**3. Tumor models**

### **3.1 Tumor models in general**

Mouse tumor-angiogenesis models include xenograft/transplant models and spontaneous models in genetically engineered mice. Xenograft/transplant models are the most widely used, due to the homogeneous and fast onset and progression of the disease (Y Cao, 2005). In these models, human tumor cells are transplanted into athymic nude mice or severely compromised immunodeficient (SCID) mice (xenograft model), or murine tumor cells are transplanted into mice of the same genetic background (homologous transplantation model). Tumor cells can be injected under the skin (heterotypic transplantation, except skin

samples. Thin sections of 15 µm should be adhered on Superfrost Plus microscope slides, subsequently fixed with cold acetone and stained with specific primary antibodies. However, it is important to note, when staining for lymphatic vessels in the adipose tissue that LYVE-1 is not as widespread on the lymphatic endothelium as podoplanin in this particular tissue (Fig. 9). Here, unlike other tissues, LYVE-1 staining mainly detects

Fig. 9. Double cryosection immunostaining of the WAT of a mouse with podoplanin (red) and LYVE-1 (green). Podoplanin-positive lymphatic staining is indicated with arrowheads

Since hypoxia is one of the driving forces of angiogenesis, detection of the presence of hypoxia in tissues could also be performed. Hypoxyprobe-1 (pimonidazole hydrochloride) staining can be used to detect cell and tissue hypoxia. Pimonidazole hydrochloride has a molecular weight of 290.8 kD, ultraviolet absorbance at 324 nm and a plasma half-life of approximately 25 minutes in mice. The detection of hypoxia in tissues can be performed by intravenous or intraperitoneal injection or oral ingestion of pimonidazole hydrochloride at a dosage of 60 mg/kg, 15-90 minutes before sacrificing mice. Dissected tissues should be fixed in 4% PFA followed by paraffin embedding. Paraffin-embedded tissue sections of 3-5 µm is further stained using a peroxidase conjugated anti-pimonidazole antibodies and

Mouse tumor-angiogenesis models include xenograft/transplant models and spontaneous models in genetically engineered mice. Xenograft/transplant models are the most widely used, due to the homogeneous and fast onset and progression of the disease (Y Cao, 2005). In these models, human tumor cells are transplanted into athymic nude mice or severely compromised immunodeficient (SCID) mice (xenograft model), or murine tumor cells are transplanted into mice of the same genetic background (homologous transplantation model). Tumor cells can be injected under the skin (heterotypic transplantation, except skin

and LYVE-1-positive non- lymphatic staining is indicated with arrows.

inflammatory cells.

Scale bar = 100 μm.

**2.8.5 Hypoxia staining**

**3. Tumor models**

counterstained with nuclei staining.

**3.1 Tumor models in general**

cancer) or into the same organ from which the tumors originate (orthotropic transplantation). It is important to remember when studying tumor angiogenesis that the tumor microvasculature might differ depending on the implantation sites. Orthotropic transplantation is considered to show a similar tumor vessel phenotype as the primary lesion, and thus more closely recapitulate the clinical situation. However, it is often easier and more accurate to follow the tumor growth in heterotypic models such as when the tumor grows under the skin, where it is easily visualized. Furthermore, depending on the tumor growth rate and size, the vessel structure in tumors can be different. Fast growing and big tumors (≥ 1.0 cm3) usually have extensive necrosis in the center, which affects the vascular network in these areas. The appropriate tumor size for evaluation of tumor angiogenesis is considered to be 0.5-0.8 cm3. Tumor cells can be modified to express high levels of angiogenic factors, such as VEGF and FGF, or reporter genes, such as green fluorescent protein (GFP), red fluorescent protein (RFP) or luciferase. These modifications allow us to study the function of specific angiogenic factors on tumor microenvironment in association with invasion and metastasis (R Cao et al, 2004). Recent technological developments have permitted us to use genetically manipulated animal models for tumor studies, in which the mice are over-expressing or deficient in angiogenesis related genes. The majority of these mice are however immunocompetent, requiring the use of murine tumor cells in such animals.

Another type of tumor-angiogenesis models are the spontaneous models. Such models are based on genetically engineered mice, where a tumor suppressor has been deleted, an oncogene is being over-expressed or both, often in a particular cell type such as the pancreatic beta-cell of the islets of Langerhans (Hanahan, 1985). These models provide a disease history that is more closely recapitulating the one in human patients, as tumors are generated from one single hyperplastic cell, and progress through steps which are highly similar to pathological progression of pre-malignant to malignant lesions in humans. In later stages of tumor development in these models, angiogenesis also becomes important and contributes to growth and metastasis of these primary lesions (Koh et al, 2010).

