Fig. 8. Time responses of *e*(*k*), *e*†(*k*), and phase traces (*e*(*k*), Δ*e*(*k*)) for Example-1 (*r* = 5.0, 6.0, 7.0, 8.0, 9.0).

Fig. 9. Backward difference Δ*e*†(*k*) vs. sampling period *h* for Example-1 (*r* = 3.0 and *r* = 9.0).

(*e*†(*k*), Δ*e*†(*k*)) of the discrete nonlinear control system when the reference inputs are *r* = 1.0, 2.0, 3.0. Figures 8 (a) and (b) show those responses when *r* = 5.0, 7.0, 9.0. Although the responses contain sustained oscillations, they do not exceed the threshold *ε* = 2.0. The input and the output of the nonlinearity lie in a parallelogram shown in Fig. 6. The robust stability in a global sense is guaranteed for all the reference inputs *r*. The above behavior can be estimated from the intersections of the highest gain of the sector and the stepwise nonlinear characteristic. Obviously, discrete-values (1.0, 2.0) and (−1.0, −2.0) lie in the outside of the stable sector.

Figures 9 (a) and (b) show the traces of backward difference Δ*e*†(*k*) when the sampling period *h* increases. As is obvious from the figure, the assumption of (10) is satisfied for *h <* 0.12 when 12 Nonlinear Control

Fig. 8. Time responses of *e*(*k*), *e*†(*k*), and phase traces (*e*(*k*), Δ*e*(*k*)) for Example-1 (*r* = 5.0,

−*ε*

*ε*

Fig. 9. Backward difference Δ*e*†(*k*) vs. sampling period *h* for Example-1 (*r* = 3.0 and *r* = 9.0).

(*e*†(*k*), Δ*e*†(*k*)) of the discrete nonlinear control system when the reference inputs are *r* = 1.0, 2.0, 3.0. Figures 8 (a) and (b) show those responses when *r* = 5.0, 7.0, 9.0. Although the responses contain sustained oscillations, they do not exceed the threshold *ε* = 2.0. The input and the output of the nonlinearity lie in a parallelogram shown in Fig. 6. The robust stability in a global sense is guaranteed for all the reference inputs *r*. The above behavior can be estimated from the intersections of the highest gain of the sector and the stepwise nonlinear characteristic. Obviously, discrete-values (1.0, 2.0) and (−1.0, −2.0) lie in the outside of the

Figures 9 (a) and (b) show the traces of backward difference Δ*e*†(*k*) when the sampling period *h* increases. As is obvious from the figure, the assumption of (10) is satisfied for *h <* 0.12 when

Δ*e*(*k*)

(b)

Δ*e*†(*k*)

(b)

*e*(*k*)

(a)

Δ*e*†(*k*)

(a)

6.0, 7.0, 8.0, 9.0).

stable sector.

Fig. 10. Discretized nonlinear characteristic and stable sector for Example-2.

Fig. 11. Time responses of *e*(*k*) and *e*†(*k*) and phase traces (*e*(*k*), Δ*e*(*k*)) for Example-2 (*r* = 1.0, 2.0, 3.0, 4.0, 5.0).

*r* = 9.0, and for *h <* 2.0 when *r* = 3.0. In either case, the assumption is satisfied in regard to *h* = 0.1.

**[Example-2]** Consider the following controlled system:

$$G(s) = \frac{K\_p(-s+8)(s+4)}{s(s+0.2)(s+16)},\tag{46}$$

where *Kp* = 1.0. Here, the same nonlinear characteristic is chosen as shown in Example-1. When the threshold *ε* = 1.0 is specified, the sectorial area of the stepwise nonlinearity for *ε* ≤ |*e*| *<* 10.0 can be determined as [0.78, 2.0]. In this example, the sampling period is chosen as *h* = 0.04. The max-min value can be calculated as follows:

$$\max\_{q} \eta(q\_{\prime}\omega\_{0}) = \eta(q\_{0\prime}\omega\_{0}) = 0.45\omega$$

Figure 13 shows the traces of backward difference Δ*e*†(*k*) when the sampling period *h* increases. As is obvious from the figure, the assumption of (10) is satisfied for *h <* 0.11 when *r* = 9.0, and for *h <* 2.2 when *r* = 3.0. In either case, the assumption is satisfied in regard to

Nonlinear Phenomena and Stability Analysis for Discrete Control Systems 201

This article analyzed the nonlinear phenomena and stability of discrete-time and discrete-value (discretized/quantized) control systems in a frequency domain. By partitioning the discretized nonlinear characteristic into two nonlinear sections and by defining a sectorial area over a specified threshold, the concept of the robust stability condition for nonlinear discrete-time systems was applied to the discrete nonlinear control systems. In consequence, the nonlinear phenomena of discrete control systems were clarified, and the robust stability of discrete nonlinear feedback systems was elucidated. The result described in this chapter will be useful in designing discrete (digital, event-driven, or packet transmission)

Desoer C. A. & Vidyasagar M. (1975). *Feedback System: Input-Output Properties*, Academic Press

Elia N. & Mitter S. K. (2001). Stabilization of Linear Systems With Limited Information, *IEEE*

Harris C.J. & Valenca M. E. (1983). *The Stability of Input-Output Dynamical Systems*, Academic

Kalman R. E. (1956). Nonlinear Aspects of Sampled-Data Control Systems, *Proc. of the*

Okuyama Y. et al. (1998). Robust Stability Analysis for Nonlinear Sampled-Data Control

Okuyama Y. et al. (1999). Robust Stability Evaluation for Sampled-Data Control Systems

Okuyama, Y. (2002). Robust Stability Analysis for Sampled-Data Control Systems in a Frequency Domain, *European Journal of Control*, Vol. 8, No. 2, pp. 99-108. Okuyama et al., Y. (2002). Amplitude Dependent Analysis and Stabilization for Nonlinear

Okuyama, Y. (2006). Robust Stability for Discretized Nonlinear Control Systems in a Global

Okuyama, Y. (2007). Nonlinear Phenomena and Stability Analysis for Discretized Control

Okuyama, Y. (2008). Robust Stabilization and PID Control for Nonlinear Discretized Systems

Systems and the Aizerman Conjecture, *Proceedings of the IEEE Int. Conf. on Decision*

with a Sector Nonlinearity in a Gain-Phase Plane, *International Journal of Robust and*

Sampled-Data Control Systems *Proceedings of the 15th IFAC World Congress*, T-Tu-M08,

Sense, *Proceedings of the 2006 American Control Conference*, pp. 2321-2326, Minneapolis,

Systems, *Proceedings of International Workshop on Modern Nonlinear Theory*, pp. 350-355,

on a Grid Pattern, *Proceedings of the 2008 American Control Conference*, pp. 4746-4751,

*Transaction on Automatic Control*, Vol. 46, pp. 1384-1400.

*Symposium on Nonlinear Circuit Analysis*, Vol. VI, pp. 273-313. Katz, P. (1981). *Digital Control Using Microprocessor*, Prentice-Hall Internatiopnal.

*h* = 0.04.

**10. Conclusions**

control systems.

**11. References**

Press.

Barcelona, Spain.

Montreal, Canada.

Seattle, USA.

USA.

(The republication by SIAM, 2009).

*and Control*, Tampa, USA, pp. 849-852.

*Nonlinear Control*, Vol. 9, No. 1, pp. 15-32.

Fig. 12. Time responses of *e*(*k*) and *e*†(*k*) and phase traces (*e*(*k*), Δ*e*(*k*)) for Example-2 (*r* = 5.0, 6.0, 7.0, 8.0, 9.0).

when the nominal gain *K* = 1.4. Hence, *α <* 0.45 and the stable area is determined as [0.77, 2.02]. This sector contains the above area. However, the stability region of control systems with linear gain *K* is given as 0 *< K <* 6.3 when the sampling period is *h* = 0.04. Obviously, the discrete nonlinear control system corresponds to a counter example of the Aizerman conjecture. Figures 11 and 12 show time responses *e*(*k*), *e*†(*k*) and phase traces (*e*(*k*), Δ*e*(*k*)), (*e*†(*k*), Δ*e*†(*k*)) of the discrete nonlinear control system, respectively. Although the nonlinear characteristic exists in the stable area for linear systems, a sustained oscillation is generated on account of a steep build-up characteristic in the lower side of the stable sector.

Fig. 13. Backward difference Δ*e*†(*k*) vs. sampling period *h* for Example-2 (*r* = 3.0 and *r* = 9.0).

Figure 13 shows the traces of backward difference Δ*e*†(*k*) when the sampling period *h* increases. As is obvious from the figure, the assumption of (10) is satisfied for *h <* 0.11 when *r* = 9.0, and for *h <* 2.2 when *r* = 3.0. In either case, the assumption is satisfied in regard to *h* = 0.04.

### **10. Conclusions**

14 Nonlinear Control

Fig. 12. Time responses of *e*(*k*) and *e*†(*k*) and phase traces (*e*(*k*), Δ*e*(*k*)) for Example-2

Fig. 13. Backward difference Δ*e*†(*k*) vs. sampling period *h* for Example-2 (*r* = 3.0 and

when the nominal gain *K* = 1.4. Hence, *α <* 0.45 and the stable area is determined as [0.77, 2.02]. This sector contains the above area. However, the stability region of control systems with linear gain *K* is given as 0 *< K <* 6.3 when the sampling period is *h* = 0.04. Obviously, the discrete nonlinear control system corresponds to a counter example of the Aizerman conjecture. Figures 11 and 12 show time responses *e*(*k*), *e*†(*k*) and phase traces (*e*(*k*), Δ*e*(*k*)), (*e*†(*k*), Δ*e*†(*k*)) of the discrete nonlinear control system, respectively. Although the nonlinear characteristic exists in the stable area for linear systems, a sustained oscillation is generated on account of a steep build-up characteristic in the lower side of the stable sector.

