Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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Since many years, he is a member of steering committee of Gdańsk branch of Polish Society of Microbiologists, a member of ESCMID, a member of editorial board of international journals, and a reviewer.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],coeditorOne:{id:"206216",title:"Dr.",name:"Agnieszka",middleName:null,surname:"Daca",slug:"agnieszka-daca",fullName:"Agnieszka Daca",profilePictureURL:"https://mts.intechopen.com/storage/users/206216/images/7512_n.jpg",biography:"Agnieszka Daca was born in 1982 in Świecie, Poland. She prepared and defended her PhD degree thesis in 2011 from the Medical University of Gdańsk and discussed the immunological changes observed in the blood of patients with SLE. 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Her nonprofessional interests are mountain climbing, skiing, and reading books.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1162",title:"Endourology",slug:"endourology"}],chapters:[{id:"59714",title:"Management of Complicated Urinary Tract Infection",slug:"management-of-complicated-urinary-tract-infection",totalDownloads:637,totalCrossrefCites:0,authors:[{id:"186524",title:"Prof.",name:"Ran",surname:"Pang",slug:"ran-pang",fullName:"Ran Pang"}]},{id:"57462",title:"Management of Urinary Tract Infections: Problems and Possible Solutions",slug:"management-of-urinary-tract-infections-problems-and-possible-solutions",totalDownloads:520,totalCrossrefCites:0,authors:[{id:"65166",title:"Prof.",name:"Giovanni",surname:"Antonini",slug:"giovanni-antonini",fullName:"Giovanni Antonini"},{id:"187348",title:"Prof.",name:"Salvatore",surname:"Di Somma",slug:"salvatore-di-somma",fullName:"Salvatore Di Somma"},{id:"222196",title:"MSc.",name:"Lorenza",surname:"Murgia",slug:"lorenza-murgia",fullName:"Lorenza Murgia"},{id:"222197",title:"Dr.",name:"Ottavia",surname:"Stalio",slug:"ottavia-stalio",fullName:"Ottavia Stalio"},{id:"222198",title:"Dr.",name:"Alyexandra",surname:"Arienzo",slug:"alyexandra-arienzo",fullName:"Alyexandra Arienzo"},{id:"222199",title:"MSc.",name:"Valeria",surname:"Ferrante",slug:"valeria-ferrante",fullName:"Valeria Ferrante"},{id:"222200",title:"MSc.",name:"Valentina",surname:"Cellitti",slug:"valentina-cellitti",fullName:"Valentina Cellitti"},{id:"222203",title:"Prof.",name:"Paolo",surname:"Visca",slug:"paolo-visca",fullName:"Paolo Visca"}]},{id:"58321",title:"Urinary Tract Infections in Renal Transplant Recipients",slug:"urinary-tract-infections-in-renal-transplant-recipients",totalDownloads:704,totalCrossrefCites:0,authors:[{id:"207724",title:"Prof.",name:"Maria Alicja",surname:"Dębska-Ślizień",slug:"maria-alicja-debska-slizien",fullName:"Maria Alicja Dębska-Ślizień"},{id:"212302",title:"Dr.",name:"Justyna",surname:"Gołębiewska",slug:"justyna-golebiewska",fullName:"Justyna Gołębiewska"}]},{id:"61815",title:"Urinary Tract Infection in Renal Allograft Recipents",slug:"urinary-tract-infection-in-renal-allograft-recipents",totalDownloads:429,totalCrossrefCites:0,authors:[{id:"228499",title:"Dr.",name:"Lovelesh",surname:"Nigam",slug:"lovelesh-nigam",fullName:"Lovelesh Nigam"},{id:"239124",title:"Dr.",name:"Aruna V",surname:"Vanikar",slug:"aruna-v-vanikar",fullName:"Aruna V Vanikar"},{id:"239127",title:"Dr.",name:"Rashmi",surname:"D",slug:"rashmi-d",fullName:"Rashmi D"},{id:"239128",title:"Dr.",name:"Kamal V",surname:"Kanodia",slug:"kamal-v-kanodia",fullName:"Kamal V Kanodia"},{id:"239129",title:"Dr.",name:"Kamlesh S",surname:"Suthar",slug:"kamlesh-s-suthar",fullName:"Kamlesh S Suthar"}]},{id:"57656",title:"Uropathogenic Escherichia coli and Fimbrial Adhesins Virulome",slug:"uropathogenic-escherichia-coli-and-fimbrial-adhesins-virulome",totalDownloads:823,totalCrossrefCites:3,authors:[{id:"45803",title:"Ph.D.",name:"Payam",surname:"Behzadi",slug:"payam-behzadi",fullName:"Payam Behzadi"}]},{id:"57854",title:"Resistant Gram-Negative Urinary Tract Bacterial Infections",slug:"resistant-gram-negative-urinary-tract-bacterial-infections",totalDownloads:648,totalCrossrefCites:1,authors:[{id:"212594",title:"Dr.",name:"Abdalla",surname:"Khalil",slug:"abdalla-khalil",fullName:"Abdalla Khalil"},{id:"212626",title:"Dr.",name:"Nashaat",surname:"Hamza",slug:"nashaat-hamza",fullName:"Nashaat Hamza"}]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"194667",firstName:"Marijana",lastName:"Francetic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/194667/images/4752_n.jpg",email:"marijana@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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1. Introduction
The current definition of biomarkers includes “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention (Biomarkers Definitions Working Group, 2001)”. Accordingly, biomarkers are usually used for detection and establishing the magnitude of a physiological derangement as well as to monitor a treatment.
The role for imaging techniques and biomarkers in the diagnosis and treatment of myocardial infarction (MI) after percutaneous coronary intervention is well-established. Many candidate biomarkers emerging from genomics and proteomics research have the potential to serve as predictive indexes for guiding the development of interventional cardiology (Gerhardt et al. 1991; Katus et al., 1991; Lindpaintner, 1997; Kong et al., 1997). Among them the undisputed role still play cardiac proteins like troponins or creatine kinase-myocardial band (CK-MB) (Alcock et al., 2010; Lim et al., 2011). Less established, however, is the employment of biomarkers to determine long-term, progressive, or dynamic risk over time in patients with advanced coronary artery disease (CAD). Biomarkers offer a means to track differential exposure as well as impact of exposure. As such, they reflect individual vulnerability, ongoing person-environment interaction, and unmeasured environmental factors that mediate the effect of exposures (Fig. 1). Essential to a vision of synergistic diagnostics is a focus on the mechanisms of diseases. Understanding what is happening on a molecular and cellular level, how disease actually begins, how cells begin to express certain proteins, influence other cells and trigger processes (atherosclerosis, thrombosis, calcification) will allow to develop in vitro diagnostics and imaging techniques to distinguish these processes. By characterizing a comprehensive set of measurable processes that capture diverse pathogenic aspects of CAD, a real-time systems view of disease activity can be generated to improve decision making.
This chapter summarize a current view on the development of new biomarkers as a prognostic platform among patients at risk of CAD and upcoming complications.
Figure 1.
Model showing relationship of a biomarker with internal and external factors which have an impact on measurable and unmeasurable features of biomarker.
2. Bone remodelling biomarkers
About 10 years ago, the hypothesis that bone remodelling biomarkers might be involved in the progression of coronary artery calcification seemed to be tricky and beyond any reasonable expectation. However, in 1995 Boström et al. first time proposed the possible mechanisms for bone formation in artery walls involving retention of pluripotent cells or osteoblastic immigration coupled with embryonic-like osteogenic program (Boström et al. 1995). The main reason for understanding the regulatory mechanisms of vascular calcification was firstly related to therapeutic approaches to prevent and possibly reverse vascular mineralization (Demer, 1997; Parhami et al. 1997). The data from clinical studies regularly report an association between bone remodeling biomarkers and the presence, severity and progression of a broad range of cardiovascular diseases. Whether they are biomarkers or rather play a causal role in mediating or protecting against vascular injury is not clear. The mechanisms underlying the postulated role of bone remodelling biomarkers in atherosclerosis probably involve inflammation and calcification processes.
This section will focus on the prognostic significance of plasma bone remodelling biomarkers levels in stable and unstable CAD.
2.1. Biology of bone remodelling of biomarkers
Vascular biomineralization in an atherosclerotic plaque results from an imbalance in osteoblast- and osteoclast-like cells and the induction of vascular or immune cells differentiation into osteogenic cells (Demer & Tintut, 2008). Osteobalsts, osteoclasts and inflammatory cells are firmly involved in bone remodelling (Fig. 2).