To study tumor angiogenesis using mouse models, control animals or tumor conditions must be incorporated in the study. If tumor cells are genetically altered, the proper control would be tumor cells which have an empty expression vector inserted in their genome instead of one coding for a particular angiogenic factor. For evaluation of treatment efficacy of anti-angiogenic drugs, the control group must be given the vehicle (solvent) in which the drug was prepared, because some vehicles can themselves affect the vascular structure in tumors. Age and genetic background of the mice must also be standardized for the different groups. Standard age for tumor experiments is 6-10 weeks. Sex is usually not important, unless the tumor cells under investigation are gender-specific such as in the case of breast or prostate cancer, but should nonetheless be the same in for example treatment and control groups.

### **3.2 Tumor models: Assessment of angiogenesis**

Dr. Judah Folkman proposed in 1971 that all tumor growth is angiogenesis-dependent (Folkman, 1971). In general, because a limiting factor of tumor growth is the supply of sufficient levels of oxygen and nutrition, fast growing tumors are characterized by a more aggressive angiogenic phenotype which arises from the ability of the tumor cells to secrete angiogenic growth factors to support and change their microenvironments. For instance, tumor cells transduced with the gene coding for VEGF grow much faster than the emptyvector transduced control cells, due to much higher intra-tumoral blood vessel density (Eriksson et al, 2002). In these tumors however, the vasculature consists of highly irregular and immature vessels and many vascular plexuses, which does not support efficient perfusion of blood compared to a less chaotic vasculature in VEGF non-transduced tumor tissue (Fig. 10). To assess angiogenesis in mouse models, there are thus three essential points which should be addressed.

Fig. 10. Histological examinations of tumor tissue. (A) H&E staining of T241 fibrosarcoma. (B) Immunohistchemical staining of T241 fibrosarcoma visualized by fluorescence microscopy. Anti-endomucin labeling of endothelial cells is shown in red and anti-α-smooth muscle actin (SMA) labeling of pericytes/smooth muscle cells is shown in green. (C) Confocal imaging of T241 fibrosarcoma. GFP transfected tumors are shown in green, anti-CD31 labeling of endothelial cells is shown in red. (D) Confocal imaging of VEGF overexpresing T241 fibrosarcoma. GFP transfected tumors are shown in green, anti-CD31 labeling of endothelial cells is shown in red.

1. Tumor size (volume). Tumor growth depends on angiogenesis. Thus, tumor size is usually proportional to the degree of which tumor-angiogenesis is induced by the tumor. If the xenograft/transplant models are used and especially if tumors are transplanted dorsally under the skin, the size is easily measured and an accurate tumor growth curve can be generated by daily measurements of tumor width (W) and length (L) using calipers and calculating the volume (V) as V=0.52\*W2\*L. If the tumor is not

tumor cells transduced with the gene coding for VEGF grow much faster than the emptyvector transduced control cells, due to much higher intra-tumoral blood vessel density (Eriksson et al, 2002). In these tumors however, the vasculature consists of highly irregular and immature vessels and many vascular plexuses, which does not support efficient perfusion of blood compared to a less chaotic vasculature in VEGF non-transduced tumor tissue (Fig. 10). To assess angiogenesis in mouse models, there are thus three essential points

Fig. 10. Histological examinations of tumor tissue. (A) H&E staining of T241 fibrosarcoma.

microscopy. Anti-endomucin labeling of endothelial cells is shown in red and anti-α-smooth

1. Tumor size (volume). Tumor growth depends on angiogenesis. Thus, tumor size is usually proportional to the degree of which tumor-angiogenesis is induced by the tumor. If the xenograft/transplant models are used and especially if tumors are transplanted dorsally under the skin, the size is easily measured and an accurate tumor growth curve can be generated by daily measurements of tumor width (W) and length (L) using calipers and calculating the volume (V) as V=0.52\*W2\*L. If the tumor is not

(C) Confocal imaging of T241 fibrosarcoma. GFP transfected tumors are shown in green, anti-CD31 labeling of endothelial cells is shown in red. (D) Confocal imaging of VEGF overexpresing T241 fibrosarcoma. GFP transfected tumors are shown in green, anti-CD31

(B) Immunohistchemical staining of T241 fibrosarcoma visualized by fluorescence

muscle actin (SMA) labeling of pericytes/smooth muscle cells is shown in green.

labeling of endothelial cells is shown in red.

which should be addressed.

visible because it is growing in a location inside the animal, special imaging systems, such as CT, magnetic resonance imaging (MRI) or bioluminesence imaging analysis (if tumors express luciferase) are needed to estimate the tumor size. The effect of antiangiogenic drugs can be inferred from the reduction in tumor growth relative to vehicle treated controls.

