3.3.3. Angle Estimation

The azimuth and elevation angle of *K* signals are included in **F** and **G**. In theory, the eigenvectors obtained by ED of these two matrices are both **T**; however, in the actual calculation process, the two eigenvalue decomposition operations are carried out independently, which can not ensure that the arrangement of eigenvectors in them is reflected well; therefore, the diagonal elements of **F** and **G** should be matched.

Suppose that **T1** and **T2** are eigenvector matrices derived from GA-ED of **Ψ***x* and **Ψ***y*, respectively. Then

$$\mathbf{O} = |\mathbf{T2}^H \mathbf{T1}|\tag{38}$$

where {| · |} is the operator that gets magnitude of every multi-vector in a matrix. For the same signal, the eigenvectors in **T1** and **T2** corresponding to matched *fk* and *gk* are related; therefore, the order of diagonal elements in **F** and **G** can be adjusted by the coordinate of the largest element in each row (or column) of **O** to complete matching.

After observing (19), *f* and *g* are multi-vectors that only have scalar and 3-gradevector, if we replace *e*123 with the imaginary unit *j* of complex number, *f* and *g* can be regarded as complex numbers. Finally, we calculate *θk* and *φk* with *fk* and *gk*, that is,

$$\begin{aligned} \theta\_k &= \tan^{-1} \left[ \frac{\text{angle}(\mathcal{g}\_k)}{\text{angle}(f\_k)} \right], \\ \phi\_k &= \sin^{-1} \left\{ \frac{\lambda}{2\pi} \text{sqrt} \text{rt} \left[ \text{angle}(\mathcal{g}\_k)^2 + \text{angle}(f\_k)^2 \right] \right\}, \end{aligned} \tag{39}$$

where angle(·) is the operator for getting phase angle. In conclusion, the steps of the GA-ESPRIT algorithm are:

Further, the corresponding relationship between the logic flow and steps of GA-ESPRIT is shown in Figure 4.

**Figure 4.** Logic flow diagram of GA-ESPRIT.


*2.3. The Impact of CIA and Bone Defect in Circulating Inflammatory Mediators*

To further investigate the systemic inflammatory impact of CIA and the combination with bone injury, 27 chemokines and cytokines were quantified in plasma. From these, epidermal growth factor (EGF), growth-regulated oncogenes/keratinocyte chemoattractant (GRO/KC), Macrophage inflammatory protein-2 (MIP-2, CXCL2), and Granulocyte-macrophage colony-stimulating factor (GM-CSF) were out of the detection range for this assay. From the remaining 23 cytokines/chemokines detected, IL-13 was not detected in the control group, but was detected in the other two animal groups and thus was considered in the analysis. Results obtained are illustrated in Figure 4 and show that, relative to the control group, CIA animals had a significant upregulation of 8 molecules and a downregulation of 5. Among the significantly up-regulated mediators IL-13, IL-2, and IL-6 were also significantly up-regulated in the CIA+FD group, while TNF-α, IL-17 A, IL-4, IL-5, and IL-12 (p70) lost significance in the CIA+FD group. Nonetheless, the tendency for increase was maintained, and in the case of IL-5 it was close to statistical significance (*p* = 0.0534). Also, Monocyte chemoattractant protein-1/C-C motif chemokine ligand 2 (MCP-1/CCL2) and Eotaxin were close to statistical significance in CIA (*p* = 0.0552 and *p* = 0.0620, respectively), and MCP-1 was significantly up-regulated for the CIA+FD group. Regarding the downregulated mediators identified in CIA plasma, Vascular endothelial growth factor (VEGF), Fractalkine, IL-18, C-X-C motif chemokine 5 (CXCL5, LIX), and Leptin, only Fractalkine and Leptin levels were statistically significantly lower in the CIA+FD group, relative to control. The only molecule that showed a significant difference between the CIA and CIA+FD groups was the regulated on activation, normal T cell expressed and secreted (RANTES) chemokine, which was significantly downregulated in CIA when compared to CIA+FD, but neither group had a significant difference to the control group.

**Figure 4.** Changes in plasma cytokine and chemokine profile in CIA and CIA+FD. Quantitative results of the multiplex cytokine/chemokine array performed for all animals in each group. Box plots represent min-to-max distribution of *n* = 4 to 5 animals per group. \* *p* < 0.05, \*\* *p* < 0.01 in relation to control group, and # *p* < 0.05 relatively to CIA group, determined by Kruskal–Wallis test and Dunn's multiple comparisons test.

#### *2.4. CIA Induction Impacts Endogenous MSC Biologic Behavior*

To evaluate the impact of chronic inflammation on endogenous MSC we next isolated and culture bone marrow-derived MSC (BM-MSC) from CIA and control animals. The number of bone marrow cells recovered was similar between the CIA and control animal groups, and after selective culture and expansion, cells obtained were highly positive for the classical stromal markers CD29 and CD90, and did not express the haematopoietic marker CD45 (Supplementary Figure S3). The bone marrow isolated cells showed the ability to differentiate into the chondrogenic, osteogenic, and adipogenic lineages (Supplementary Figure S4). All together, these data confirmed the successful isolation of MSC from bone marrow of both CIA and control animals.

BM-MSC were then used to evaluate the impact of the cells source microenvironment on their biological properties and behavior. CIA and control BM-MSC were metabolic active along 1, 3, and 7 days of culture, but CIA-MSC showed lower metabolic activity that did not increase along time, when compared to control cells (Figure 5A). In line with these results, the Ki-67 fluorescent immuno-staining analysis also showed a tendency for reduced proliferation of CIA-MSC (Figure 5B,C). To access if exposure to the inflammatory microenvironment could be conditioning, MSC proliferation control cells were cultured in the presence of TNF-α or IL-4. Interestingly, even low doses of TNF-α produced significant reductions in the percentage of proliferating cells, while IL-4 had no significant impact (Figure 5D).

**Figure 5.** MSC metabolic activity and proliferation capacity. (**A**) Metabolic activity was measured at 1, 3 and 7 days of MSC culture by resazurin assay. RFU: relative fluorescence units. Statistical differences were evaluated by 2-way ANOVA followed by Turkey's multiple comparison test (**B**) Representative images of nuclei (Dapi, blue) and Ki-67 immunostaining (pink, white arrows) of control and CIA-derived MSC after 2 days in culture. Scale bar: 50 μm. (**C**). Percentage of Ki-67+ control and CIA-derived MSC, across different experiments. Statistical differences were evaluated by Mann–Whitney test. (**D**) Percentage of Ki-67+ control- derived MSC after 2 days in absence/presence of 10 ng/mL or 100 ng/mL of TNF-α or IL-4. Box plots represent min-to-max distribution of *n* = 4 to 5 animals per group. \* *p* < 0.05 by Friedman test followed by Dunn's multiple comparisons test.

The endogenous differentiation ability of BM-MSC from CIA and control animals was evaluated at 3 and 7 days of culture, in the absence of chemical and molecular inductors. The results obtained showed that CIA animals-derived MSC had increased expression of osteogenic genes, with significantly higher ALP expression, relative to control cells, at 7 days, and a significant increase in RUNX2 expression from 3 to 7 days in CIA-MSC, which was not observed in control-MSC (Figure 6A). When analyzing chondrogenic differentiation capacity of CIA-MSC in basal conditions, results indicate a significantly higher expression of ACAN at 3 days compared to control cells, and that significantly decreased by day 7, while expression of SOX9 did not show significant differences between the cells of CIA and control animals, or along time (Figure 6B). Then we explored if exposure to cytokines could impact MSC differentiation in the absence of chemical induction. Results obtained show that exposure to TNF-α or IL-4 increased ALP gene expression with IL-4 exposure producing significant increases, even when compared to the positive control, using chemical inductors (Figure 6C). Interestingly, IL-4 exposure led to increased ACAN expression, to levels similar to those obtained with chondrogenic induction and for CIA- MSC, while TNF-α did not lead to the increases observed for MSC from CIA animals (Figure 6D).