Figure 2.
Bone remodelling osteoblasts and osteoclasts differentiation. Figure was produced using Servier Medical Art.
Figure 3.
The role of osteoprotegerin (OPG) in pre-osteoclast differentiation. OPG trap and neutralize a soluble receptor activator of nuclear factor kappa-B ligand (RANKL) which activates osteoclasts by its receptor (RANK). Figure was produced using Servier Medical Art.
Mesenchymal stem cells are precursors for pre-osteoblasts. Osteoblasts activity leads to bone formation and mineralization, their differentiation and activity is mostly regulated by RANKL (receptor activator of nuclear factor kappa-B ligand) inducers, such as: vitamin D (1,25[OH]2D), glucocorticosteroids, parathormone (PTH), prostaglandins (PGE2), lipopolysaccharides (LPS), histamine and pro-inflammatory cytokines: interleukins (IL-1 and IL-11), tumor necrosisi factor alfa (TNF-α) and others (Eriksen, 2010). RANKL is a surface-bound molecule (also known as CD254). It is found on osteoblasts and serves to stimulate osteoclasts by RANK (receptor activator of nuclear factor kappa-B) activation and RANK/RANKL axis has a core regulatory role in osteoblasts and osteoclasts signalling (Fig. 3) (Caidahl et al., 2010).
2.2. Osteoprotegerin and osteopontin as risk factors of coronary artery disease
Several studies suggest the involvement of bone remodeling biomarkers in coronary artery disease and related atherosclerotic disorders (Van Campenhout & Golledge, 2009; Venuraju et al. 2010). Prime regulators of bone remodelling, such as osteoprotegerin (OPG) and osteopontin (OPN), are significantly and independently associated with inflammatory processes and arterial hypertension and may exert substantial influence on the severity of cardiovascular disease. (Stępień et al., 2011)
OPG is a soluble glycoprotein widely expressed in most human tissues including the bone (osteoblasts) and the vasculature (endothelial and vascular smooth muscle cells, VSMC) (Collin-Osdoby, 2004; Schoppet et al., 2002) that is implicated in the regulation of bone and vascular calcification. OPG is a member of the tumor necrosis factor (TNF)-related family and a part of the OPG/RANKL/RANK triad. OPG acts as a soluble secreted decay receptor for a receptor activator of nuclear factor kappa-B ligand (RANKL) and neutralize this essential cytokine required for the osteoclasts differentiation (Hsu et al., 1999) (Fig. 3).
RANKL expressed on osteoblastic, stromal and T cells binds to RANK (osteoclast differentiation factor) on the surface of osteoclasts, monocytic and dendritic cells and mediates a cell-to-cell signal responsible for osteoclastogenesis (Yasuda et al. 1998). Additional roles in immunological responses include the RANK-RANKL binding between dendritic and T cells which enhances the immunostimulatory capacity of dendritic cells and T cell proliferation (Green & Flavel 1999).
It was observed that opg-knockout mice (OPG -/-) develop early onset osteoporosis and arterial calcification (Bucay et al., 1998) and the restoration of the gene prevented osteoporosis progression and arterial calcification (Min et al., 2000). Increased OPG level has been observed in men with advanced CAD and plasma OPG level has proved to be an independent predictor of myocardial ischemia in asymptomatic diabetic patients (Avignon, 2007; Schoppet et al., 2003). Moreover, increased OPG has been related to the number and vulnerability of plaques as well as in carotid artery (Kadoglou et al., 2008; Vik et al. 2010) or coronary vessels (Palazzuoli at al., 2008), which suggests its involvement in the coronary disease progression (Mikami et al., 2008; Pedersen et al., 2010). Elevated OPG in plasma is univariable predictors of coronary artery calcification (CAC) progression (Anand et al., 2007). The sensitivity of OPG for detecting of CAC score higher than 200 Agatston units was 80% in patients with predialysis diabetic nephropathy (Schoppet et al., 2003). However, in the large Norwegian study by Pederesen et al. (Pedersen et al., 2010), adjustment for conventional risk factors attenuated the risk estimates for OPG levels. Only the subgroup of patients with stable angina pectoris (SA) with levels above the 90th percentile was at risk all-cause mortality: 1.94 (1.18, 3.18), p=0.01; CAD mortality: 2.29 (1.16, 4.49), p=0.02; and MI: 1.76 (1.02, 3.06), p=0.04.
In patients with acute coronary syndromes (ACS) the baseline OPG concentrations were strongly associated with increased long-term mortality (hazard ratio [HR] for log transformed OPG level 1.7 [range 1.5 to 1.9] p<0.0001) and heart failure hospitalizations (HR 2.0 [range 1.6 to 2.5]; p < 0.0001) but weaker with recurrent MI (HR 1.3 [range 1.0 to 1.5]; p = 0.02) and not with stroke (HR 1.2 [range 0.9 to 1.6]; p = 0.35). The association remained significant after adjustment for conventional risk markers (Omland et al., 2008). In apparently healthy individuals (the European Prospective Investigation into Cancer in Norfolk – EPIC Norfolk cohort) high serum concentrations of OPG and soluble RANKL were associated with an increased risk of future CAD (Semb et al., 2009). OPG showed a significant association with the risk of future coronary events in both sexes. This association remained statistically significant after adjustment for traditional cardiovascular risk factors (i.e. age, diabetes, systolic blood pressure, smoking, total cholesterol and HDL cholesterol).
OPN is secreted as a calcium-binding glycophosphoprotein that has been implicated in bone remodeling and inflammation as well. Similarly to OPG, osteopontin is widely distributed in different human cells including osteoblasts, lymphocytes, macrophages, endothelial cells and vascular smooth muscle cells (Brown et al., 1992).
OPN is a cytokine and has the ability to stimulate migration of macrophages and osteoclasts (Giachelli et al, 1998; Suzuki et al., 2002) and proliferation of osteoclasts and vascular smooth muscle cells (Giachelli et al, 1998; Liaw et al., 1994). A growing body of experimental evidence suggests that OPN overexpression plays an essential role in modulating compensatory cardiac fibrosis and hypertrophy (Xie et al., 2004; Singh et al. 2010). OPN acts through different integrins and thus has a great potential to regulate populations of different cells on the molecular and cellular levels (Bazzichi et al., 2009; Burke et al., 2009). OPN plays a pivotal role in inflammation and atherosclerotic plaque formation in an animal model (Scatena et al., 2007). Recent data has indicated a high predictive value of OPN for secondary manifestations of atherosclerotic disease (e.g. cardiovascular death, myocardial infarction, stroke, and endovascular interventions) in a 3-year follow-up of patients undergoing carotid surgery (de Kleijn et al., 2010).
Baseline levels of OPN are independent predictors of future adverse cardiac events in patients with chronic coronary syndrome (CCS), and may be useful for risk stratification (Minoretti et al., 2006). Recent data have indicated a high predictive value of OPN for secondary manifestations of atherosclerotic disease (e.g. cardiovascular death, MI, stroke and endovascular interventions) in a 3-year follow-up of patients undergoing carotid surgery. In a prospective study by Gogo et al. (Gogo et al., 2006), the association between angiographically quantified coronary artery calcification and OPG was not found. Detection of coronary calcification by coronary angiography may underestimate the calcification burden, thus synergistic diagnostics of coronary calcification should utilize more sensitive techniques of MSCT (Willemsen et al., 2009). However, in patients with CAD undergoing
percutaneous coronary intervention (PCI) the highest OPN levels were associated with both plaque progression and restenosis in a stent (p=0.003). In addition, OPN, IL-6, and CRP were higher in patients with ACS than in those with CCS (analysis of variance: p<0.001, p<0.05 and p<0.05, respectively) (Mazzone et al, 2011).
A question arises as to whether peripheral vascular function (calcification markers) matches the coronary arteries (calcification) and thus, whether it may serve as a surrogate marker to identify individuals with increased hazard of CAD and mortality (de Kleijn et al., 2010; Lieb et al., 2010; Scatena et al., 2007). Therefore bone-matrix proteins combined with cardiovascular imaging could be potential markers for vulnerable coronary artery plaques.