### **3.3 Lymphangiogenesis and lymphatic metastasis**

It is well known that blood vessels can support tumor growth by providing oxygen and nutrients, and removing waste products, but the function of tumor lymphatic vessels remains poorly understood (Y Cao, 2008).

Lymphatic networks consist of lymphatic capillaries, collecting lymphatic vessels, and lymph nodes. Unlike blood vessels, lymphatic capillaries consist of one layer of lymphatic endothelial cells (LECs), discontinuous basement membrane and few vascular smooth muscle cells (VSMCs). Lymphatic vessels lack tight junctions between endothelial cells, but are instead equipped with one-way lymphatic valves which give these vessels the ability to collect fluids and macromolecules from the tissue and transport it back to the circulation. On the other hand, these features of the lymphatic endothelium also mean that malignant cells can easily enter into and disseminate via the lymphatic system, leading to lymphatic metastasis. Tumor lymphangiogenesis is therefore associated with cancer metastasis. In some common cancers, such as lung and breast cancer, lymphatic metastasis is the dominant route for tumor metastasis. Invasion of intra- or peri-tumoral lymphatics may result in dissemination of malignant cells to the lymphatic system, leading to lymphatic metastasis in regional lymph nodes (Y Cao Y, 2005; R Cao et al, 2004).

Similar to blood vessels, lymphatic vessels are quiescent in healthy individuals. The formation of lymphatic vessels in tumors is a multistep process that involves LEC proliferation, migration, tube formation and remodeling, which require up-regulation of lymphangiogenic stimulators and down-regulation of lymphangiogenic inhibitors (Y Cao, 2005). Lymphatic vessel growth may represent the imbalanced consequence between positive and negative regulators tipping toward positive regulation. Understanding the molecular mechanisms that control lymphangiogenesis is therefore an important step in the development of therapeutic agents in the prevention and treatment of cancer metastasis.

Among the list of lymphangiogenic factors, members of the VEGF family are the best characterized. VEGF-A, which binds to VEGFR-2 and VEGFR-1, PlGF and VEGF-B which bind to VEGFR-1 and especially VEGF-C and VEGF-D which bind to VEGFR-3 are lymphangiogenic factors, the -C and -D isoforms being the most potent, which regulate both physiological and pathological lymphangiogenesis. VEGF-C/VEGF-D-VEGFR-3-mediated signals are also critical for the sprouting of the first lymphatic vessel from the developing veins in the embryo. This signaling pathway is essential for differentiation of endothelial progenitor cells into the lymphatic lineage (Kukk et al, 1996; Alitalo et al, 2005).

Primary tumors produce several lymphangiogenic factors, including VEGF-A, VEGF-C, VEGF-D, Insulin-like growth factor (IGF), hepatocyte growth factor (HGF) and PDGF-B. These factors induce angiogenesis and lymphangiogenesis both in the local environment and in the regional lymph nodes. Furthermore, they play an important role in establishing the pre-metastatic niche. The term pre-metastatic niche describes the adaptations of for example the lymph nodes which are needed to allow disseminating tumor cells, arriving at a later stage, to meet optimal conditions for growth in that particular site. In addition to signals produced by the tumor cells themselves, inflammatory cells such as macrophages are recruited to tumors by a wide range of tumor cell-derived cytokines and growth factors. At the tumor site, inflammatory cells play a critical role in mediating lymphangiogenesis most likely through the secretion of several lymphangiogenic cytokines.

To detect lymphatic vessels in or around tumors, immunohistochemical staining with lymphatic specific markers, such as VEGFR-3, LYVE-1 and podoplanin is recommended (see Fig. 13). Lymphangiogenesis can be evaluated using all of the models described in this chapter, by using antibodies against one or a combination of these factors either in whole mount staining or on thin sections of frozen or paraffin embedded tissue (Fig. 13).