**Figure 6.** The capacity of MSC differentiation is affected by arthritis induction. The differentiation of control and CIA-MSC were evaluated in absence of chemical inductors, at 3 and 7 days of culture by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for (**A**) osteogenic marker genes ALP and RUNX2, and (**B**) chondrogenic differentiation genes SOX9 and ACAN. Box plots represent min-to-max distribution of *n* = 2 to 3 different animals per group, in 2 independent experiments. \* *p* < 0.05 by Kruskal–Wallis test followed by Dunn's multiple comparisons test. The effect of 10 ng/mL or 100 ng/mL of TNF-α and IL-4 in the differentiation of control MSC, were evaluated in basal conditions at 3 days by RT-qPCR for (**C**) osteogenic and (**D**) chondrogenic markers. Cells cultured with osteogenic and chondrogenic inductors were used as positive controls for osteogenic (osteo+) and chondrogenic (chond+) differentiation. \* *p* < 0.05, \*\* *p* < 0.01 determined by Friedman test followed by Dunn's multiple comparisons test.

Taken together, these results indicate that exposure to hallmark arthritis cytokines like TNF-α can partially mimic the CIA impact on MSC.

#### **3. Discussion**

In the current study, arthritis was induced in all animals and reached a plateau in its score by 17 to 21 days after CII-IFA (Collagen type II-Incomplete Freund's adjuvant) immunization. This is in line with previous reports on this model [26]. Histologically, our data shows hind paws swelling and inflammation in digital joints, visible by the infiltration of inflammatory cells and hyperplasia of the synovial membrane, which constitute hallmarks of human RA pathology [27] and have been previously reported in CIA animal model [11,28,29]. The CIA model was combined with a critical size femoral bone defect model in a load-bearing site without the need for fixation material [30] and without significant acute implications for animal welfare and paw swelling. Enlargement of secondary lymphoid organs was similar between CIA and CIA+FD at 3 days post-bone injury, albeit slightly decreased percentages of proliferating cells in CIA+FD were observed. This is in line with previous work in the CIA model, such as in the report by Ibraheem where the injection of different collagen types combined with adjuvant to induce arthritis in rats led to enlarged lymph nodes with histologic alterations [31] and the reports of splenomegaly in the most severe states of adjuvant-induced arthritis [11,32]. Also, a previous report induced a mid-shaft femur fracture with fracture fixation in the CIA rat model and showed delayed healing in CIA animals, but the authors do not analyze inflammation in their study [33]. In fact, the current study is, to the best of our knowledge, the first one addressing the interplay between inflammation and bone injury in these RA models.

To determine the systemic immune cell response to CIA induction and the combination with bone injury, we evaluated the main immune cell populations that are the key cell players involved in the development and maintenance of the inflammation in CIA model [10], in blood and secondary lymphoid organs. Our results show a significant increase in B cells in blood and draining lymph nodes of CIA animals and also increased myeloid cells in blood. This change in proportions may suggest that these cells migrate from the blood to the inflamed tissues. A previous report in the mouse CIA model described similar increases in the percentage of B cells, which they correlate with increased anti-collagen antibodies in serum [34]. Activated B cells have been reported to produce anti-collagen antibodies that bind to cartilage collagen and form an immune complex, generating a localized inflammatory response and attracting monocytes, granulocytes, and T cells to the joint [10,17].

Interestingly, CIA animals responded to the bone injury with significant increases in myeloid cells and their activation, particularly in secondary lymphoid organs. Myeloid cells are important regulators that contribute to the perpetuation of inflammation in RA [35]. Moreover, in a murine model of early RA, increased myeloid dendritic cells were reported in draining lymph nodes before the appearance of joint histological changes, indicating their role in the early stage of murine RA [36]. A significant decrease in T cell proportions was found in lymph nodes of CIA rats, accompanied by a decrease in CD4/CD8 ratios. These data may suggest that T cells are attracted to the joints, but this requires further confirmation. The immune response mediated by T cells is described as crucial for the development of mouse [10,17] and rat CIA, with athymic rats being resistant to CIA induction, but T cell transfer alone was reported to have induced only a mild response [10]. The role of NK cells in RA and CIA is not yet clear, with some evidence that they may contribute to [37,38] or protect against [39] inflammation. Here, we did not observe differences in the percentage of NK cells in peripheral blood as described in a previous study in CIA mice [34], but observed an increase in the CIA+FD group. The same work showed a decrease in splenic NK cells proportion at day 37, different from the increasing tendency that was observed in the current study at day 21 after CIA induction. Interestingly, the changes observed here in systemic immune cell proportions are different from those previously observed in the same model of bone injury, but in healthy animals at day 6 post-surgery [40]. This might be explained by the inflammatory conditions, but also by different times post-injury, as in the same model in healthy animals we observed an acute response at day 3, and resolution of that response at day 14 post-surgery [41].

In the current study, a broad quantitative analysis of plasma cytokines/chemokines comparing control, CIA, and CIA+FD animals was performed. We found increased levels of the hallmark cytokine TNF-α in the plasma of CIA animals. High TNF-α levels in affected joints or in peripheral blood serum are strongly associated with RA disease severity [42,43]. This pro-inflammatory cytokine was described in different RA animal models [43,44], including the transgenic mouse expressing human TNF-α [45] and the CIA model [12,13]. TNF-α has also been reported as accelerating arthritis signals when injected into animals [43]. In response to TNF-α, RA synovial fibroblasts are described to produce MCP-1/CCL2, which was also found increased in the plasma of CIA animals in the current study, particularly after bone injury. MCP-1 is a potent chemokine that mediates monocyte ingress and activation in joints [46]. This was clearly demonstrated in vivo by the injection of a rabbit model with MCP-1/CCL2, resulting in a marked macrophage infiltration of the affected joints [47]. Macrophages have been previously described as a major source of MCP-1/CCL2 [48]. In our results, CD68+ macrophages were also found in the inflamed synovium.

Levels of IL-17, IL-2, and IL-5 were also increased in CIA animals, and maintained the same tendency (albeit not always significant) in the CIA+FD group. These cytokines are related to the T cell function and response in vivo. Systemic IL-17 levels intensify RA and induce a chronic and erosive form of the disease [49]. A recent report in CIA model in Dark Agouti rats demonstrated that the Th-17 response in draining lymph nodes is greater in females than in males, correlating the Th-17/Treg (regulatory T cell) axis with sexual differences in CIA severity [50]. IL-2 is a positive regulator of T cell proliferation, and administration of IL-2 can mediate a pro-inflammatory effect on CIA mice, increasing the animals' arthritic scores [51]. A recent report shows that IL-2-Anti-IL-2 monoclonal antibody immune complexes can inhibit CIA by increasing Treg cell functions [52]. IL-5 is a cytokine with pleiotropic effects on target cells, including eosinophils and B cells, inducing cell proliferation, survival, and differentiation [53], its role in RA remains unknown, but a recent work found elevated serum levels of IL-5 in 59% of seropositive RA patients [54]. Also, it was reported that lymph node cells isolated from CIA-susceptible mice lines showed a higher number of IL-5 producing cells in culture than cells from resistant mice [55]. Fractalkine was found downregulated in CIA animals with and without bone injury, which is not in agreement with reports indicating amelioration of arthritis symptoms and joint destruction when Fractalkine is suppressed [56,57]. Also, Leptin was found significantly reduced in the plasma of CIA and CIA+FD animals, but its role in arthritis is still controversial. In the mouse CIA model, Leptin injection has been reported to worsen disease via Th-17 cells [58], but has also been reported as anti-inflammatory when collagen-antibody-induced arthritis is induced in mice overexpressing Leptin [59]. Also, its levels have been reported as increased in other models of RA and in patient's plasma, but reduced at the joint level, indicating its consumption [60]. RANTES (or CCL5) was the only molecule that showed a significant difference between the CIA and CIA+FD groups. A recent report on bioinformatic analysis of human RA gene expression data indicates that CCL5 might have a negative impact on the development of RA [61], while another recent report in mouse indicates that peritoneal levels of several CC chemokines, including CCL5, could be related with mouse strain susceptibility to experimental arthritis [62]. However, as in our study, neither group had a significant difference to control, and so the biological meaning of this result remains unclear.