Δ*e*(*k*)

(b)

Δ*e*†(*k*)

(b)

*e*(*k*)

(a)

Δ*e*†(*k*)

(a)

*r* = 9.0).

(*r* = 5.0, 6.0, 7.0, 8.0, 9.0).

This article analyzed the nonlinear phenomena and stability of discrete-time and discrete-value (discretized/quantized) control systems in a frequency domain. By partitioning the discretized nonlinear characteristic into two nonlinear sections and by defining a sectorial area over a specified threshold, the concept of the robust stability condition for nonlinear discrete-time systems was applied to the discrete nonlinear control systems. In consequence, the nonlinear phenomena of discrete control systems were clarified, and the robust stability of discrete nonlinear feedback systems was elucidated. The result described in this chapter will be useful in designing discrete (digital, event-driven, or packet transmission) control systems.

#### **11. References**

Okuyama, Y. (2009). Discretized PID Control and Robust Stabilization for Continuous Plants on an Integer-Grid Pattern, *Proceedings of the European Control Conference*, pp. 514-519, Budapest, Hungary.

16 Nonlinear Control

202 Applications of Nonlinear Control

Okuyama, Y. (2009). Discretized PID Control and Robust Stabilization for Continuous Plants

Budapest, Hungary.

on an Integer-Grid Pattern, *Proceedings of the European Control Conference*, pp. 514-519,

### *Edited by Meral Altinay*

A trend of investigation of Nonlinear Control Systems has been present over the last few decades. As a result the methods for its analysis and design have improved rapidly. This book includes nonlinear design topics such as Feedback Linearization, Lyapunov Based Control, Adaptive Control, Optimal Control and Robust Control. All chapters discuss different applications that are basically independent of each other. The book will provide the reader with information on modern control techniques and results which cover a very wide application area. Each chapter attempts to demonstrate how one would apply these techniques to real-world systems through both simulations and experimental settings.

Photo by Elen11 / iStock

Applications of Nonlinear Control

Applications of

Nonlinear Control

*Edited by Meral Altinay*


**Advances in Natural or Synthetic Nanoparticles for Metastatic Melanoma Therapy and Diagnosis**

**Maria Beatrice Arasi** † **, Francesca Pedini** † **, Sonia Valentini, Nadia Felli \*,**‡ **and Federica Felicetti** ‡

Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, 00161 Rome, Italy<EMAIL_ADDRESS>(M.B.A<EMAIL_ADDRESS>(F.P<EMAIL_ADDRESS>(S.V<EMAIL_ADDRESS>(F.F.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +39-06-4990-2145

† Co-first authors.

‡ Co-last authors.

Received: 10 September 2020; Accepted: 7 October 2020; Published: 9 October 2020

**Simple Summary:** Malignant melanoma is the most aggressive skin cancer; its incidence is constantly growing in the white population. In the advanced stage of the disease, after metastatic dissemination, patients have a poor prognosis. Nanomedicine represents a new frontier in cancer treatment; in this field, synthetic and natural nanoparticles (NPs) may represent an important therapeutic and diagnostic opportunity. This review provides an overview of current knowledge in this area: several kinds of NPs, like PLGA, chitosan, liposome and gold-NPs, are used to increase the specificity of drug delivery, allowing a dose reduction and, consequently, a lower toxic effect. Particular attention is given to exosomes (EXOs), an example of natural NPs, important both in conveying molecules with a therapeutic function and in the diagnostic field.

**Abstract:** Advanced melanoma is still a major challenge in oncology. In the early stages, melanoma can be treated successfully with surgery and the survival rate is high, nevertheless the survival rate drops drastically after metastasis dissemination. The identification of parameters predictive of the prognosis to support clinical decisions and of new efficacious therapies are important to ensure patients the best possible prognosis. Recent progress in nanotechnology allowed the development of nanoparticles able to protect drugs from degradation and to deliver the drug to the tumor. Modification of the nanoparticle surface by specific molecules improves retention and accumulation in the target tissue. In this review, we describe the potential role of nanoparticles in advanced melanoma treatment and discuss the current efforts of designing polymeric nanoparticles for controlled drug release at the site upon injection. In addition, we highlight the advances as well as the challenges of exosome-based nanocarriers as drug vehicles. We place special focus on the advantages of these natural nanocarriers in delivering various cargoes in advanced melanoma treatment. We also describe the current advances in knowledge of melanoma-related exosomes, including their biogenesis, molecular contents and biological functions, focusing our attention on their utilization for early diagnosis and prognosis in melanoma disease.

**Keywords:** nanoparticles; exosomes; extracellular vesicles; melanoma; therapy; diagnosis; prognosis

#### **1. Introduction**

Advanced cutaneous melanoma is a highly aggressive and drug-resistant cancer [1,2]. The main problem associated with treating melanoma is its low response rate to conventional therapy. Nevertheless, the rapidly developed fields of melanoma research are introducing new potential

therapeutic approaches, focusing on developing new efficient drugs and delivery systems. Before 2011, metastatic melanoma was considered incurable, leading to death within 18 months of diagnosis [3,4]. The therapies used were based on the administration of chemotherapeutic agents such as dacarbazine and temozolomide [5]. In order to circumvent the intrinsic resistance against chemotherapy, researchers explored the possibility of potentiating the immune response by using immune stimulators, such as interleukin 2 (IL-2), interferon alpha (IFN-α), ipilimumab and thymosin alpha 1 [6,7]. Afterwards, as result of genome sequencing and identification of the main mutation, a series of therapeutic agents have been discovered; about half of all melanomas have mutations in the BRAF (v-raf murine sarcoma viral oncogene homolog B1) gene [8,9]. These mutations constitutively activate the MAPK (mitogen-activated protein kinase) pathway, resulting in uncontrolled cell proliferation, tumor development and growth [1,10]. Several tyrosine kinase inhibitors (TKIs) have been developed that block the MAPK pathway and inhibit cell growth and survival. The use of TKIs has shown a significant improvement of patient overall survival and the combinations therapy with BRAF/MEK inhibitors is now accepted as the standard care for BRAF-muted advanced melanoma. Unfortunately, in most treated patients, responses to these inhibitory molecules are transient, and, in a few months, a therapy-resistant phenotype occurs. Recently, the inhibition of the programmed cell death 1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) axis, a suppressor of T-cell response, turned out to be an effective therapeutic strategy with a low toxicity profile and contributed to better clinical outcomes compared to chemotherapy and also to other immune mediated approaches with cytotoxic T lymphocyte-associated 4 (CTLA-4) inhibition [11–13]. However, substantial heterogeneity exists in metastatic melanoma response to treatments; therefore, the development of multimodality strategies is the major challenge to fight this deadly disease. Several types of nanoparticles (NPs) and nanovesicles have been explored for their employment as drug carriers in melanoma therapy and their functionalization with specific molecules, such as antibodies, can generate different smart nanodrugs for application in melanoma therapy (Figure 1). Herein, we will discuss the new frontiers in the treatment of patients with unresectable or metastatic melanoma, paying special attention to new anticancer molecules and their delivery through natural or synthetic nanoparticles. *Cancers* **2020**, *12*, x 2 of 18 therapeutic approaches, focusing on developing new efficient drugs and delivery systems. Before 2011, metastatic melanoma was considered incurable, leading to death within 18 months of diagnosis [3,4]. The therapies used were based on the administration of chemotherapeutic agents such as dacarbazine and temozolomide [5]. In order to circumvent the intrinsic resistance against chemotherapy, researchers explored the possibility of potentiating the immune response by using immune stimulators, such as interleukin 2 (IL-2), interferon alpha (IFN-α), ipilimumab and thymosin alpha 1 [6,7]. Afterwards, as result of genome sequencing and identification of the main mutation, a series of therapeutic agents have been discovered; about half of all melanomas have mutations in the BRAF (v-raf murine sarcoma viral oncogene homolog B1) gene [8,9]. These mutations constitutively activate the MAPK (mitogen-activated protein kinase) pathway, resulting in uncontrolled cell proliferation, tumor development and growth [1,10]. Several tyrosine kinase inhibitors (TKIs) have been developed that block the MAPK pathway and inhibit cell growth and survival. The use of TKIs has shown a significant improvement of patient overall survival and the combinations therapy with BRAF/MEK inhibitors is now accepted as the standard care for BRAF-muted advanced melanoma. Unfortunately, in most treated patients, responses to these inhibitory molecules are transient, and, in a few months, a therapy-resistant phenotype occurs. Recently, the inhibition of the programmed cell death 1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) axis, a suppressor of T-cell response, turned out to be an effective therapeutic strategy with a low toxicity profile and contributed to better clinical outcomes compared to chemotherapy and also to other immune mediated approaches with cytotoxic T lymphocyte-associated 4 (CTLA-4) inhibition [11–13]. However, substantial heterogeneity exists in metastatic melanoma response to treatments; therefore, the development of multimodality strategies is the major challenge to fight this deadly disease. Several types of nanoparticles (NPs) and nanovesicles have been explored for their employment as drug carriers in melanoma therapy and their functionalization with specific molecules, such as antibodies, can generate different smart nanodrugs for application in melanoma therapy (Figure 1). Herein, we will discuss the new frontiers in the treatment of patients with unresectable or metastatic melanoma, paying special attention to new anticancer molecules and their delivery through natural or synthetic nanoparticles.

**Figure 1. Figure 1.**  Step-by-step representation of a nanoparticles-based delivery system for melanoma therapy. Step-by-step representation of a nanoparticles-based delivery system for melanoma therapy.

#### **2. Nanoparticles and Drug Delivery**

Nanomaterials are delivery vehicles used to protect the intended drug against degradation and enhance its stability. Using a nanotechnology treatment for several types of cancer enhances targeted delivery to cancer cells and uptake, as well as reducing cytotoxic side effects on normal tissues [14].

