**2.7. Infective endocarditis in children**

**PATHOGEN EARLY PVE\* (%)**

Adapted from Wang et al. [53]. \*

42 Contemporary Challenges in Endocarditis

**Table 2.** Causative organisms for early and late PVE.

surpasses greatly that of the pacemaker [79–81].

**2.5. Ventricular-assist devices infection**

**2.4. Cardiovascular-implantable electronic devices infection**

valve endocarditis.

*Staphylococcus aureus* **36 18** *Coagulase‐negative Staphylococci* **17 20** *Enterococcus* **8 13** *Viridans streptococci* **2 10** *Streptococcus bovis* **2 7** HACEK **0 2** Fungi **9 3** Other **6 14** Culture negative **17 12**

*N* **= 53**

HACEK: *Hemophilus, Aggregatibacter spp., Cardiobacterium hominis, Eikenella corrodens, Kingella kingae*. PVE: Prosthetic

The most commonly used cardiovascular-implantable electronic device (CIED) are permanent pacemaker, cardiac resynchronization therapy, and implantable cardioverter-defibrillator. Most of these are implanted using transvenous leads. This practice had dramatically reduced the risk of infection associated with the procedure. Nevertheless, complication by infection remains a problem that can lead to significant morbidity, mortality, and elevated costs [73– 75]. Reports of CIED infection vary according to different series and range from 0.13 to 19.9% [76–78]. In a 16-year survey of Nationwide Inpatient Sample (NIS) from 1993 to 2008, the rate of CIED implantation increased 4.7% annually. The incidence of CIED infection remained stable until 2004, but increased almost twice in a 4-year period (2004–2008) from 1.53 to 2.41%, respectively [75]. The rate of infection associated with implantable cardioverter-defibrillator

Patients who receive ventricular-assist devices (VADs) usually have various comorbidities, including a state of immune compromise. The risk of infection varies depending on the duration of VAD support [82]. Higher rates of infection are observed in the destination therapy group compared with the group where VAD is used as a bridge to transplantation [82]. Hravnak reported that registry patients with implant duration longer than 60 days were twice as likely to develop infection than those patients supported for less than 30 days [83]. The reported rates of infection in patients with VAD range from 13 to 80% and depend on multiple factors, including comorbidities, type of device implanted, and duration of VAD support [84]. Infection of VAD can present as three different syndromes: driveline infection (most frequent)

Early refers to IE within 2 months and late after 2 months, according to Wang et al.

**LATE PVE\* (%)** *N* **= 331**

> As in adults, trends in children IE are related to the evolution of care in the sick child, particularly children born with congenital heart disease. The incidence of children IE provides limited data, mostly based on inpatient admission which could not represent accurately the general population. In a report between 1933 and 1972, the incidence was 0.22–0.55 cases per 1000 pediatric hospital admissions [91]. A retrospective review between 1972 and 1982 found an incidence of 1/1280 pediatric admissions [92]. Later, in a multicenter study, the incidence of IE slightly decreased, ranging from 0.005 to 0.12 cases per 1000 pediatric admissions [93]. In other report including 47,518 patients, from 1998 to 2010, congenital heart disease was found as the major underlying condition associated to IE in children in high-income countries, with a cumulative incidence of 6.1 per 1000 children [94]. The distribution of IE between boys and girls is balanced in contrast with series in adults in whom men have a higher tendency to suffer the condition [94, 95]. Rheumatic fever is rare in developed countries, nevertheless is commonly found in low-income countries. In the presurgical era, the proportion of IE in children with rheumatic heart disease ranged from 30 to 50% [96]. A single center report covering seven decades found that IE occurred in 31% of rheumatic heart disease patients in presurgical era, compared to era 3 (1992–2004) with only 1.1% of patients having the condition [97]. Approximately 50% of cases of pediatric IE complicating congenital heart disease have had previous cardiac surgery, especially palliative shunts of complex cardiac repair [98]. Risk of postoperative IE in children depends greatly on the type of surgery; for example, a study from Oregon found a relatively low incidence of IE after tetralogy of Fallot repair (1.3%), ventricular septal repair (2.7%), atrial septal repair (2.8%), and aortic coarctation repair (3.5%). Nevertheless, a high incidence of IE was found in aortic stenosis (valve replacement) with a cumulative incidence at 25 years of 13.3% [99]. The rate of IE in structurally normal hearts is lower than those with a predisposing condition (22 vs. 78%), respectively [100]. A major risk factor to develop IE in an anatomically normal heart is an indwelling vascular catheter [101].

#### **2.8. Infective endocarditis in adults**

An important condition related to IE in the elderly is the congenital bicuspid aortic valve. In a prospective multicenter study, it was present in 16% of cases of native valve endocarditis [102]. Degenerative cardiac lesions assume an important role in the development of IE without underlying valve disease. In one study, degenerative lesions were present in 50% of patients with native valve IE older than 60 years [103]. Calcified mitral annulus is a common finding in elderly women but rarely complicate with IE (3.8%) [104]. Even not a classical condition related to IE, idiopathic hypertrophic subaortic stenosis may represent up to 5% of incidence of the infection [105]. And there is a higher mortality rate correlation if a murmur is present (up to 36% of patients with hypertrophic aortic stenosis and IE) [105]. Another condition associated with IE is the mitral prolapse syndrome. In different series, the range of IE in those patients with mitral valve prolapse can go from 11 to 23% [106, 107]. In another study, 8.6% of patients with mitral valve prolapse who were monitored prospectively for 9–22 years developed IE [108]. This syndrome must be suspected in patients with mid-systolic click with or without a late systolic murmur. This condition is not uncommon and has been found in 0.5– 20% of otherwise healthy people, especially young women. It has become apparent that a significant proportion of patients with mitral valve prolapse have an anthropometrically distinct habitus, suggesting that this condition is only an element of a generalized developmental syndrome [109]. It may be useful to have in mind these characteristics to help identify patients with a high risk of developing IE. Having valvular redundancy and thickened leaflets may increase the risk of IE [103]. The combination of mitral valve prolapse and men older than 45 years also may increase the risk of IE [110]. In a detailed case-control study, 25% of patients with IE had mitral valve prolapse; the odds ratio (8.2 of 95% confidence interval, 2.4–28.4) indicated a substantially higher risk for IE in patients with mitral valve prolapse than for those without it [111]. Another study found that mitral valve prolapse IE presented with more subtle symptoms, less mortality, and responded better to antimicrobial therapy than other types of left-sided IE, even though recognition of the infection was delayed [112].

#### **2.9. Infective endocarditis in drug abusers**

All estimations of IE incidence in drug abusers are hindered because there are no enough data reporting the exact number of victims of illicit drug-abuse epidemic. Reports from the United States present an incidence of IE in intravenous drug abusers that range from 2 to 5% per year [113] or 1.5–2 cases per 1000 years of IV drug abuse with men more commonly affected [114]. Although congenital cardiac disease and right-sided heart instrumentation are associated with IE, IV drug abusers retain the majority of cases. Intravenous drug users and those with HIV primarily consist of relatively young adults [115]. Acute infection accounts for approximately 60% of hospital admissions among drug abusers and IE is responsible of 5–15% of these episodes [116]. The presence of IE in a drug addict is difficult to predict, especially from history and physical examination findings alone [117, 118]. More than 60% of IV drug abusers with IE do not have an underlying preexisting valvular disease [119]. Although cocaine use by an intravenous drug abuser should raise the suspicion of IE infection [120], the most credible predictors of IE in febrile intravenous drug users are visualization of vegetations by echocardiography and the presence of embolic phenomena [118]. Up to 13% of cases of IV drug abusers with febrile episodes have an echocardiographically demonstrated IE [118]. Although leftsided native valve endocarditis may be present in this group of patients, the tricuspid valve is more commonly affected in intravenous drug users [121, 122]. Only two-third of patients with proven IE diagnostic presented with heart murmurs on admission [116]. The frequency of valvular involvement is tricuspid alone or in combination with other valves, 52.2%; aortic alone, 18.5%; mitral alone 10.8%; and mitral and aortic combined, 12.5% [123]. Most of these patients are young (20–40 years old), and men are more commonly affected than women with a ratio of 4:1–6:1. Approximately 66% of the patients have extravalvular compromise which may help in the diagnosis [124–126]. Although there are studies reporting infection rate reductions (such as HIV, hepatitis, or abscess) with the implementation of a needle-exchange program [127, 128], to date, there are no conclusive evidence showing reduction in IE among this special group.


**1. Introduction**

The sustainability of the mining industry faces a challenging scenario for mineral processing, with low grade ore deposits, water scarcity and seawater as an alternative to freshwater. One of

the operational priorities is to close the water cycle, a process that can be successful with proper management of the thickening stages. Generally, the concentration stages are carried out at alkaline pH, which means that both particles and flocculant molecules (such as hydrolyzed polyacrylamide) carry anionic charges. Although reagents may have a low affinity with the particles, it is sufficient to form porous aggregates with low density, which facilitate the transport of the thick pulp from the bottom of the thickener to the tailing dams. The fact that industrial water has some ionic charge can be beneficial. There is evidence in the literature that the presence of ions in the liquor is necessary for adequate flocculation, because ions allow the adsorption of polymeric molecules on the charged surfaces [1–3]. There is also evidence that flocculants may exhibit different behaviors when interacting in saline media. For example, studies have indicated that flocculated kaolinite shows decreasing sedimentation rates in environments with high salinity [4,5], while other studies show the opposite [6]. We recently demonstrated that the increase in salinity promotes the adsorption of polyelectrolytes on mineral surfaces but decreases the size of the polymer in solution, resulting in a competitive effect that can benefit or impair particle agglomeration. Thus, varied behaviors are expected [7,8].

There is great interest in studying flocculation from a microscopic level because the mechanisms involved during the aggregation of colloidal particles determine the efficiency of thickeners. Various particle characterization techniques are available, such as laser analysis, dynamic image analysis (DIA), and microscopy [9], however, the focused beam reflectance measurement (FBRM) has significant advantages for flocculation, since it can be used directly in feed slurries, allowing online monitoring of aggregate size without sampling and without dilution [10–14]. Through this technique, the effect of shear rate has been studied in several works [15–18]. In some cases, a maximum aggregate size is reached in a short time after the addition of the flocculant, but then fragmentation of the aggregates and polymer depletion occurs. Interestingly, the systems studied by He et al. [19] have shown that the maximum floc size is reached at low shear rate values and that any further increase in the intensity of the mixture leads to fragmentation.