3. Microparticles
Microparticles (MP) are sub-micron sized cell membrane/cytoplasmic fragments that are released from the cell surface. There are two well-known cellular processes that can lead to the formation of MPs: chemical and physical cell activation (by agonists or shear stress, respectively), and apoptosis (Jimenez et al., 2003). However, the mechanisms that take place during MP formation are still not revealed. It seems that, the flopping of phosphatidylserine (PS) to the outer layer of the plasma membrane is pivotal. Finally, this process leads to the formation and shedding of MPs from activated or apoptotic cells. In resting condition the membrane asymmetry is maintained by an aminophospholipid translocase with flippase activity. Bilayer asymmetry is disrupted in the consequence of the inhibition of flippase activity by calcium influx. Increased calcium ions concentrations activate calcium-dependent calpains, which disturb cytoskeleton, promote the shedding of MPs (Morel et al., 2011) and stimulate scramblase and floppase activities, which lead to the collapse of the membrane asymmetry (Freyssinet & Toti, 2010).
MPs are qualitatively and quantitatively diverse and vary in diameter between 0.1 and 1.5 µm and may harbor a number of cell surface proteins (Fig. 4). MPs are released from various cell types such as circulating blood cells (platelets, lymphocytes T and B, monocytes and erythrocytes) and cells of the vessel wall (endothelial and smooth muscle cells) (Amabile et al., 2010).
Figure 4.
A platelet microparticle is carrying not only specific membrane adhesion proteins (P-selectin, integrins – e. i. GPIIbIIIa, ), but also may harbour and transfer tissue factor (TF) which has its procoagulant potential and other functional effectors (E-selectin, von Willebrand factor, arachidonic acid, thromboxane A2), that can regulate aggregation, adhesion molecule expression, cell proliferation, apoptosis and endothelial migration. MPs may capsule messenger molecules (miRNA, DNA ?), cytokines, growth factors and calpains. Figure was produced using Servier Medical Art.
MPs from numerous cellular sources have been described in human plasma. They have received increasing attention as potential biomarkers of cell damage and activation or biovectors in blood coagulation, inflammation and cancer (Benameur et al., 2009; Hoyer et al., 2010). In several pathological states like dilated cardiomyopathy, chronic renal failure or cerebrovascular disease, MPs were used as biomarkers to identify a disease or to detect complications linked to a given disease (Bulut et al. 2011; Faure et al., 2006; Jung et al., 2009). Numerous clinical studies have evaluated their usefulness in the stratification of patients at risk for vascular disorders and to monitor response to treatment. Circulating MPs may serve as a marker for cardiovascular events in CAD patients or as a predictor of acute allograft rejection after heart transplantation (Morel et al., 2008; Sinning et al., 2010).
3.1. Microparticles discrimination and enumeration
The high level of microparticles’ diversity may create a problem with compatible masurement of MPs using different analytical methods. The number of microparticles depend on the detection technique and a wide range of pre-analytical variables, i.e. blood collecting and handling, plasma preparation and storage conditions. Therefore, optimization and standardization of detection methods are important to define microparticles correctly and to avoid falsely high or low quantification. Even minor protocol changes significantly affected MP levels (Ayers et al., 2011).
3.1.1. Flow cytometry in MP analysis
Several research have evaluated the impact of these different parameters to propose a pre-analytical protocol for MP analysis. Three ISTH Scientific and Standardization Subcommittees (SSC Vascular Biology, DIC, and Haemostasis &Malignancy) have initiated a project aimed at standardizing the enumeration of cellular MPs by means of flow cytometry method (FCM). The first collaborative workshop was set to establish the resolution and a threshold levels of the flow cytometers currently used in laboratories. Additionally, the interinstrument reproducibility of platelet MP enumeration in human plasma was analyzed (Lacroix et al., 2010). The study included 40 laboratories and 59 flow instruments were validated according to the protocol based on Megamix beads calibration to discriminate microparticles between 0.5 μm and 0.9 μm using the forward scatter (FS) channeling (FSC) parameter (FS/FSC). After that, selected laboratories received PFP samples prepared as frozen aliquots by the core laboratory, to avoid any preanalytic-linked variability. The authors found high discrepancy among Becton Dickinson instruments, as well within low, medium and high values of MP: coefficients of variation were 78%, 60% and 91%, respectively. Whereas interlaboratory reproducibilities were 30%,15% and 17% for low, medium and high values among Beckmann Coulter instruments. These data indicate that standardization of platelet MPs enumeration by FCM dependents on intrinsic characteristics of instruments. Moreover, standardization by calibrated beads such is useful tool for MP enumeration, however, calibrated beads do not reflect real condition of MPs in human plasma.
3.1.2. Indirect methods for MP enumeration
Alternative methods for MP enumeration based on TF-activity/antigen or platelet glycoprotein GPIb-integrin have been already described (Huisse et al, 2009; Kuriyama et al., 2010). The activity of tissue factor is evaluated using a chromogenic substrate for factor Xa, thus the ability of MPs to promote factor X activation in the presence of factor VII using a chromogenic activity assay is utilized (Huise et al, 2009). Alternatively, TF antigen or activity can be measured in plasma or whole blood (Key NS & Mackman N, 2010). However, determination of microparticle size is not possible by such approaches.
3.1.3. Pre-analytical variability in MP determination
The analysis of different protocols used in MP preparation showed that washing, centrifugation, filtration of buffer and long-term freezing influenced significantly the MP quantification (Ayers et al., 2011; Dey-Hazra et al., 2010). Freezing samples at -80°C decreased MP levels (Ayers et al., 2011; Shah et al., 2009). The second collaborative workshop was dedicated to propose a common pre-analytical protocol useful for standardization of pre-analytical variables in determination of MPs (Scientific and Standardization Committee 2010).
3.1.4. Specific antigens in MP discrimination
There are two main features of native MPs: the small size and the anionic phospholipid - PS on the outer leaflet of their membrane. In addition, MPs carry surface membrane antigens reflecting their cell of origin, including those induced by cellular activation, cell injury or apoptosis. These properties permit detection of specific subpopulations, such as endothelial, leucocyte or platelet-derived MPs (Diamant et al., 2004).
PS is specifically bound to annexin V and is recommended as a distinguish marker for MP enumeration (Bulut et al., 2011; Shah et al. 2009). However, a number of evidence suggests that some vesicles derived from endothelial cells are PS-negative by annexin-V labelling (Jimenez et al., 2003; Sekuła et al., 2011). In platelet-poor plasma obtained from healthy donors, 80% of platelet-derived MPs failed to bind annexin V (Connor et al., 2010). In this case, a phalloidin-staining of actin filaments could be helpful in discrimination of MPs and other cell fragments (Mobarrez et al., 2010). Washing samples as well as double centrifugation result in decreased annexin-V (Ayers et al. 2011).
3.1.4.1. Platelet MPs
Platelets constitute the main source of circulating procoagulant MPs under many physiological and pathophysiological situations (Geiser, et al., 1998; Huise et al, 2009; Kuriyama et al., 2010). Procoagulant platelet derived MPs are enriched in P-selectin (CD62P), cell surface protein (CD63), integrins: GPIIbIIIa (α2bβ3), GPIIb (α2b, CD41), GPIIIa (β3, CD61) and GPIb (CD42b), tissue factor (CD142, TF) or calpains (Figure 4).
Patients with unstable angina (UA) and AMI had a significantly increased number of procoagulant MPs: GPIIbIIIa-positive, CD62P-positive and CD41-positive (Huisse et al., 2009; Morel et al., 2004; Stankiewicz et al., 2007; van der Zee et al. 2006). The total number of platelet-derived MPs were numerically higher in patients with no recanalisation compared to patients with recanalisation (Huisse et al., 2009). However, we observed paradoxically lower number of CD62P-positive platelets in whole blood obtained from patients with ACS, than from SA patients, but the level of soluble P-selectin in plasma was significantly higher than in those with ACS (Figure 5). We may suspect that soluble P-selectin levels are derivatives of platelet origin MPs (Chung et.al., 2009).
3.1.4.2. Tissue factor-bearing MPs
It was shown by cell sorting with the specific marker CD42b that under resting conditions, blood-borne TF was mainly harbored by platelet-derived MPs (Müller et al, 2008). In acute coronary syndromes, TF triggers the formation of intracoronary thrombi following endothelial injury, activation of macrophages and apoptotosis of smooth muscle cells (SMCs) and macrophages (Morel et al, 2008). Apoptotic (annexin V-positive) MPs support a number of TF-positive MPs from different origin. Apoptotic macrophages and SMCs are the main source of membrane-bound TF and they contribute to TF accumulation. Formation of TF triggering MPs rich in PS provides a suitable anionic phospholipid surface for assembly of the tenase and prothrombinase complexes and thrombin activation (Del Conde et al., 2005).