Although the use of MSC as therapeutics for RA is being intensely studied, the effect of CIA induction and its inflammatory milieu on the biological behavior of endogenous bone marrow-derived MSC had not been explored so far. Growing evidence indicates that bone marrow is actively involved in the RA disease process. In RA patient samples, the interaction between synovial inflammatory tissue and bone marrow reportedly results from the disruption of cortical bone leading to bone marrow invasion [63]. Furthermore, previous work described in vivo a significant enlargement of vascularized bone canals that link the bone marrow and the synovium tissue, in joints with synovial hyperplasia [64]. Also, it has been suggested that bone marrow-derived immature mesenchymal cells may migrate through the joint space to replace synovial cells, similar to what happens with inflammatory cells [65,66]. In fact, even if bone marrow is not primarily affected, this interaction, together with the aberrant cytokine production, can potentially affect the biological behavior, namely the survival and proliferation of bone marrow cells [67].

The current work shows that culture-expanded MSC from CIA animals were morphologically and immunophenotypically similar to controls, expressing the characteristic mesenchymal markers (Figure S1C). Nevertheless, the metabolic activity and percentage of proliferative cells were lower in the CIA-MSC cultures. This had not been described in RA animal models thus far, but agrees with what was described in cells derived from RA patients [68,69]. RA-MSC display proliferation defects in culture without significant differences in the percentages of apoptotic cells [68,69], and high levels of the cell cycle inhibitor p21, which can mediate the defects in cell growth and replicative capacity [69]. Mice treated with anti-TNF-α at an early CIA stage showed a reduction in the number of MSC in the bone marrow and synovium [64]. Conversely, a previous report described that the proportion of CD34+ bone marrow progenitor cells increase after the anti-TNF-α treatment in RA patients, with the number of bone marrow mononuclear cells in clonogenic assays being higher when compared to the pretreatment values [67]. In our study, a significant reduction in the percentage of proliferative MSC was observed in control cells exposed to TNF-α, an important pro-inflammatory cytokine found increased in the plasma of CIA animals. This reduction in healthy MSC proliferation in the presence of TNF-α could have consequences for MSC-based therapies for RA.

Our results show increased osteogenic gene expression of CIA-MSC in basal conditions when compared to control MSC. Contrasting data has been reported on the osteogenic and chondrogenic potential of MSC isolated from RA patients. Previous reports indicate that ALP expression was comparable between the cells from RA and healthy donors [68,69], and that RA MSC do not differ significantly from the normal cells on their chondrogenic differentiation [70]. MSC differentiation in the CIA animal model has been analyzed in response to the chemical/molecular stimulation protocols commonly used in vitro, but not in basal conditions, and further detailed analysis is still required. Previous work showed that control and CIA-derived mouse BM-MSC respond to osteogenic stimulation with similar increases in RUNX2 and ALP gene expression [71]. In our work the cells were isolated from the same site and from animals with the same age and sex. Thus, our results support that the alterations in proliferative and differentiation capacities are not site or age-dependent, but are likely associated with the disease condition of the animals. We also explored if CIA conditions could be mimicked by exposure to TNF-α and observed an increase, albeit not statistically significant in ALP gene expression. This agrees with previous reports showing that exposure to these concentrations of TNF-α impairs MSC proliferation/survival [72]. The same authors also found impaired MSC response to osteogenesis induction upon exposure to these concentrations of TNF-α, but the response of the cells in the absence of osteogenic induction was not analyzed. In the RA model of human TNF-α transgene, previous reports showed the deleterious effects of high levels of this cytokine on fracture repair [45], and in the CIA model fracture healing was also impaired [33]. Further studies are required to analyze if such impairments are related to changes in MSC proliferation and differentiation, and the role of chronic inflammation in bone repair [72].

In conclusion, arthritis induction creates a systemic inflammatory environment, with changes in immune cells and circulating cytokine/chemokine levels. Also, bone marrow-derived MSC isolated from CIA animals display impaired proliferation and increased differentiation in basal conditions. Further studies are needed to clarify the relation between exposure to inflammatory cytokines like TNF-α and MSC phenotype impairments, and their consequences for bone repair in RA. Furthermore, the current work supports the viability of using the CIA model to investigate the mechanisms of bone repair/regeneration in chronic inflammatory conditions, and the development of therapeutic strategies more appropriate to treat bone fractures in RA patients.

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

#### *4.1. Bovine Collagen Type II Emulsion Preparation*

Bovine collagen type II (CII, 2 mg/mL, Chondrex, Redmond, WA, USA) was added in a 1:1 proportion of incomplete Freund's adjuvant (IFA, Chondrex, Redmond WA, USA) and homogenized

in an ice water bath, according to the manufacturer's instructions. Then, the emulsion was kept at 4 ◦C until injection, without this period exceeding 1 h.


**1. Introduction**

Metal matrix composites (MMCs) are one of the most advanced potential materials in thermal management applications. The addition of nano/micro-sized fillers into metal matrix can reinforce the mechanical, thermal, and electrical performance of MMCs. To date, only relatively simple MMC designs with limited functionality have been produced by various solid (e.g., powder metallurgy) and liquid (e.g., stirring and squeeze casting) methods [1].

Diamond/copper composites are the excellent examples of MMCs. Diamond is a nextgeneration thermal management material, and the density of these composites is below that of pure copper. Many researchers employed diamond particles to tailor copper's coefficient of thermal expansion (CTE) and enhance the thermal conductivity (TC) [2]. Although these diamond-particle-reinforced MMCs have great prospects in heat dissipation, it's not easy to fabricate complex structure due to the complexity of processing diamond-based materials. With high energy density, small laser spot, and high cooling rate, selective laser melting (SLM) is a unique additive manufacturing (AM) technology that is used to construct complex designs [3], which has drawn the attention from industry and academia [4]. The method adopts a high-energy laser beam for melting metal powders layer by layer in

**Citation:** Zhang, L.; Li, Y.; Li, S.; Gong, P.; Chen, Q.; Geng, H.; Sun, M.; Sun, Q.; Hao, L. Fabrication of Titanium and Copper-Coated Diamond/Copper Composites via Selective Laser Melting. *Micromachines* **2022**, *13*, 724. https:// doi.org/10.3390/mi13050724

Academic Editor: Yee Cheong Lam

Received: 8 April 2022 Accepted: 28 April 2022 Published: 30 April 2022

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

**Copyright:** © 2022 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/).

3D computer aided design (CAD) data [5,6], after which the micro-melted metal pool cools to form precise metal parts [7–11]. Thus, combining SLM's ability to create complex designs with an MMC's excellent performance could amplify the capabilities and reshape the application [12,13].