Several researchers have described the kinetics of growth and fragmentation of aggregates using population balance models (PBM) [20–22] of a wide variety of systems, such as coagulation and flocculation [23], crystallization [24], reduction in mill size [25], and polymerization [26]. A century ago, Smoluchowski [27] was the first to formulate the PBM equations to describe the coalescence growth phenomena. Since then, this procedure has been the basis for describing new systems with mechanisms of aggregation of particles with increasingly richer physics, which has required modifications to the original equations.

Consideration of the fractal dimension as an indicator of the irregular structure of the aggregates has been key [20,28,29]. The fractal dimension has allowed describing the decrease in aggregate size over time due to restructuring mechanisms [30–32]. In all these cases, the fractal dimension increases while flocculation takes place. Recently, Jeldres et al. [33] showed that the fractal dimension of aggregates of flocculated mineral tailings in seawater decreases, while flocculation time and/or mixing intensity increase. These results are important because they define the mechanisms involved during the growth of aggregates, such as fragmentation, collision efficiency, permeability of structures [32,34] and flocculant depletion [21,35].

In this work, PBM is used to describe the flocculation kinetics of quartz-kaolinite tailing particles flocculated in seawater with a high-molecular-weight anionic polymer for a range of mixing intensities. The effect of the shear stress on the flocculation kinetics and on the aggregation and breakage rates of aggregates is analyzed. Optimum mixing conditions are determined to achieve the largest aggregate size. The structure of the aggregates during flocculation is described by a constant and time-dependent fractal dimension. Finally, an attempt is made to determine whether the representation of flocculation kinetics requires a constant average fractal dimension or a time-varying fractal dimension.

#### **2. Model**

The PBM equations used in this work were derived from from the work proposed by Spicer and Pratsinis [36] for particle grouping, which combines the discretization approach of Hounslow et al. [37] for aggregations and Kusters et al. [38] for breakage, in which the aggregate size distribution is discretized into a number of bins or channels according to a geometric progression of the volume distribution of the aggregates, that is, *Vi*+1 = 2*Vi*. The population balance equation is given by:

$$\begin{aligned} \frac{dN\_i}{dt} &= \sum\_{j=1}^{i-2} 2^{j-(i-1)} Q\_{i-1,j} N\_{i-1} N\_j + \frac{1}{2} Q\_{i-1,j-1} N\_{i-1} N\_{i-1} - \sum\_{j=1}^{i-1} 2^{j-i} Q\_{i,j} N\_i N\_j - \sum\_{j=1}^{i\_{\text{max1}}} Q\_{i,j} N\_i N\_j\\ &- S\_i N\_i + \sum\_{j=1}^{i\_{\text{max2}}} \Gamma\_{i,j} S\_j N\_j \end{aligned} \tag{1}$$

where *Ni* is the number concentration of aggregates in bin *i*. The first two term on the right-hand side of Equation (1) describe the formation of aggregates of size *i* from smaller aggregates. The following two terms describe the loss of aggregates of size *i* to greater aggregates. The fifth term on the right-hand side represents the loss of aggregates of size *i* by fragmentation. The last term represents the gain of aggregates of size *i* by fragmentation of larger aggregates. The superscript *max*1 and *max*2 stand for the maximum number of intervals used to represent the complete aggregate size spectrum by aggregation and fragmentation, respectively. Functions *Q*, *S* and Γ represent aggregation rate, fragmentation rate, and breakage distribution function of aggregates, respectively. All the functions in Equation (1) are empirical in origin and thus parameters involved need to be determined by solving Equation (1) against experimental data. The functions are described next.

#### *2.1. Aggregation Kernel*

The function *Q* is the aggregation kernel defined by the product between the collision frequency (β) and the capture efficiency (α):

$$Q\_{i,j} = \beta\_{i,j} \alpha\_{i,j} \tag{2}$$

#### Collision frequency β

Fluid flow can penetrate and pass through particle aggregates [39], meaning that the genuine collision frequency is considerably lower than that predicted from rectilinear flow models. To incorporate permeability effects, we use a parameter called 'fluid collection efficiency' η which is the ratio between flow passing through an aggregate and that approaching it (undisturbed). Veerapaeni and Wiesner [40] proposed the function in Equation (3) to account for the collision frequency which includes permeability and fractal dimension, as:

$$\beta\_{i,j} = \frac{1}{6} \left( \sqrt{\eta\_i} d\_i + \sqrt{\eta\_j} d\_j \right)^3 G \tag{3}$$

where *di* and *dj* are the diameters of the aggregates of sizes *i* and *j*, respectively, *G* is the mean value of shear rate, and η is derived from the Brinkman extension to Darcy's law in function of a dimensionless permeability ξ, that is:

$$\eta\_{i} = \frac{9(\xi\_{i} - \tanh \xi\_{i})}{(38\xi\_{i} + 2\xi\_{i}^{2} - 3 \tanh \xi\_{i})} \tag{4}$$

where ξ*i* is defined as ξ*i* = *di*/2 √ *Ki* and *Ki* is the permeability, for which the expression from the Li and Logan [41] is used:

$$K\_i = \frac{d\_i^2}{72} \left( 3 + \frac{3}{1 - \phi\_i} - \sqrt[3]{\frac{8}{1 - \phi\_i} - 3} \right) \tag{5}$$

The porosity φ is related to fractal dimension (*df*) by the expression of Vainshtein et al. [42]:

$$\phi\_l = 1 - \mathbb{C}\left(\frac{d\_l}{d\_0}\right)^{d\_f - 3} \tag{6}$$

where *C* is a packing coefficient (generally assumed to be 0.65) and *d*0 is the primary particle diameter; *di* and *d*0 are related by the expression by Mandelbrot [43]:

$$d\_i = d\_0 \left(\frac{2^{i-1}}{C}\right)^{\frac{1}{d\_f}} \tag{7}$$

Collision efficiency α

Several models allow estimating collision efficiency, depending on the type of aggregate and the additive used. In this work, the three-parameter expression of Vajihinejad and Soares [35] is used to capture the depletion of adsorbed high-molecular-weight polymer of the type used here and its rearrangement on the surface of the particles, that is:

$$
\alpha\_{i,j} = (\alpha\_{\text{max}} - \alpha\_{\text{min}})e^{-k\_d t} + \alpha\_{\text{min}} \tag{8}
$$

where α*max* and α*min* are maximum and minimum collision efficiency, respectively, at steady state conditions and *kd* is a decay constant.

#### *2.2. Breakage Kernel*

The *S* function in Equation (1) is the fragmentation rate and the Γ function is the breakage distribution function; these two represent the breakage kernel. The *S* term is difficult to predict since it has not been theorized; it is common to adjust it to size distribution data. For this work, a two-parameter power law function of the aggregate mass is used [44]:

$$S\_{\bar{l}} = s\_1 G^{s\_2} d\_{\bar{l}} \tag{9}$$

where *s*1 and *s*2 are the fitted parameters.

For the breakage distribution function, a binary distribution without parameters is used:

$$
\Gamma\_{i,j} = \begin{cases}
\frac{V\_j}{\nabla\_i^2} \text{ for } j = i + 1 \\
0 \text{ for } j \neq i + 1
\end{cases}
\tag{10}
$$

#### *2.3. Shear Rate*

The shear rate required by the aggregation and breakage kernels is calculated from:

$$G = \left(\frac{\varepsilon \rho\_{\text{sus}}}{\mu\_{\text{sus}}}\right)^{\frac{1}{2}}\tag{11}$$

where ε is the average energy dissipation rate given by:

$$
\varepsilon = \frac{N\_p N^3 D^5}{V} \tag{12}
$$

where *Np* is the impeller power number which for a plane disk with gentle agitation, as is our case, is 0.6 [45], *N* is the rotation speed, and *D* and *V* are the diameter of the impeller and the working volume of the vessel, respectively. The density of the suspension ρ*sus* is calculated from:

$$
\rho\_{\text{sus}} = \left(\frac{w}{\rho\_s} + \frac{1 - w}{\rho\_w}\right)^{-1} \tag{13}
$$

where *w* is the mass solid fraction of the solution and ρ*s* and ρ*w* are the solid and water densities, respectively. Finally, the viscosity of the solution μ*sus* is measured.

#### *2.4. PBM Solution*

In this work, the PBM equation (Equation (1)) is used to describe the flocculation kinetics of synthetic copper tailings in seawater. A solver based on a numerical differentiation formula for stiff ODEs (ode23s) is used in MATLAB environment. The numerical solution requires parameters which are obtained by minimizing the objective function:

$$\text{OF}(\alpha\_{\text{max}}, \alpha\_{\text{min}}, k\_d, s\_1, s\_2) = \sum\_{t\_i}^{t\_f} \left( d\_{\text{ag,xp}} - d\_{\text{ag,g,mod}} \right)^2 \tag{14}$$

where *dagg*,*exp* is the experimental diameter of the aggregates and *dagg*,*mod* is the volume-weighted mean aggregate diameter from the model:

$$d\_{mod} = \frac{\sum\_{i=1}^{\max} N\_i d\_i^4}{\sum\_{i=1}^{\max} N\_i d\_i^3} \tag{15}$$

To solve Equation (14) the MATLAB function *fminsearch* is used; this function uses the Nelder–Mead direct search to find the minimum of an unconstrained multivariable function.

Two criteria validate the model fit and predictions, one is the coefficient of determination (*R*2) that measures the closeness of the model values to the experimental values:

$$\mathbf{R}^2 = 1 - \frac{\sum\_{i=1}^{\max} \left( d\_{a\text{\ $g\$ }, \text{exp}, i} - d\_{a\text{\ $g\$ }, \text{mod}, i} \right)^2}{\sum\_{i=1}^{\max} \left( d\_{a\text{\ $g\$ }, \text{exp}, i} - d\_{a\text{\ $g\$ }, \text{exp}} \right)^2} \tag{16}$$

The other is the goodness of fit:

$$\text{GofF}(\%) = 100 \frac{\left\langle d\_{\text{agg. exp}} \right\rangle - \text{std}}{\left\langle d\_{\text{agg. exp}} \right\rangle} \tag{17}$$

where std stands for the standard error calculated from:

$$\text{stdd} = \left(\frac{1}{n-f} \sum\_{t\_i}^{t\_f} \left( \left< d\_{a\text{ g.g., exp}} \right> - \left< d\_{a\text{ g.g., mod}} \right> \right)^2 \right)^{\frac{1}{2}} \tag{18}$$

where *n* is the number of data values and *f* is the number of parameters to be fitted. A GoF of 90% or higher means that the proposed model is able to predict the flocculation kinetics [10]. Finally, conservation of the total volume of particles is verified after every integration to ensure that the simulations are maintaining the particle population.