**6. Conclusions**

Since their emergence about a decade ago, social networks have rapidly grown and infiltrated every aspect of our daily lives. With rapidly expanding reliance on their platforms, social networks like Facebook and Twitter are becoming a goldmine of personal information and user behavior data which makes the study of these networks of prime importance. The valuable information stored within these platforms makes them the target of malicious entities which try to compromise the privacy of the users which may further lead to unwanted disclosure of the sensitive attributes of the network.

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In this chapter, we have reviewed a novel privacy measure that quantifies the resistance of a large social network against a privacy violating attack. We reviewed some efficient algorithms to compute this measure in social graph and revisited the privacy violation properties in 8 real-world networks. The current theoretical and empirical results for ð Þ *k*, ℓ -anonymity pave the way for further investigation of this measure, as well as addressing its shortcomings and limitations.


*Review* **Balance Control in Obese Subjects during Quiet Stance: A State-of-the Art**

#### **Veronica Cimolin 1,\*, Nicola Cau 2, Manuela Galli 1 and Paolo Capodaglio 2**

Received: 3 February 2020; Accepted: 4 March 2020; Published: 7 March 2020

**Abstract:** Obese individuals are characterized by a reduced balance which has a significant effect on a variety of daily and occupational tasks. The presence of excessive adipose tissue and weight gain could increase the risk of falls; for this reason, obese individuals are at greater risk of falls than normal weight subjects in the presence of postural stress and disturbances. The quality of balance control could be measured with different methods and generally in clinics its integrity is generally assessed using platform stabilometry. The aim of this narrative review is to present an overview on the state of art on balance control in obese individuals during quiet stance. A summary of knowledge about static postural control in obese individuals and its limitations is important clinically, as it could give indications and suggestions to improve and personalize the development of specific clinical programs.

**Keywords:** posture; stability; force platform; stabilometry; posturography; obesity

#### **1. Introduction**

Postural stability can be explained as the ability of one's motor control to maintain the standing posture, even during external perturbations. It is often described as static (quiet standing) or dynamic (maintaining a stable position while undertake a prescribed movement) [1].

Postural stability is achieved when a subject can regain equilibrium after being disturbed. Postural control requires the integration of inputs from various sensory systems, which are: (a) the proprioceptive system (receptors in joints, muscles, tendons, skin) [2], (b) the visual system [3] and (c) the vestibular system (semicircular canals and macular otoliths) [4]. Sensorimotor integration and balance regulation rely on the cerebellum; most of the evidence of the function of the cerebellum in humans comes from patients with cerebellar damage who display balance abnormalities and gait ataxia [5].

This multimodal (integrating visual, vestibular, and proprioceptive inputs) and redundant control causes chaotic center of mass (CoM) oscillations known as postural sway [6–8]. In clinics the quality and integrity of balance control is generally assessed using various measures of postural sway [6].

Instrumental evaluation with platform stabilometry or static posturography is a technique where CoM fluctuations are represented by the center of foot pressure (CoP) displacements [9] and it consists in the measurement of forces exerted against a force platform during quiet stance. This method is widely used in clinical settings to evaluate the integrity of the postural control system and to obtain functional markers on fine competencies and their development in different testing conditions (i.e., eyes open vs. eyes closed, feet position, and presence of external stimuli) [10]. Commonly, the study of properties of the CoP trajectory in clinics is performed using traditional time series, in particular analyzing length, displacement excursion, velocity and frequency analysis. The main advantages of these analysis techniques are the simplicity of the experimental set-ups and the safety for the subjects evaluated, a particularly important consideration in pathological conditions [11]. However, some limitations are

present: (a) the lack of a normal pattern due to intra- and inter-individuals variability; (b) the difficulties inherent to standardizing the measurement experimental set-up (reproducibility of the experimental protocol, environmental conditions, random errors, signal processing); and (c) the presence of highly coupled parameters measured by means of the force platform [12,13]. Due to the presence of these limitations, it is necessary to have reliable methods available to extract physiologically significant information from the platform data [11,14]. Alongside the study of properties of the CoP trajectory using traditional time series, some advanced mathematical methods have been developed applying a dynamic approach, such as entropy and fractal dimension (FD) analysis [11,15,16]. Even if previous studies [14,17,18] suggested that these methods could represent reliable techniques complementary to the analysis in time and frequency domain in order to evaluate postural control, their use in clinical setting is not yet widespread.