Chemotherapeutic agents generally exhibit a short plasma half-life due to rapid clearance and fast biodegradation; these elements, together with the low specificity, make it necessary to use high doses, resulting in severe toxic effects. In addition, since cancer diagnosis is often late, cancer cells have already invaded other parts of the body [15].

Nanotechnologies are based on the use of different polymeric systems whose modifiable physical and chemical systems provide tools to improve different aspects of the therapy. Some examples are the release of the drug in a tissue-specific way, the decrease in side effects, the protection from drug degradation, the possibility to reduce the frequency of administrations and the toxicity of the substance itself [16]. Depending on their specific structural characteristics, nanoparticles can easily penetrate tissues and cells, circumscribe the biological effect of the drug on a specific cell type and modify the pharmacokinetic properties, thus allowing a prolonged drug release over time. The Food and Drug Administration (FDA) approved only a few nanodrugs for use and clinical trials, as they have been shown to either target and directly kill tumor cells or improve, overall, targeted chemotherapy drug delivery. The formulation of these nanoparticles increases the drug concentration into the tumor by 100–400% [17].

#### *2.1. PLGA*

The FDA and the European Medicine Agency (EMA) approved PLGA nanoparticles (PLGA-NPs) in 2011 as a system for conveying nucleic acids, drugs and other molecules for cancer and other diseases. PLGA is a biocompatible and biodegradable polymer (poly-lactic acid and poly-glycolic acid) whose degradation produces non-toxic substances, such as water and carbon dioxide [18]. The PLGA-NPs' cellular uptake depends on both the size of the NP and the composition of the plasma membrane of the cells themselves [19]. The different physical characteristics of PLGA-NPs, such as their size and morphology, can be determined by varying the parameters of the synthesis method used. Nanoparticles have a large surface area that can be functionalized with different agents to make the vehicle target-specific [20].

In the last period, several publications highlighted the efficiency of PLGA-NPs in delivering anticancer agents; in a recent work, Arruda and his co-workers showed that the encapsulation of a peptide with antitumoral activity, P20 (CSSRTMHH), within the PLGA-NPs conjugated to the C peptide with a homing function (CVNHPAFACGYGHTMYYHHYQHHL), strongly increases their functionality. The therapeutic effect of these NPs was evaluated in a syngeneic model of metastasis and was shown to produce the same inhibitory effect of free P20 at fivefold higher concentration, reducing by 28% the number of lung nodules [21].

In another recent study, trastuzumab has been used as a recognizing unit attached to NPs for Her-2 positive breast cancer therapy. Targeted nanoparticles were prepared by combining PLGA/ polyethylenimine (PEI)/lipid nanoparticles (PPLNs) with trastuzumab through the electrostatic adsorption method to deliver the antitumor drug docetaxel. The results showed that trastuzumab-PPLNs had a cell-targeting effect and could effectively inhibit the proliferation of cancer cells. This work demonstrates that trastuzumab-PPLNs are a promising treatment for breast cancer and can be considered a proof of concept for anticancer therapies, including melanoma [22].

Following a similar approach, Kou et al. selected paclitaxel as anti-metastatic and anti-angiogenic model drug to produce paclitaxel-loaded PLGA-NPs (Ptx-NPs), coated with a polylysine cationic SM5-1 single-chain antibody (Ptx-NP-S) which binds to a membrane protein specifically expressed in melanoma, hepatocellular carcinoma (HCC) and breast cancer cells. The authors demonstrated that Ptx-NP-S not only showed very high affinity to SM5-1 but also was internalized by the target cells. They proved a significantly enhanced cytotoxicity of Ptx-NP-S when compared to non-targeted paclitaxel-loaded PLGA-NPs, suggesting SM5-1 single-chain antibody-polylys as a good candidate to synthesize cancer-targeted PLGA nanoparticles [23].

Another important field of application of PLGA-NPs is as peptide transporters in vaccines. Vaccines could be promising tools for treating cancers, including melanoma [24]; however, several studies obtained unsatisfying results, possibly because of the low stability of peptides and delivery approaches. Thus, the appropriate and efficient peptide carrier systems still continue to be one of the major obstacles. In order to overcome this difficulty, Li and coworkers recently used PLGA-NPs as a new method to deliver tumor-associated antigen (TAA) peptides to dendritic cells (DC) [25].

#### *2.2. Chitosan*

Chitosan is a bio-poly-amino-saccharide cationic polymer obtained from chitin deacetylation moieties formed by the repetition of two units: N-acetyl-D-glucosamine and D- glucosamine [26]. The molecular weight and the extent of deacetylation of the chitosan influence its properties; this is employed specially to prepare micro and nanoparticles capable of conveying nucleic acids or medicines. The chitosan backbone consists of multiple free amino and hydroxyl groups that can be functionalized. Versatile approaches have been reported for the construction of chitosan-based nanomaterials, such as nanogels, NPs, micelles, liposomes, nanofibers and nanospheres [27]. The polymer's success is due to its properties such as: biocompatibility and biodegradability, FDA approval in clinical use, the possibility of modifying its surface and to complex it in nanoparticles conveyed against cells and/or target tissues. Chitosan exhibits no or minimal toxicity, which made it widely regarded as a safe material in drug delivery [28]. Various cargos, hydrophilic and also hydrophobic, can be incorporated into chitosan-NPs. Their release is influenced by solubility, diffusion, and size of the nanoparticles [29]. Many publications show innovative ways for antitumor drug delivery based on environmental response and targeting principles [30,31].

A recent study evaluated the effect of doxorubicin encapsulated into chitosan/alginate nanoparticles to inhibit viability and tumor growth of melanoma cells in a mouse melanoma model. The experiments revealed that the encapsulation of doxorubicin could be considered advantageous because it increases intracellular accumulation and produces a long-lasting cytotoxic effect [32]. The cationic nature of chitosan makes it suitable to complex anionic nucleic acids, protecting them from degradation by nucleases in serum [33].

One of the possible applications of this property is the transport of two specific categories of non-coding RNAs called microRNA (miRs) and short interfering RNA (siRNAs) [34,35]. MiRs regulate the expression of messenger RNAs (mRNAs) at a post-transcriptional level. They are implicated in various aspects of tumor progression, including proliferation, apoptosis, and epithelial–mesenchymal transition (EMT), and they can promote or suppress tumorigenesis and metastatization, thus acting either as onco-suppressors or oncogenes [14,36–38].

The possibility of protecting these molecules from enzymatic degradation using chitosan-NPs represents a very important therapeutic option. SiRNAs have broad potential as therapeutic agents because they are able to silence any gene in a reversible way. To achieve the clinical potential of siRNAs, delivery materials are required to transport them in the cells of target tissues.

Rostami and coworkers, in a recent study, developed the alginate-conjugated trimethyl chitosan (ATMC) nanoparticles loaded with specific siRNAs. In particular, in the cancer cells the activation of S1P/sphingosine-1-phosphate receptor 1 (S1PR1) and IL-6/glycoprotein 130 (GP130) genes induces STAT3 phosphorylation, which in turn stimulates their expression.

On this basis, the authors investigated the potential anticancer effect by silencing the S1PR1 and GP130 genes, through ATMC-NPs containing specific siRNAs; the results obtained in different cancer cell lines, including melanoma B16F10, although in need of further study, have been encouraging [35].

#### *2.3. Liposomes*

Liposomes are small lipid vesicles (50 to 100 nm) generated from cholesterol, which reduces their permeability and increases their in vivo and in vitro stability [39]. Liposomes, categorized by the composition and mechanism of intracellular delivery, fall under five types: conventional liposomes (CL), pH-sensitive liposomes, cationic liposomes, immunoliposomes, and long-circulating liposomes (LCL). Liposomes with modified surfaces have also been developed using several molecules, such as glycolipids or sialic acid [40]. To prolong their circulation time, liposome surfaces have been coated with a hydrophilic polymer, such as polyethylene glycol (PEG), thus increasing repulsive forces between liposomes and plasma components [41]. Liposomes seem to be an almost ideal drug-carrier system due to their morphology being similar to that of cellular membranes and for their ability to incorporate various substances.

Recently, Gao et al. demonstrated that liposome-PEI encapsulation improved the uptake of n-Butylidenephthalide (BP) through enhancement of cell endocytosis, indicating that the BP/lipo PEG PEI complex (LPPC) has great potential in melanoma treatment. In particular, because BP is metabolized very rapidly, its antitumor role, already demonstrated in various types of cancer, including melanoma, would be limited. To avoid this problem, the researchers developed the complex BP/LPPC, improving both the uptake of this drug by the cells and its stability over time [42].

Yin et al. prepared R8-dGR peptide modified paclitaxel (PTX) and hydroxychloroquine (HCQ) co-loaded liposomes (PTX/HCQ-R8-dGR-Lip) to enhance delivery through recognition of integrin αvβ3 receptors and neuropilin-1 receptors on B16F10 melanoma cells. The results showed that R8-dGR modified liposomes (R8-dGR-Lip) enhanced tumor-targeting delivery in vitro and in vivo [43].

Health authorities have approved different liposomal formulations of small molecule therapeutics; their ability to deliver macromolecules, such as nucleic acid-based therapeutics plasmid DNA (pDNA) and siRNA, to disease sites is under investigation. In a recent study, lipid nanocapsules were used to combine two anti-melanoma strategies, gene therapy by using Bcl-2 siRNA and ferrocifens as novel anticancer agents. The results showed a 50% reduction in tumor volume in tumor-bearing animals compared to the control group [44].

Sadhu et al. utilized liposomes to deliver glutathione disulfide to melanoma cells. These liposomes slowed down tumor proliferation by 85–90% in melanoma-bearing mice and significantly improved the survival rate [45]. Interestingly, lipid constituents of liposomal vesicles can be tailored to achieve particular immunogenic responses. Manipulation of surface charge density in cationic liposomes can regulate immune response [46].