#### **3. Materials and Methods**

#### *3.1. Materials*

Quartz particles with a density of 2.65 g/cm3 acquired from Donde Capo (Chilean Store, Santiago, Chile) were used, which were pulverised and screened with -#325 mesh. The SiO2 content, estimated by XRD analysis, was higher than 99 wt%. Kaolin particles were acquired from Ward's Science (Rochester, NY 14586, Rochester, United States), and the XRD analysis indicated 84% kaolinite and 16% halloysite. Seawater from the coast of Antofagasta (Chile) was provided by the Department of Marine Resources from the University of Antofagasta and subsequently filtered by UV for the removal of microorganisms. The ionic composition was determined with varied chemical methods depending on the ion type. By atomic absorption spectrometry: Na+ 10.5 g/L, Mg2+ 1.42 g/L, Ca2+ 0.38 g/L, K+ 0.40 g/L. The composition of Cl− was determined by argentometric method and was 19.1 g/L. The concentration of HCO3− was determined by acid–base volumetry to be 0.17 mg/L. NaOH was used as a pH modifier; anionic SNF704 and high molecular weight were used as a flocculant.

#### *3.2. Flocculation Kinetics*

A suspension of 270 g was prepared, considering a total solids content of 8 wt%, corresponding to a mixture of 80 wt% quartz and 20 wt% kaolin. The slurry was vigorously mixed for 30 min using a 30 mm diameter turbine-type stirrer, placed in the axial position, ina1L capacity, 100 mm diameter vessel. The stirrer was placed 20 mm above the vessel bottom. Subsequently, the mixing rate was reduced to 250 rpm, and an established volume of a solution (seawater and polymer) was added, at a proportion fixed by the required polymer dosage. The size distribution was traced using the FBRM technique, which consists of a sensor that measures reflection pulses of a laser beam on suspended particles in the range of 0.25–1000 μm. The main advantage of this technique is that the data is collected in-situ and in real-time; therefore, it does not require sampling or isolation that could contribute to changes in the size and distribution of aggregates by the eventual breakage or agglomeration. The FBRM probe was submerged vertically in the reaction vessel, 10 mm over the stirrer and 20 mm off-axis. The measurement began by tracking the primary particles of the tailings, and after 1 min, the flocculant was added with subsequent growth and destruction of aggregates for 3 min. The flocculation assays where done at pH 8, under ambient temperature and different mixing intensities.

#### *3.3. Viscosity*

The calculation of the shear rate requires the viscosity of the slurry. For this, a flocculation process was carried out with the methodology defined in Section 3.2. Subsequently, an aliquot of 56 mL was taken for rheological analysis. An Anton Paar MCR 102 rheometer with the RheoCompass Software was operated in controlled-rate mode, and a sandblasted #CC39 with bob-in-cup geometry was used to reduce wall slip effects. The gap generated between both concentric cylinders was 1.5 mm. Each sample remained for 15 min, ensuring that the rheological properties were obtained in steady state. The range of shear rate was preset between 10–300 s−1.

#### *3.4. Sedimentation*

After a pre-set flocculation period (10–80 s), the pulp was gently poured into closed cylinders of 300 cm3 (35 mm internal diameter) and then slowly inverted twice by hand (the whole cylinder rotation process took 4 s). After 10 min of settling, the supernatant fluid was rescued and stirred to homogenize the suspended solids. Then, a 50 mL aliquot was used for turbidity measurements in a Hanna HI98713 turbidimeter, which performed ten readings in 20 s, delivering the average of the point readings.

#### *3.5. Conditions*

Table 1 summarizes parameters and conditions used in this study. With these conditions, shear rate was calculated for all the cases, using Equations (11)–(13).

**Table 1.** Input parameters and conditions.

#### **4. Results**

#### *4.1. Initial Particle Size*

Solving the PBM equations requires an initial particle number concentration for each size class and per unit of volume of suspension (*N*0,*i*), for that, a number distribution from experimental volume distribution is obtained from:

$$N\_{0,i} = \phi \frac{v(d\_{0,i})}{V\_{0,i}} \tag{19}$$

where *v*(*d*0,*i*) is the experimental volume fraction of particles with diameter *d*0,*i*, obtained from Equation (7), and *V*0,*i* is the volume of the primary particles following the geometric progression *V*0,*i* = 2*i*−1*V*1. Figure 1 shows the normalized volume for synthetic tailings in seawater at pH 8. *v*(*d*0,*i*) is obtained by interpolating the data in Figure 1 at the required values of *d*0,*i*. The initial distribution is sensitive to the shear rate, where the tendency is for the particles to aggregate as the intensity of mixing increases. The initial primary particle size is set to 5 μm, which is the mean minimal size with non-zero value in the distribution.

**Figure 1.** Normalized initial volume distribution of particles of synthetic tailings in seawater at pH 8 for different mixing intensities (shear rate G).

#### *4.2. Fractal Dimension*

The fractal dimension defines the structure of the aggregates and appears in several equations describing particle agglomeration. In this study, the fractal dimension (Figure 2) is obtained from combined sedimentation tests with aggregate size measurements using the FBRM probe. The experimental data of hindered settling velocity, mean particle size, and mean aggregate size were used to extract the fractal dimension from the settling velocity model of Heath et al. [11]. In previous work, we found that the fractal dimension of particle tailing aggregates is very close to constant for low values of shear rate [46], in agreement with results from Heath et al. [15,21]. The results of Figure 2 confirm that, for low shear rate, the fractal dimension is a constant independent of flocculation time. However, when the shear rate values are high, the fractal dimension decreases monotonously over time, indicating that fragmentation of aggregates leads to lower-dimension Euclidean structures. In this work, the consequences of using constant fractal dimension in the determination of flocculation kinetics and aggregation and breakage functions for all flocculation conditions are analyzed. The results are compared with those obtained using fractal dimension as a function of time. Note that the constant fractal dimension is defined here as the average between the lowest and highest fractal dimensions for the same system and identical flocculation conditions.

**Figure 2.** Temporal evolution of the fractal dimension during flocculation of synthetic tailing particles in seawater at pH 8 as a function of shear stress (G).

#### *4.3. Flocculation Kinetics Modelling*

The flocculation kinetics of synthetic tailing particles in seawater are shown in Figure 3 as a function of shear stress. While the open circles correspond to experimental data, the solid lines correspond to the best fit with the PBM and the fractal dimension from Figure 2. Low shear requires a long time to give life to small aggregates, while high shear in a short time leads approximately to the same small aggregates; in the first case collisions and captures are unimportant, while in the second case the ruptures are important. There is clearly a mixing intensity that maximizes the size of the aggregates; however, the necessary flocculation time is critical because it defines the utility or lack of utility of the chosen flocculant. In the case of synthetic tailing particles in seawater with the flocculant chosen in this work, the optimum size of the aggregates is ca. 225 μm when the shear rate is 163 s−1. The PBM captures all the complex stages of particle flocculation, that is, initial growth of aggregates by particle bridging and subsequent size reduction as a result of fragmentation.

**Figure 3.** Flocculation kinetics of synthetic tailing particles in seawater at pH 8 as a function of shear stress (G). Open circles correspond to experimental data and solid lines to the best fit with the population balance model (PBM). (**A**): Shear rate range from 89–251 s−1. (**B**): Shear rate range from 300–462 s−1.

Figure 3 shows qualitatively that the model correctly describes the experimental data for all mixing intensities. The quantitative results of GoF and R2 summarized in Table 2 show that the quality of the PBM is very good in all the cases analyzed and that the effect of representing the fractal dimension of the aggregates as a constant independent of the flocculation time is a correct decision, at least to represent the flocculation kinetics.

**Table 2.** Quantitative results of GoF and R2 when the PBM is used with constant fractal dimension (*df* mean) or variable (*df* var) fractal dimension dependent on flocculation time.

#### *4.4. Aggregation, Breakage, and Permeability Modelling*

The aggregation, rupture, and permeability functions of particle aggregates are the building blocks to describe the flocculation kinetics, in the present case of synthetic tailing particles. Therefore, it is of great interest to evaluate the quality of the PBM representation of these functions when using the fractal dimension of the aggregates as a constant or variable dependent on the flocculation time. Figure 4 summarizes the four results.

**Figure 4.** PBM representation of collision frequency (**a**), collision efficiency (**b**), aggregate permeability (**c**) and fragmentation rate (**d**) for the largest aggregates considering constant fractal dimension of the aggregates (dashed lines) and time-dependent fractal dimension (solid lines) for different mixing intensities. Aggregate size range is 200–600 μm (bin *i* = 15).

As expected, the collision frequency increases and the collision efficiency decreases with mixing intensity (Figure 4a,b). The collision frequency remains constant when the fractal dimension is considered constant and grows over time when the fractal dimension is considered dependent on the flocculation time. The collision efficiency shows an exponential decay with flocculation time, at low shear rate the efficiency decreases rapidly when the fractal dimension is used as a function of time, and at high shear rate the collision efficiency is not so different if the fractal dimension is constant or variable. The results suggest that when aggregates are more porous and irregularly structured they are less likely to adhere once they collide.

The permeability of the aggregates increases with the intensity of the mixing and does so more strongly when the shear rate is very high. Permeability is constant when considering a constant fractal dimension and increases with flocculation time when considering a fractal dimension dependent on flocculation time. The product of the collision frequency and the collision efficiency is the aggregation rate; it is expected to be quite different when using constant or variable fractal dimension, however, to represent the optimal mixing (163 s−1) that leads to the larger aggregates (225 μm) the results of Figure 4a,b for collision frequency and collision efficiency, respectively, and the results of Figure 4c for

aggregate permeability, suggest that there are no large differences if one or the other fractal dimension is used.

The fragmentation rate is not a monotonous function of the mixing intensity for both constant and variable fractal dimensions (Figure 4d). For low shear rate values, the rate decreases when the mixing intensity increases, and for high shear rate values, the rate increases with the mixing intensity. The fragmentation rate is constant when the constant fractal dimension is used and in general it is very different from the fragmentation rate obtained with a variable fractal dimension. Under optimal mixing conditions (163 s−1), the results of Figure 4c suggest that the fragmentation rate is better represented by the variable fractal dimension, the use of a constant fractal dimension can lead to significant deviations.

It is well known that compensations between aggregation and breakage rates lead to a very good representation of flocculation kinetics; the results in Figure 3 are a good demonstration. However, in addition, this study reveals that under optimal mixing conditions the representation of flocculation kinetics is equally good with a constant or variable fractal dimension.