Figure 5.
Platelet activation measured as a percentage of surface P-selectin-positive (CD62+) platelet (PLT), and by monocyte/platelet aggregates (MO/CD61+) and neutrophil/platelet aggregates (N/CD61+) in peripheral blood from patients with stable angina (SA) and acute myocardial infarction (AMI), and by levels of soluble P-selectin in patients with stable angina (SA), and acute myocardial infarction (AMI). Data are expressed as medians. *p<0.05,***p<0.00001 for the comparison.
Additionally, an increased number of TF-positive (CD142-positive) MPs in patients with ACS was observed (Figure 6) (Huisse et al., 2009; Steppich et al., 2005). Moreover, elevated levels of different origin TF-bearing MPs were significantly higher within the occluded coronary artery than in peripheral blood samples (Morel et al. 2009). It suggests their contrubution in coronary atherothrombosis and in situ formation of procoagulat MPs.
3.1.4.3. Endothelial microparticles
Endothelial microparticles (EMPs) are an emerging marker of endothelial cell (EC) activation and dysfunction and their circulating numbers are elevated in a number of pathologic states including cardiovascular disease. Many studies suggest that endothelial cell-derived MPs have a paracrine role and contribute to the development of endothelial dysfunction in most cardiovascular diseases: CAD, ACS, MI, hypertension and congestive heart failure. Moreover, diabetes, end-stage renal failure and pulmonary or venous embolism are strong factors bringing about EMP shedding [Bal et al., 2010; Chirinos et. al., 2005; Faure et al., 2006; Morel et al., 2004]. In this case patients have marked activation of endothelial, platelet, and leukocyte MPs.
Endothelial-derived microparticles (EMPs) may carry different endothelial originating coagulation factors, for example TF, which contribute to the clot formation and lysis (Chou et al., 2004; Stępień et al., 2007b). Patients with AMI displayed higher levels of all MPs than patients with SA and CD31-positive EMPs appeared the main source of procoagulant MPs (Morel et al., 2004). In patients with ACS significant correlations between both the total
Figure 6.
Representative dot plot of circulating microparticles (MPs) in a patient with acute coronary syndrome (ACS) and in a control voluntary. A, C - flow cytometry gating logic, MPs were initially gated by forward (FCS-H) and side scatter (SSC-H) in logarithmic scale; B, D - fluorescence plots show MPs binding of annexin V-FITC (FL1-H) and anti-CD142-PE (FL2-H) monoclonal antibody.
number as well as the level of CD34, CD51 and CD142 were observed (Stankiewicz et al., 2007). Moreover, increased number of EMPs (E-cadherin/CD144-positive MPs) was an independent predictor of future cardiovascular events (HR 2.42 [range 1.03 to 5.68), p=0.04), but not for all-cause mortality (HR 2.10 [range 0.83 to 5.32] p=0.12) in patients with heart failure (Nozaki et al., 2010) and the assessment of EMPs improved prediction of future cardiovascular events in patients with CAD (Nozaki et al., 2009).
4. Clotting
Clotting is a rapid and highly dynamic process, which involves both platelets and coagulation factors. To monitor the clotting process a lot of instrumentations and methods are engaged: i) clotting times: the activated partial thromboplastin time (aPTT) and the prothrombin time (PT); ii) thromboelastography; iii) assessment of thrombin generation markers and thrombin inhibitors; iv) the real-time monitoring of thrombin generation. This section will focus on the prognostic significance of thrombin generation markers in stable and unstable CAD.
4.1. Markers of thrombin generation in CAD patients
Antithrombin (AT) appears to be the most important stoichiometric inhibitor which forms equimolar complexes with thrombin molecules – TAT (thrombin-antithrombin) complexes. A concentration of TAT complexes measured in peripheral venous blood and in blood collected at the site of microvascular injury reflect thrombin generation. TAT complexes are expressed during clot formation and there are (alike fibrinopeptide A and F 1+2 fragments) markers of thrombin activation (Pelzer et al., 1988; Pelzer et al., 1991). These markers are elevated in pro-thrombotic conditions In patients with cardiovascular disease, the detection of a prothrombotic state may have two major implications: i) to extend the duration and ii) to monitor the dose of anticoagulation after cardiac intervention. The thrombin plasma activity is very firmly associated with CAD.
The potential coagulation activity in plasma can be evaluated by the rate of thrombin formation and the total amount of formed thrombin is measured by means of chromogenic or fluorescence methods (Devreese et al., 2007; Hemker et al., 2002). This thrombin potential in plasma can be assessed by different methods and the Calibrated Automated Thrombogram (CAT) applies a fluorogenic substrate. A chromogenic substrate is used in Behring Coagulation System (BCS). In both methods thrombin generation is activated by diluted recombinant tissue factor (TF), but in the BCS method a non-defined fibrin aggregation inhibitor is present. Both methods are applied in diagnostics. In CAT a calibration factor is measured in a plasma sample identical to that in which thrombin generation is being determined and the course of the calibration factor is assessed during the entire measurement (Figure 7). Thrombin generation assays seem to be useful in endogenous TF assessment (Ollivier et al. 2010; Stępień et al., 2007a).
4.2. Blood sampling for coagulation markers assessment
The most important think in coagulation diagnostics is to apply a reliably sampling method. To ensure accurate measurement samples must be collected in the circumstances under which false elevations of molecular markers of hemostatic and fibrinolytic activation will not occur. Thus, atraumatic antecubital venipuncture into vacutainer containing buffered sodium citrate is essential and the contamination with calcium or magnesium should be avoided (van den Besselaar et al., 2007; Stegnar et al., 2007). To avoid activation of coagulation by tissue thromboplastin, each collection of citrated plasma should be preceded by a serum tube. Duration of needle puncture, rather than duration of tourniquet use, produced the greatest elevation in plasma levels of TAT and F1+2 (Omote et al., 2008).
Figure 7.
The rate of thrombin formation is presented as the thrombin concentration against time curve. Three parameters are presented: lag time (Tlag), peak height (Cmax) and endogenous thrombin potential (ETP).
4.3. Prognostic value of thrombin generation in cardiac events
Increased circulating levels of thrombin and its markers characterize ACS (Ardissino et al., 2003; Takano et al., 1991). Plasma F1+2, normally about 1 nM, is roughly 1.5-2-fold higher than observed in SA patients, reaching maximum values in AMI (Ardissino et al., 2001). U-shaped relationship between plasma prothrombin fragment 1+2 levels and the risk of developing cardiac death or renewed myocardial infarction was observed. Intermediate levels (1.5-1.9 nM) were associated with the lowest risk, whereas both higher (>1.9 nM) and lower (< 1.5 nM) values were associated with an increased risk (RR 1.56 [range 1.25 to 2.28] and RR 1.35 [range 1.11 to 1.86], respectively) (Ardissino et al., 2003). Hypercoagulable state measured as thrombin-antithrombin complexes (TAT) levels and as calibrated automated thrombogram reflects vascular impairment in CAD patients (Stępień et al., 2007a). It was observed that high TAT levels may predict mortality in chronic heart disease group after adjustment for classic risk factors (Marcucci et al., 2006). In empirical reconstruction, simulated maximum thrombin levels (p<0.01) and rates (p<0.01) were 50% higher with ACS while the initiation phases of thrombin generation were shorter than in patients with stable CAD (Brummel-Ziedins et al., 2008). Elevated levels of thrombin derivatives are associated with clinical risk factors for stroke (Lane et al., 1983; Takano et al., 1991). Elevated thrombin concentration reflects hypercoagulable state in patients with hypertension (Hoeper et al, 1998; Kłoczko et al., 1996), hyperglycaemia (Undas et al., 2008) and hypercholesterolemia (Wada et al., 1992; Sanguigni et al., 2005; Undas et al., 2005).
5. Conclusion
Endothelial and platelets activation leading to cardiovascular complications can be evaluated quantitatively by measurement of plasma levels of circulating MPs. Moreover, a multiple biomarkers strategy that includes bone remodeling biomarkers (OPG, OPN) and clotting properties can provide better risk stratification of cardiovascular events. Development and discovery of new biomarkers may improve clinical assessment of patients
who might benefit more from treatment. Synergistic strategies in diagnostics seem to be more advantageous than routine method in prognosis and patients’ management.