Fu and coworkers developed paclitaxel (PTX)-loaded liposomes functionalized with trans activator of transcription (TAT), the most frequently used cell-penetrating peptide, and cleavable PEG. Under physiological conditions, the TAT peptide is shielded by a PEG layer and so liposomes exhibit a good stability in the bloodstream. When liposomes arrive at the tumor site, exogenous reducing agents could remove PEG, causing TAT exposure and facilitating cell internalization. In this study, the authors showed a tumor inhibition of around 70% in B16F1-bearing mice [47].

Lastly, the liposomes may also be used as an adjuvant to boost the protective or therapeutic immune response [48].

#### *2.4. Gold Nanoparticles*

Gold nanoparticles (AuNPs) are a type of nanoparticles produced in various sizes and shapes (i.e., gold nanospheres, nanorods, nanocages, nanoshells, and nanostars). They are dependent on the synthetic methods adopted for their preparation [49]. These nanoparticles can be prepared in a broad range of core sizes (1 to 150 nm), which makes it easier to control their dispersion. The presence of a negative charge on the surface of gold nanoparticles makes them easily modifiable.

Literature reports that various types of gold nanoparticles are effective tools in interfering with human cancer cell biology, including melanoma [50]. Indeed, their application in therapeutic development and in cancer diagnosis could provide a potential step forward in cancer theranostics [51]. Thanks to their physical, chemical and biological features, AuNPs can be used in imaging technology, such as computed tomography, ultrasound and magnetic resonance and a few of them have been approved by the FDA in medicine [52].

Kim et al. demonstrated the use of AuNPs as a contrast agent for the quantitative molecular combining of high-resolution photoacoustic tomography (PAT) of melanomas in vivo. To avoid the limitations of conventional diagnostic techniques, the authors exploited the enormous optical absorbing gold nanocages (AuNCs), obtaining a 300% increase in the contrast when AuNCs were bioconjugated with [Nle4,d-Phe7]-α-melanocyte-stimulating hormone compared to the control nanocages [53].

Through the addition of various biomolecules such as drugs, target ligands and genes, the nanoparticles could function in an enhanced way. To overcome in vivo delivery barriers, Au nanocomplexes are normally modified with functional moieties such as stabilizing materials, target ligands, and bio responsive linkers [54,55].

Conjugation with the anti-nucleosome monoclonal antibody (mAb 2C5) of PEGylated liposomes improves cancer cell-specific delivery targeting the matrix metalloprotease 2 in the tumor microenvironment [56]. Many studies showed that the use of magnetic fields or altered temperatures and light improve the delivery of gold particles including anticancer agents [57].

#### *2.5. Other Nano-Agents*

Other nanoparticles have a potential use as anti-melanoma agents (Table 1) [58–65].

**Table 1.** Several types of nanoparticles used in melanoma treatment.

Abbreviations: PAMAM, poly-amidoamine; DAB, diaminobenzidine; PEA, palmitoylethanolamide; PEG, polyethylene glycol; ROS, reactive oxygen species; 5-FU, 5-Fluorouracil; IFN-γ, interferon-gamma.

#### *2.6. Specific Drug Delivery*

The ideal drug carrier would have the ability to deliver the drug to the cell/tissue/organ in a specific way to make the cancer treatment more efficient. A potentially powerful technology is represented by targeted nanoparticles. The most used approach is to conjugate to nanoparticles antibodies or antibody-derivatives which must be able to overcome the physiological barriers and conditions that limit access to the target, and to bind target cells through specific antibody–receptor interactions [66].

Primary tumors as well as metastatic tumors overexpress some antigens on their surfaces. Particularly, for melanoma treatment, the literature shows the specificity of many antigens.

Secreted protein acidic and rich in cysteine (SPARC) was deepened in the tumorigenicity and progression of melanoma, resulting as a nonstructural matricellular protein [67]. The conjugation of nanoparticles with a peptide able to specifically recognizing the SPARC protein, has made it possible to obtain an important tool that exhibited high tumor affinity with a very low binding in negative cell lines. In in vivo targeting experiments, an increase of SPARC-targeted nanoparticles was observed in tumor tissue as compared with the control group. Moreover, a very important result was the detection of both primary tumors and metastatic growth obtained by using the fluorescent version of these nanoparticles [68].

 Saeed and coworkers in a recent work described that the single-chain variable fragment (scFv) antibodies directed against MAGE-A1 peptide, presented by human leukocyte antigen A1 (HLA-A1), in short M1/A1, have been coupled to liposomes to achieve specific melanoma targeting. Through experiments by flow cytometry and confocal microscopy, the authors demonstrated the bind between M1/A1-positive and B-cells as well as with melanoma cells and the internalization by these cells [70].

The chondroitin sulfate proteoglycan 4 (CSPG4) is a highly specific marker of the nevomelanocyte lineage and has been utilized to target melanoma. In a recent work, Falvo and coworkers produced nanoparticles conjugated to a monoclonal antibody specific for CSPG4 encapsulating cisplatin molecules. Flow cytometry showed a specific binding to CSPG4 (+) melanoma cell lines. Selectivity towards melanoma was also confirmed in xenograft models: targeted nanoparticles significantly inhibited growth of melanoma xenografts whereas modestly affected breast carcinoma xenografts [71].

These studies highlight the importance of the potential active targeting to improve the outcome of a nanoparticle-based therapy.

#### *2.7. Natural Nanoparticles*

All cell types of the body release extracellular Vesicles (EVs), which are present in all body fluids. They are defined by their mechanism of cellular production and include exosomes (EXOs), microvesicles (MVs) and apoptotic bodies [72]. The diameter of the EXOs ranges from 30 to 150 nm; they are secreted by most cell types into the extracellular microenvironment under normal and pathological conditions and can be found in blood, sweat, saliva, urine, amniotic fluids, breast milk, cerebrospinal fluids and malignant ascites [73–76]. EXOs have a membrane consisting of a lipid bilayer and contain various biomolecules, including proteins, lipids, RNA and DNA, suggesting their involvement in the regulation of various biological functions through different molecular mechanisms [77–79].

When EXOs are taken up by other cells, their cargo (mRNAs, miRs and protein) is transferred and influences the phenotype of recipient cells [80,81]. EXOs mediate cell-to-cell communication and are involved in different physiological and pathological processes, also playing an important role in cancer [82]. Like other tumors, melanoma cells modify their microenvironment to enable growth and metastasis via direct cellular interactions or the indirect release of soluble factors and/or EXOs.

Similarly to synthetic nanoparticles, EXOs can be considered drug delivery systems and possess important advantages such as long-term circulatory capability, good biocompatibility, minimal toxicity and intrinsic ability to target specific cells [83]. Moreover, one of the most useful properties of EXOs is their ability to cross the cytoplasmic membrane [84] and the blood/brain barrier [85].

Altogether, these characteristics make EXOs a very interesting drug delivery system [86]. Recently, several studies have focused on specific modification of EXOs surface to make them a proper candidate for cellular uptake and target specificity [87]. In particular, it has been proved that modification of EXO–tetraspanin complexes strongly influences target cell selection both in vitro and in vivo [88], improving the targeting of tissues and cell types of interest. Other modified transmembrane proteins are

the platelet-derived growth factor receptors [89]. In 2018, Kim et al. engineered macrophage-derived EXOs loaded with the anticancer drug paclitaxel to improve their circulation time in the blood and allow them to target pulmonary metastases. By using this modified EXOs, the drug can selectively deliver to target cancer cells and also can increase the survival rate in an in vivo lung cancer model [90].

In the last decade, several studies have focused on immune system stimulation for the treatment of different cancers. Dendritic cell-derived EXOs (dexosome) have been successfully engineered to target helper T cells to stimulate cytotoxic T cell proliferation, influence T cell differentiation and create an anti-tumor environment [91]. Dexosomes have entered clinical trials for colorectal cancer [92], non-small cell lung cancer [93] and metastatic melanoma [94]. Particularly in melanoma cancer, EXOs are studied as therapeutic cell-free vaccines, e.g., dendritic cell-derived EXOs loaded with MAGE3 antibodies were administered subcutaneously or intradermally to stimulate the immune response of melanoma stage IIIb/IV patients obtaining positive clinical effects [95].

Moreover, Zhu et al. analyzed the fundamental role of EXOs derived from natural killer cells to exert cytotoxic effects on melanoma cells, suggesting this could be a potential immunotherapeutic strategy for cancer [96]. More recently, Luigini L. et al. performed a homemade test for the analysis of natural killer exosomes derived from plasma of melanoma patients and healthy donors. Their results showed that these vesicles could be used to improve cancer treatment in combination with NK cells or immune checkpoint-based therapies [97].

Among the new therapeutic approaches, very interesting results were reviewed by Chillà A. et al. In their paper, they refer to the therapeutic use of some particular cells used as natural nanoparticles [98]. For example, the possibility to target an experimental melanoma with red blood cells (RBCs) and platelet membrane-coated gold nanoparticles containing curcumin was described. In this study, the natural cell membranes have been used as biointerfaces that interact with the host environment. Platelet membrane coating has been used to target cancer cells while RBC membrane coating has been used to provide clearance by macrophages [99]. This cell-mediated delivery would provide controlled dosing, no systemic toxicity, and high tolerance by the patient and could be possibly evaluated as a therapeutic strategy in melanoma [98].

#### *2.8. Natural Nanoparticles for Metastatic Melanoma Diagnosis*

Early diagnosis and effective therapy are undoubtedly the primary issues for prolonging the survival of cancer patients. At present, the solid biopsy used for diagnosis is mostly the basis for treatment of cancer. Unfortunately, this method has considerable limitations as it is invasive and sometimes not feasible. Accumulating evidence over the past few decades has highlighted the potential use of circulating molecular biomarkers as a promising platform for the noninvasive diagnosis and definition of prognosis. As previously reported, the cargos incorporated in EXOs reflect the contents and the pathological status or signaling alterations of the original cells. Moreover, the cargos are protected from degradation and are isolable from patients' body fluids. Thus, analyses of circulating EXOs and their contents in blood could represent a promising strategy in cancer diagnosis advancement and monitor patients' therapeutic response.

