◦C) for 90 min. The solution was centrifuged for 5 min at 10,000 rpm× *g* (4 ◦C) before injecting into the GC-MS."

#### 4.2.2. GC-MS Analysis

Samples (2 μL) of the derivatized solution were injected into the GC-MS system (GC-2010 Plus gas chromatograph coupled to a 2010 Plus single quadrupole mass spectrometer; Shimadzu Corp., Kyoto, Japan). Chromatographic separation was achieved with a 30 m 0.25 mm CP-Sil 8 CB fused silica capillary GC column with 1.00 μm film thickness (Agilent, J&W Scientific, Folsom, CA, USA), with helium as carrier gas. The initial oven temperature of 100 ◦C was maintained for 1 min and then raised by 6 ◦C/min to 320 ◦C with a further 2 min of holding time. The gas flow was set to obtain a constant linear velocity of 39 cm/s and the split flow was set at 1:5. The mass spectrometer was operated with electron impact ionization (70 eV) in full scan mode in the interval of 35–600 *m*/*z* with a scan velocity of 3333 AMU/sec and a solvent cut time of 4.5 min. The complete GC program duration was 40 min.

#### *4.3. Metabolites Identification*

Metabolite identification was performed according to Troisi et al. [44]. Briefly, untargeted metabolites were identified by comparing the mass spectrum of each peak with the NIST library collection (NIST, Gaithersburg, MD, USA). The linear index difference max tolerance was set to 10, while the minimum matching spectra library search was set to 85% (level 2 identification according to Metabolomics Standards Initiative [MSI]) [45]. Fifteen samples out of the over 200 signals per sample (7.5%) produced by gas chromatographic–mass spectrometry were not investigated further because they were not consistently found in other sets of samples (either too low in concentration or of poor spectral quality to be confirmed as metabolites).

#### *4.4. Statistical Analyses*

Data regarding milk and mozzarella cheese quality are expressed as mean ± SE. Differences were assessed by Student's t-test, and *p* < 0.05 value was considered significant. The chromatographic data were tabulated with one sample per row and one variable (metabolite) per column. Data pre-treatment consisted of normalizing each metabolite peak area to that of the internal standard (2-iso-propyl malic acid) followed by generalized log transformation and data scaling by autoscaling (mean-centered and divided by standard deviation of each variable). Statistical analysis of data from three biological replicates for each farm was performed by ANOVA Bonferroni correction (*p*-value < 0.05) by applying GraphPad PRISM software. Only metabolites significantly different between the farms were considered for further analyses. Principal component analyses (PCA) and heatmap representations were conducted by the online tool ClustVis (http://biit.cs.ut.ee/clustvis/). Unit variance scaling was applied to rows (metal/metabolite values in each farm) and single value decomposition (SVD) with imputation was used to calculate principal components. The heatmaps were generated clustering columns (Farms) by correlation distance and McQuitty linkage. Samples were classified considering geographical area of origin (North/South) and also the presence or absence of PDO trademark (Yes = Y; No = N).

Moreover, partial least square discriminant analysis (PLS-DA) [46] was performed using the statistical software package R (Foundation for Statistical Computing, Vienna, Austria). Class separation was achieved by PLS-DA, which is a supervised method that uses multivariate regression techniques to extract, via linear combinations of original variables (X), the information that can predict class membership (Y). PLS regression was performed using the plsr function included in the R pls package [47]. Classification and cross-validation were performed using the corresponding wrapper function included in the caret package [48]. A permutation test was performed to assess the significance of class discrimination. In each permutation, a PLS-DA model was built between the data (X) and the permuted class labels (Y) using the optimal number of components determined by cross validation for the model based on the original class assignment. Variable importance in projection (VIP) scores were calculated for each metabolite. The VIP score is a weighted sum of squares of the PLS loadings, taking into account the amount of explained Y-variation in each dimension.

**Author Contributions:** Conceptualization, G.N. and G.C.; data curation, A.S. and G.N.; formal analysis, G.M. and D.D.N.; investigation, A.S.; methodology, F.V.; resources, G.C.; software, F.V.; validation, G.M. and D.D.N.; writing—original draft, A.S.; writing—review and editing, M.D. and G.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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

#### **References**

**Sample Availability:** Samples of the compounds extract of milk and mozzarella cheese are available from the authors.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*


**Author details**

Sevgi Marakli\* and Nermin Gozukirmizi

\*Address all correspondence to<EMAIL_ADDRESS>
Istanbul University, Faculty of Science, Department of Molecular Biology and Genetics, Vezneciler, Istanbul, Turkey