Balance is a fundamental element in carrying out daily life activities. Aging and various pathologies increase the postural instability which can lead to falls [19]. In particular, some anthropometric measurements (height and weight) are demonstrated to significantly influence both postural sway and postural stability [12,20,21]. The excessive presence of fat adds mass to different regions and modifies the body geometry [22]: in this way the altered CoM position leads to balance impairments [23].

It is estimated that in 2016, more than 1.9 billion adults aged 18 years and older were overweight; of these over 650 million were obese [24]. A recent paper suggests that by 2030 nearly one in two adults in the United States will be obese and nearly one in four adults will have severe obesity [25]. Patients with obesity have intrinsically reduced postural stability and balance as compared with their normal-weight counterparts. This reduced stability increases linearly with body mass index (BMI). They are at increased risk of falling at any age [26]. Although many studies investigated the risk of falling in obese individuals, balance exercises in these individuals are often not implemented in the rehabilitation program. After implementing a rehabilitation program with specific balance exercises, patients with obese have shown to improve their balance control. Maffiuletti et al. [19] showed that just 4-min of specific balance training incorporated into the physical exercise routine improved postural stability in patients with severe obesity. The recommended exercise program for patient with obesity is a multicomponent 90-min exercise program that includes 15-min balance training, 15-min flexibility, 30-min aerobic exercise and 30-min high-intensity resistance training [27]. Different aspects of balance control can be addressed:

The consensus is that obese individuals exhibit poor postural stability [28,29]; the aim of this review will be to present an overview on the state of art on balance control in obese individuals during quiet stance. A summary of our current knowledge about static postural control in obese individuals is important from a clinical perspective, as it could allow for the development of clinical programs that are more oriented towards the needs of this population, even avoiding the risk of comorbidity due to falls.

#### **2. Methods**

We conducted an extensive search of the relevant literature, with a focus on studies/articles published in the last 15 years (2004–2019). The literature search was performed in November 2019 on the following electronic databases: Web of Science, PubMed MEDLINE, Scopus, and Mendeley. Customized queries including keywords and Boolean logic with AND/OR operators were entered in this form: "(balance OR posture OR stability) AND (posturography) AND (obesity)", with document type set to "Article". The search was limited to full original articles written in English. Bibliographies of identified papers were hand searched for supplemental relevant items.

All included studies had to meet the following criteria: overweight and obese adults (18–75 years; with body mass index (BMI) ≥ 25 Kg/m2); studies assessing obese patients with genetic obesity; evaluations performed with force platform. We excluded studies that were not primarily focused on the evaluation of quiet standing evaluation (dynamic balance) in obese subjects, or non-adult participants and elderly subjects.

#### **3. Results**

A total of 27 records were retrieved from the electronic databases. Eight items were added by visual inspection of reference lists and review articles. After removing seven duplicates, titles and abstracts screening led to exclude four papers. Out of the remaining 24 articles, 12 failed to meet inclusion criteria. The main reasons for exclusion were as follows: studies assessing elderly or non-adult patients; patients with neuropathy; the presence of other pathologies (multiple sclerosis). The selection process is summarised in Figure 1.

**Figure 1.** Diagram of study selection.

Table 1 presents a summary of the papers on postural ability in obese individuals with the demographic characteristics of the evaluated individuals and some details of the experimental set-up (trial duration, conditions, feet position, analyzed parameters).

Summaryofthemaindetailsofthereviewed

#### *Appl. Sci.* **2020**, *10*, 1842

101

#### *3.1. Study Population*

The sample size of participants ranged from n = 11 to n = 180 (mean age range: 19 years to 60 years). A total of 860 subjects were involved (approximately 60% females and 40% males), but we could not exclude subjects overlapping in studies conducted by the same research group.

#### Experimental Set-up and Parameters

In terms of the experimental set-up, the common postural techniques are used in terms of trial duration (30 s or 60 s) and conditions (eyes open and eyes closed). On the contrary, some differences were found as for the feet position; this could represent a bias as it is demonstrated that anthropometric measurements (e.g., maximum foot width), and the foot position could influence balance [12]. Almost all the studies have been conducted using parameters obtained by time-domain analysis of the CoP trajectory (length, displacement excursion, velocity).

To the best of our knowledge, most studies in obese individuals have adopted the traditional time-domain approach. However, a growing number of studies have been designed to explore other approaches for the analysis of the CoP trajectories during quiet standing and to characterize the preferential involvement of specific neuronal loops in postural regulation (frequency), its irregularity and complexity (entropy), its chaotic pattern (fractal dimension method), etc. [40].