For instance, PD-L1 expression in tumor tissues is widely used in selecting patients who will benefit from treatment with immunotherapy. Recently, evaluating the role of exosomal PD-L1 expressed in melanoma patients treated with anti-PD-1/PD-L1 antibodies, Danesi R. and his group showed that exosomal PD-L1 expression increased in subjects with disease progression and decreased in patients responding to treatment, while no significant changes were observed in patients with disease stabilization [100]. In the same year, Chen G. et al. [101] showed that Interferon-γ (IFN-γ) up-regulates PD-L1-containing EXOs secretion and changes during the course of anti-PD-1 therapy. In accordance, a very recent prospective study to evaluate exosomal-PD-L1 expression from 100 melanoma patients treated with immune checkpoint inhibitors and BRAF/MEK inhibitors, showed that tumor EXOs carrying PD-L1 had immunosuppressive properties since they were as efficient as the melanoma cell at

inhibiting T-cell activation. This study provided a rationale for monitoring the EXO-PD-L1 level as a potential predictor of treatment response in melanoma patients [102].

Functional nucleic acids contained in EXO (miRs, long non-coding RNAs, circulating RNAs, mRNAs, and DNA) are chemically stable and resistant to degradation by RNAases. Numerous previous studies have shown that exosomal miRs could be potential diagnostic and prognostic biomarkers for melanoma [103]. In particular, González and his group, in one of the first publications that identified circulating exosomal miRs in patients with melanoma, reported that the EXOs from patients with melanoma contain a level of miR-125b that is significantly lower compared to that contained in EXOs from disease-free melanoma patients or healthy controls [104]. Consistently, miR-125b appears to function as a tumor suppressor in melanoma, by direct regulation of c-Jun and MLK3 protein expressions [105,106] and inducing cell senescence [107]. Recently, Huber et al. have described seven miRs, including miR-125b-5p, that were released in extracellular vesicles and associated with myeloid-derived suppressor cells (MDSCs) and resistance to treatment with immune checkpoint inhibitors in melanoma patients [108]. Moreover, it was reported that the regulation of the miR-125b-5p/EIF5A2 axis in melanoma, through the long intergenic non-protein coding RNA 520 (LICN00520), promotes the proliferation, invasion and migration of melanoma cells. The authors suggest that long non-coding RNA (lncRNA) expression level may be possibly evaluated in blood circulation as a therapeutic biomarker [109].

Another report by Tengda et al. reported that the analysis of a panel of five exosomal miRs, in particular miR-532-5p and miR-106b, was able to distinguish patients with or without metastases, patients with stage I–II from patients with III–IV of disease and patients who received pembrolizumab treatment from those not treated [110].

Most studies have shown the potential role of tumor-derived EVs, mainly EXOs, as biomarkers to predict cutaneous malignant melanoma (CMM) outcome and resistance to MAPKis [110–112]. A recent work published by Svedman has led to the identification of the EV-derived miRs, let-7g-5p and miR-497-5p, that can be used as putative good predictive biomarkers after MAPKi treatment in metastatic CMM patients [113]. As reported by Lunavat et al., the expression of miR-211–5p is up-regulated upon vemurafenib and dabrafenib treatment in EXOs and melanoma cells. Interestingly, the authors suggested that stable expression of miR-211–5p reduces sensitivity to BRAF inhibition by regulating cellular proliferation. The authors indicate this miR as a promising prognostic biomarker in melanoma [112].

MiR-21, one of the most frequently up-regulated miRs in solid tumors, controls important tumor suppressor genes as well as genes involved in carcinogenesis, such as phosphatase and tensin homolog (PTEN), programmed cell death protein 4 (PDCD4), phosphoinositide 3-kinase (PI3K), Sprouty and reversion inducing cysteine rich protein with Kazal motifs (RECK) [114–118]. As a result, miR-21 has been proposed as a plausible diagnostic and prognostic biomarker, as well as a therapeutic target for several types of cancer [119]. Moreover, the use of exosomal miR-21 as a potential biomarker has been proposed. Specifically, a systematic review provided evidence that is consistent with this hypothesis although the limited number of each cancer type analyzed should be considered. In addition, the author suggested that a combination of miR panels or cancer antigens may be a good strategy to have better diagnostic results or prognostic predictions in most circumstances [120].

MiR-21 plays a pivotal role in melanomagenesis and is released in EXOs produced from melanoma cells [121]. In an interesting work, Pfeffer et al. performed a miR profiling on RNA prepared from plasma-derived EXOs from specific patient cohorts [122]. As stated in the cited review [120], they found that a panel of five miRs (miR-17, miR-19a, miR-21, miR-126, and miR-149) was expressed at higher levels in plasma-derived EXOs from patients with metastatic melanoma. In addition, these miRs were found to be over-expressed in patients with metastatic sporadic melanoma compared to familial melanoma patients or healthy controls. This panel is proposed as a predictive biomarker to monitor remission as well as relapse following therapeutic intervention [122]. Of interest, miR-21 and miR-146a up-regulations were found in vitreal EXOs of patients with uveal melanoma (UM) as a consequence of dysregulation arising from tumor cells. Data shown in that paper also suggest the possibility of detecting these miRs in VH (vitreous humor) and that serum of UM patients could be considered a potential circulating marker [123]. In addition, the plasma level of miR-21 was positively correlated with tumor burden in metastatic melanoma patients [124] and its relative expression was positively correlated with the tumor nodes and metastasis (TNM) stage of melanoma [125]. All these studies confirmed that the expression of miR-21 in melanoma patients can be used as a potential serum biomarker for melanoma metastasis diagnosis and disease assessment.

Several proteins involved in melanoma progression and metastasis have been identified in EXOs and could be considered as possible prognostic biomarkers [126].

A large increase in total exosomal protein derived from malignant melanoma occurs during disease progression. Specifically, each melanoma cell line appears to exhibit an individual type of heat shock protein (HSP)70 expression, likely reflecting a selection during tumor progression and therapy [127]. Very interesting results were obtained from Peinado and coworkers who identified a melanoma-specific exosomal signature in blood from patients with stage IV melanoma that included expression of tyrosinase-related protein-2 (TYRP-2), very late antigen-4 (VLA-4), HSP70, HSP90 and mesenchymal epithelial transition (MET) oncoprotein [128]. More recently, Alegre et al. evaluated the presence of the melanoma biomarkers melanoma inhibitory activity (MIA) protein, S100B and tyrosinase-related protein 2 (TYRP2) in EXOs derived from serum obtained from stage IV melanoma patients, melanoma-free patients and healthy controls. They observed that MIA and S100B can be detected in EXOs and their quantification presents diagnostic and prognostic utility in melanoma patients [129].

Recently, lncRNAs have emerged as an important regulatory factor in tumor growth and transformation [130]. In particular, Cantile et al. have analyzed the fundamental role played by HOTAIR in the malignant transformation and progression of melanoma cells. Moreover, they have identified this lncRNA in the blood and have suggested its potential role as circulating marker [131]. Numerous reports have shown that exosomal lncHOTAIR could be a sensitive liquid biomarker of different types of cancers, such as glioblastoma multiform, breast cancer and non-small-cell lung cancer (NSCLC) [132–134]. However, the potential use of EXOs for HOTAIR detection in melanoma cells has not yet been explored.

In a very recent and interesting study, the authors describe a comprehensive proteomic analysis of 426 human samples from tissue explants (TEs), plasma and other body fluids. They reported that the "tiny packages of materials released by tumors", called EVPs (extracellular vesicles and non-vesicular particles <50 nm), may serve as biomarkers for detecting a number of different types of cancers (including melanoma) in its early stages. In particular, the analysis of some specific proteins (e.g., versican, tenascin C, and thrombospondin 2 can distinguish tumors from normal tissues with 90% sensitivity and 94% specificity. Moreover, they have defined a panel of tumor-type-specific EVP proteins in TEs and plasma, which can classify tumors of unknown primary origin [135].

#### **3. Conclusions**

Advanced melanoma is a rapid metastasizing and drug-resistant cancer [1,2]. Despite the availability of new therapies discovered in recent years, metastatic melanoma remains a highly lethal disease. Recent advances in nanotechnologies opened up new perspectives for treating melanoma, potentially effective in overcoming the limitations of conventional approaches: toxicity to normal tissues, development of drug resistance and rapid blood clearance. Nanotechnologies are based on the use of different polymers whose specific characteristics are adapted to different aspects of therapies. Both the Food and Drug Administration (FDA) and European Medicines Agency (EMA) recently approved several types of nanoparticles as a system for conveying nucleic acids, drugs and other molecules for cancer and other disease treatment, including melanoma [17,18,28]. More optimized nanocarriers for the delivery of drugs in a specific way that is able to make the melanoma treatment more efficient is represented by targeted nanoparticles. Melanoma-specific antibodies are conjugated

to nanoparticles, thus overcoming the physiological barriers and conditions to reach the target and to recognize and actively bind to target cells through specific antibody–receptor interactions [66].