However, diamond and copper have extremely poor wettability, causing weak interfacial bonding and large interfacial thermal resistance [14,15]. There are two general solutions applied for this issue [16]. The first is matrix alloying, which leads the alloying element, for example, titanium [15], boron [17,18], zirconium [19,20], or chromium [21], into the copper matrix. However, it is difficult to precisely control the additive amount, thus resulting in excess elements residual in matrix, thereby decreasing the TC of MMC. The second is surface modification, which uses strong carbide-forming elements, like titanium [22], chromium [23,24], tungsten [25], or molybdenum [26], onto the diamond particle surface. The abovementioned four metal layers can react with diamond and partly dissolve in a copper matrix. Since the formation free energy of titanium carbide (TiC) is sufficiently low, it can easily form carbide in a short time and chemically bond with diamond. The interface layer formed serves as a bridge connecting copper and diamond, thereby increasing the composite's TC. Sun et al. [27] reported that the copper/diamond composites with titanium-coated diamond particle composites were synthesized by mechanical alloying, after annealing at 800 ◦C, the TC of a 40 vol.% diamond/copper composite reached 409 W/mK. The titanium-coating combination to the surface metallization of diamond particles helps to improve the copper/diamond interface and obtains a high TC within the composite material. Zhang et al. [28] reported that copper coated diamond powder in a variable mass ratio of diamond and copper was fabricated through an electroless plating process, before the coated powder was sintered through spark plasma sintering, the TC of a 70 vol.% diamond/copper composite reached 404 W/mK. In conclusion, they all have these disadvantages of the single sample shape and the relatively terrible interface bonding.

This research investigates the comparison for the thermal and mechanical performance between copper-coated diamond/copper combined materials manufactured by SLM and those with titanium-coated diamond/copper combined materials. In the current research, dense complex diamond/copper combined materials with titanium and copper-coated diamond particles were successfully 3D printed by implementing different forming processes and 3D printing strategies. A series of experiments were carried out via SLM single-line scanning to determine the appropriate processing parameters, and a cubic sample was prepared to characterize the composites' morphology and microstructure. The optimization of 3D printing parameters and strategy herein can be useful to develop new approaches for the further construction of a wider range of diamond-particle-reinforced MMCs [29]. In this work, the dense 1 vol.% copper-coated diamond/copper composite manufactured via SLM displays good interfacial bonding of the copper matrix and diamond reinforcement, and excellent thermal and mechanical performance were firstly revealed, which would serve as a promising candidate for thermal management material.

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

#### *2.1. Materials*

The micron-level pure gas atomized copper powders were purchased from China Metallurgical Research Institute (purity of 99.99%), the particles of which were primarily spherical, allowing them to improve the fluidity and bulk density in comparison with polyhedral particles [30]. The average powder particle size of 18.856 ± 15 µm was below that of the laser spot (30 µm). The diamond particles were purchased from Henan Yuxing Sino-crystal Micro-diamond Co., Ltd., with an average size of approximately 25 µm.

In this experiment, copper and titanium were directly deposited on the diamond particle surface via electroless plating (Figure 1a) and evaporation methods (Figure 1b), respectively. Table 1 lists the mix ratios of copper powder and coated diamond particles, combined in a ball mill at 100 rpm for 3 h, before which were dried for 3 h at 60 ◦C and then sifted through a 400 mesh.

then sifted through a 400 mesh.

In this experiment, copper and titanium were directly deposited on the diamond particle surface via electroless plating (Figure 1a) and evaporation methods (Figure 1b), re-

**Figure 1.** Schematic diagram of depositing copper and titanium on the diamond particle surface via (**a**) electroless plating and (**b**) evaporation process, respectively; (**c**) The preparation process for rectangular contour and cubic samples. **Figure 1.** Schematic diagram of depositing copper and titanium on the diamond particle surface via (**a**) electroless plating and (**b**) evaporation process, respectively; (**c**) The preparation process for rectangular contour and cubic samples.

**Table 1.** The coated diamond/copper composite compositions.

#### *2.2. Preparation of the Titanium and Copper-Coated Diamond/Copper Components through SLM*

In order to obtain the high-quality titanium and copper-coated diamond/copper samples, the SISMA MYSINT100 system with a neodymium-doped yttrium aluminum garnet fiber laser (wavelength: 1060 nm; maximal output laser power, 180 W; laser spot size, 30 µm) was used to investigate the detailed parameters from singlet to cubic formation in high-purity N2 atmosphere (residual oxygen content < 0.5 vol.%). The titanium and copper-coated diamond/copper powders were protected from oxidation. Rectangular contour (1 × 3 mm2 ) and cubic (5 × 5 × 5 mm3 ) samples were used for the monorail and block experiments, separately (Figure 1c). The chessboard laser scanning strategy was used for the cubic samples, each layer was separated into four squares, and the scanning direction in each square was perpendicular to the adjacent square. The no-hatch laser scanning strategy was used for the rectangular contour samples. A single-line scan was performed via SLM to determine the appropriate processing parameters, which were then used to prepare the cubic samples for the morphology and microstructure characterization.

The hatch distance indicated the degree to which the titanium and copper-coated diamond/copper powders were repeatedly scanned by the laser. At the same laser power and scanning rate, the smaller the hatch distance, the greater the laser's influence on the composites. A variety of volumetric laser energy density (*D*) was adopted to determine the finished part parameters. *D* is computed through Equation (1):

$$D = \frac{P}{h \times t \times v} \tag{1}$$

in which *P*, *v*, *h* and *t* refer to the laser power, scanning rate, hatch distance and layer thickness, respectively.

#### *2.3. Characterization Techniques*

The surface morphology and microstructure were observed via optical microscopy (OM, Leica A205, Wetzlar, Germany) and scan electron microscopy with energy dispersive spectroscopy (SEM-EDS, Hitachi-Su8010, Hitachi High-Tech, Clarksburg, MA, USA). Prior to the microscopic observation, the specimens were polished and etched (10 s within a mixed solution of 100 mL distilled water, 5 mL HCl and 5 g FeCl3). The width of a single weld pool within the printed composites was assessed via image analysis with ImageJ. Titanium-coating and copper-coating thickness on the diamond particle surface were determined through focused ion beam (FIB) and SEM. The coated diamond cross-section specimens were prepared using FIB milling. X-ray diffraction (XRD, Bruker D8 Advance, Rheinstetten, Germany) employing Cu Kα radiation at 40 KV and 40 mA within the scope of 2*θ* = 5◦–90◦ was used to test the phase structure with a step size of 0.02◦ . The composite density was determined adopting Archimedes' law. A Netzsch LFA 427 Transient Laser Flash machine and the calorimetric technology were used for measuring the thermal diffusivity and specific heat, respectively. The TC was obtained by multiplying the composite density, thermal diffusivity, and specific heat. A carbon spray was used to coat the top and bottom surfaces of the specimens (Ø 12.7 × 3 mm) with graphite to improve their capacity for absorbing the applied energy. The mean TC of each specimen was determined by measuring three parallel positions. Finally, samples (3 × 4 × 25 mm3 ) were used to test the bending strength and CTE. The bending strength test was performed applying an Instron 5569 Universal Testing machine, and a dilatometer (DIL 402C, Netzsch, Selb, Germany) was used to examine the CTE of the composite materials from room temperature

to 400 ◦C. The roughness of the top surface was measured by a laser scanning confocal microscope (OLYMPUS OLS4100) with Gaussian filtering to evaluate the quality of 1 vol.% copper-coated diamond/copper combined materials surfaces, the OLS4100 can correctly identify a measuring position and easily perform roughness measurement of a target micro area, the accuracy of height measurement was less than 0.2µm error per 100µm.