Acknowledgments
The author is a Secretary of the Board of the Polish College of Laboratory Medicine (KMLP). KMLP is a multispecialty society dedicated to the advancement of education, development and management in clinical biochemistry, hematology, immunology, toxicology, pathology and cytology, clinical genetics, microbiology and molecular biology.
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Introduction",level:"1"},{id:"sec_2",title:"2. Bone remodelling biomarkers",level:"1"},{id:"sec_2_2",title:"2.1. Biology of bone remodelling of biomarkers",level:"2"},{id:"sec_3_2",title:"2.2. Osteoprotegerin and osteopontin as risk factors of coronary artery disease",level:"2"},{id:"sec_5",title:"3. Microparticles",level:"1"},{id:"sec_5_2",title:"3.1. Microparticles discrimination and enumeration",level:"2"},{id:"sec_5_3",title:"3.1.1. Flow cytometry in MP analysis",level:"3"},{id:"sec_6_3",title:"3.1.2. Indirect methods for MP enumeration",level:"3"},{id:"sec_7_3",title:"3.1.3. Pre-analytical variability in MP determination",level:"3"},{id:"sec_8_3",title:"3.1.4. Specific antigens in MP discrimination",level:"3"},{id:"sec_8_4",title:"3.1.4.1. Platelet MPs",level:"4"},{id:"sec_9_4",title:"3.1.4.2. Tissue factor-bearing MPs",level:"4"},{id:"sec_10_4",title:"3.1.4.3. Endothelial microparticles",level:"4"},{id:"sec_14",title:"4. Clotting",level:"1"},{id:"sec_14_2",title:"4.1. Markers of thrombin generation in CAD patients",level:"2"},{id:"sec_15_2",title:"4.2. Blood sampling for coagulation markers assessment",level:"2"},{id:"sec_16_2",title:"4.3. Prognostic value of thrombin generation in cardiac events",level:"2"},{id:"sec_18",title:"5. Conclusion",level:"1"},{id:"sec_19",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'AlcockR. F.RoyP.AdoriniK.LauG. T.KritharidesL.LoweH. C.BriegerD. B.FreedmanS. B.2010Incidence and determinants of myocardial infarction following percutaneous coronary interventions according to the revised Joint Task Force definition of troponin T elevation. Int J Cardiol. 1401April 2010), 6672\n\t\t\t'},{id:"B2",body:'AmabileN.RautouP. E.TedguiA.BoulangerC. M.2010Microparticles: key protagonists in cardiovascular disorders. Semin Thromb Hemost.\n\t\t\t\t\t368November 2010), 907916\n\t\t\t'},{id:"B3",body:'AnandD. 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Chair and Department of Clinical Biochemistry,Jagiellonian University Faculty of Medicine, Krakow, Poland
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\n
1. Introduction
\n
Science fiction has motivated intelligent minds to enhance the quality of living for the last century. A well-known example in fantasy is bionic limbs controlled by the mind. Individuals who have lost or permanently damaged limbs can benefit from procuring an aesthetically pleasing and fully functioning bionic replacement to restore or improve their quality of life. The field of biomaterials engineering has been making monumental advances by producing devices such as biosensors, bioMEMS, and artificial hearts [1–3]. There is a continuous growth in population today, demanding the attention from biomedical fields to improve lifestyles and create better body functionality. Although current devices’ interfaces with the human body have come a long way, there is still a long way to go in the fabrication methods of the required scaffolds.
\n
This chapter outlines one single pathway of research done to broaden the opportunities in the biomaterials industry. The bioactivity of laser-treated silicon is investigated through the use of in vitro testing. This research investigates of the trends of different laser parameters, including power, frequency, scanning speed, line spacing, and overlap number.
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1.1. Challenges in the biomaterials industry
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The current challenges in the biomedical field include finding biocompatible and bioactive organic or inorganic materials and simplifying manufacturing processes. Devices that are implanted inside the body require materials that are biocompatible; the behavior of this material when interacting with the human body must not have any toxic effects and must perform a specific task. For a material to be bioactive, it requires to be biocompatible and have a biological effect and provoke a positive and controlled biological response. Current biocompatible materials that are commonly used are gold, titanium, polymers, bioceramics, and composites [4–8].
\n
Silicon was chosen as the material for this research due to its abundance and semiconductor abilities. Microfabricated silicon is widely used today in the microelectronics and photovoltaics industry [9, 10]. Silicon in its pure form is not biocompatible [11, 12]. However, silicon can be packaged in a biocompatible material such as titanium [13, 14]. It has been found that porous silicon is biocompatible [11]. The current method used for creating a porous layer is etching with hydrochloric acid. Acid etching is a long process that requires the use of dangerous chemicals and is consequently environmentally friendly. The challenge that silicon faces in the biomaterials industry is to find a superior surface alteration method.
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\n
\n
1.2. Laser processing
\n
Technology that easily controls and creates an accurate pattern on a microscale is required in the microelectronics industry. A good solution to this criterion is a laser, which has been commonly used for surface texturing of steel [15, 16]. It was found that this method of surface treatment allowed the generation of micropores with different characteristics. Unlike acid etching, a laser is great for the modification of silicon since it is very clean, high resolution, and controllability of intensity and depth of penetration. The Nd:YAG pulsed laser is a particularly good solution since it is cost-effective, stable, and has the required high power range. Another advantage to this method is that there are no chemicals involved, which eliminates the complex processes of preparation and environmental concerns. Above all, using a laser is a single-step process. The economic and simplistic benefits that are associated with this approach are valuable to the biomedical industry.
\n
High-end picosecond and femtosecond pulsed laser systems have also been used for generation of porous silicon particles [17, 18]. In this research, it is found that a Nd:YAG nanosecond pulse laser can achieve the desired biocompatible silicon. The nanosecond laser is much more economical and commercially available than the faster pulse lasers. The nanosecond laser is also currently used in the medical industry for procedures such as eye and dental surgeries [19, 20].
\n
\n
\n
\n
2. Laser processing and surface characterization
\n
Microfabrication with lasers is becoming increasingly popular in many industries including biomaterials [21]. Laser irradiation introduces surface irregularities and chemical changes to the silicon surface. The laser irradiates a simple line pattern onto pure silicon with <100> orientation. There are a number of methods used to analyze the condition of the laser-processed substrat. Images of the samples are taken with field emission scanning electron microscopy (FESEM) and 3D optical microscopy.
\n
\n
2.1. Laser system
\n
The laser used in this research is a SOL-20 1064 nm Nd:YAG nanosecond laser by Bright Solutions Inc. The JD2204 Sino Galvo two-axis Galvo scanner has an input of 10 mm and a beam displacement of 13.4 mm. The theoretically determined spot size diameter is 19 μm. The laser pulses can range from lengths of 6 to 35 ns, frequencies of 10 to 100 kHz, powers up to 20 W, and scanning speeds up to 3000 mm/s. For this research, scanning speeds of 100–1000 mm/s, powers of 7, 10, and 15 W, and frequencies of 25, 70, and 100 kHz are used. Line spacing varies from 0.025, 0.05, and 0.10 mm. Overlap number, or number of times the laser repeats the same pattern, varies from 1, 2, and 3. The manipulation of these parameters is easily executed through the laser-operating software.
\n
\n
\n
2.2. Biocompatibility evaluation
\n
The biocompatibility of a material is influenced by surface roughness, reflectivity, and chemical content of the substrate. The chemical content is assessed using micro-Raman and energy-dispersive X-ray (EDX) analysis. The surface roughness is determined with the use of 3D optical microscopy, and the reflectivity is determined with light spectroscopy. The biocompatibility is also determined with the use of simulated body fluid, which is a form of in vitro assessment—testing done outside of the body. Simulated body fluid is a solution that mimics the ion concentration of human blood plasma. When a biocompatible material is submerged in the liquid, hydroxyapatite forms on the surface [22, 23]. The submerged samples in the SBF are kept in an incubator at 36.5°C for up to 6 weeks.
\n
Cell interactions with the laser-processed silicon substrate are also evaluated with cultured mouse embryonic fibroblasts (NIH 3T3). Cells are seeded at 2400 cells/cm2 in triplicate and incubated for 72 h at 37°C. The samples are incubated under 5% CO2 in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% heat-inactivated calf serum. Phosphate-buffered saline (PBS) was then used to rinse the nonadherent cells from the samples overnight at 4°C. Staining was then done to the samples with phalloidin (1:2000 dilution) and draq5 (1:10,000 dilution) overnight for fluorescence imaging.