The discovery of EXOs as natural carriers of RNA, DNA, proteins and lipids have highlighted the potential use of these vesicles as circulating molecular biomarkers for both diagnostic and therapeutic purposes. Due to their easy handling and their ability to transfer molecules between cells, EXOs may be a very performant delivery system [86]. Moreover, naturally secreted EXOs loaded with therapeutic molecules may optimize efficacy of treatment while also reducing off-target delivery. Specifically, molecules may be included in EXOs that are difficult to deliver intracellularly without the use of a carrier (ncRNA, recombinant proteins, small-molecule drugs). Particularly in melanoma, EXOs are also studied as a therapeutic cell-free vaccine [95]. The purification of pure populations of EXOs from EXO-secreting cell lines, unlike those released from autologous primary cells, have immunogenic and oncogenic potential. However, the modification of the complex structure of naturally secreted EXOs may limit their functional activity and their pharmaceutical acceptability. As a potential alternative, some authors have proposed the development of EXO-mimetics manufactured using selected components of EXOs that can be incorporated into synthetic nanoparticles to enhance their stability, immunogenicity, targeting, and uptake [136]. However, the main and useful components of exosomes to therapeutic delivery have not been identified yet.

Although the high number of publications in this field suggests the high diagnostic and prognostic potential of EVs in melanoma, our knowledge remains limited. Several factors hinder the real use of EVs or EXOs as biomarkers. For example, the differences in the methods used by researchers to isolate EXOs from patients' blood introduce heterogeneity in the composition of the different preparations, thus precluding the identification of the active components. Furthermore, different populations of vesicles are found in the plasma of patients and the isolation of those released by melanoma tumor cells would be of great scientific importance. In this regard, a recent publication by Ferrone et al. suggests the use of an antibody directed against CSPG4 antigen to separate exosomes released by melanoma from those secreted by normal cells [137].

In conclusion, natural or synthetic nanoparticles are promising tools to design new therapeutic, diagnostic and prognostic approaches to cancer and, in particular, to melanoma. Studies are needed to fill the gap between the large amount of evidence available in the preclinical setting and the limited evidence obtained in the real clinical situation.

**Funding:** This work was supported by the following grants: MAECI, Progetti di Grande Rilevanza, Domanda di contributo; L. 401/90 (PGR01049) to N.F.; Regione Lazio "Progetto di Gruppo di Ricerca finanziato ai sensi della LR Lazio 13/08" (Protocollo 15263) to Mauro Biffoni (M.B.) for an M.B.A. fellowship.

**Acknowledgments:** We thank Mauro Biffoni (M.B.) for critical reading of the manuscript and Deborah Paradiso for English-language editing and proofreading.

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

#### **References**

© 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/).

*Review*


*3.2. Conditioning Factors*

Landslide occurrence is considered to be affected by a variety of natural and anthropogenic factors representing the conditions of a given region. These conditioning factors can be separated into two main categories: (a) the preparatory factors which create suitable conditions for a landslide by changing the state of a slope from stable to marginally stable, and (b) the triggering factors which initiate a landslide by changing the state of a slope from marginally stable to unstable [43]. Morphological and hydro-lithological conditions of the region of interest are represented by natural preparatory factors, whereas the human interventions on it are represented by anthropogenic preparatory factors. The triggering factors mainly represent climatic and seismic conditions related to rainstorms and earthquakes, respectively.

**Figure 2.** Multi-temporal Google Earth images: (**a**,**c**) before the landslides; (**b**,**d**) after the landslides. The red dashed lines indicate the location of the landslide before it happened, and the red solid line shows the scar of the landslide after it occurred.

Since no official guidelines are used by the scientific community for the selection of factors, the characteristics of the study area, data availability and a literature review [29,30] were taken into account for this study. In total, fourteen conditioning factors were selected, including both preparatory and triggering factors. In particular, the altitude, slope angle, slope aspect, profile curvature, plan curvature, stream density, stream power index (SPI), topographical wetness index (TWI), lithology, proximity to faults and soil type were used as natural preparatory factors; the land use/cover and proximity to roads were used as anthropogenic preparatory factors, and the mean annual rainfall was used as a triggering factor.

Defined as the height above a reference point (typically above the mean sea level), altitude is an important conditioning factor due to its gravitational potential energy. In general, the higher the slope angle is, the higher the likelihood of failure. Therefore, steep slopes are more prone to failures. The slope aspect is defined as the azimuth-based orientation of terrain and is highly related to exposure to sunlight; evapotranspiration; and rainfall's effects on weathering, soil, vegetation cover and root development [44]. Expressed by different types, such as plan and profile, the curvature indicates the runoff and erosion factors of water. The plan curvature is perpendicular to the maximum slope direction, whereas the profile curvature is parallel to the same direction [45]. By retaining more rainfall water and erosion-induced sediment than convex slopes, concave slopes are correlated with higher likelihoods of failure.

Considering its effects on groundwater recharge, stream density constitutes another important factor for landslide activity. This factor determines the ratio of the total length of streams to the extent of the study area. A high stream density is linked to low surface water infiltration and thus mass movements with high velocity [46]. SPI is another hydrological factor that measures the erosive power of the streams. On the other hand, TWI quantifies the moisture content of the surface [32].

Lithology is one of the most crucial factors for LS assessments, since different lithological formations have different slope instability performances in terms of strength and permeability. In a tectonically active country such as Greece, the faults seem to be associated with extensive fractured zones and steep relief anomalies presenting favorable conditions for landslides [35]. Hence, landslides are usually found in proximity to faults. Additionally, different soil types can have different impacts on surface infiltration and groundwater flow, depending on their particular physical and mechanical properties [47].

Changes in land use/cover as a result of human activities such as cultivation, deforestation and forest logging can significantly affect the occurrence of landslides. Proximity to roads can also reflect the human impact on landslides, as road construction at the base of a slope tends to degrade its stability.

Rainfall—causing an increase in the pore water pressure and a reduction in the shear strength of the soil [48]—is a basic triggering mechanism for not only the development of new landslides but also the re-activation of old ones. Particularly in Greece, rainfalltriggered landslides are among the most frequent and devastating disasters [38]. It is worth mentioning that since the majority of earthquakes that occurred in the study area during the last two decades were characterized by relatively low magnitudes (with M w between 3.0 and 3.5) and great depth (greater than 15 km) [49], seismic factor was not included in the analysis.

As is shown in Table 2, all the above conditioning factors were represented by GISsupported data formats. Most of them were in raster format (grids), but others were converted from vector (point, line, or polygon features) to a raster format with 25 m spatial resolution.

#### *3.3. Geographical Detector (GeoDetector)*

GeoDetector is a spatially-based multivariate statistical model which was developed in 2010 by Wang et al. [50]. It can detect the spatially stratified heterogeneity of a given phenomenon according to the basic principle that if a determinant is associated with the phenomenon, then there may be some similarities between their spatial distributions. Furthermore, it can reveal the driving forces behind the phenomenon by quantifying the impacts of individual determinants and of their pairwise interactions. The phenomenon under investigation as a dependent variable can be represented by either numerical continuous or discrete classified (stratified) data, and the determinants as explanatory variables exclusively by classified data.

**Table 2.** Summary of the datasets representing the conditioning factors.

In the case of LS, GeoDetector can detect whether a conditioning factor (explanatory variable) causes the spatial stratified heterogeneity of landslide occurrence (presence or absence of a landslide, dependent variable) or not. In particular, it can quantify the degree of impact of each factor on the landslide occurrence using a q-statistic calculated as follows [51]:

$$q = 1 - \frac{\sum\_{h=1}^{L} N\_h \sigma\_h^2}{N\sigma^2} \tag{1}$$

where *h* = 1, 2, ... , *L* is a given class (stratum) of an explanatory variable; *L* is the number of classes; *Nh* and *N* are the numbers of samples in class *h* and entire study area, respectively; and *σh* and *σ* are the variance of dependent variable in class *h* and entire study area, respectively. Ranging from 0 to 1, the higher the q value is, the more this explanatory variable contributes to the dependent variable. A *p*-statistic, an indicator of statistical significance for each explanatory variable, is also calculated by a non-central F-distribution:

$$p(q < x) = p\left(F < \frac{N-L}{L-1} \frac{x}{1-x}\right) = 1-a \tag{2}$$

where *a* is the probability of q being higher than or equal to *x*. In a 95% confidence interval, an explanatory variables with a *p* value greater than 0.05 is considered to have a statistically insignificant relationship with the dependent variable and could be eliminated from the model.

By estimating the value of q-statistic corresponding to the interaction of two explanatory variables, GeoDetector can also quantify the degree of the interactive impact of each pair of conditioning factors on landslide occurrence. As is shown in Table 3, based on the

comparison of this value with the individually estimated values, the type of interaction can be then determined.

**Table 3.** Types of interaction between two explanatory variables (X1 and X2).

#### *3.4. Information Value (IV)*

IV is a bivariate statistical model which was initially proposed by Yin and Yan [52] and later modified by van Westen [53]. It includes class-level estimations of weight values based on the spatial associations between the landslide occurrence and each class of each conditioning factor. The IV for a given factor class is derived from a mathematical formula of the ratio of landslide density in this class to the landslide density in entire study area (or factor):

$$IV = \ln\left(\frac{Np\text{ix}(Si)/Np\text{ix}(Ni)}{\sum Np\text{ix}(Si)/\sum Np\text{ix}(Ni)}\right) \tag{3}$$

where *Npix(Si)* is the number of landslide pixels within the factor class *i*, and *Npix(Ni)* is the number of all pixels in the same class. The calculated value can be either positive or negative, and the higher (or lower) it is, the more (or less) significant the contribution of the relevant factor class to landslide occurrence.

#### **4. LS Assessment by Hybrid Modeling**

Considering the functionalities and data requirements of the two models composing the GeoDIV hybrid model, two GIS-based data processing procedures initially took place under the general methodological framework (Figure 3). These procedures were the (non)landslide sampling and the factor preparation. For sampling, the landslide inventory dataset was divided into two subsets used as inputs in the model's training (training dataset) and validation (validation dataset), respectively. Among the amount of 60 landslides contained in the inventory, 80% of them (48 in amount) were randomly selected for the training dataset in this study. The remaining 20% (12 in number) constituted the validation dataset. Based on the sizes of mapped landslides and the spatial resolution of obtained factor data, the entire study area was then tiled into grid pixels of 25 × 25 m as the basic analysis unit, resulting in 188 training and 41 validation landslide pixels. The IV model required only a landslide dataset, whereas the GeoDetector model required both landslide and non-landslide datasets. Hence, in order to construct the dependent variable for GeoDetector, an equal number of pixels from the not landslide-affected part of study area were also selected in a random way for the training dataset (totally 376 pixels). The target values of 0 and 1 were assigned to the non-landslide and landslide pixels, respectively, making the dependent variable a binary classified dataset.