#### *4.5. Optimized Parameters*

The optimization of the PBM model is performed against experimental flocculation data resulting in five parameters: the maximum and minimum collision efficiency (α*max*, α*min*), the collision efficiency decay constant (*kd*), and two breakage rate kernel parameters (*s*1 and *s*2). Of great interest is the effect of mixing intensity and the fractal dimension, constant or variable, on these parameters. The three parameters that describe the collision efficiency versus shear rate are presented in Figure 5, for the two conditions of fractal dimension. In general, α*max* and α*min* decrease with the shear rate, α*min* = 0 for the whole range of shear rate, except for at the lowest rate, and *kd* increases with the shear rate. The effect of the fractal dimension either constant or variable is small except at very high shear rate for α*max* and *kd*. α*min* is not affected by the choice of fractal dimension. For the optimal mixing conditions (163 s−1), the three parameters of the collision efficiency can be estimated considering a constant fractal dimension. Finally, the two parameters of the breakage rate (*s*1, *s*2) change erratically with the shear rate but are not affected by the choice of the fractal dimension, whether constant or variable. For the optimal mixing conditions, the breakage rate parameters can be determined assuming a constant fractal dimension.

**Figure 5.** Experimental (symbols) vs. calculated (continuous lines) time evolution of the mean diameter of aggregates of tailing particles as a function of pH in seawater. Constant pH condition at 8.

#### *4.6. Prediction Capability*

To test the predictive capacity of PBM with the parameters determined in the preceding sections, new experiments were carried out to determine the kinetics of flocculation of tailing particles in seawater at 185 and 250 rpm. Pulps with the same composition and percentage of solids were

considered as in the trials in Section 4.3. The size of primary particles was again 5 μm. Also, the same flocculant and the same operating conditions were used, that is, temperature, flocculant dose, agitation time, and pH. Figure 5 shows the experimental and modeling results. The parameters for PBM were interpolated from Figure 2 for the fractal dimension, Figure 6 for the aggregation kernel and Figure 7 for the breakage rate for each pH. The adjustments are generally good, with GoF of 87.17 for 185 rpm and 92.02 for 250 rpm, which demonstrates the predictive capacity of PBM. Unlike what was found in our previous paper [46] where pH has little influence in kinetics, the shear rate has marked nonlinear dependence on the results of kinetics. Therefore, modeling was possible with good prediction for intervals of 30 s–1.

**Figure 6.** Optimum aggregation parameters vs. shear rate for constant and variable fractal dimension, (**a**) maximum and minimum collision efficiencies and (**b**) collision efficiency decay constant *kd*.

**Figure 7.** Optimum breakage parameters vs. shear rate for constant and variable fractal dimension, (**a**) *s*1 and (**b**) *s*2.

#### **5. Conclusions**

PBM equations were used to describe the aggregation kinetics of synthetic copper tailings in seawater with a high molecular weight polyacrylamide at pH 8 and a range of mixing intensities. It is common to consider that the fractal dimension of the aggregates is constant throughout the flocculation process, however in this work we found that when the mixing intensity is high, >100 s–1, the fractal dimension of the aggregates decreases monotonously over time, indicating that fragmentation of aggregates leads to lower-dimension Euclidean structures. This study revealed that compensations between aggregation and breakage rates lead to a very good representation of the flocculation kinetics of the particle tailings in seawater, and in addition, that the representation of flocculation kinetics under optimal conditions is equally good with a constant or variable fractal dimension. The mixing intensity of 163 s–1 was found to maximize the size of the tailing aggregates, that is, ca. 225 μm at ca. 20 s. The aggregation and breakage functions and corresponding parameters are sensitive to the choice of the fractal dimension of the aggregates, whether constant or time-dependent; however, under optimal conditions, a constant average of the fractal dimension is sufficient. The predictive capacity of the model can be used to find the optimal flocculation conditions based on a few experimental flocculation data at different mixing intensities, and the optimal flocculation time can be used to make decisions regarding the effectiveness of the flocculant used.

**Author Contributions:** G.R.Q. and W.H.L. contributed in research and wrote paper, N.T., P.R. and R.I.J. contributed in project administration, L.A. contributed resources and P.G.T. contributed in review and validation. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Conicyt Fondecyt 11171036 and Centro CRHIAM Project ANID/FONDAP/15130015.

**Acknowledgments:** The authors thank Centro CRHIAM Project Conicyt/Fondap/15130015. RIJ thanks CONICYT Fondecyt 11171036. Pedro Robles thanks the Pontificia Universidad Católica de Valparaíso for the support provided.

**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/).


*Article* **Subpopulations of Circulating Cells with Morphological Features of Malignancy Are Preoperatively Detected and Have Differential Prognostic Significance in Non-Small Cell Lung Cancer**

**Emanuela Fina 1,\* , Davide Federico 2,3, Pierluigi Novellis 4,† , Elisa Dieci 4,†, Simona Monterisi 1 , Federica Cioffi 4 , Giuseppe Mangiameli 3,4, Giovanna Finocchiaro 5 , Marco Alloisio 3,4 and Giulia Veronesi 4,\* ,†**

**Simple Summary:** Lung cancer is by far the main cause of cancer-related deaths among both men and women. Early detection of malignant nodules and non-invasive monitoring of disease status is essential to increase the chance of cure. In this study, we analyzed the frequency and the biological features of circulating tumor cells, i.e., cells released from the tumor and in transit in the bloodstream, in patients with a diagnosis of non-small cell lung cancer undergoing surgical resection, with the aim to develop a blood-based diagnostic test and to promptly identify patients at risk of post-operative disease recurrence.

**Abstract:** Background: Non-small cell lung cancer (NSCLC) frequently presents when surgical intervention is no longer feasible. Despite local treatment with curative intent, patients might experience disease recurrence. In this context, accurate non-invasive biomarkers are urgently needed. We report the results of a pilot study on the diagnostic and prognostic role of circulating tumor cells (CTCs) in operable NSCLC. Methods: Blood samples collected from healthy volunteers (*n* = 10), nodule-negative high-risk individuals enrolled in a screening program (*n* = 7), and NSCLC patients (*n* = 74) before surgery were analyzed (4 mL) for the presence of cells with morphological features of malignancy enriched through the ISET® technology. Results: CTC detection was 60% in patients, while no target cells were found in lung cancer-free donors. We identified single CTCs (sCTC, 46%) and clusters of CTCs and leukocytes (heterotypic clusters, hetCLU, 31%). The prevalence of sCTC (sCTC/4 mL ≥ 2) or the presence of hetCLU predicted the risk of disease recurrence within the cohort of early-stage (I–II, *n* = 52) or advanced stage cases (III–IVA, *n* = 22), respectively, while other tumor-related factors did not inform prognosis. Conclusions: Cancer cell hematogenous dissemination occurs frequently in patients with NSCLC without clinical evidence of distant metastases, laying the foundation for the application of cell-based tests in screening programs. CTC subpopulations are fine prognostic classifiers whose clinical validity should be further investigated in larger studies.

**Keywords:** circulating tumor cells; lung cancer; early diagnosis; cancer biomarkers

**Citation:** Fina, E.; Federico, D.; Novellis, P.; Dieci, E.; Monterisi, S.; Cioffi, F.; Mangiameli, G.; Finocchiaro, G.; Alloisio, M.; Veronesi, G. Subpopulations of Circulating Cells with Morphological Features of Malignancy Are Preoperatively Detected and Have Differential Prognostic Significance in Non-Small Cell Lung Cancer. *Cancers* **2021**, *13*, 4488. https://doi.org/10.3390/ cancers13174488

Academic Editor: David Wong

Received: 31 July 2021 Accepted: 26 August 2021 Published: 6 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Lung cancer is the tumor with the highest fatality rate worldwide, both in males and females [1], due to its aggressiveness and biological heterogeneity, with non-small cell lung cancer (NSCLC) representing the most frequent histological subtype [2,3]. Importantly, since approximately 75% of patients present symptoms when the disease has advanced locally or disseminated at distant sites [4], lung cancer mortality is also a large consequence of late diagnoses. Therefore, early detection is a key for improving patient's survival [5], and the US National Lung Screening Trial (NLST), the Nelson study and other non-randomized trials have actually demonstrated a significant reduction in mortality (20–30%) and morbidity upon screening programs based on a thoracic scan by low-dose computed tomography (LDCT) [6,7]. At present, uncertainties on high costs, risk of radiation exposure and false positives are obstacles to the large-scale implementation of such screening in Europe [8–10], and controversies exist in the management of subjects with indeterminate or premalignant nodules, which have to be monitored for a long time, and in some cases biopsied, increasing the risk of subsequent morbidities [11]. Moreover, notwithstanding the considerable advantages for patients who are diagnosed with the early-stage disease compared to those with unresectable tumors [12,13], unfortunately, 30% to 50% of cases who receive an indication for local treatment experience disease recurrence and die despite surgery with curative intent [14,15]. Lung cancer still lacks accurate biomarkers, and staging is no longer considered an accurate prognostic factor since patients with the disease at the same stage may undergo recurrence with variable incidence [16,17]. In this context, both non-invasive diagnostic tests and novel prognostic and predictive biomarkers are urgently needed to better stratify patients at risk of recurrence upon surgery and adjuvant therapies [18–20].

The measurement of blood biomarkers is an attractive approach to monitor cancer appearance and evolution: (i) suitable to be repeated, (ii) minimally invasive and (iii) believed to represent the systemically diffused expression of tumor heterogeneity [21–23]. Among all possible non-invasive biomarkers, circulating tumor cells (CTCs) are largely informative as they represent cancer cells in transit in blood, with the expected ability to re-seed the site of origin and/or to colonize distant organs [24], and which can be enumerated and characterized at DNA, RNA, protein and morphological level, thus providing access to a considerable amount of information. Importantly, hematogenous dissemination is now considered an early event in tumor progression [25], and CTCs were actually observed in the blood of patients without clinically detectable metastases in several solid tumors [26]. In lung cancer, a seminal work demonstrated that CTC analysis with the Isolation by Size of Epithelial/Tumor cells (ISET®) technology, which enables the vast majority of hematopoietic cells to be excluded by blood sample filtration through a porous membrane, can anticipate the detection of malignant nodules by computed tomography scan in patients with chronic obstructive pulmonary disease (COPD) who had eventually developed stage IA tumor [27], fostering the introduction of cell-based tests in diagnostic trials.

CTCs can be enriched and detected by several techniques based on the physical and biological differences between cancer cells and blood cell types; however, accurate detection is hampered by their rarity and phenotypical heterogeneity [28]. Size-based enrichment of CTCs coupled to cytomorphological analysis are unbiased with respect to the expression of protein markers and may reach a sensitivity of one cell per blood volume [29,30]. On the basis of these considerations, we have analyzed blood samples of lung cancer-free individuals and operable NSCLC patients before surgery, using the ISET® technology. Previous works in early-stage NSCLCs demonstrated that CTCs can be observed by ISET® in about 50% of patients and that they have prognostic significance in the preoperative setting [31,32], while in a multicentric screening trial, CTC analysis was able to identify only 26% of lung cancers detected at first LDCT scan [33].