\n
\n
\n
2.3. Temperature evaluation
\n
Different frequencies, powers, and pulse widths change the behavior of the laser pulses. Determining the temperature will help investigate the pulse energy and how it affects the surface topographic properties and chemical structure. The surface temperature is modeled using the two-dimensional heat equation in cylindrical coordinates. The heat equation in cylindrical coordinates is found in Eq. (1).
where κ is the heat conduction coefficient, ρ is the material density (kg/m3), c\n\np\n is the specific heat (J/kgK), and \n\n\n\nq\n˙\n\n\n\n is the rate at which energy is generated per unit volume of the medium (W/m3) [24]. Since it is assumed that κ, ρ, and c\n\np\n are constant, and there is no energy generation within the silicon (\n\n\n\n\nq\n˙\n\n\n=\n0\n\n\n), Eq. (1) can be simplified into Eq. (2).
where a = κ/ρc\n\np\n, which is the thermal diffusivity (m2/s). The boundary conditions for this equation include the initial temperature being room temperature, the pulse intensity is at its maximum during the pulse at z = 0, the intensity is zero between pulses, and the temperature change is zero at r = ∞, and z = ∞. With these conditions, the single-pulse temperature change of a high absorption material, ΔT, for a square pulse, can be obtained (Eq. (3)) [25].
where I\n\nmax\n (Eq. (4)) is the peak intensity which is the peak power divided by the spot area, P\n\nmeasured\n is the experimental measured power, f is the pulse frequency, τ is the pulse duration, γ is equal to the Fresnel energy reflectivity (R) subtracted from 1 (1–R) with a R value of 0.325 and a γ value of 0.675, κ is Silicon’s diffusivity with a value of 9.07×10−3 m2/s, K is Silicon’s conductivity with a value of 155 W/mK, τ is the laser pulse duration, W is the beam’s filed radius (1/e) with a value of 1.94×10−5 m, z is the ablation pit depth, and r is the ablation pit radius. Using this equation with the assumption that the pulse is square-shaped, the temperature can be determined at the center of ablation (r = 0) and at the surface (z = 0). From Eq. (3), an analytical expression can be made to determine the depth of the ablated groove at the center of the ablation with respect to radius by using the mean value theorem.
where β is an experimentally determined correction factor of 0.5 and ΔT\n\nB\n is silicon’s boiling temperature of 2972 K. A more accurate representation of the experimental results found in Eqs. (6) and (7) will determine the ablation depth after a train of pulses. Each consecutive pulse adds to the penetration of the preceding pulse, S, resulting in a deeper groove.
where S is the spatial separation between each consecutive pulse, v_scan is the scanning speed in m/s, and f is the pulse frequency.
\n
\n
\n
\n
3. Effect of scanning parameters
\n
The three scanning parameters discussed in this section are overlap number, line spacing, and scanning speed. Each of these is easily set through the laser-operating software. These parameters have a direct effect of the surface topography and oxidation levels of the silicon substrates.
\n
\n
3.1. Topography analysis
\n
The field emission scanning electron microscope images are an effective way of investigating the physical results from the laser ablation on the silicon samples. \nFigure 1\n shows the effect of different overlap numbers. At 1 overlap (OL), the line pattern is distinct and relatively clean. When the OL number is increased to 2, the line pattern is less definite and contains more irregularities. Finally, increasing the overlap number once again to 3, the line pattern is almost unrecognizable with a substantial amount of imperfections.
\n
Figure 1.
FESEM images of pattern overlaps of 1, 2, and 3 with a line spacing of 0.10 mm, a laser power of 10.5 W, a frequency of 100 kHz, and a scanning speed of 400 mm/s.
\n
The effect of different line spacing was then investigated with FESEM. The overlap number was kept constant at 1. At the largest line spacing of 0.10 mm, the line pattern is discrete. At the smallest line spacing of 0.025 mm, the amount of imperfections is high with no distinctive line separation. The effect of line spacing can be seen in \nFigure 2\n.
\n
Figure 2.
FESEM images of line spacing of 0.025, 0.05, and 0.10 mm with an overlap number of 1, a laser power of 13.3 W, a frequency of 100 kHz, and a scanning speed of 400 mm/s.
\n
Knowing that a higher overlap number and a smaller line spacing made for the highest level of laser-ablated substrate, a sample with 0.025 mm line spacing and three overlaps was created to observe the surface characteristics. The high magnification FESEM image in \nFigure 3\n of this sample shows a nanofibrous substrate. These nanofibers are the result of a high-energy reactive plume that forms on the surface during laser ablation [26]. The plume generates a heat-affected zone that causes the silicon ions to react with the oxygen ions, creating these nanoscale SiO2 fibers [27–29].
\n
Figure 3.
Presence of nanofibers detected on FESEM image of sample with three overlaps and 0.025 mm line spacing with power of 13.3 W, frequency of 100 kHz, and a scanning speed of 400 mm/s.
\n
The effect of scanning speed was then investigated with 3D optical microscopy. The data of the results from scanning speeds of 100, 200, 500, 800, and 1000 mm/s are mapped and compared in \nFigure 4\n. It is expected that the lower scanning speeds have larger depths due to a higher number of pulses ablating the surface area. However, with a closer look at \nFigure 4\n, the lower scanning speeds have shallow depths and a relatively high wall of built-up material along the sides of the groove. At higher scanning speeds, when the pulses are farther apart, there is less penetration on the surface, leading to a shallower groove. At lower scanning speeds, the high-temperature ablated material from the walls caves back into the deep groove and solidifies into a much shallower groove than initially dredged. Scanning speeds of 200 and 800 mm/s show a deep groove with a smaller amount of material built up than the 100 mm/s sample.
\n
Figure 4.
Profile data from the 3D optical microscope for scanning speeds of 100, 200, 500, 800, and 1000 mm/s at an overlap number of 1, a power of 15 W, and a frequency of 100 kHz.
\n
Eqs. (6) and (7) are then used to find the theoretical ablation depths at various scanning speeds. Observing the trend in \nFigure 5\n, it is clear that the ablation depth decreases with increasing scanning speed. Both the experimental data and theoretical results are in close agreement.
\n
Figure 5.
Ablation depth after a train of pulses at difference scanning speeds at a power of 15 W and a frequency of 100 kHz.
\n
\n
\n
3.2. Bioactivity assessment
\n
Each sample was submerged in simulated body fluid (SBF) for 6 weeks and kept at a constant temperature of 36.5°C. The samples were then emerged from the fluid and assessed with energy-dispersive X-ray (EDX). The SBF-soaked samples were found to contain a traces of phosphorous and calcium, which is indicative of the presence of a bone-like apatite. Hydroxyapatite is formed by the nucleation of calcium phosphate ions [5, 27]. The silicon oxide layer created by the laser plume has a negative charge, which attracts the positively charged calcium phosphate. The resulting substrate contains this bone-like apatite, which was seen on the laser-treated silicon samples. The EDX results from the sample with three overlap and 0.025 mm line spacing can be seen in \nFigure 6\n.
\n
Figure 6.
EDX results of sample with three overlaps, 0.025 mm line spacing, 400 mm/s, a power of 13.3 W, and a frequency of 400 mm/s.
\n
This presence of bone-like apatite confirms that the biocompatibility of pure silicon was enhanced with nanosecond laser pulses. A smaller line spacing and higher overlap number generates more SiO2 nanofibers, which provides a favorable site for the nucleation of apatite.
\n
\n
\n
\n
4. Effect of frequency
\n
The range of frequencies used in this section is 25, 70, and 100 kHz. For these experiments, the scanning speed was kept constant at 100 mm/s, the power at 15W, and the overlap number at 1.
\n
\n
4.1. Topography analysis
\n
\n\nFigures 7\n and \n8\n show the topography changes in the frequency samples. A lower frequency produces a wider and shallower groove, while the higher frequencies yield a thinner yet deeper ablated groove. The theoretical results in for a single pulse in \nFigure 9\n show that the groove increases in depth and decreases in width as frequency increases, which is in close agreement with the experimental results.
\n
Figure 7.
3D optical microscopy images of samples with frequencies of 25, 70, and 100 kHz at a power of 15 W, a scanning speed of 100 mm/s, and 1 overlap.
\n
Figure 8.