*4.3. Metabolomic Analysis*

We prepared urine samples for NMR by thawing and centrifuging them for 15 min at 18,630× *g* at 4 ◦C. We added the supernatant (350 μL) to bi-distilled water (350 μL) and to a D2O solution (200 μL) of TSP (3-(trimethylsilyl)-propionic-2,2,3,3-d4 acid) 10 mM and of NaN3 2 mM. A 1M phosphate bu ffer had been used to set the D2O solution to pH of 7.00 ± 0.02. After a further centrifugation, we recorded 1H-NMR spectra at 298 K with an AVANCE III spectrometer (Bruker, Milan, Italy), at a frequency of 600.13 MHz, equipped with Topspin software (Ver. 3.5).

According to Zhu et al. [17], we suppressed the signals from broad resonances using a CPMG-(Carr-Purcell-Meiboom-Gill) filter composed of 400 echoes with a of 400 s and a 180◦ pulse of 24 s, for a total filter of 330 ms. We also applied pre-saturation, to reduce the signal from water. We employed Topspin software to apply a line broadening of 0.3 Hz and to adjust the phase of each spectrum. We set the recycle delay to 5 s, by considering the relaxation time of the protons under investigation. We employed R computational language [45] for any further processing of spectra, quantification of molecules, and data mining, with custom scripts.

We aligned the spectra by using the TSP signal as a reference (−0.017 ppm). We adjusted the baseline of each spectrum by distinguishing irregularities of the baseline from genuine signals, according to the "rolling ball" idea [46], implemented in the R package "baseline" [47]. We performed the assignment of the signals by comparing chemical shift and multiplicity with the libraries (Ver. 10) of Chenomx software (Chenomx Inc., Canada, v. 8.3).

According to Dieterle et al. [48], water intake behavior can change the dilution of urine as much as five times, obscuring any trend in metabolite concentrations. We removed this confounding factor by calculating, for each sample, the ratio between the area of TSP peak and the intensity of the spectrum. This allowed us to estimate the dilution of each sample and to select the one with the mostly representative dilution. We used this sample as a reference by quantifying the molecules from the added TSP. We then normalized the other samples towards the reference by probabilistic quotient normalization (PQN) [48].


*3.3. The Particle Swarm Optimization–Butterfly Optimization Algorithm*

In the process of searching, it is simple for the PSO algorithm to fall into the local optimal solution. The main drawback of the BOA is its extended convergence time and significant oscillations during the process. To address the limitations of the PSO and BOA algorithms, the PSO-BOA algorithm incorporates the BOA search mechanism into the PSO algorithm. Specifically, the algorithm selects the search method based on comparing the generated random number with the predetermined switching probability '*p'*. To enhance the algorithm's ability to identify the global optimal value, the PSO-BOA algorithm integrates two strategies. On the one hand, the strategy randomizes the spatial position of individual particles during the local search process, which explores a diverse search space and optimizes the quality of individual particles. On the other hand, the strategy utilizes both local and global optimal particles to update the position and speed of particles in the global search process, enabling the algorithm to exploit the current best solutions and refine the search trajectory toward the global optimal value. Simultaneously, the PSO-BOA algorithm adjusts the sensory modality *c* and the inertia weight *ω* in the iteration process, which accelerates the convergence rate and improves the local search performance.


*3.8. In Vivo Gene Therapy in KB Tumor Xenografts*

Among the FA-PEG-modified siRNA lipoplexes, LP-HAPC-2mol%FA-PEG2000 lipoplexes strongly suppressed the gene silencing effect in KB cells (Figures 2A and 7). Therefore, we evaluated the anti-tumor effect by direct injection into KB tumor xenografts with LP-HAPC-2mol%FA-PEG2000 lipoplexes of PLK1 siRNA. Injection of LP-HAPC-2mol%FA-PEG2000 lipoplexes with Cont siRNA or PLK1 siRNA was performed a total of three times, with 2 days between each injection. The anti-tumor effect on KB tumor xenografts was evaluated by measurement of tumor volume (mm3) and weight (mg) (Figure 10A,B). Intratumoral injections of LP-HAPC-2mol%FA-PEG2000 lipoplexes with PLK1 siRNA inhibited tumor growth compared with the Cont siRNA, although the difference was not significant. However, intratumoral injections of LP-HAPC-2mol%PEG2000 lipoplexes with PLK1 siRNA also inhibited growth compared with Cont siRNA, indicating that the in vivo anti-tumor effect by PEG-modified siRNA lipoplexes was not correlated with the in vitro one (Figure 8). This result suggested that the optimal formulation of PEG- or FA-PEG-modified cationic liposomes in FR-selective gene silencing effect might be different between in vitro and in vivo transfection studies.