Here, we have described atypical cells and searched for cells with morphological features of malignancy upon staining with standard cytological colorants, with the aim to assess the prognostic significance of CTC count and possible CTC subpopulations in

operable NSCLC and to further explore the applicability of a CTC-based test in screening programs by comparison with a cohort of lung cancer-free individuals.

#### **2. Materials and Methods**

#### *2.1. Study Design*

We designed and carried out a prospective observational study, following the STROBE guidelines and the approval by the Ethics Committee of Humanitas Research Hospital in Rozzano (Milan, Italy). Signed informed consent was obtained from all patients. Consecutive patients with a confirmed diagnosis of NSCLC, aged 18 years or older, not pregnant, treatment naïve, without prior cancer detected within the previous 5 years or a second malignancy if there was evidence of active disease and candidate to surgical resection were enrolled from January 2017 to September 2018 and admitted in the Thoracic Surgery Division of Humanitas Cancer Center. During this phase of the study, blood samples were collected from lung cancer patients detected outside the smoker's health multiple action (SMAC) screening program, the same day or the day before surgical intervention. Blood samples for CTC analysis were also collected from high-risk individuals enrolled in the SMAC screening program before the LDCT scan, starting from May 2019. Inclusion criteria were: heavy smokers, i.e., ≥30 packs per year, for more than 30 years, or formersmokers aged 55 years or older, who had ceased smoking within the 15 years prior to enrollment in the study, absence of symptoms of lung cancer, such as worsening of cough, hoarseness, hemoptysis and weight loss. Exclusion criteria were: previous diagnosis of lung cancer, extrapulmonary cancer history in the last 5 years (excluding in situ tumors or skin epidermoid tumor), chest CT scan performed in the last 18 months, severe lung or extrapulmonary diseases that may preclude or invalidate appropriate therapy in case of diagnosis of malignant pulmonary neoplasia. CTC analysis was performed on individuals without LDCT-detected pulmonary nodules. Blood samples from a group of healthy volunteers aged 30–50 years were analyzed as a negative control to optimize the target cell identification. The data were collected by a review of electronic medical records. The TNM staging manual of the American Joint Committee on Cancer (AJCC) 8th edition was applied. Cases considered for survival analyses include patients whose disease recurrence or death was clearly documented and attributed to NSCLC, while patients whose status was not available were excluded.

#### *2.2. Blood Collection and CTC Enrichment*

Samples of peripheral venous blood were drawn from patients or lung cancer-free donors using a 21G needle, collected in K2EDTA BD Vacutainer® tubes (Becton Dickinson Italia, Milan, Italy), preserved at room temperature under gentle agitation and processed within 1.5 h using the Isolation by Size of Epithelial/Tumor cells (ISET®) technology (Rarecells® Diagnostics, Paris, France). Briefly, 10-mL blood samples were diluted 1:10 with a proprietary Rarecells® Buffer, which lysates red blood cells, and fixed with 37% formaldehyde solution (Sigma-Aldrich, St. Louis, MO, USA) at a final concentration of about 0.7% for 10 minutes and under gentle agitation. The blood was filtered through a filtration block containing a polycarbonate membrane, which hosts ten porous filter spots (8-µm-diameter cylindrical pores), each spot representing the filtration product of 1 mL of blood. The membranes were stored at 4 ◦C until cytological staining.

#### *2.3. CTC Detection Method and Identification Criteria*

Four ISET membrane spots per sample, which are the equivalent of 4 mL of blood, were stained using May–Grünwald and Giemsa colorants following these steps: incubation with a pure May–Grünwald solution (Sigma, St. Louis, MO, USA) for 5 min, then with May–Grünwald 50% diluted in distilled water for 5 min and finally with a Giemsa solution (Sigma) 10% diluted in distilled water for 20 min. Stained membranes were mounted with Organo/Limonene MountTM mounting medium (Sigma) and examined under a bright-field microscope (Olympus BX51) using a 10× objective. Areas of interest were

subsequently digitalized at a 40× magnification for cytomorphological analysis. All images were analyzed by a referral cytopathologist, without knowledge of disease status and outcome, on the basis of the classical morphological criteria of malignancy, also described by Hofman and colleagues [34], and other morphological criteria to define populations of non-malignant circulating cells, as reported by Wechsler [35]. Images of atypical cells recorded during the analysis of a training set of healthy volunteers were also taken into account to exclude cells with uncertain malignancy. CTCs were defined as cells presenting with a nucleus-to-cytoplasm ratio >0.75 and nucleus diameter >20 µm, and with at least one of the following characteristics: nuclear border irregularities or nuclear hyperchromasia. Clusters were defined as groups of at least two CTCs or groups of at least one CTC and at least another cell type without features of malignancy, juxtaposed or in direct contact at the cell membrane level. Clusters of cells with homogenous chromatin staining and nucleus-to cytoplasm ratio <0.70, generally oval-shaped, were defined as clusters of epithelial-like cells. Large macrophage-like cells were defined as cells with a longer diameter >30 µm, with low nucleus-to-cytoplasm ratio, abundant pale basophilic cytoplasm and showing several cell shapes, such as fusiform, tadpole-like, round or oblong. Naked nuclei were defined as hyperchromatic and irregularly shaped nuclei with a longer diameter >20 µm and without apparent cytoplasm. Samples were called CTC-positive when at least one CTC was observed in a total of four filter spots, which corresponds to 4 mL of blood. All samples were considered evaluable for cytomorphological analysis according to cellularity, the prevalence of damaged cells and staining quality.

#### *2.4. Spike-in Experiments*

A549 ATCC® CCL-185™and NCI-H460 [H460] ATCC ® HTB-177™ lung cancer cell lines were kindly provided by the European Institute of Oncology in Milan. Cells were propagated according to the instructions provided by the American Type Culture Collection (Manassas, VA, USA). Before performing spike-in experiments, the cells were detached with Trypsin-Versene® solution (Lonza, Basel, Switzerland), counted using the Trypan Blue 0.4% solution as a vital dye exclusion assay (viability >99% in all tests) and injected in 10 mL of blood collected from healthy volunteers at a dilution of 1000 cells per milliliter of blood (i.e., 1000 cells per membrane spot). The spiked-in samples were incubated for 30 min at room temperature under gentle agitation until chemical fixation and filtration as described before. The membranes were stained with Hematoxylin (Histo-line Laboratories Srl, Milan, Italy) for 3 min and Eosin Y aqueous solution (Histo-line Laboratories Srl) for 1 min, or with May–Grünwald and Giemsa solutions (Sigma) as described before and observed at the Olympus BX51 under a 20× magnification objective.

#### *2.5. Statistical Analysis*

Associations between categorical variables were tested by Fisher's exact test using contingency tables. Differences in discrete variables were tested using the Mann–Whitney and Kruskal–Wallis test. Linear regression analysis and Pearson's correlation coefficient *r* were used to estimate the correlation between cell numbers and age. Cox proportionalhazards regression was used to investigate the prognostic role of CTC status or number on recurrence-free survival, with relative hazard ratios (HR) and 95% confidence interval (CI). Significance in the probability of time-to-event was tested by log-rank test. Each selected factor was investigated in univariable analysis. All tests for the comparison of experimental groups were performed using GraphPad Prism, version 7.04. Survival and Cox regression analyses were performed, and Kaplan–Meier plots were constructed using MedCalc, version 12.7, and SPSS, version 26.0, respectively. All tests were two-sided, and significance statements refer to a *p*-value < 0.05.

#### **3. Results**

#### *3.1. Cancer Cell Hematogenous Dissemination Is a Frequent Event in NSCLC Patients without Clinical Evidence of Metastasis*

We performed the prospective collection of blood samples before surgical intervention (*n* = 74), and we analyzed blood samples of young volunteers (*n* = 10) and high-risk lung cancer-free individuals (*n* = 7), as assessed by LDCT as negative controls (Table 1). The number of cases locally treated with curative intent was 32, 20 and 17 for stages I, II and III, respectively. Five patients had stage IV disease at baseline: three underwent only diagnostic surgical procedure at pleural or lung nodules, while two underwent surgery with radical intent, one case presenting with a big, excavated lung lesion with the paraneoplastic syndrome and no possibility to undergo chemotherapy, the other case underwent segmental resection plus resection of a local pleural lesion with partial decortication for intraoperative diagnosis of a small pleural metastasis. In the control cases, cells with features of malignancy were not observed, while some nuclei larger than 20 µm, hyperchromatic or with heterogeneous chromatin stain, without apparent cytoplasm, sometimes overlapping, were found in 7 out of the 17 (41%) cases (Supplementary Figure S1). Images were digitalized and taken into account during the morphological analysis of patients' blood samples in order to exclude indeterminate atypical figures and avoid misleading interpretations. Of the total population of NSCLC patients, 44 cases (59.5%) were called CTC-positive, as they had at least one cell with clear features of malignancy detected in 4 mL of blood, 57.5% with stage I–III and 80% with stage IVA tumor (*p*-value = 0.4922). The CTC status did not correlate with patient demographics nor with smoking habits, as also with pathologic tumor stage, histology and invasiveness features, except for a statistically significant higher CTC positivity rate observed in cases without peritumoral neoplastic angioinvasion. Interestingly, the proportion of CTC-positive cases with stage I or II NSCLC was considerable (57.7%) with respect to the limited amount of blood analyzed in this study (Table 1).

**Table 1.** *Cont*.

a current/former versus never smokers. b stage I/II versus III/IV. c adenocarcinoma versus squamous cell carcinoma. d G1/G2 versus G3/G4. e PL0 vs. PL1/PL2/PL3.