**Severe Traumatic Brain Injury (TBI) Modulates the Kinetic Profile of the Inflammatory Response of Markers for Neuronal Damage**

**Cora Rebecca Schindler 1,\*, Thomas Lustenberger 1, Mathias Woschek 1, Philipp Störmann 1, Dirk Henrich 1, Peter Radermacher 2 and Ingo Marzi 1**

Received: 1 May 2020; Accepted: 26 May 2020; Published: 1 June 2020

**Abstract:** The inflammatory response plays an important role in the pathophysiology of multiple injuries. This study examines the effects of severe trauma and inflammatory response on markers of neuronal damage. A retrospective analysis of prospectively collected data in 445 trauma patients (Injury Severity Score (ISS) ≥ 16) is provided. Levels of neuronal biomarkers (calcium-binding Protein B (S100b), Enolase2 (NSE), glial fibrillary acidic protein (GFAP)) and Interleukins (IL-6, IL-10) in severely injured patients (with polytrauma (PT)) without traumatic brain injury (TBI) or with severe TBI (PT+TBI) and patients with isolated TBI (isTBI) were measured upon arrival until day 5. S100b, NSE, GFAP levels showed a time-dependent decrease in all cohorts. Their expression was higher after multiple injuries (*p* = 0.038) comparing isTBI. Positive correlation of marker level after concomitant TBI and isTBI (*p* = 0.001) was noted, while marker expression after PT appears to be independent. Highest levels of IL-6 and -10 were associated to PT und lowest to isTBI (*p* < 0.001). In all groups pro-inflammatory response (IL-6/-10 ratio) peaked on day 2 and at a lower level on day 4. Severe TBI modulates kinetic profile of inflammatory response by reducing interleukin expression following trauma. Potential markers for neuronal damage have a limited diagnostic value after severe trauma because undifferentiated increase.

**Keywords:** multiple trauma; traumatic brain injury (TBI); risk prediction; biomarker; IL-6; IL-10; posttraumatic inflammation; S100b; NSE; GFAP

#### **1. Introduction**

Polytrauma (PT) and severe traumatic brain injury (TBI) caused by road traffic accidents and falls are the main causes of death and disability in young patients under 45 years with immense socioeconomic impact through loss of productivity, medical and rehabilitation costs [1,2]. The treatment of patients with multiple organ injuries poses a particular challenge due to different injury patterns and severity, but also due to the complex immune response [3]. Post-traumatic exaggerated immunomodulation often leads to postinjury complications, multiple organ failure (MOF) or death and are predictors of mortality in trauma [4,5]. The brain is vulnerable to damage and failure due to its high metabolic rate and limited intrinsic energy reserve. The (neuro-)inflammatory environment subsequently leads to cell death and neurodegeneration in secondary brain damage but also to neuro reparative mechanisms in later stages. Secondary brain injury due to inflammatory processes is one of the main reasons for worsening of outcome [6–8]. Detecting the severity of injuries at an early stage, predicting their development and preventing secondary damage is of highest clinical interest, with biomarkers playing an important role [9]. One of the most studied serum biomarkers in TBI is the calcium binding protein B (S100B). The oligomeric cytoplasmic protein is predominantly found in astrocytes and involved in cellular processes and signal transduction [10]. Neuron-specific enolase 2 (NSE) is mainly found in the cytoplasm of neurons and neuroendocrine tissue and is involved in glycolysis in both neuronal cells and erythrocytes. Its detection in blood is considered as marker for neuronal damage [11,12]. The gliafibrillary acidic protein (GFAP) is a monomeric intermediate filament concentrated in the astroglial cytoskeleton. GFAP is brain-specific and is released into the peripheral blood circulation after death of astrocytes [13]. After multiple injuries and/or TBI, both the membrane integrity of brain cells and the integrity of the blood–brain barrier (BBB) is disturbed [14]. S100B, NSE and GFAP are released into the blood, with kinetic profile being related to injury pattern and (neuro-)inflammatory process [15,16]. Pro- and anti-inflammatory cytokines such as interleukin-6 and -10 (IL-6, IL-10) are released in response to tissue injury and lead to both reactive and restorative inflammatory processes [17]. Although interleukins are clear markers for immune activation, further investigation is needed to determine the extent to which they reflect brain damage as diagnostic factors [18]. In the acute phase reaction, IL-6 is known to regulate inflammation, immunity and neural development. An acute local and systemic release in brain tissue, blood and cerebrospinal fluid can be observed as a reaction to injury [19,20]. IL-10 is commonly known as an anti-inflammatory cytokine that performs immunomodulatory functions and is particularly important in the resorption phase. Its expression increases with the pathology of the Central Nervous System (CNS), promoting survival of nerve and glial cells and attenuating inflammatory responses [21]. Understanding the (neuro)immunological effect of TBI on the systemic inflammatory response is a key factor in the development of early targeted therapies [8]. Although several studies have investigated systemic cytokine levels in severely injured trauma patients [22,23] the specific influence of severe trauma or TBI on the neuronal and inflammatory response and expression of brain-specific biomarkers has not been described.

The aim of the present study was to characterize the systemic profiles of neuronal biomarkers S100b, NSE, GFAP and the pro- and anti-inflammatory markers IL-6 and Il-10 in severely injured patients and to assess the influence of severe TBI on the state of acute inflammation in these patients. Furthermore, it will be shown to what extent neuronal biomarkers used in TBI have sufficient significance to assess the influence of initial or indirect secondary intracranial damage in polytrauma.

#### **2. Experimental Section**

The study was performed at the University Hospital Frankfurt, Goethe University after approval by the Institutional Review Board (89/19) in accordance with the Declaration of Helsinki and following STROBE guidelines [24]. Written informed consent was obtained for enrolled patients or their legally authorized representatives in accordance with ethical standards.

We retrospectively reviewed a cohort of severely injured trauma patients admitted to the emergency department (ED) of the University Hospital of the Goethe University Frankfurt from 2012 to 2016. All clinical data were prospectively taken during the quality documentation of the TraumaRegister der Deutschen Gesellschaft für Unfallchirurgie (DGU)®, Berlin, Germany. All trauma patients in the ED were treated according to the Advanced Trauma Life Support (ATLS® American College of Surgeons, Chicago, IL, USA) standard and the polytrauma guidelines [25]. Injury severity from trauma was calculated using the Injury Severity Score (ISS) based on the Abbreviated Injury Scale (AIS) score which assigns each injury a severity level between 1 (mild) and 6 (maximum) in different regions (head, face, thorax, abdomen, extremities and external injuries) [26,27]. The New Injury Severity Score (NISS) was calculated to better correlate the assessment measure with the polytrauma of the patients [28]. We evaluated 445 trauma patients with an ISS ≥ 16 between 18 and 80 years of age. Patients who met inclusion criteria were stratified into 3 groups: isolated severe TBI ((isTBI); AIShead ≥ 4, all other body

areas AIS ≤ 1, polytraumatized patients with severe TBI ((PT + TBI); AIShead and AIS of other body area ≥3;) and polytraumatized patients without relevant brain injury ((PT); AIShead ≤ 1;). Patients with known pre-existing immunological disorders, immunosuppressive medication, burns, concomitant acute myocardial infarction, thromboembolic events and patients who died within 5 days of hospital admission (incomplete serial blood samples) were excluded. Documentation containing further information on demography (age and sex) and injury.

Serial venous blood samples were obtained from traumatized patients on admittance to the ED through day 1–5 daily after trauma. Following baseline sample, subsequent blood was collected following the standard hospital procedures in pre-chilled ethylenediaminetetraacetic acid (EDTA) tubes (BD vacutainer, Becton Dickinson Diagnostics, Aalst, Belgium) and stored on ice. Blood was centrifuged at 2000× *g* for 15 min at 4 ◦C and the supernatant (serum) was stored at −80 ◦C until analysis. IL-6 and IL-10 concentrations were measured by IL-6/IL-10 Eli-pair Enzyme-linked Immunosorbent Assay ((ELISA); Diaclone, Hoelzel Diagnostica, Cologne, Germany) according to the manufacturer's instructions. The following markers of brain injury were assessed using commercially available ELISA assays (Bio-Techne GmbH (R & D Systems GmbH, Wiesbaden, Germany): S100B (DY1820-05 Human S100B; assay range: 47–3000 pg/mL)), glial fibrillary acidic protein (DY2594-05 GFAP; assay range: 0.3–20 ng/mL) and neuron-specific enolase 2 (DY5169-05 Human Enolase 2/Neuron-specific Enolase; assay range: 78–5000 pg/mL), after sample dilution as needed.