**Figure 10.** In vivo siRNA therapy of KB tumor xenografts with FA-PEG-modified PLK1 siRNA lipoplexes in mice. When the average volume of the xenograft tumors reached 80 mm3 (day 0), LP-HAPC-2mol%PEG2000 and LP-HAPC-2mol%FA-PEG2000 lipoplexes with 10 μg of Cont siRNA or PLK1 siRNA were injected directly into the tumor three times (day 0, 2, and 4). Tumor volume was measured at days 0, 2, 4, 6, and 8 (**A**). Tumor volume (%) was calculated as relative to each tumor volume at the day 0. The mice were sacrificed at day 8, and then the excised tumors were weighed (**B**). Each result represents the mean + SD (*n* = 3–4).

#### **4. Discussion**

Several studies have investigated the application of FA-PEG-modified cationic liposomes for siRNA delivery into FR-expressing cells [20–23], although, many studies have reported on the use of FA-PEG-modified liposomes for the delivery of anticancer drugs and plasmid DNA [36,37]. For siRNA delivery with FA-PEG-modified cationic liposomes, cationic lipids such as dioctadecyldimethylammonium chloride (DODAC) [20,21], 1,2-dioleoyl-3-trimethylammoniumpropane (DOTAP) [23], and 3β-(*N*-(*N* ,*N* -dimethylaminoethane)-carbamoyl)cholesterol (DC-Chol) [22] have been used, and FA-PEG-lipids were included at 0.5–2.5 mol% in the liposomal formulations. Previously, we reported that inclusion of 1 mol% FA-PEG2000-DSPE into lipid-based cationic nanoparticles composed of OH-Chol and Tween80 could enhance siRNA delivery into tumor cells [30]. However, there have been no reports about the effect of cationic lipid type in FA-PEG-modified cationic liposomes on FR-targeting. Therefore, in this study, we used three types of cationic cholesterol derivatives for the preparation of FA-PEG-modified siRNA lipoplexes, and we examined the effect of cationic lipid type in FA-PEG-modified cationic liposomes on FR-mediated siRNA transfection in tumor cells.

Cationic cholesterol derivatives contain the following parts: a cholesteryl skeleton, a cationic head group, and a linker bound between the cholesteryl skeleton and the cationic head group. The linker between the hydrophilic and hydrophobic parts influences the gene delivery efficacy of cationic liposomes [38]. Furthermore, the introduction of a hydroxyethyl group into the cationic head group of cationic cholesterol derivatives can enhance in vitro gene transfection by cationic liposomes [39–41]. Previously, we synthesized HAPC-Chol, OH-Chol, and OH-C-Chol as cationic cholesterol derivatives with a hydroxyethyl group in the cationic head group, and demonstrated

that cationic liposomes composed of their cationic cholesterol derivatives exhibited effective siRNA transfection activity [12–14,42]. Therefore, in this study, we used their cationic cholesterol derivatives, and prepared three types of FA-PEG-modified siRNA lipoplexes for the evaluation of FR-mediated siRNA transfection.

In in vitro transfection studies, FA-PEG-modification of LP-HAPC, LP-OH, and LP-OH-C could increase the gene silencing effect of siRNAs via efficient cellular uptake, compared with PEG-modification (Figures 2 and 4). In OH-Chol-based cationic liposomes, the increase in FA-PEG2000-DSPE in LP-OH-FA-PEG2000 increased the amount of siRNA taken up by the cells (Figure 4B). However, it also decreased the gene silencing activity (Figure 2C), indicating that increased FA-PEG-modification of LP-OH could increase cellular uptake of the siRNA lipoplexes via FR, but siRNA might not be efficiently delivered into the cytoplasm or not easily released from the siRNA lipoplexes after FR-mediated endocytosis. In LP-OH-C lipoplexes, the increase in FA-PEG2000-DSPE in LP-OH-C-FA-PEG2000 decreased the cellular uptake of the siRNA lipoplexes (Figure 4C), but it did not greatly decrease the gene silencing activity (Figure 2D), indicating that the LP-OH-C-1–3mol%FA-PEG2000 might deliver siRNA efficiently into cytoplasm after the endocytosis, although the increase of FA-PEG-modification in LP-OH-C decreased the cellular association. In contrast, the increase in FA-PEG2000-DSPE in LP-HAPC-FA-PEG2000 did not greatly affect the amount of siRNA taken up by the cells (Figure 4A) and the gene silencing effect (Figure 2A), suggesting that FA-PEG-modification of LP-HAPC could mediate cellular uptake via FR, and deliver siRNA efficiently into cytoplasm after the endocytosis. These results indicated that the cellular association via FR and gene silencing efficiency by FA-PEG-modified siRNA lipoplexes were strongly affected by the cationic lipid type in cationic liposomes, and HAPC-Chol will be a better cationic lipid in FR-mediated transfection by cationic liposomes, compared with OH-Chol and OH-C-Chol. However, it was not clear why the cationic lipid type in FA-PEG-modified cationic liposomes affected cellular association via FR and the gene silencing effect. The linker groups of cationic cholesterol derivatives control the flexibility of the cationic head groups. HAPC-Chol and OH-C-Chol have a carbamate-type linker, and OH-Chol has a carboxamide-type linker. Previously, we reported that the difference in the linker group between carboxamide and carbamate in cationic cholesterol derivatives affected cellular association with siRNA nanoplexes [42]. In addition, HAPC-Chol has a slightly longer linker compared with OH-Chol and OH-C-Chol. Therefore, we speculated that the difference in the linker group of these cationic cholesterol derivatives might affect the interaction between the cationic head group on cationic liposomes and the anionic cell membrane and/or endosomal escape after transfection with FA-PEG-modified siRNA lipoplexes.