**Figure 3.** Methodological framework for the development of the hybrid GeoDIV model.

In regard to factor preparation, the raster layers of conditioning factors on a continuous numerical scale (altitude, slope angle, profile curvature, plan curvature, stream density, SPI, TWI, proximity to faults, proximity to roads and mean annual rainfall) were divided into a number of discrete classes (Figure 4). In this study, the number of categories and their relative break values were mainly determined by the "natural breaks (Jenks)" classification method [54]. In this method, class breaks identify the most similar within-group values and maximize the differences between classes according to the deviations about the median [55]. Additionally, the raster layers of factors originally on a discrete classified scale (slope aspect, lithology, soil type, and land use/cover) were prepared by grouping them into more or less common initial classes (Figure 4).

After the data processing procedures, the GeoDIV model was implemented. A database was firstly created as the result of the matching of the sample of 376 training data with each factor layer. Including the fourteen classified factors as independent variables and the landslide presence or absence (binary target value of 0 and 1) as the dependent variable were determined in the GeoDetector software, developed by Xu and Wang [56], to determine the impacts of the factors and their pairwise interactions on the spatial stratified heterogeneity of landslide occurrence represented by the training sample. This determination included the calculation of q values for the factors and their pairwise interactions (Tables 4 and 5). To incorporate in the model only the factors with statistically significant relationships with landslide occurrence, the estimated *p* values (Table 4) of the factors were also exploited for factor selection. Despite the requirement for *p* values less than 0.05 in the 95% confidence interval, factors such as altitude, slope angle, plan curvature, stream density, TWI, proximity to faults, proximity to roads, lithology, soil type and land use/cover remained in the model. Conversely, slope aspect, profile curvature, SPI and mean annual rainfall were not qualified to be further analyzed by the model, indicating that there were statistically insignificant relationships (i.e., *p* values greater than 0.05) between them and landslide occurrence in the same confidence interval.

**Figure 4.** *Cont.*

**Figure 4.** Conditioning factors: (**a**) altitude; (**b**) slope angle; (**c**) slope aspect; (**d**) plan curvature; (**e**) profile curvature; (**f**) stream density; (**g**) SPI; (**h**) TWI; (**i**) lithology; (**j**) proximity to faults; (**k**) soil type; (**l**) mean annual rainfall; (**m**) land use/cover; (**n**) proximity to roads.

\* indicate the factors eliminated from GeoDetector according to the *p* values.

**Table 5.** The q-statistic values for the pairwise interactions between the conditioning factors, calculated using GeoDetector.

Subsequently, by matching only the 188 landslide training data with each layer of statistically significant factors, the landslide density for each of their classes was estimated. The IVs were then calculated by Equation (2) to determine the impact of each class on landslide occurrence (Figure 5).

**Figure 5.** *Cont.* 74

**Figure 5.** The estimated IVs for the classes of conditioning factors qualified from the factor selection: (**a**) altitude; (**b**) slope angle; (**c**) plan curvature; (**d**) stream density; (**e**) TWI; (**f**) lithology; (**g**) proximity to faults; (**h**) soil type; (**i**) land use/cover; (**j**) proximity to roads. NA values (or no bars) indicate "not applicable" for these classes.

By using the q values from GeoDetector as factor-level weights and IVs as class-level weights, the overall landslide susceptibility (LS) score was estimated through a GIS-based weighted linear combination of statistically significant factors:

$$LS = \sum\_{j=1}^{n} \mathcal{W}\_{l} \times s\_{i,j} \tag{4}$$

where *Wj* is the weight of a given factor *j*, *si,j* is the weight for a given class *i* of factor *j* and *n* is the number of factors. The spatial distribution of the estimated overall score was visualized by a LS map divided into five classes ("very low", "low", "moderate", "high" and "very high" susceptibility) according to the "natural breaks (Jenks)" method (Figure 6).

**Figure 6.** The landslide susceptibility map produced by the hybrid GeoDIV model.


**5.9. Parainfluenza virus**

associated with coexisting bacterial pneumonia. Pneumonia is usually characterized by a proteinaceous exudate and alveolar necrosis. There is a variable polymorphonuclear inflammatory

Human metapneumovirus (hMPV) is in the Paramyxoviridae family and was initially described in the Netherlands in 2001 [46]. It is a pleomorphic-shaped virus with protein projections from the surface. hMPV is a ubiquitous organism and almost all children have been exposed to it by age 5 years, Morbidity in lower respiratory tract infections in children and infants was reported to bronchiolitis (59%), croup (18%), asthma (14%), and pneumonia (8%) [47]. Reinfection continues to occur throughout life. The virus is spread via droplet and fomite

The severity of infection increases with older age and with complications such as immunosuppressive conditions or cardiopulmonary disease. Adult hospitalizations with hMPV infection are associated with chronic obstructive pulmonary disease (COPD) exacerbations, pneumonia and bronchitis. Severe pneumonitis requiring intensive care is required in immu-

The influenza viruses are RNA viruses in the family Orthomyxoviridae, they are enveloped, single-stranded, and are the most common viral cause of pneumonia. Influenza has three serotypes A, B, and C. Influenza type A can infect livestock allowing a reservoir for infection and opportunity for epidemics in humans. For this reason it is usually the most virulent pathogen. The structure of influenza virus includes two envelope glycoproteins, known as hemagglutinin (H) and neuraminidase (N).The hemagglutinin enables infectivity of the virus by attaching to cellular sialic acid residues, whereas the N protein allows spread of the virus to other cells through cleavage of the new virus. Severe pneumonia complications can occur in high-risk individuals [48]. Two influenza types have emerged of particular importance: H5N1 avian influenza strain and the novel H1N1 swine influenza strain. In the influenza A (H1N1) pandemic of 2009–2010, the World Health Organization estimated approximately 16,000 deaths. The majority of these deaths corresponded to patients with underlying risk factors contributing to worse outcomes Influenza type B causes illness seems to occur more in closed populations, e.g., boarding schools. Influenza type C is less common and occurs as

Measles is a respiratory tract virus that causes a febrile illness with rash in children and a mild pneumonia in healthy adults. It is a single-stranded RNA virus in the Paramyxoviridae family and the genus *Morbillivirus*. It comprises a nucleocapsid surrounded by an envelope. Measles is a highly contagious disease that results from infection with measles virus and is still responsible for more than 100,000 deaths every year [50]. Measles virus is transmitted by

response [45].

198 Contemporary Topics of Pneumonia

exposure.

**5.7. Influenza virus**

sporadic cases [49].

**5.8. Measles virus**

**5.6. Human metapneumovirus**

nocompromised hosts (e.g., hematologic malignancies).

Parainfluenza virus (PIV) consists of nucleocapsids, which propagate in the cytoplasm of infected cells, with hemagglutinin present in the virion envelope. PIV is a common virus infection of childhood. PIV is second in importance to only RSV in causing children pneumonia and bronchiolitis in infants younger than 6 months and lower respiratory tract disease. Transmission is through direct contact or large-droplet spread.

Although there are four subtypes of PIV, PIV types 1 and 2 tend to peak during the fall season where as type 3 is endemic year-round. Recurrent upper or lower respiratory tract infections occur throughout life because Immunity is short term. The infections vary from a self-limiting illness to life-threatening pneumonia especially in immunocompromised hosts leading to lung injury and respiratory failure [52]. In one study, hematopoietic stem cell transplant (HSCT) patients with PIV progressed to develop pneumonia. Of 44% of these patients with pneumonia there was a mortality rate of 37% [53].


**6.1 Diet and stress modulation of hypertension**

134 Modern Telemetry

is inserted in the abdominal cavity (for rats) or under the skin (for mice). Interestingly, with the miniaturization of electronic components and probably because of the pressure of animal rights activists, there has been an impressive decrease in the number of publications using telemetry in large animals (cats, dogs, swine, monkeys) and a significant increase in those employing rodents (Kramer et al., 2001). Our own experience is with the transmitters from Data Sciences International (DSI, St-Paul, MN, USA), but other manufacturers are present on the market. For a review of the progress in radiotelemetry in small animals, please refer to Kramer et al. (2001). Similarly, the interested reader can consult the original publication by Mills et al. (2000) describing the characteristics of the mouse transmitters from DSI as well as the review article by Huetteman & Bogie (2009) that describes in details

Because of the surgery needed for the installation of the transmitters, radiotelemetry can be considered an invasive technique. But, at the same time, when recovery from surgery is optimal, it is the least invasive method since the measurements are achieved in the usual environment without external stressors such as the direct intervention of the technician. Furthermore, measurements are performed not only on undisturbed animals, but also in freely moving animals, which contrasts with all the other measurement techniques. Indwelling catheters attached to tethering devices is the only method allowing some movement of the conscious animals but generates noise and stress and does not come close to what can be achieved by telemetry. Blood pressure can be performed continuously on a beat-to-beat basis and during the night, a period not always practical for the experimenter. It allows the monitoring of the circadian rhythm impact and greatly reduces the variation in the mean blood pressure when data are averaged over several hours. For instance, Van Vliet (2003) has shown that the 95% confidence interval of the mean of 24 hours of blood pressure measurements in a group of 9 mice was 8 mm Hg as compared to 14 mm Hg for 30-minutes average and 22 mm Hg for a single time point. In SHR, systolic blood pressure is 20-40 mm Hg higher and heart rate 100 bpm faster when assessed by tail-cuff or directly by indwelling catheters (Bazil et al., 1993). In mice, while the tail cuff results are highly correlated with direct arterial pressure (r=0,86, p<0,01), the tail-cuff values are 20 mm Hg higher on average (Krege et al., 1995). For both species, the lowest blood pressure values are obtained with implantable radiotelemetry because the method is devoid of stress and performed in freely moving animals without anaesthesia (Irvine et al., 1997; Mills et al., 2000; Kuneš et al., 2008).