Nevertheless, these alternative methodologies have not been used in obese patients. To our knowledge, only few researches [17,37] quantified posture using the FD approaches in addition to the time and frequency domain in genetic obese patients (Prader-Willi syndrome and Down syndrome). The results showed that patients presented higher CoP fluctuations in both AP and ML directions with longer CoP trajectory than the control group [35,41–43] and similar values as for the frequency analysis. Furthermore, the pathological individuals presented larger excursions of CoP with the same velocity of oscillation if compared to normal weight individuals. In addition, patients with genetic obesity exhibited higher FD values, which were higher in patients with Down syndrome. According to these results, the time domain parameters do not seem to clearly highlight early anomalies in postural maintenance: the traditional method does not investigate the chaotic fluctuations of CoP trajectories. Frequency analysis and dynamical system theory could identify early changes and may match time frequency analysis to assess balance. However, the clinical interpretation of the results is not completely clear and requires further in-depth investigation.

#### *3.2. Gender E*ff*ects*

Gender differences (considering in particular gynoid and android shape) are observed in terms of body mass distribution, even if android fat distribution is also found in females, especially in postmenopausal women: nevertheless, the research for possible gender-specific differences in balance has reported contradictory findings. Some studies were conducted just on a specific gender (males or females).

#### 3.2.1. Males

Hue et al. [30] observed that obese men presented higher instability, in both the antero-posterior (AP) and medio-lateral (ML) CoP excursions, than normal weight individuals; an improvement was found in severely obese men after weight loss [31] and specific balance training [19]. This result supports the suggestion that body weight is an important predictor of postural stability [30].

#### 3.2.2. Females

Blaszczyk et al. [33] observed a significant postural instability in all obese patients; CoP fluctuations were higher in patients with the highest body mass index (>40 kg/m2). In another study, Cienliska-Swider et al. [38] quantified CoP characteristics in a group of obese women with android type of obesity as compared with a group of obese women with gynoid type of obesity, standing with eyes open and closed. They found that women with abdominal obesity showed a larger sway range in the AP direction under both conditions and a greater maximal CoP velocity than subjects with gynoidal obese type under the eyes closed condition. Women with abdominal obesity seem to exhibit greater postural instability in comparison with women with gynoid fat distribution.  After body weight reduction, ML static stability decreased, directly connected to a change in the base of support. Recently, Hita-Contreras [36] analyzed the relationship between body weight/body fat distribution and postural balance and their correlation with falls in postmenopausal women, which are characterized by weight gain and increased central adiposity. In the obese group, higher excursions were found in the antero-posterior direction under both eyes-open and eyes-closed conditions, as well as for the CoP velocity. In particular, in overweight and obese individuals with an android body fat distribution a good correlation with the risk of falling was found. Similar results were found in obese older women, suggesting that obesity has a negative impact on the capacity of older woman to adequately use proprioceptive information for posture control [44].

#### 3.2.3. Males vs. Females

If instability was generally observed both in obese males and female, it is not clear whether gender could influence balance. Cruz-Gomez et al. [34] assessed the influence of BMI group (lean/overweight/obese) and gender on the postural sway of adolescents and adults during quiet upright stance in four conditions (eyes open/closed on hard/soft surface). The postural stability of obese subjects decreases with eyes closed, if compared to normal weight individuals, with no influence of the gender. Thus, they found no interactions between the BMI group and the gender, independently of the age of the subjects. On the contrary, in another study, Menegoni et al. [32] identified a gender-specific effect in obese individuals: both genders displayed instability in AP direction, while only males presented ML destabilization. The difference in male and female body tissue distribution could potentially offer two related explanations to these outcomes.  AP instability is exacerbated in both populations by greater overall mass which leads to higher ankle torque. The male distribution of mass, however, results in increased loading of the pelvic girdle which, in turn may contribute to greater ML CoP excursion. Similar, yet alternatively, the anatomically lower position of fat storage associated with the female gynoid shape has a consequently lower CoM than the male android shape.