#### *3.2. Subpopulations of Circulating Atypical Cells Differentiate Operable NSCLC Patients from Lung Cancer-Free Individuals*

The morphological analysis of cytological samples prepared on ISET membranes revealed the presence of a heterogeneous population of circulating atypical cells, which includes different subsets observed at a variable frequency within the two cohorts of patients and controls. We identified three subpopulations of cells with features of malignancy, hereafter referred to as (i) single CTCs (*n* = 71 cells), i.e., not in direct contact with other cells (sCTC, Figure 1a–c), (ii) homotypic CTC clusters (homCLU, *n* = 2 clusters), i.e., groups of slightly overlapping CTCs, and (iii) heterotypic clusters (hetCLU, *n* = 40 clusters), i.e., CTCs in direct contact with leukocytes (Figure 1d–f), mainly monocytes (62.5%) and neutrophil granulocytes (12.5%); we also observed (iv) a subpopulation of large cells, hereafter referred to as atypical macrophage-like cells (Figure 1g–i), (v) a subpopulation of clusters of epithelial-like cells (Figure 1j–l) and (vi) a considerable number of nuclei with a longer diameter >20 µm, in some cases with irregular membrane border and/or hyperchromasia, each of them apparently without the classical cytoplasmic rim observed in CTCs, in some cases similar to those observed in healthy donors (Supplementary Figure S1), hereafter referred to as naked nuclei (Supplementary Figure S2). In order to exclude the possibility that clusters were a result of technical artifacts due to the CTC enrichment procedure, we

performed spike-in experiments with A549 and NCI-H460 cell lines at a dilution of about 1000 cells per milliliter in a total of 10 mL of blood from three healthy donors. Spiked-in, filtered and stained tumor cells were typically round-shaped and showed nucleus diameters larger than 20 µm, a nucleus-to-cytoplasm ratio around 90% and nuclear hyperchromatism. We did not observe the formation of homotypic clusters after filtration and staining, except for some doublets of NCI-H460 cells, and leukocytes were randomly interspersed among cancer cells. *Cancers* **2021**, *13*, x FOR PEER REVIEW 8 of 16

**Figure 1.** Subpopulations of atypical circulating cells differentiate patients with operable non-small cell lung cancer from lung cancer-free individuals. Images depict (**a**–**c**) single cells with morphological features of malignancy and (**d**–**f**) heterotypic clusters of cells with features of malignancy physically interacting with normal cells, such as (**d**) neutrophil granulocytes, (**e**) monocytes or (**f**) other indeterminate leukocytes, detected in patients, (**g**–**i**) atypical macrophage-like cells, (**g**) oblong or (**h**) tadpole-like, both detected in patients, and irregularly shaped (**i**), detected in healthy donors, and (**j**–**l**) clusters of epithelial-like cells, detected in patients, on porous membranes stained with May–Grünwald and Giemsa. Objective magnification 40×. **Figure 1.** Subpopulations of atypical circulating cells differentiate patients with operable non-small cell lung cancer from lung cancer-free individuals. Images depict (**a**–**c**) single cells with morphological features of malignancy and (**d**–**f**) heterotypic clusters of cells with features of malignancy physically interacting with normal cells, such as (**d**) neutrophil granulocytes, (**e**) monocytes or (**f**) other indeterminate leukocytes, detected in patients, (**g**–**i**) atypical macrophage-like cells, (**g**) oblong or (**h**) tadpole-like, both detected in patients, and (**i**) irregularly shaped, detected in healthy donors, and (**j**–**l**) clusters of epithelial-like cells, detected in patients, on porous membranes stained with May–Grünwald and Giemsa. Objective magnification 40×.

In the cohort of 74 NSCLC patients, sCTC and hetCLU were detected in 34 (45.9%) and 23 (31.1%) cases, respectively, while both CTC subsets were observed in 14 (18.9%) cases. Homotypic clusters were observed in two cases only (2.7%). Neither the presence nor the prevalence or number of sCTC correlated with patients' demographics, smoking habits and tumor features, except for males and smokers, where at least two sCTC were detected at a significantly higher frequency compared to females and never smokers (Supplementary Table S1 and Figure 2a). Neither the presence nor the number of hetCLU correlated with the clinicopathological features (Supplementary Table S2 and Figure 2b).

**Figure 2.** The number of single CTCs (sCTC) and heterotypic CTC clusters (hetCLU) is not associated with the clinicopathological features of stage I–IVA non-small cell lung cancers. Dot plots

May–Grünwald and Giemsa. Objective magnification 40×.

**Figure 1.** Subpopulations of atypical circulating cells differentiate patients with operable non-small cell lung cancer from lung cancer-free individuals. Images depict (**a**–**c**) single cells with morphological features of malignancy and (**d**–**f**) heterotypic clusters of cells with features of malignancy physically interacting with normal cells, such as (**d**) neutrophil granulocytes, (**e**) monocytes or (**f**) other indeterminate leukocytes, detected in patients, (**g**–**i**) atypical macrophage-like cells, (**g**) oblong or (**h**) tadpole-like, both detected in patients, and irregularly shaped (**i**), detected in healthy donors, and (**j**–**l**) clusters of epithelial-like cells, detected in patients, on porous membranes stained with

**Figure 2.** The number of single CTCs (sCTC) and heterotypic CTC clusters (hetCLU) is not associated with the clinicopathological features of stage I–IVA non-small cell lung cancers. Dot plots **Figure 2.** The number of single CTCs (sCTC) and heterotypic CTC clusters (hetCLU) is not associated with the clinicopathological features of stage I–IVA non-small cell lung cancers. Dot plots representthe distribution of (**a**) sCTC or (**b**) hetCLU count (median number, line) in 4 mL of peripheral blood according to the tumor stage, the histological subtype (LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma), the visceral pleura invasion and the lymph-node involvement. Differences were not significant (*p*-value > 0.05) by Kruskal–Wallis (tumor stage) or Mann–Whitney test.

Cells with features of atypical large macrophages were found in 12 out of 74 (16.2%) patients, and 2 out of 17 (12%) controls, whereas naked nuclei were observed in both cohorts, although with a two-fold increased frequency in patients (61/74, 82.4%) compared to controls (7/17, 41%; *p*-value = 0.0011). Interestingly, clusters of epithelial-like cells without apparent features of malignancy were detected in 9 out of 74 patients (12.2%) and none of the control cases. The number of patients called CTC-positive, who also had at least one atypical large macrophage, naked nucleus or cluster of epithelial-like cells were 11 (25%), 37 (84.1%) and 4 (9.1%), respectively.

#### *3.3. The Prevalence of Single Circulating Tumor Cells Predicts the Risk of Recurrence in Patients with Surgically Treated Stage I–II NSCLCs*

We then explored the utility of CTCs in serving as prognostic biomarkers to identify patients with an early-stage tumor at higher risk of disease recurrence. All stage I and II cases underwent surgical resection with curative intent. Six out of the 52 patients received post-operative adjuvant platinum-based with either vinorelbine or gemcitabine chemotherapy and/or radiotherapy. The disease status of all patients was monitored by clinical and radiological exams, except for eight cases (two stage I and six stage II) who were lost at follow-up (*n* = 3) or had died for unknown causes (*n* = 5). The median (range) observation time was 28 (1–47) months, and the total number of recurrence events was 13 (seven at intrathoracic level, five at distant sites and missing information in one case). The risk of disease recurrence in stages III and IV compared to stages I and II patients were HR 95%CI 3.45 (1.37–8.67), *p*-value = 0.0006, with an equal number of events (13) per group. In the stages I and II cohort, neither the tumor stage nor the grading or the lymph-node status were able to discriminate patients at higher risk of early disease recurrence. The numbers of stages I and II cases out of 44 evaluable for disease recurrence and with at least one, two, three or five sCTC, or at least one hetCLU, or CTC-positive irrespective of the subset type, were 21 (47.7%), 8 (18.2%), 5 (11.4%), 1 (2.3%), 15 (34.1%) and 26 (59.1%), respectively. No differences were found when grouping patients according to the overall CTC status or the presence of hetCLU, while patients with a prevalence of at least two or three CTCs in 4 mL of blood had a statistically significant shorter recurrence-free survival probability (HR 95% CI, cut-off two CTC/4 mL: 5.15 (1.10–24.33), *p*-value = 0.0009; cut-off three CTC/4 mL: 3.99 (0.47–33.57), *p*-value = 0.0216) compared to the counterpart with

a more favorable CTC count (Figure 3 and Supplementary Table S3), demonstrating that the number of sCTC was the most relevant predictor of prognosis in early-stage operable NSCLCs. mL: 3.99 (0.47–33.57), *p*-value = 0.0216) compared to the counterpart with a more favorable CTC count (Figure 3 and Supplementary Table S3), demonstrating that the number of sCTC was the most relevant predictor of prognosis in early-stage operable NSCLCs.

*Cancers* **2021**, *13*, x FOR PEER REVIEW 9 of 16

*Patients with Surgically Treated Stage I–II NSCLCs*

Whitney test.

represent the distribution of (**a**) sCTC or (**b**) hetCLU count (median number, line) in 4 mL of peripheral blood according to the tumor stage, the histological subtype (LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma), the visceral pleura invasion and the lymph-node involvement. Differences were not significant (*p*-value > 0.05) by Kruskal–Wallis (tumor stage) or Mann–

*3.3. The Prevalence of Single Circulating Tumor 00000Cells Predicts the Risk of Recurrence in*

We then explored the utility of CTCs in serving as prognostic biomarkers to identify patients with an early-stage tumor at higher risk of disease recurrence. All stage I and II cases underwent surgical resection with curative intent. Six out of the 52 patients received post-operative adjuvant platinum-based with either vinorelbine or gemcitabine chemotherapy and/or radiotherapy. The disease status of all patients was monitored by clinical and radiological exams, except for eight cases (two stage I and six stage II) who were lost at follow-up (*n* = 3) or had died for unknown causes (*n* = 5). The median (range) observation time was 28 (1–47) months, and the total number of recurrence events was 13 (seven at intrathoracic level, five at distant sites and missing information in one case). The risk of disease recurrence in stages III and IV compared to stages I and II patients were HR 95%CI 3.45 (1.37–8.67), *p*-value = 0.0006, with an equal number of events (13) per group. In the stages I and II cohort, neither the tumor stage nor the grading or the lymph-node status were able to discriminate patients at higher risk of early disease recurrence. The numbers of stages I and II cases out of 44 evaluable for disease recurrence and with at least one, two, three or five sCTC, or at least one hetCLU, or CTC-positive irrespective of the subset type, were 21 (47.7%), 8 (18.2%), 5 (11.4%), 1 (2.3%), 15 (34.1%) and 26 (59.1%), respectively. No differences were found when grouping patients according to the overall CTC status or the presence of hetCLU, while patients with a prevalence of at least two or three CTCs in 4 mL of blood had a statistically significant shorter recurrence-free survival probability (HR 95% CI, cut-off two CTC/4 mL: 5.15 (1.10–24.33), *p*-value = 0.0009; cut-off three CTC/4

**Figure 3.** The prevalence of single circulating tumor cells (sCTC) correlates with reduced recurrencefree survival in stages I and II non-small cell lung cancers. Kaplan–Meier plots showing the time-toevent probability of disease recurrence according to (**a**) the presence of CTCs irrespective of the cell subset, (**b**,**c**) the prevalence of sCTC or (**d**) the presence of heterotypic circulating tumor cell clusters (hetCLU).