Measuring blood pressure in conscious unrestrained animals

Quantifying relationship between blood pressure and other variables Studying blood pressure-dependent and independent effects following

Measuring blood pressure continuously over time

Quantifying hypertension or changes in blood pressure

Table 3. Recommendations for the use of telemetry for measuring blood pressure in animals

Table 3 summarizes the recommended use and advantages that apply to direct blood pressure measurements methods and especially to telemetry. The only application for which the AHA indicates that direct methods are not recommended is for screening of large

Measuring blood pressure variability

interventions

(adapted from Kurtz et al., 2005).

the surgical procedures to implant DSI transmitters in rats and mice.

**Recommended for:**

The work by Šedová et al. (2004) from our group illustrates the various concepts that we have presented here and take advantage of telemetric measurement of blood pressure to draw conclusions that would have been impossible to obtain otherwise. The manuscript describes the effects of diet-induced obesity in the SHR. Because the cardiovascular response to stress is a significant predictor of hypertension, it looked more specifically at the effects of the diet on the stress response and the global impact on cardiovascular morbidity.  Blood pressure and heart rate were measured by telemetry at week 0 and week 12. In addition to basal blood pressure recorded for 3 consecutive days each time, blood pressure determination in response to a 30 minutes immobilisation stress was also performed at weeks 0 and 12.

As expected, immobilisation stress in restrainers for 30 minutes was able to increase blood pressure and heart rate in both groups. While there was no difference in the blood pressure response to stress in both groups between week 0 and 12, there was a significant lag in the return to the baseline after stress in the high-fat diet group at week 12 as compared to week 0. This was significant for systolic and diastolic blood pressure as well as for heart rate. No difference was observed in the response to stress in the control animals in the stress or poststress periods. Furthermore, because it allowed the measurement of the circadian pattern of blood pressure, telemetry could reveal a blood pressure increase during the night in the animals receiving the high-fat diet as compared to the normal-chow fed controls. As we have seen from the immobilisation stress data, this would have been impossible to detect with a one-time recording in stressful conditions by the tail-cuff method. Needless to say, measuring blood pressure during stress and after stress with any other techniques could not

Radio-Telemetry in Biomedical Research - Radio-Telemetry Blood Pressure

telemetry is essential for quality phenotyping in genetic research.

displaying genetic heterogeneities that dilute the loci effects.

**7. General conclusion**

**pressure**

Measurements in Animal Models of Hypertension, How It Revolutionized Hypertension Research 137

markers, good phenotyping is essential if one wants to rely on the results. Given the important consequences if one wants to pursue the study of a candidate locus, we think that

**6.3 Dissection of hypertension: Positional cloning of quantitative trait loci for blood**

Finally, we should not forget to mention the work from Alan Deng who dissects hypertension in the rat and was able to find several significant blood pressure quantitative trait loci (Deng, 2007). His systematic approach uses telemetry as the sole method for blood pressure determination. The blood pressure values are obtained from adult rats from inbred strains. He does, however, perform several genetic manipulations in order to be able to reveal the loci. This approach allowed him to report several loci, the chromosomal position of which are later refined by producing sub-congenics animals (Chauvet et al., 2008). He was also able to reveal some gene-diet interactions and gene-gene interactions. For instance, he was able to demonstrate that a minor effect locus (named C18QTL1) could exert a major effect when it was transferred onto another genetic background. The normotensive Lewis rat genetic background was preventing this locus from exerting its full effect (Charron et al., 2005). This indicates that the normotensives also possess high blood pressure genes, but their effects are counterbalanced by 'normotensive' genes. This demonstrates the genetic buffering capacity of the genome and probably explains why the GWAS performed in humans do not yield the desired major high blood pressure loci: the population are

In this review, we have tried to demonstrate the importance of the environmental component of hypertension. When recognizing this importance, we think that it becomes easier to select the right tools to measure blood pressure in animal models as well as in humans. Human studies tend to indicate a decrease of the prevalence of hypertension whereas it may only be the reflection of the new standardized methods for the measurement of blood pressure devoid of stress. We have shown that rodents used for hypertension research are also stress sensitive and that the most commonly used method for blood pressure determination exacerbates the stress perceived by the animals: it increases blood pressure and body temperature. Since we have presented evidences indicating that stress and temperature can in turn be involved in the development of hypertension, it seems essential to subtract their influence when studying hypertension. Therefore, we think that telemetry is the sole method to perform blood pressure measurement in animal models of hypertension because it enables to subtract the stress artefacts or, conversely, to study them. Furthermore, no genetic studies should be performed in animals if telemetry is not used to characterize the blood pressure levels. Some differences are small and inadequate phenotyping could ruin a GWAS by revealing spurious statistical associations and diverting researchers to wrong candidate genes. On the other side, in an effort to standardize the techniques and eliminating human interference and bias, human epidemiological and genetic studies tend to eliminate the stress component from the measurement of blood pressure. As we have shown, this approach is desired in animal studies especially because we are dealing with inbred strains and can test our hypothesis is several backgrounds. Small effect blood pressure loci have more chance of being 'true' and can be verified in congenics

have yielded such results. We think that telemetry is the most appropriate mean for measuring blood pressure in this kind of work involving the modulation of blood pressure by environmental stressors. We also want to underline the fact that stress is always present when dealing with blood pressure measurements in rodents. Given its impact on blood pressure, it has to be taken into consideration even when it is not specifically under study.

This example is a good illustration of the study of the gene × environment interactions: We had to increase the genetic variance in order to be able to monitor changes that would not be visible otherwise. The first challenge was the use of an obesity-inducing diet that revealed differences in blood pressure response to stress and in the night period of the circadian pattern. The second challenge was the stress test where we could observe a delay in the return to the baseline in the high-fat diet treated rats. This is very interesting in the light of another finding: an augmentation of the cardiac mass in the rats fed a high fat diet. With the telemetric data, we can hypothesize that the increase in cardiovascular reactivity observed in the night (active period of the rats) and following a stress could explain an increased workload for the heart and the hypertrophy after only 12 weeks of high-fat diet. The effect of the diet would thus be more important when the animals are awake, active and experiencing daily stressors. This interpretation would be impossible without the telemetric data.

#### **6.2 Genetic determinants of emotionality and stress response in mice**

Some strains of mice such as the A/J strain display high anxiety levels. The A/J and C57Bl6 strains are the progenitors of the recombinant congenic strains AcB/BcA. With the help of this recombinant congenic strains panel, Thifault et al. (2008) unveiled quantitative trait loci of the stress response and of emotionality in mice. For the stress response, a 30-minute immobilisation stress was carried out. In these mice, it is characterized by two phases: initial hypothermia followed by thermogenesis. For emotionality, open-field test (number of fecal boli) and elevated plus maze (enclosed arm duration and emergence latencies) were employed. The results unveiled several genomic loci associated with the differential response in the recombinant congenic strains panel. Of major interest, stress loci overlapped with candidate loci for cardiovascular diseases. For instance, the BcA70 strain showed a down regulation of the *Atp1a2* gene in the heart and brain as compared with the parental C57Bl6 strain (p<0,001 and p<0,05, respectively), and this is in accordance with the high emotionality of the knock-outs for that gene and its involvement in salt-sensitive hypertension.

These remarkable data, resulting from complex analysis of genomic and phenotypic results would not have been possible without telemetry. The body temperature recordings from the stress test were performed with the telemetry implants and a strain distribution pattern over 2,5°C was obtained and could be used to unveil significant loci associated. This temperature difference could not have been monitored with precision with any other means. As for blood pressure, telemetry allowed the monitoring of body temperature continuously and the circadian pattern was obtained. It showed a significant strain x diet effect with the AcB strains (mostly A/J background) displaying a lower body temperature under high-salt diet, and the strain differences were steeper during the dark phase of the circadian cycle. Again, these subtle observations would not have been possible without continuous monitoring by telemetry.

This study unveiled genetic loci responsible for the differential responses to many of the stressors and tests performed. Since it is easy to get spurious association with genetic markers, good phenotyping is essential if one wants to rely on the results. Given the important consequences if one wants to pursue the study of a candidate locus, we think that telemetry is essential for quality phenotyping in genetic research.

#### **6.3 Dissection of hypertension: Positional cloning of quantitative trait loci for blood pressure**

Finally, we should not forget to mention the work from Alan Deng who dissects hypertension in the rat and was able to find several significant blood pressure quantitative trait loci (Deng, 2007). His systematic approach uses telemetry as the sole method for blood pressure determination. The blood pressure values are obtained from adult rats from inbred strains. He does, however, perform several genetic manipulations in order to be able to reveal the loci. This approach allowed him to report several loci, the chromosomal position of which are later refined by producing sub-congenics animals (Chauvet et al., 2008). He was also able to reveal some gene-diet interactions and gene-gene interactions. For instance, he was able to demonstrate that a minor effect locus (named C18QTL1) could exert a major effect when it was transferred onto another genetic background. The normotensive Lewis rat genetic background was preventing this locus from exerting its full effect (Charron et al., 2005). This indicates that the normotensives also possess high blood pressure genes, but their effects are counterbalanced by 'normotensive' genes. This demonstrates the genetic buffering capacity of the genome and probably explains why the GWAS performed in humans do not yield the desired major high blood pressure loci: the population are displaying genetic heterogeneities that dilute the loci effects.