#### *3.3. General Considerations*

In general, body weight is considered a predictor of postural instability [30], from adolescence onwards [26]. In obese individuals there is significantly greater forward CoP displacement during dynamic standing balance activities [46]. Excessive body weight affects posture linearly with the increase of BMI (0.39 < rho < 0.60, p < 0.05) [47,48], similarly to the later stages of pregnancy [31], the center of gravity shifts forward, lumbar lordosis increases together with the pelvic forward tilt, dorsal kyphosis and secondary cervical lordosis become more pronounced [49]. Otherwise, increased CoP parameter values and therefore increased postural instability during quiet stance has been reported in both morbidly and slightly obese subjects [39]. Two hypotheses have been proposed to explain the presence of higher oscillations in obese individuals in comparison with normal weight subjects: a) the reduction of plantar sensitivity due to the hyper activation of the plantar mechanoreceptors for the continuous pressure of supporting the large mass; b) the presence of high mechanical request in obese subjects due to a whole body center of mass further away from the axis of rotation causing a greater gravitational torque [30].

#### **4. Conclusions**

According to this narrative review, the research investigating the effect of adiposity on postural balance is limited and, to date, has primarily focused on parameters related to time-domain approach during bipedal stance.

Because of the elevated mass to height ratio of excessively muscular people, BMI becomes a relatively ineffective method for differentiating between highly overweight subjects and bulky, yet fit, muscular subjects. This could be a potential explanation for the discrepancies found in studies using BMI as their sole classification metric. The relationship between excessive adiposity and balance may be better explained using different measures of fat percentage. Rather, by including the ratio of circumference between waist and hip, researchers could add a quantitative parameter (in addition to the traditional BMI) which characterizes the shape of a subject and subsequently ensure better homogeneity amongst groups.

Considering that the prevalence of obesity is increasing at a rapid rate all over the world, health systems will have to face the growing problems directly related to obesity. It is now known that an increase in BMI is associated with an increase in functional limitation, decreased stability and an increased risk of falls. Complications associated with falls are often more difficult to treat in obese individuals in comparison with normal-weight subjects. Targeted physical activity and rehabilitation would seem to lead to improvements in terms of balance in obese people; it is still unclear whether a regular exercise program and weight loss could be the first steps in countering the reduced balance related to obesity. Balance exercises are often neglected in rehabilitation programs; evidence exists that majority of patients with obesity complain of dizziness, but they seem to underestimate the risk of fall; considering the increased risk of fall, balance exercises for the patient with obesity should be implemented even in the absence of specific balance disorders.

The main limitation of this review is mainly related to the age of the evaluated patients. A more extensive review could be conducted evaluating also the effects of age (elderly) and non-adult participant's (children and adolescents). In addition, it could be interesting to consider dynamic balance training in these patients, which has been proven improving balance performance and decreases falls in these subjects.

**Author Contributions:** V.C. contributed to conceptualization, Writing—original draft, Writing—review & editing; N.C. Data curation, Formal analysis, Writing—original draft; M.G. contributed to conceptualization, Supervision, Writing—review & editing; P.C. Project administration, Supervision, Writing–review & editing. 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**

© 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/).


*3.1. Materials Characterization*

Low-frequency ultrasound exposure, such as cavitation and microwaves, ensured complete destruction and removal of all cellular and bone marrow components, including bone cells, stroma, myeloid cells, and lipids from the spongiosa. Quality control of the scaffold delipidization was performed using physical and biochemical methods listed below. After lyophilization in a sublimation unit, the humidity of the biomaterial did not exceed 5%.

Exposure of the material to hydrochloric acid resulted in the final destruction of bone cells and demineralization of the scaffold.

The shelf life of lyophilized demineralized spongiosa Lyoplast® is three years after gamma-ray sterilization. Storage and transportation of the finished product does not require any special temperature conditions.


**2. Methods**

### *2.1. Sample Preparation*

Each sponge of same size was pre-dried and injected with 0.2 mL of 5 M CaCl2 solution. It was ensured that the solution was evenly distributed. The sponge was then dried in the oven at 60 ◦C for 12 h on a Petri dish with a lid. The mass was measured to quantify the salt content in the sponge. The sponge salt samples were then ready for water absorption measurement. The salt control was created by evenly spreading out an equal mass of salt as the salt content in the sponge on a Petri dish.

### *2.2. Moisture Absorption Time Dependence Measurement*

The materials were placed on a microbalance inside a temperature- and humidityregulated chamber. Weight changes of the samples were monitored and logged over time using a custom program written in in LabVIEW. Measurements were carried out at a constant temperature of 27 ◦C and a humidity level of 80% RH. After the absorption measurement, the sponge was put in the oven for 12 h at 60 ◦C to run the next trial.