#### *3.4. The Presence of Heterotypic Clusters of CTCs Predicts the Risk of Recurrence in Patients with Surgically Treated Stage III–IVA NSCLCs*

We assessed the clinical significance of CTCs in the group of patients presenting with operable NSCLC at advanced stages (III and IV). Seventeen out of the 22 patients received post-operative adjuvant platinum-based with either vinorelbine or gemcitabine chemotherapy and/or radiotherapy or targeted therapy with gefitinib or afatinib for stage IV EGFR mutated tumors. The disease outcome of all patients was monitored by clinical and radiological exams, except for four stage III cases that were lost at follow-up (*n* = 1) or had died for unknown causes (*n* = 3). The median (range) observation time was 17; (1–33) months, and the total number of recurrence events was 13 (5 at intrathoracic level, 6 at distant sites and missing information in 2 cases).

In advanced NSCLC patients, neither the tumor stage nor the grading were able to accurately identify cases at higher risk of early disease recurrence, although a slight trend toward statistical significance was obtained when grouping according to the tumor stage (Supplementary Table S4). The numbers of stages III and IV cases out of 18 evaluable for disease recurrence and with at least one, two, three or five sCTCs, or at least one hetCLU, or CTC-positive irrespective of the subset type, were 8 (44.4%), 6 (33.3%), 3 (16.7%), 3 (16.7%), 5 (27.8%) and 12 (66.7%), respectively. According to the survival analysis based on the CTC status, a slight trend toward statistical significance was observed when considering the overall CTC population, while no difference was found when grouping patients

according to the prevalence of sCTC. Interestingly, cases presenting with at least one hetCLU in 4 mL of blood had a statistically significant shorter recurrence-free survival probability (HR 95%CI: 3.44 (0.76–15.50), *p*-value = 0.0129) compared to patients without hetCLU (Figure 4 and Supplementary Table S4), providing evidence for the first time of the prognostic significance of hetCLU in advanced stage operable NSCLCs. tients according to the prevalence of sCTC. Interestingly, cases presenting with at least one hetCLU in 4 mL of blood had a statistically significant shorter recurrence-free survival probability (HR 95%CI: 3.44 (0.76–15.50), *p*-value = 0.0129) compared to patients without hetCLU (Figure 4 and Supplementary Table S4), providing evidence for the first time of the prognostic significance of hetCLU in advanced stage operable NSCLCs.

*Cancers* **2021**, *13*, x FOR PEER REVIEW 10 of 16

*with Surgically Treated Stage III–IVA NSCLCs*

distant sites and missing information in 2 cases).

(hetCLU).

**Figure 3.** The prevalence of single circulating tumor cells (sCTC) correlates with reduced recurrencefree survival in stages I and II non-small cell lung cancers. Kaplan–Meier plots showing the time-toevent probability of disease recurrence according to (**a**) the presence of CTCs irrespective of the cell subset, (**b**,**c**) the prevalence of sCTC or (**d**) the presence of heterotypic circulating tumor cell clusters

*3.4. The Presence of Heterotypic Clusters of CTCs Predicts the Risk of Recurrence in Patients*

We assessed the clinical significance of CTCs in the group of patients presenting with operable NSCLC at advanced stages (III and IV). Seventeen out of the 22 patients received post-operative adjuvant platinum-based with either vinorelbine or gemcitabine chemotherapy and/or radiotherapy or targeted therapy with gefitinib or afatinib for stage IV EGFR mutated tumors. The disease outcome of all patients was monitored by clinical and radiological exams, except for four stage III cases that were lost at follow-up (*n* = 1) or had died for unknown causes (*n* = 3). The median (range) observation time was 17 (1–33) months, and the total number of recurrence events was 13 (5 at intrathoracic level, 6 at

In advanced NSCLC patients, neither the tumor stage nor the grading were able to accurately identify cases at higher risk of early disease recurrence, although a slight trend toward statistical significance was obtained when grouping according to the tumor stage (Supplementary Table S4). The numbers of stages III and IV cases out of 18 evaluable for disease recurrence and with at least one, two, three or five sCTCs, or at least one hetCLU, or CTC-positive irrespective of the subset type, were 8 (44.4%), 6 (33.3%), 3 (16.7%), 3 (16.7%), 5 (27.8%) and 12 (66.7%), respectively. According to the survival analysis based on the CTC status, a slight trend toward statistical significance was observed when considering the overall CTC population, while no difference was found when grouping pa-

**Figure 4.** The presence of heterotypic circulating tumor cell clusters (hetCLU) correlates with reduced recurrence-free survival in stages III and IVA non-small cell lung cancers. Kaplan–Meier plots showing the time-to-event probability of disease recurrence according to (**a**) the presence of CTCs irrespective of the cell subset, (**b**,**c**) the prevalence of single CTCs (sCTC) or (**d**) the presence of hetCLU.

#### **4. Discussion**

In this study, we have observed that cells with morphological features of malignancy can be detected in 4 mL of peripheral blood in 60% of patients with operable NSCLC and that the CTC frequency is not dependent on the tumor stage. We have found that in NSCLC, cells with features of malignancy can circulate in physical contact with leukocytes, mainly monocytes, forming aggregates of two cells in the majority of cases, here called heterotypic clusters, with an overall frequency of 31%, without significant differences according to the disease stage. We have also documented the presence and frequency of other subpopulations of cells in patients, such as clusters of epithelial-like cells, large macrophage-like cells and naked nuclei >20 µm, which were found both in patients and lung cancer-free controls. Importantly, our study revealed different clinical messages hidden in CTCs based on the subset type as, compared to other tumor-related factors, only the CTC number at baseline was able to inform early-stage patients' risk and time of recurrence, whereas heterotypic clusters represented the most informative subpopulation of CTCs with respect to disease outcome in the advanced stage setting.

Contrarily to the traditional view that cancer cells disseminate via blood vessels within a time window closer to the clinical manifestation of secondary lesions rather than to the

initiation of a tumor, CTCs are now widely recognized as events that can be detected at a considerable frequency even in patients presenting with early-stage or locally advanced disease. Since CTCs are known to be heterogeneous within the same patient, and a gold standard approach for accurate detection has not been developed yet [36], it is clear that the operational definition for the measurement of CTCs may change from study to study along with the technical approach. One of the first reports on CTC analysis in stage I–III NSCLC demonstrated that the CTC detection rate obtained by the capture of cells expressing EpCAM and cytokeratins 8/18/19 was lower compared to parallel samples analyzed by a size-based isolation approach coupled to cytomorphological analysis [32]. However, two main biological and technical aspects should be taken into account: first, some CTCs might have shaped their makeup while undergoing the epithelial-to-mesenchymal transition (EMT), thus downregulating the expression of surface adhesion proteins [37] and remaining undetectable to EpCAM-based capture; second, neutrophils have a low-to-absent expression of CD45 and, importantly, may be non-specifically bound by antibodies for some intracellular proteins, including cytokeratins [38], thus leading to CTC misidentification. Following the first demonstration that the early detection of CTCs was able to anticipate stage I lung cancer diagnosis by radiological scan in COPD patients [27], the majority of successive studies was performed using CTC enrichment methods based on the physical properties of tumor cells. The CTC positivity data obtained in our study population was 59.5%, which is slightly higher compared to the study by Hofman and colleagues in a case series of 210 NSCLC patients undergoing radical surgery [32], although the difference is not negligible if considering that we have analyzed 4 mL rather than 10 mL of blood. In other studies, the CTC detection rate was 80% by morphological analysis on 10 mL of blood from 40 chemonaïve stage IIIA and IV cases [39] and 69.5% by immunostaining for EpCAM and CD45 upon peripheral blood mononuclear cell collection and subsequent filtration in 82 cases with any stage [40].

In addition to the population of single CTCs, other subsets of cells are emerging as possible diagnostic biomarkers, as they showed high specificity in distinguishing between patients from lung cancer-free individuals: CTC clusters [41–43], which are aggregates of at least two CTCs in physical contact, held together through intercellular junctions [44], also known as circulating tumor microemboli, which can appear with infiltrated or surrounding platelets [35], or aggregates of cancer cells with non-malignant stromal or immune cells [44], as also large macrophage-like cells [35] and tumor-macrophage hybrid cells [45,46], which instead express both epithelial and leukocyte/macrophage-specific markers. The frequency of CTC clusters may vary depending on the technical approach and the disease stage [41–43,47]; therefore, further studies are needed to confirm the origin of this subset of CTCs. However, it has been becoming increasingly clear that CTC clusters may act as predictors or players in therapy resistance [48,49], and experimental evidence showed that polymorphonuclear/myeloid-derived suppressor cells interact with CTCs and promote their metastatic potential [50]. Remarkably, a non-conventional approach for CTC isolation recently showed that heterotypic clusters could be detected at a relevant frequency in many non-metastatic and metastatic solid tumors [51]. Therefore, heterotypic clusters can represent a promising biomarker and therapeutic target. Furthermore, the role of clusters of epithelial-like cells that we have observed in our cohort of patients is worth being clarified.

Studies with other technical approaches provided interesting results on the clinical role of CTCs in NSCLC in the operative setting. It was reported that cytokeratin- and EGFR-positive cells enriched by an immunomagnetic approach could be observed in stage I–III NSCLC patients at different frequencies before and 1 month after surgery and that post- but not pre-surgery detected cytokeratin-positive CTCs were associated with diseasefree survival [52]. In 2019, a work with the CellSearchTM system showed that tumor cells collected from the pulmonary vein during surgical procedures could be observed in 48% of cases, that using a cut-off of at least 18 CTCs in 7.5 mL of blood it was possible to predict disease relapse and that mutations in CTCs largely overlapped with those found in metastases detected 10 months later [53]. Other authors challenged the effect of surgery

on CTC kinetics and found a significant decline in EpCAM-enriched CTCs a few days after surgery in all patients, and that early rebound of CTC counts was associated with disease recurrence [54]. Our data provide further evidence of the role of CTCs detected in the peripheral venous blood, and importantly, of the different significance that CTC subsets may have in classifying patients at risk of relapse when detected before surgery. Longitudinal studies are of interest to assess CTC kinetics in response to post-surgery administered systemic therapies.