Experimental profile data from the 3D optical microscope for frequencies of 25, 70, and 100 kHz at an overlap number of 1, a power of 15 W, and a scanning speed of 100 mm/s.
\n
Figure 9.
Theoretical profile data for a single pulse for frequencies of 25, 70, and 100 kHz at an overlap number of 1, a power of 15 W, and a scanning speed of 100 mm/s.
\n
\n
\n
4.2. Temperature analysis
\n
The temperature is determined for each frequency with Eq. (3) and can be found in \nFigure 10\n. The higher temperatures are associated with the lower frequencies on both z-axis and r-axis. By increasing the frequency, the pulse energy decreases which results in lower temperatures and a smaller heat-affected zone [30]. Due to reduced size of the heat-affected zone, the shape of the groove consequently changes size as well. This results in the thinner grooves at higher frequencies. Each recurring pulse adds to the penetration of preceding pulse. Higher frequencies have more pulses, resulting in a deeper penetration of the surface, which develops a trench with a larger depth.
\n
Figure 10.
The single-pulse temperature on the surface of the silicon with respect to radius.
\n
\n
\n
4.3. Bioactivity assessment
\n
Mouse embryonic fibroblast cell interactions were examined for each frequency. The cell count in \nFigure 11\n establishes that there are a higher number of cells in the higher frequency grooves. The cells show a strong preference for the treated areas and show avoidance in the untreated areas. There is also a presence of fibronectin within the cells, which is an ECM protein secreted during embryonic development and wound healing, potentially leading to collagen deposition and tissue morphogenesis [30, 31]. These results confirm that the biocompatibility is enhanced with higher frequencies.
\n
Figure 11.
The number of cells within the laser-treated groove for each frequency as well as the amount of cells outside the groove within 100 μm from the edge of the groove [30].
\n
\n
\n
\n
5. Effect of power
\n
Laser power immensely influences the surface properties when treatment is done to a material. The power for this section varies from 7, 10 to 15 W. For these experiments, the frequency was kept constant at 100 kHz, the scanning speed was set to 400 mm/s, and the overlap number was 1.
\n
\n
5.1. Topography analysis
\n
The FESEM images of each power sample are shown in \nFigure 12\n. The experimental 3D optical microscopy profile data is shown in \nFigure 13\n. These results show that at higher powers, the groove will increase in both width and depth. Unlike the frequency trends, the size of the heat-affected zone increases with power. The theoretical single-pulse depths found with Eq. (5) are shown in \nFigure 14\n. These results also show that the groove width and depth increase with power and are in close agreement with the experimental results.
\n
Figure 12.
FESEM images of samples with powers of 7, 10, and 15 W at a frequency of 100 kHz, a scanning speed of 400 mm/s, and 1 overlap.
\n
Figure 13.
Experimental profile data from the 3D optical microscope for powers of 7, 10, and 15 W at an overlap number of 1, a frequency of 100 kHz, and a scanning speed of 400 mm/s.
\n
Figure 14.
Theoretical profile data for a single pulse for powers of 7, 10, and 15 W with an overlap number of 1, a frequency of 100 kHz, and a scanning speed of 400 mm/s.
\n
\n
\n
5.2. Temperature analysis
\n
The temperature is determined for each power with Eq. (3) and can be found in \nFigure 15\n. As expected, the higher temperatures are found with higher powers. At lower powers, the heat-affected zone is smaller, allowing for both a thinner and shallower groove. When the pulse power is increased, the temperatures in the high-density plume are increased, causing more generation of the SiO2 nanofibers [26].
\n
Figure 15.
The single-pulse temperature on the surface of the silicon with respect to radius. Tb is the boiling temperature of silicon at 3538 K.
\n
\n
\n
5.3. Bioactivity assessment
\n
Samples were once again assessed with fibroblast culturing for each power. When viewing the cell interactions under the microscope, cells were accumulated inside the laser-treated area as expected. Interestingly, the cell count was low directly beside the grooves and began to become more concentrated farther away from the edge of the groove. The fibroblasts avoided the zones immediately beside the grooves on each side. This can be seen in \nFigure 16\n.
\n
Figure 16.
Fibroblast cells avoiding the zones beside the grooves for powers of 7, 10, and 15 W.
\n
This phenomenon is a result from the shockwave that is generated from the high-energy plume during laser ablation [26]. The shockwave transfers energy to the surface with results in high intensity thermal stress. The thermal shock causes a small zone directly beside the ablated areas, which contains residual stress. When the power is increased, there is a larger transfer energy, resulting in a larger stress zone. These residual stress zones contain mismatched crystal orientations due to tensile stresses causing crystal distortion.
\n
Knowing that the power level of the laser pulse can control the residual stress zone size and cell behavior, this research can provide opportunities in cell manipulation and cell programming.
\n
\n
\n
\n
6. Summary
\n
This chapter aims to introduce nanosecond laser processing for the enhancement of biocompatibility of pure silicon for various biomedical technologies. These results can contribute to the design of manufacturing processes of innovative biomedical devices to enhance the quality of living for a number of individuals. This research investigates the trends of various laser parameters including three scanning parameters (line spacing, overlap number, and scanning speed), pulse frequency, and laser power. Biocompatible in vitro assessment was conducted through the use of simulated body fluid (SBF) and cell culturing with NIH 3T3 fibroblasts. Samples with smaller line spacing and higher overlap numbers showed more generation of SiO2 nanofibers, which were shown to be biocompatible under SBF assessment. Biocompatibility increased with frequency due to the SiO2 being more prominent on high frequency samples and containing more fibroblast cell proliferation. Fibroblasts also showed preference to higher powers. However, the heat-affected zone immediately outside the ablated areas showed a mismatch of crystal orientations causing residual stress. These stress zones were avoided by cells, which led to promising results for the potential in cell programming and manipulation.
\n
\n\n',keywords:"silicon, nanofibers, Nd:YAG laser, fibroblasts",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/56144.pdf",chapterXML:"https://mts.intechopen.com/source/xml/56144.xml",downloadPdfUrl:"/chapter/pdf-download/56144",previewPdfUrl:"/chapter/pdf-preview/56144",totalDownloads:729,totalViews:166,totalCrossrefCites:0,dateSubmitted:"October 20th 2016",dateReviewed:"May 22nd 2017",datePrePublished:"December 20th 2017",datePublished:"February 14th 2018",dateFinished:null,readingETA:"0",abstract:"The increasing demand for new biomaterials and fabrication methods provides an opportunity for silicon to solve current challenges in the field. Laser processing is becoming more common as the public begins to understand its simplicity and value. When an abundant material is paired with a reliable and economic fabrication method, biomedical devices can be created and improved. In this chapter, different laser parameters of the Nd:YAG laser are investigated and the topographic and physical trends are analyzed. The biocompatibility is assessed for scanning speed, line spacing, overlap number, pulse frequency, and laser power with the use of simulated body fluid (SBF) and fibroblast culturing (NIH 3T3). Not only can nanosecond pulses increase the biocompatibility of silicon by generating silicon oxide nanofibers, but the substrate becomes bioactive with the manipulation of cell interactions.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/56144",risUrl:"/chapter/ris/56144",signatures:"Candace Colpitts and Amirkianoosh Kiani",book:{id:"5951",title:"Biomaterials in Regenerative Medicine",subtitle:null,fullTitle:"Biomaterials in Regenerative Medicine",slug:"biomaterials-in-regenerative-medicine",publishedDate:"February 14th 2018",bookSignature:"Leszek A. Dobrzański",coverURL:"https://cdn.intechopen.com/books/images_new/5951.