### **3. Results and Discussion**

### *3.1. Preliminary Experiment*

Four types of sponges were tested to decipher the most effective sponge: cellulose, soundproof, magic, and Dacron (Figure 2a). The results were normalized to the salt's mass in the respective sponges. Hence, it is evident that the cellulose sponge was the most effective, as seen in the water uptake vs. time graphs in Figure 2b. The water uptake percentage was defined by *massmoisture absorbed masssalt* × 100%. Control measurements of each of the sponges without salts did not show any water absorption effects. Thus, in the next sections, we focus our investigation on the salt–cellulose sponge systems only.

**Figure 2.** (**a**) Sponge types tested. Each container has a respective sponge and control; change in mass was measured every 15 min for 6–7 h. (**b**) Sponge type curves. Graph shows water uptake by various commercial sponges. They were soaked in 5M solutions, dried, and left to absorb. The vertical dotted lines indicate a new trial. The weight changes during the drying phase were not tracked, except for the beginning and final weight.

#### *3.2. Salt-Cellulose Sponge Water Uptake Time Dependent Measurements*

The efficacy of the cellulose sponge was examined by testing its maximum absorption capacity by exposing it to a controlled atmosphere until the salts stopped absorbing moisture, as can be seen in Figure 3. There are several key observations. First, we found that the water uptake dynamics fit very well to a double exponential model (Equation (1)), as listed below:

$$\mathcal{W}(t) = \mathcal{W}\_{\text{saturated}} - A\_1 e^{\frac{-t}{\overline{\tau}\_1}} - A\_2 e^{\frac{-t}{\overline{\tau}\_2}}$$

where *W*(*t*) is the water uptake percentage at a given time, *Wsaturated* is the maximum water uptake percentage, and *A*1, *A*2, *τ*1, and *τ*2 are fitting constants. We confirm that a single exponential model cannot explain such behavior. This suggests that there are two types of water uptake mechanisms in our system. Secondly, it is evident that the sponge salt sample exhibit faster water uptake rate. Finally, the sponge salt sample was found to exhibit a higher maximum water uptake, *Wsaturated*, of ~305% while for the control salt sample, it was found to be ~272%. The salt sponge sample performed better than most of the previously reported moisture absorbers in the literature (Table 1).

### *3.3. Proposed Model*

Based on the enhancement observed from the water uptake dynamics, we propose a physical model as illustrated in Figure 4. In this model, salts are spread around the pores of the sponge. When the salts absorb moisture, the absorption is homogenous throughout the sponges. This, in effect, causes even salt precipitate distribution upon drying. As a result, the optimal amount of surface area of the salt species is maintained; for example, it does not suffer from an agglomeration problem. The optimal surface area would cause an improved water absorption rate. The infusion of the salts into the cellulose sponge results in an increase in both maximum water uptake and absorption rate. More analysis is needed to explain the improved maximum water uptake and the two absorption mechanisms that were observed in our system. Nevertheless, it is clear that the simple infusion of salts into the cellulose sponge provided significant improvements that are valuable in the context of atmospheric moisture clean water generation.

**Figure 4.** Proposed physical model of the salt-cellulose sponge system. Cellulose sponge with minimal agglomeration.

### *3.4. Peltier Device Prototype*

To recover the absorbed moisture as clean water, a Peltier-based distillation unit was prototyped (Figure 5). The unit was equipped with three water recovery channels to maximize the transfer of water droplet from the evaporation to the condensation chambers. The primary channel was set directly under the acute end of the roof due where most of the vapor was accumulating. The secondary channel directly transferred moist air to the collection chamber using a small DC motor pump. Finally, a tertiary channel was set up across from the acute end of the slanted roof to capture the flow of the larger water droplets

**Figure 5.** Prototype of the Peltier-based clean water generator. (**a**) Side view showing the three water channels to transfer water from the top evaporation to bottom condensation/collection chambers. (**b**) front view. (**c**) View of the Peltier module.

The device uses a single piece of Peltier module (9 V, 2 A) in a small chamber with a diameter of 8 cm and generates ~5 mL of clean water/hour from atmospheric moisture. Although further optimization is necessary, we note that this system does not waste any sacrificial clean water, as in most reverse osmosis or water distillation systems. Further optimization is also possible, including the incorporation of solar-powered energy sources, development of a multi-module systems, and better material choice to encourage formation and flow of water droplets on the walls of the evaporation chamber.