Size-based approaches have already been shown to increase the sensitivity in detecting CTC clusters in metastatic NSCLCs compared to epitope-dependent methods. In 2011, vimentin-positive and cytokeratin-negative CTC clusters were described in three out of six metastatic NSCLC patients [55], and later it was found that circulating tumor microemboli, which were defined as clusters of at least three CTCs, could be observed in 43% of patients using ISET® but were undetectable by CellSearchTM, which captures cells by anti-EpCAM antibodies [39]. Moreover, CTC clusters isolated by a biomarker-independent, size-based microfluidic method could be observed in 96% of patients with metastatic NSCLC, and 75% of them were EpCAM-negative [47]. In our study, with stage I–IVA NSCLC patients, the frequency of homotypic CTC clusters was negligible. The marker-free technical approach based on the direct evaluation of cytological samples enabled the visualization of CTCs in contact with leukocytes, as also of other atypical cells. The application of a filtration method coupled to morphological analysis brings many advantages. However, a crucial point that should be addressed is the reproducibility of the analysis based on cytomorphological criteria. Although they have been already defined and blindly validated by a team of 10 cytopathologists on 808 blood samples analyzed by the ISET method, such criteria hold the same limits as those used in routine cytology [56]. In our study, cells with features of malignancy were not found in lung cancer-free individuals, following blinded analysis. Larger cohorts of individuals at risk of lung cancer and the inter-reader variability should be evaluated in multicentric studies in order to increase the robustness of the CTC test and to foster its application in the clinical routine. The identification of a panel of biomarkers for CTC detection in NSCLC is also of crucial importance in this context. Searching for lung cancer-specific markers is a long-standing issue for pathologists, which consequently affects CTC studies. An attentive look at CTC and metastasis biological hallmarks may help to identify markers alternative to cytokeratin, and in fact, a recent paper showed that the glycolysis enzyme hexokinase 2 (HK2) increased the detection of CTCs in a cohort of 18 stage III lung adenocarcinoma patients without clinical evidence of distant metastases from 39% when considering cytokeratin-positive to 61% when considering HK2-positive cell subsets [57].

To summarize the results of this study, we have discovered a population of heterotypic CTC clusters in early-stage NSCLC and provided first evidence of the differential prognostic significance of single CTC count, using a low cut-off (two CTCs in 4 mL of blood), and of the presence of heterotypic clusters, in operable NSCLC patients with stages I or II and stages III or IVA, respectively, demonstrating that looking at CTC subsets rather than the overall CTC population can help to refine the classification of patients at risk of disease recurrence, and outperforming classical primary tumor-related markers; we have performed the analysis on cytological samples corresponding to 4 mL only compared to larger blood volumes (from 7.5 to 10 mL) used in other studies, scoring as CTC-positive about 60% of patients and none of the control individuals, and we have documented and described other subsets of circulating atypical cells occurring with different frequency in patients and lung cancer-free donors.

#### **5. Conclusions**

The ISET® technology for CTC enrichment coupled to cytomorphological analysis was proven as a promising approach for the development of non-invasive biomarkers in NSCLC. With a view to the implementation of a CTC-based test in screening programs, studies with larger case series and the introduction of molecular analyses to infer the origin

of atypical cells would be desirable. CTC subsets also deserve consideration to be included in the clinical routine among the standard prognostic factors in order to early identify patients at risk of recurrence and refine the therapeutic strategies accordingly.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/cancers13174488/s1, Figure S1.   Single circulating tumor cell (sCTC) prevalence and non-small cell lung cancer (NSCLC) patients' characteristics; Table S2. Circulating tumor cell cluster (cCTC) status and NSCLC patients' characteristics; Table S3. Recurrence-free survival probability in patients with operable stage I–II NSCLC according to standard clinico–pathological parameters and CTC status; Table S4. Recurrencefree survival probability in patients with operable stage III–IV NSCLC according to standard clinicopathological parameters and CTC status.

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

**Funding:** This work was supported by specific grants by LILT (5X1000 program year 2013), the Umberto Veronesi Foundation (Milan, Italy), TRANSCAN-2 Joint Transnational Call 2016 (JTC 2016) project code TRANSCAN-045 CLEARLY, Italian Ministry of Health (Finalizzata Ministeriale 2016) project code PE-2016-02364336. E. Fina is the recipient of a postdoctoral research fellowship awarded by the Umberto Veronesi Foundation.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of IRCCS Humanitas Research Hospital.

**Informed Consent Statement:** Informed consent was obtained from all patients involved in the study.

**Data Availability Statement:** Data is contained within the article or supplementary material.

**Acknowledgments:** The authors wish to acknowledge all donors for participating in the study. A special thanks also to Patrizia Paterlini-Bréchot and her team from the Université Paris Descartes and Rarecells Diagnostics for technical support during the starting phase of the project.

**Conflicts of Interest:** The study funders had no role in the design of the study, the collection, analysis, and interpretation of the data, the writing of the manuscript, and the decision to submit the manuscript for publication. The authors declare no competing non-financial interests but the following competing financial interest: G.V. received honoraria from Ab Medica SpA (Milan, Italy). All the other Authors declare no conflict of interest.

#### **References**


Preface

Autonomous Underwater Vehicles (AUVs) are remarkable machines that revolutionized the process of gathering ocean data. Their major breakthroughs resulted from successful developments of complementary technologies to overcome the challenges associated with autonomous operation in harsh environments. This book brings together the work of many experts in several domains related to the design, development and deployment of AUVs.

During the last decades, AUVs have gone through notable developments. In the late eighties and early nineties, the first prototypes required a tremendous effort and ingenious engineering solutions to compensate for the technological limitations in terms of computational power, batteries, and navigation sensors. To deploy these expensive vehicles navigating autonomously in a very unforgiving environment, and expecting them to return safely was a true act of faith in engineering, a scaled version of the early efforts in space technology.

The initial developments continued steadily and, by the end of the last century, AUVs have gradually moved from the controlled academic environment into challenging operational scenarios, covering scientific, commercial and military applications. As the technology matured, many different solutions were effectively demonstrated, in various sizes and configurations, and a few evolved into commercial products.

Underwater robotics is a peculiar field of knowledge, bringing together specific complementary knowledge in mechanical and electrical engineering, and also in computer science. In the last decade, with the impressive improvements in computational power, battery technology, and miniaturization of electronic systems, AUVs became less cumbersome and more amenable to be used as test beds for new techniques for data processing. As smaller, lighter, and less expensive equipment became available, the access to operational vehicles was further facilitated and more and more prototypes became accessible for testing new algorithms and solutions. The geographic span of valuable scientific work with field results was extended to include a larger number of researchers, not only from leading scientific institutions but also from more modest laboratories in emerging countries. This has resulted in an exponential increase in AUV development and deployment, alone or in fleets, with arguably many thousands of hours of operations accumulated around the world, and corresponding amount of data. Autonomous Underwater Vehicles became a common tool for all communities involved in ocean sampling, and are now a mandatory asset for gathering detailed ocean data at very reasonable costs.

Most of the advances in AUV capabilities aimed at reaching new application scenarios and decreasing the cost of ocean data collection, by reducing ship time and automating the process of data gathering with accurate geo location. Although this yielded significant improvements in efficiency, new approaches were also envisaged for a more productive utilization of this new tool. With the present capabilities, some novel paradigms are already being employed to further exploit the on board intelligence, by making decisions on line based on real time interpretation of sensor data. In many organizations, this ability is also being applied to allow the AUVs to conduct simple intervention tasks.

The design of Autonomous Underwater Vehicles is governed by a complex tradeoff between the critical requirements of the planned missions, and the main constraints on fabrication, assembly and operational logistics. Contrary to the early tendency to develop general purpose vehicles, the current pursuit of efficiency has pushed the concept of specific vehicles for specific tasks, frequently taking advantage of modular designs to accelerate the assembly time.

In the last years, there have been a great number of publications related to underwater robotics, not only in traditional engineering publications, but also in other fields where the robotic solutions are being used as a tool to validate scientific knowledge. There are also numerous conferences held each year, addressing all aspects of AUV development and usage. Both have served to report the major breakthroughs and constitute a foremost source of reference literature. This book collects a set of self contained chapters, covering different aspects of AUV technology and applications in more detail than is commonly found in journal and conference papers. The progress conveyed in these chapters is inspiring, providing glimpses into what might be the future for vehicle technology and applications.

> **Nuno A. Cruz** INESC Porto - Institute for Systems and Computer Engineering of Porto Portugal

**Part 1**

**Vehicle Design**

**0**

**1**

**Development of a Vectored Water-Jet-Based**

The applications of underwater vehicles have shown a dramatic increase in recent years, such as, mines clearing operation, feature tracking, cable or pipeline tracking and deep ocean exploration. According to different applications, the mechanical and electrical configuration and shape of an underwater vehicle are different. For instance, manipulators are necessary when doing mines clearing operation or some other tasks which need to deal with environment. If an underwater vehicle is used for underwater environment detection or observation, it is better to make this vehicle smaller and flexible in motion that it can go to smaller space easily. If the vehicle needs high speed moving in the water then a streamline

Different structures with different size of underwater vehicles are developed. Most of these underwater vehicles are torpedo-like with streamline bodies, like (Sangekar et al., 2009). And there are some small size AUVs like (Allen et al., 2002) and (Madhan et al., 2006). And also there are some other AUVs adopt different body shape, such as (Antonelli & Chiaverini, 2002). Meanwhile, the propulsion system is one of the critical facts for the performance of underwater vehicles, because it is the basis of control layers of the whole system. Propulsion devices have variable forms, for instance, paddle wheel, poles, magneto hydrodynamic drive,

Paddle wheel thrusters are the most common and traditional propulsion methods for underwater vehicles. Usually, there are at least two thrusters installed on one underwater vehicle, one for horizontal motion and the other for vertical motion. The disadvantages of paddle wheel thrusters are obvious, for example, it is easy to disturb the water around the underwater vehicles. Meanwhile, the more the paddle wheel thrusters are used, the weight,

The steering strategies of traditional underwater vehicles are changing the angular of rudders or using differential propulsive forces of two or more than two thrusters. Of course, there are vectored propellers being used on underwater vehicles. Reference (Cavallo et al., 2004) and (Le Page & Holappa, 2002a) present underwater vehicles with vectored thrusters. Reference (Duchemin et al., 2007) proposes multi-channel hall-effect thrusters which involves vector propel and vector composition. Reference (Le Page & Holappa, 2002b) proposes an autonomous underwater vehicle equipped with a vectored thruster. At the same time, the design of vectoring thrusters used on aircrafts is also an example of vectored propulsion

**1. Introduction**

body is required.

sails and oars.

noise and energy consumption increases.

system (Kowal, 2002), (Beal, 2004) and (Lazic & Ristanovic, 2007).

**Spherical Underwater Vehicle**

Shuxiang Guo and Xichuan Lin

*Kagawa University*

*Japan*


**Author details**

Seema Chhabra1 \*, Smrity Sahu1 , Keshav Sharma1 , Maryada Sharma2 , Lekha Rani1 , Ranjana Minz1 and Sunil Dogra3

1 Department of Immunopathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India

2 Department of Otolaryngology and Head and Neck Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, India

3 Department of Dermatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India

\*Address all correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.