In our study, the inclusion of PEG2000-DSPE or FA-PEG2000-DSPE into the formulations of LP-HAPC, LP-OH, and LP-OH-C were optimal at 2 mol%, 1 mol%, and 2 mol%, respectively, for selective FR-mediated gene silencing (Figures 2 and 7). In addition, LP-HAPC-2mol%FA-PEG2000, LP-OH-1mol%FA-PEG2000, and LP-OH-C-2mol%FA-PEG2000 lipoplexes with PLK1 siRNA selectively suppressed the growth of tumor cells via down-regulation of PLK1 mRNA (Figures 8 and 9), indicating that their FA-PEG-modified siRNA lipoplexes were selectively taken up by the FR-expressing cells, and then suppressed the expression of target genes in the cells. However, in in vivo transfection, both LP-HAPC-2mol%PEG2000 and LP-HAPC-2mol%FA-PEG2000 lipoplexes with PLK1 siRNA inhibited tumor growth compared with Cont siRNA, indicating that the in vivo anti-tumor effect of PEG-modified siRNA lipoplexes was not correlated with the in vitro one. We speculated that siRNA lipoplexes after intratumoral injection were subjected to a strict environment surrounded by tumor cells, resulting in induction of non-specific uptake into tumor cells. Therefore, in in vivo transfection, PEG-modification of LP-HAPC with more PEG2000-DSPE might be needed for suppression of the anti-tumor effect by PEG-modified lipoplexes with PLK1 siRNA. However, we reported previously that inclusion of 1 mol% FA-PEG2000-DSPE into lipid-based cationic nanoparticles composed of OH-Chol and Tween 80 could efficiently deliver siRNAs into KB cells, and FA-PEG-modified nanoplexes of HER-2 siRNA introduced by intratumoral injection significantly inhibited the tumor growth of KB xenografts compared with

Cont siRNA, but PEG-modified nanoplexes did not [30], indicating that the in vivo anti-tumor effects by PEG-modified and FA-PEG-modified siRNA nanoplexes were well correlated with the in vitro ones. These findings suggested that the FR-selective gene silencing by PEG- and FA-PEG-modified cationic liposomes might also be affected by the liposomal formulation. Further studies must be performed to investigate liposomal formulations to improve FR-selective transfection in vivo. In this study, we selected an intratumoral injection as a route of administration for FA-PEG-modified siRNA lipoplexes; however, in future study, we will need to evaluate anti-tumor effect after intravenous injection of FA-PEG-modified siRNA lipoplexes.


**8. Conclusion**

LIA is a good alternative compared to other traditional pain management techniques. LIA is safe and effective to achieve good outcomes, early mobilisation and decreasing length of stay without jeopardising clinical outcomes. Fast track arthroplasty is a holistic approach to patients who undergo total hip and knee arthroplasty, a journey or care that starts with setting patient's mind and expectation, optimising medical status, intraoperative LIA infiltration, decrease the usage of narcotics either in spinal or post-operative medication, discourage usage of PCA or IDC, encourage day of operation mobilisation and optimising post-operative physiotherapy protocols, post-operative exercise protocol.

*Fast Track Arthroplasty Using Local Infiltration Analgesia DOI: http://dx.doi.org/10.5772/intechopen.99433*