Continuous normally distributed variables were summarized using means ± standard error of the mean (*SEM*), while categorical or continuous variables with skewed distributions were summarized using means ± standard deviation (*SD*). The *p*-values for categorical variables were derived from the two-sided Fisher's exact test, and for continuous variables from the Mann–Whitney U test or the Kruskal–Wallis test. Significant values were adjusted by the Bonferroni post hoc test. Spearman's rank correlation coefficients were calculated to determine correlations between inflammatory and neuronal biomarkers and injury characteristics. A *p*-value < 0.05 was considered to be statistically significant. Values are reported as means for continuous variables and as percentages for categorical variables. All analyses were performed using the Statistical Package for Social Sciences (SPSS for Mac©), version 26 (SPSS Inc., Chicago, IL, USA).

#### **3. Results**

#### *3.1. Demographics and Clinical Injury Characteristics*

Table 1 shows the demographic and clinical characteristics stratified by injury pattern. During the 5-year study period a total of 445 severely injured patients (ISS ≥ 16) was evaluated. A total of 104 patients met the inclusion criteria. Of these, 43 patients were severely injured without TBI (PT), 35 patients were polytrauma patients with severe TBI (PT+TBI) and 26 patients suffered from isolated severe TBI (isTBI). A total of 76.0% of the trauma patients were men. Patients with isolated TBI were significantly older (61 ± 3 years, *p* < 0.001) than those in other groups, about 70% of them were older than 55 years. Mean ISS of the patients with isolated TBI was significantly lower (24 ± 1, *p* < 0.01) but in NISS (*p* = 0.135) no significant difference was found. Computed tomographic findings of patients with PT severe TBI or isolated severe TBI usually suffered not only from one type of intracranial hemorrhage but from several entities simultaneously.

#### *3.2. Systemic Profiles of Neuro Markers in Relation to Injury Pattern*

Figure 1 displays neuronal serum marker levels over the time course from ED admission to hospital day 5 stratified by injury pattern.

**Table 1.** Demographic and clinical injury characteristics stratified by injury pattern.

Abbreviations: PT = Polytrauma, isTBI = isolated Traumatic Brain Injury, SEM = Standard Error of Mean, y = years, ISS = Injury Severity Score, NISS = New Injury Severity Score, AIS = Abbreviated Injury Score, ICH = intracerebral hemorrhage, SDH = subdural hematoma, SAH = subarachnoid hemorrhage, EDH = epidural hematoma.

(**c**)

**Figure 1.** Neuronal serum marker levels (mean ± Standard Deviation) over the time course from admission to Emergency Department (ED) through to hospital day 5 stratified by injury pattern. \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001. (**a**) Neuron-specific enolase 2 (NSE) (**b**); glial fibrillary acidic protein (GFAP); (**c**) calcium-binding Protein B (S100b).

After trauma, the NSE expression is highest on the day of admission with maximum serum level of 8 ± 1 ng/mL (PT), 13 ± 2 ng/mL (PT + TBI) and 7 ± 1 ng/mL (isTBI) and steadily decreases over time. Significantly higher GFAP expression was found in the PT cohort (day 1: 23.80 ± 7.94 pg/mL) comparing to isTBI (day 1: 5.82 ± 2.33 pg/mL, *p* = 0.038) 24 to 72 h after trauma.

For S100b highest marker expression was found in PT patients (495 ± 150 pg/mL) on time of admission, followed by the PT + TBI group (333 ± 128 pg/mL). After isolated TBI the S100b expression was over all lower (71 ± 15 pg/mL) comparing PT and PT + TBI.

#### *3.3. Systemic Profiles of Inflammatory Markers in Relation to Injury Pattern*

Figure 2 shows inflammatory serum marker levels over the time course from ED admission through to hospital day 5 stratified by injury pattern.

> 37 377%,

**Figure 2.** Inflammatory serum marker levels (mean ± Standard Deviation) over the time course from emergency department (ED) admission through to hospital day 5 stratified by injury pattern. \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001. (**a**) Interleukin (IL)-6 (**b**); Interleukin (IL)-10.

In PT (day 1: 400 ± 71 pg/mL) and isTBI (day 1: 98 ± 22 pg/mL) group IL-6 peaked on hospital day 1 while in PT + TBI group highest IL-6 level was detected on admission (267 ± 86 pg/mL). All groups showed significant cytokine drop over time course. Over the entire time course, IL-6 levels were significantly higher in the PT cohort (*p* < 0.001) as well within the first 24 h in PT + TBI group (*p* < 0.001) compared to the isTBI group.

All groups showed highest IL-10 expression on admission (PT 148 ± 33 > PT + TBI 111 ± 30 > isTBI 31 ± 11 pg/mL) with significant decline from day 2 in the PT (− 71.2%) and PT + TBI (− 73.6%) groups and from day 4 in isTBI (− 9.5%) group. Within 24 h after trauma, IL-10-level was significantly higher in the PT (*p* < 0.001) and PT + TBI (*p* < 0.05) cohorts according to isolated TBI.

#### *3.4. Relation between Marker Expression and Injury Severity*

On admission we found significant positive correlation of increased IL-10 expression and injury severity (Spearman correlation (*rho*), ISS *r* = 0.4, *p* = 0.04; NISS *r* = 0.5, *p* = 0.01) as well as and positive correlation between increased cytokine expression (IL-10 *r* = 0.6, *p* = 0.001; IL-6 *r* = 0.5, *p* = 0.01) and higher risk of mortality (Revised Injury Severity Classification Score (RISC II)) in severely injured patients (PT) within 24 h after severe trauma (PT).

In PT + TBI group the S100b expression showed significant positive correlation with injury severity (ISS *r* = 0.4, *p* = 0.02), mortality risk (RISC II *r* = 0.4, *p* = 0.02) and severity of TBI (AIShead *r* = 0.4, *p* = 0.05) up to 72 h after trauma.

In isTBI cohort we found negative correlation between GFAP expression and age (*r* = − 0.4, *p* = 0.03). After isolated TBI the inflammatory response correlated 24 h after trauma with the ISS (IL-6 *r* = 0.6, *p* = 0.01), NISS (IL-6 *r* = 0.6, *p* = 0.003; IL-10 *r* = 0.5, *p* = 0.04) and severity of head injury (AIShead/IL-10 *r* = 0.6, *p* = 0.002) as well as with risk of mortality (IL-10 *r* = 0.6, *p* = 0.004).

#### *3.5. Inflammatory Status (IL-6*/*IL-10 Ratio) and Kinetic Profile of Neuro Markers*

Table 2 displays the relation between inflammatory response and neuronal markers expression stratified by injury pattern. Figure 3 shows inflammatory status (IL-6/IL-10 ratio) together with the kinetics of neuro marker expression during the time course. The comparison of the marker levels within a group in percentage ratio serves to illustrate the proportionality.

**Table**

=Neuron-specific enolase 2, GFAP = glial fibrillary acidic protein, S100b = calcium-binding Protein B.

**Figure 3.** Pro-inflammatory response (Interleukin (IL)-6/Interleukin (IL)-10 ratio, grey-hatched) and neuronal marker expression (Neuron-specific enolase 2 (NSE), glial fibrillary acidic protein (GFAP), calcium-binding Protein B (S100b)) after (**a**) polytrauma (PT); (**b**) polytrauma + traumatic brain injury (TBI); (**c**) isolated TBI (isTBI). ED = Admission to Emergency Department.