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"15880",title:"Prof.",name:"Leszek A.",middleName:null,surname:"Dobrzański",slug:"leszek-a.-dobrzanski",fullName:"Leszek A. Dobrzański"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"183144",title:"Dr.",name:"Amirkianoosh",middleName:null,surname:"Kiani",fullName:"Amirkianoosh Kiani",slug:"amirkianoosh-kiani",email:"a.kiani@unb.ca",position:null,institution:{name:"University of New Brunswick",institutionURL:null,country:{name:"Canada"}}},{id:"198976",title:"Ms.",name:"Candace",middleName:null,surname:"Colpitts",fullName:"Candace Colpitts",slug:"candace-colpitts",email:"j4y1x@unb.ca",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Challenges in the biomaterials industry",level:"2"},{id:"sec_2_2",title:"1.2. Laser processing",level:"2"},{id:"sec_4",title:"2. Laser processing and surface characterization",level:"1"},{id:"sec_4_2",title:"2.1. Laser system",level:"2"},{id:"sec_5_2",title:"2.2. Biocompatibility evaluation",level:"2"},{id:"sec_6_2",title:"2.3. Temperature evaluation",level:"2"},{id:"sec_8",title:"3. Effect of scanning parameters",level:"1"},{id:"sec_8_2",title:"3.1. Topography analysis",level:"2"},{id:"sec_9_2",title:"3.2. Bioactivity assessment",level:"2"},{id:"sec_11",title:"4. Effect of frequency",level:"1"},{id:"sec_11_2",title:"4.1. Topography analysis",level:"2"},{id:"sec_12_2",title:"4.2. Temperature analysis",level:"2"},{id:"sec_13_2",title:"4.3. Bioactivity assessment",level:"2"},{id:"sec_15",title:"5. Effect of power",level:"1"},{id:"sec_15_2",title:"5.1. Topography analysis",level:"2"},{id:"sec_16_2",title:"5.2. Temperature analysis",level:"2"},{id:"sec_17_2",title:"5.3. Bioactivity assessment",level:"2"},{id:"sec_19",title:"6. Summary",level:"1"}],chapterReferences:[{id:"B1",body:'\nTurner AP, Pickup JC. Diabetes mellitus: Biosensors for research and management. Biosensors. 1985;1(1):85-115\n'},{id:"B2",body:'\nGrayson AR, et al. A BioMEMS review: MEMS technology for physiologically integrated devices. Proceedings of the IEEE. 2004;92(1):6-21\n'},{id:"B3",body:'\nCopeland JG, et al. Cardiac replacement with a total artificial heart as a bridge to transplantation. The New England Journal of Medicine. 2004;351(9):859-867\n'},{id:"B4",body:'\nLi X, et al. The use of nanoscaled fibers or tubes to improve biocompatibility and bioactivity of biomedical materials. Journal of Nanomaterials. 2013;2013:14\n'},{id:"B5",body:'\nRadmanesh M, Kiani A. Effects of laser pulse numbers on surface biocompatibility of titanium for implant fabrication. Journal of Biomaterials and Nanobiotechnology. 2015;6:168\n'},{id:"B6",body:'\nSwetha M, et al. Biocomposites containing natural polymers and hydroxyapatite for bone tissue engineering. International Journal of Biological Macromolecules. 2010;47(1):1-4\n'},{id:"B7",body:'\nYue Z, et al. Controlled delivery for neuro-bionic devices. Advanced Drug Delivery Reviews. 2013;65(4):559-569\n'},{id:"B8",body:'\nVallet-Regí M, Colilla M, González B. Medical applications of organic–inorganic hybrid materials within the field of silica-based bioceramics. Chemical Society Reviews. 2011;40(2):596-607\n'},{id:"B9",body:'\nGreen ML, et al. Ultrathin (<4 nm) SiO2 and Si–O–N gate dielectric layers for silicon microelectronics: Understanding the processing, structure, and physical and electrical limits. The Journal of Applied Physics. 2001;90(5):2057-2121. DOI: http://dx.doi.org/10.1063/1.1385803\n\n'},{id:"B10",body:'\nJeong S, et al. Hybrid silicon nanocone-polymer solar cells. Nano Letters. 2012;12(6):2971-2976. Available: http://dx.doi.org/10.1021/nl300713x. DOI: 10.1021/nl300713x\n\n'},{id:"B11",body:'\nBuckberry L, Baylis S. Porous silicon as a biomaterial. Materials World. 1999;7:213-215\n'},{id:"B12",body:'\nShaoqiang C, et al. Hydroxyapatite coating on porous silicon substrate obtained by precipitation process. Applied Surface Science. 2004;230(1):418-424\n'},{id:"B13",body:'\nMwenifumbo S, et al. Cell/surface interactions on laser micro-textured titanium-coated silicon surfaces. The Journal of Materials Science: Materials in Medicine. 2007;18(1):9-23\n'},{id:"B14",body:'\nMyllymaa S, et al. Adhesion, spreading and osteogenic differentiation of mesenchymal stem cells cultured on micropatterned amorphous diamond, titanium, tantalum and chromium coatings on silicon. Journal of Materials Science: Materials in Medicine. 2010;21:329-341\n'},{id:"B15",body:'\nVilhena LM, et al. Surface texturing by pulsed Nd:YAG laser. Tribology International. 2009;42(10):1496-1504. DOI: dx.doi.org/10.1016/j.triboint.2009.06.003\n'},{id:"B16",body:'\nRadmanesh M, Kiani A. Nd: YAG laser pulses ablation threshold of stainless steel 304. Materials Sciences and Applications. 2015;6(07):634\n'},{id:"B17",body:'\nBonse J, et al. Femtosecond laser ablation of silicon–modification thresholds and morphology. Applied Physics A. 2002;74(1):19-25\n'},{id:"B18",body:'\nKiani A, et al. Leaf-like nanotips synthesized on femtosecond laser-irradiated dielectric material. The Journal of Applied Physics. 2015;117(7):074306\n'},{id:"B19",body:'\nWood JP, et al. Nanosecond pulse lasers for retinal applications. Lasers in Surgery and Medicine.2011;43(6):499-510\n'},{id:"B20",body:'\nAl-Hadeethi Y, et al. Data fitting to study ablated hard dental tissues by nanosecond laser irradiation. PLoS One. 2016;11(5):e0156093\n'},{id:"B21",body:'\nKhademhosseini A, et al. Microscale technologies for tissue engineering and biology. Proceedings of the National Academy of Sciences of the United States of America. 2006;103(8):2480-2487. DOI: 0507681102 [pii]\n'},{id:"B22",body:'\nCanham LT. Bioactive silicon structure fabrication through nanoetching techniques. Advanced Materials. 1995;7(12):1033-1037\n'},{id:"B23",body:'\nKamitakahara M, et al. Bioactivity and mechanical properties of polydimethylsiloxane (PDMS)-CaO-SiO2 hybrids with different PDMS contents. The Journal of Sol-Gel Science and Technology. 2001;21(1-2):75-81\n'},{id:"B24",body:'\nIncropera FP, et al. Introduction to convection. In: Anonymous, editors. Introduction to Heat Transfer. 5th ed. Danvers, MA, USA: John Wiley & Sons; 2007. pp. 57\n'},{id:"B25",body:'\nHendow ST, Shakir SA. Structuring materials with nanosecond laser pulses. Optics Express. 2010;18(10):10188-10199\n'},{id:"B26",body:'\nTavangar A, Tan B, Venkatakrishnan K. Study of the formation of 3-D titania nanofibrous structure by MHz femtosecond laser in ambient air. The Journal of Applied Physics. 2013;113(2):023102\n'},{id:"B27",body:'\nTavangar A, Tan B, Venkatakrishnan K. The influence of laser-induced 3-D titania nanofibrous platforms on cell behavior. Journal of biomedical nanotechnology. 2013;9(11):1837-1846\n'},{id:"B28",body:'\nKiani A, Venkatakrishnan K, Tan B. Optical absorption enhancement in 3D nanofibers coated on polymer substrate for photovoltaic devices. Optics Express. 2015;23(11):A569-A575\n'},{id:"B29",body:'\nColpitts C, Kiani A. Synthesis of bioactive three-dimensional silicon-oxide nanofibrous structures on the silicon substrate for bionic devices’ fabrication. Nanotechnology and Nanomaterials. 2016;6:1-7\n'},{id:"B30",body:'\nColpitts C, et al. Mammalian fibroblast cells show strong preference for laser-generated hybrid amorphous silicon-SiO2 textures. Journal of Applied Biomaterials and Functional Materials. 2016. DOI: D2673E51-9D76-4504-A673-13AAC5CE7AD3 [pii]\n'},{id:"B31",body:'\nVega ME, Schwarzbauer JE. Collaboration of fibronectin matrix with other extracellular signals in morphogenesis and differentiation. Current Opinion in Cell Biology. 2016;42:1-6. DOI: S0955-0674(16)30055-2 [pii]\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Candace Colpitts",address:null,affiliation:'
Silicon Hall: Laser Micro/Nanofabrication Facility, Department of Mechanical Engineering, University of New Brunswick, NB, Canada
Silicon Hall: Laser Micro/Nanofabrication Facility, Department of Mechanical Engineering, University of New Brunswick, NB, Canada
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