Kinetic profiles of PT and PT-TBI groups showed similar course from ED admission through to hospital day 5. While the PT and PT+TBI groups continued to decrease over time until 29.3% (PT) and 22.9% (PT + TBI), the TBI group showed constantly elevated NSE expression (52.3%) 5 days following trauma. All groups showed synchronous decline over time with a maximum reduction of GFAP expression to 64.2–70.0%. For S100b we found a drop down to 24.1% in PT and 33.9% in PT + TBI group within 24 h. After isolated TBI consistent S100b expression over time course (min. 80.9%, day 3) with a secondary peak on day 4 (145.0%) was found.

Based on the IL-6/IL-10 ratio we identified two phases of pro-inflammation for all groups: increase from admission to day 3 with peak after 48 h and secondary from day 3 to day 5 with peak on day 4. The pro-inflammatory quotient after 48 h was higher in the PT + TBI group (ratio = 34.8) than after PT (ratio = 15.0) and isTBI (ratio = 11.6). The lowest pro-inflammatory reaction was found for isTBI.

#### *3.6. Relation between Inflammatory Response and Neuronal Markers*

After severe PT a significant correlation was found between IL-6 and IL-10 level (*r* = 0.8, *p* < 0.001) on admission and day 1 to day 5. Over the entire time course, a positive correlation was observed between S100b and NSE and IL-6 expression with statistical significance from day 4 (NSE *r* = 0.04, *p* = 0.01, IL-6 *r* = 0.4, *p* = 0.04) and GFAP.

After PT with severe TBI, GFAP and S100b expression showed statistically significant synchronous decrease (*r* = 0.3, *p* = 0.04) over the entire time course. Positive correlation was found as well between NSE and S100b (*r* = 0.3, *p* = 0.04) from day 3.

A positive relation of GFAP with NSE (admission: *r* = 0.3, *p* = 0.05) and with S100b (*r* = 0.5, *p* = 0.001) levels over the entire time course and between NSE (*r* = 0.4, *p* = 0.01) and with S100b on admission, day 2, 3 and 5 was measured in patients with isTBI.

#### **4. Discussion**

Of 104 patients included, 75% were male and the cohort with isolated TBI was significantly older than the PT and PT+TBI group. Polytrauma is the leading cause of death in Western countries for people up to 45 years of age, in a male-to-female ratio of 2.6:1. Older patients cause most TBI-related hospitalizations and deaths [1,29]. In this study it was shown that the application of both the already used and the more recent biomarkers S100b, NSE and GFAP have limited value in the assessment of TBI in polytraumatized patients. Nevertheless, biomarkers are certainly the optimal complement to clinical and radiological findings to assess the course and outcome of the injury sufficiently. Biomarkers can reveal the extent of cell death, provide early indications of occult or later visible organ damage, and thus have an immense clinical value.

#### *4.1. S100b Missing Diagnostic Value after Severe Trauma*

One of the most often studied serum biomarkers in TBI is S100b [7]. Due to its high molecular weight, the protein is only able to pass through the BBB, if it has an increased permeability caused by injury [14]. Therefore, this marker was used to detect (sensitivity 81–100%) and predict the outcome of a severe TBI. The potential pitfalls of S100b in the different areas are usually related to its specificity (20–67%) and sensitivity in the detection and assessment of intracranial injury. Consistent cut-off levels (51 pg/mL to 210 ng/mL at admission) are difficult to find in the literature [10,12,15]. In this study, the highest serum levels for S100b were measured in polytrauma patients (495 ± 150 pg/mL) at admission, followed by the PT + TBI group (333 ± 128 pg/mL) and isolated TBI (71 ± 15 pg/mL). S100b is thus expressed lowest after isTBI, although not significantly. S100b is also expressed in peripheral nervous tissue in addition to the CNS. Furthermore, muscle tissue, chondrocytes and adipocytes were identified to release S100b after injury [3]. TBI could only be detected with a predictive power of 67% in pediatric polytrauma patients with TBI and fracture of long bones [30]. Orth-Nissen et al. found higher serum levels of S100b in patients with multiple injuries than in patients with isolated TBI or without TBI. This suggests that elevated serum concentration of S100b after trauma seems to be significantly influenced by an extra-cerebral injury and is an indicator for a global tissue and organ damage [31]. It was shown that pro-inflammatory cytokines such as IL-6 induce an increase of S100b and GFAP secretion via the mitogen-activated protein kinase (MAPK) pathway [32]. Furthermore, a positive correlation between neuro marker expression over time was found after PT + TBI and isolated TBI. Possibly this is an indicator of congruent marker expression after neuronal damage in TBI.

#### *4.2. Non-Specific Increase of NSE Expression by Cofounding E*ff*ect after Severe Trauma*

NSE is localized in the cytoplasm of neurons, erythrocytes, platelets and neuroendocrine cells. It has been discussed as a biomarker for neuronal damage and has been associated with the severity of injury and clinical outcome after severe TBI. In the literature, serum levels after trauma ranged between 6.5–21.2 ng/mL [9,12,30]. In this study it was shown that after polytrauma with TBI, the NSE expression is highest on the day of admission with a maximum serum level of 13 ± 2 ng/mL (PT + TBI) > 8 ± 1 ng/mL (PT) > 7 ± 1 ng/mL (isTBI) and decreases steadily until day 5. A possible explanation for this finding could be an NSE release due to hemolysis after polytrauma. Another possibility might be the release of neuronal NSE due to reperfusion damage in the course of polytrauma with subsequent BBB disruption by immune reaction. Serum protein S100b and NSE increased temporarily as a result of multiple trauma associated with hemorrhagic shock might lead to cerebral hypoperfusion and brain damage in porcine model [33,34]. Due to its expression in neuroendocrine cells NSE is also studied in association with malignant and inflammatory lung diseases and first studies analyzing NSE release after lung injury have been published [35,36]. Methodological and technical problems such as slow elimination and potential artifacts in trauma patients call into question their diagnostic value as a screening tool [33]. It is striking that the isTBI group still shows an increased NSE expression (isTBI

52.3% > PT + TBI 22.9%, PT 29.3%) 5 days after the trauma, which may be an expression of severe neuronal damage during the secondary brain swelling and injury.

#### *4.3. GFAP Expression Is Induced by Multiple Injuries*

GFAP was largely considered brain-specific and is released into the peripheral bloodstream after death of astrocytes [11]. Meanwhile GFAP expression is also described in Schwann cells, myoepithelial cells, chondrocytes and fibroblasts [37]. An acute intracerebral hemorrhage leads to an immediate mechanical destruction of the astroglia with subsequent release of GFAP into the bloodstream [38]. According to Lei et al., human GFAP serum levels are elevated from admission and during the first 5 days after severe TBI and are predictive of the neurological outcome after 6 months. In the literature, different GFAP cut off levels from 6.8 pg/mL to 0.7 ng/mL are described after TBI for unfavorable outcome [13,39]. In the present study, significantly higher GFAP levels in the PT group (day 1: 24 ± 8 pg/mL) compared to isTBI (day 1: 6 ± 2 pg/mL, *p* < 0.05) were measured 24 to 72 h after trauma. Hsieh et al. demonstrated that overa6h period of reperfusion after intestinal ischemia, microglial cells and astrocytes were significantly activated in the brain [40]. Polytrauma increases GFAP and neutrophil expression and leads to secondary brain damage in the presence of exacerbated neuroinflammation, edema and disruption of the BBB [41]. In the isTBI cohort, a negative correlation between GFAP expression and age (*r* = − 0.4, *p* = 0.03) was found. It is possible that GFAP is less expressed by the significantly older isTBI patients.

#### *4.4. Severe Traumatic Brain Injury (TBI) Modulates Kinetic Profile of Inflammatory Response*

The acute inflammatory reaction plays an important role in immune defense, but can also exert serious adverse consequences, if the excessive immune response leads to systemic inflammation, secondary organ damage and subsequent MOF [3,21]. After TBI, activation of glial cells, microglia and astrocytes as well as infiltration of blood leukocytes takes place within minutes, releasing pro- and anti-inflammatory mediators and various growth factors and triggering a local neuroinflammation as well as a systemic inflammatory response. In this case, the brain functions as both target and effector organ [42]. Among these mediators, interleukins play a particularly prominent role in the development of posttraumatic complications [18,21]. IL-6 is secreted in increased amounts early after trauma. Serum concentrations in available studies range from 47 to 245 pg/mL [8,19,43]. Following TBI, IL-6 levels increase with peak concentrations of 93 to 269 pg/mL in human serum and IL-6 levels greater than 100 pg/mL in the first 24 h after trauma have been associated with severe brain injury [8,18,44]. In this study, IL-6 expression in polytraumatized patients with and without concomitant head injury was significantly higher compared to isTBI (day 1: PT 400 ± 71, PT + TBI 230 ± 53 pg/mL, > isTBI 98 ± 22 pg/mL, *p* < 0.001); all groups showed a relevant cytokine decrease (*p* < 0.001) over time.

IL-10 is an anti-inflammatory cytokine that regulates and limits acute inflammation in response to trauma to prevent tissue damage, such as secondary brain damage after TBI [21,45]. IL-10 plays a crucial role in inflammatory and autoimmune diseases of the intestine, chronic infections, tumor development, neuroinflammation, -degeneration as well as multiple organ dysfunction syndrome (MODS) and MOF after polytrauma, where IL-10 levels from 21.0 to 340.7 pg/mL were shown [42,46]. In this study, the highest IL-10 levels were measured on admission and a continuous decrease until day 5 was shown. The overall increase within the first 24 h was significantly higher in PT (148 ± 33 pg/mL, *p* < 0.001) and PT+TBI (111 ± 30 pg/mL, *p* < 0.05) than after isTBI (31 ± 11 pg/mL). Previous studies described a correlation of IL-6 with the severity of injury (ISS) or insult and mortality, especially during the first post-traumatic hours. IL-6 is associated with the occurrence of MODS and sepsis together with IL-10 within 24 h after trauma [23]. In the present study, IL-10 was positively correlated with ISS and NISS on the day of admission and day 1.

In addition, a positive correlation between IL-6 and IL-10 expression with the mortality risk (RISC II) was shown. Thus, the inflammatory reactions are most likely associated with the severity of injury (ISS: PT + TBI 37 > PT 30 > isTBI 24, *p* < 0.01) and the extent of tissue trauma itself. Interestingly, polytraumatized patients with severe head trauma showed—although not statistically significant—lower IL-6 values than severely injured patients without TBI despite a higher ISS. This corresponds to already published results from 2016, which raised the question of an immunosuppressive role of TBI [8]. With increasing severity of TBI (AIShead isTBI 4.5 > + TBI 4.1 > PT 0.3), an overall lower cytokine expression was observed. Based on the IL-6/IL-10 ratio, two phases of pro-inflammation can be identified for all three groups from hospital admission to day 3 with a first peak after 48 h and from day 3 to day 5 with a second peak on day 4. Overall, the pro-inflammatory quotient was higher after PT and PT + TBI than after isTBI, with the first phase being strongest after PT + TBI. It is possible that the limited cytokine expression and depressed pro-inflammatory ratio indicates an immunomodulatory effect after severe TBI, but the pro-inflammatory immune response was ultimately relatively strongest after PT + TBI. Data on the predictive ability of IL-6 serum and the neurological outcome are limited and contradictory. While in the pediatric TBI serum IL-6 showed no association with the neurological outcome [47], others showed that high IL-6 correlates with a poor neurological outcome [44]. The determination of cytokine levels can serve not only as a prognostic value for the development of complications, but also for the early identification of patients who are at risk of suffering a "second hit" by renewed immune stimulus in the form of a secondary intervention.

An important pathophysiological factor in the development of posttraumatic complications is the dysfunction of the external (skin) and internal paracellular blood and organ barriers, including the brain (BBB), air and gut blood barriers, resulting in tissue flooding with immune cells and microbial invasion [48]. IL-6 modulates the expression of tight junction proteins in cerebral microvasculature of sheep, and the release of adhesion molecules in plasma of polytrauma patients (ISS ≥ 18) correlates with disease severity and organ dysfunction [49]. Furthermore, the hemorrhagic shock is associated with barrier dysfunction of the gut and development of MODS after the trauma and can lead to a traumatic endotheliopathy by glycocalyx degradation, like in the CNS [50]. Studies have shown that paracellular hyperpermeability due to inflammatory reactions and abnormal release of neurotransmitters is the basis for intestinal barrier dysfunction after TBI [51]. The increased permeability of the organ barriers allows metabolites to enter the bloodstream and to detect specific biomarkers [14]. Identifying the severity of injuries at an early stage, predicting their development in order to prevent secondary damage is of paramount interest in trauma treatment, with the detection of biomarkers playing an important role [43].

#### *4.5. Limitations of the Study*

The most important limitation is the retrospective nature of the data analysis; however, all clinical data were acquired in a prospective manner, and only the cytokines and the neuro-specific markers were measured later-on. Furthermore, patients with isolated TBI were significantly older than the comparison groups. In a recently published multicenter study, older patients with blunt trauma showed significantly lower plasma cytokine and chemokine concentrations, including IL-6 levels, than patients <55 years of age [52]. The patients included in the isTBI cohort also suffered from several types of intracranial bleeding simultaneously, which does not allow differentiation between bleeding entities and marker expression. In the present study we found a difference between patient's age of the PT and PT+TBI vs. isTBI group and in their interleukin expression. Ultimately, no conclusions can be drawn as to how age may have altered the inflammatory response. The variance of the individual values is large, specifically of neuronal markers, which is due to potential confounders such as volume administration, blood products, shock, which are difficult to standardize.

#### **5. Conclusions**

Although no significant correlation between cytokine expression and release of neuronal markers after trauma could be shown, it is clear that the application of the biomarkers NSE, GFAP and S100b have limited value in the assessment of TBI in polytrauma patients, the levels are not substantially elevated. Possible cause could be a modulation of the kinetic profile of the inflammatory response

following severe traumatic brain injury (TBI). IL-6 and IL-10 levels were lower in patients with multiple injuries and concomitant TBI compared to those without severe TBI, suggesting a reduced visible systemic inflammatory response due to traumatic brain injury. In addition, significant correlations were shown between IL-6 and IL-10 levels and injury severity after TBI, indicating the general posttraumatic inflammatory course.

**Author Contributions:** Conceptualization, I.M., D.H. and T.L.; methodology, D.H. and T.L.; validation, T.L.; formal analysis, C.R.S.; investigation, P.R. and C.R.S.; resources, I.M. and P.R.; data curation, C.R.S.; writing—original draft preparation, C.R.S.; writing—review and editing, P.R., P.S., D.H. and I.M.; visualization, M.W.; supervision, I.M. and D.H.; project administration, I.M. and D.H. All authors have read and agreed to the published version of the manuscript.

**Acknowledgments:** P.R. was funded by the CRC 1149.

**Conflicts of Interest:** C. R. Schindler, T. Lustenberger, M. Woschek, P. Störmann, D. Henrich, P. Radermacher, I. Marzi declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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