Parameter estimates for the model in Eq. (1) fitted on daily observations from January 2015 to June 2017.
\\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\\nWe 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!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased 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\nWe 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!
Note: Edited in March 2021
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Karyotype",slug:"blood-parameters-in-human-fetuses-with-congenital-malformations-and-normal-karyotype",totalDownloads:1175,totalCrossrefCites:0,authors:[{id:"95095",title:"Dr.",name:"Chantal",surname:"Bon",slug:"chantal-bon",fullName:"Chantal Bon"}]},{id:"33792",title:"Placental Angiogenesis and Fetal Growth Restriction",slug:"placental-angiogenesis-and-fetal-growth-restriction",totalDownloads:2402,totalCrossrefCites:1,authors:[{id:"33248",title:"Dr.",name:"Stavros",surname:"Sifakis",slug:"stavros-sifakis",fullName:"Stavros Sifakis"},{id:"124223",title:"Dr.",name:"Nikolaos",surname:"Vrachnis",slug:"nikolaos-vrachnis",fullName:"Nikolaos Vrachnis"}]},{id:"33793",title:"The External Version in Modern Obstetrics",slug:"external-cephalic-version",totalDownloads:2206,totalCrossrefCites:0,authors:[{id:"74956",title:"Dr",name:"Esther",surname:"Fandiño García",slug:"esther-fandino-garcia",fullName:"Esther Fandiño García"},{id:"86840",title:"Dr.",name:"Juan Carlos",surname:"Delgado 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One of the cancer types known to affect the gastrointestinal (GI) tract and rated third most commonly diagnosed form irrespective of gender is colorectal cancer (CRC).
In a context outlined by alarming figures of both prevalence (with a lifetime CRC risk of 5.1%) and high mortality rate, CRC being second in line among cancer-related causes of deaths in both genders, advances in therapy have become particularly significant; thus, in addition to established liver resection, outcomes in survival rate have been greatly improved in recent years due to better means for earlier detection, advances in chemotherapy and therapies based on biological agents.
Largely studied nowadays, factors influencing the likelihood of developing CRC are a close family history of genetic changes in that respect (20% of cases), notably associated with certain genetic syndromes such as the Lynch syndrome (hereditary nonpolyposis colorectal cancer, HNPCC) in ca. 3% of cases [1] and familial adenomatous polyposis with its variant the Gardner syndrome (in a further ca. 1% of CRC instances).
HNPCC-related CRC risk factors include early onset (ca. 44 average age) triggered by an autosomal dominant inheritance, development in 70% of cases in splenic flexure proximity as well as a surplus of synchronous (18% of all patients) and metachronous CRC (45% of patients) following segmental resection or hemicolectomy. In addition to a CRC cause, Lynch syndrome frequently also results in other carcinoma types such as ovary and endometrial, other gastrointestinal location (stomach, small intestine), pancreas as well as transitional carcinoma in the renal pelvis and the ureter [2].
This high metastatic disease potential is the main cause of CRC lethal outcome and may be attributed to the contribution of a specific gene (I(MACCI)); already isolated [3], this is a transcriptional factor able to influence the expression of the hepatocyte growth factor and therefore associated with proliferation of CRC cells, scattering and new tissue invasion and further tumour growth and metastasis, as shown in cell cultures and animal studies in mice. MACCI close involvement as contributor to occurrence of metastasis makes it a novel target for CRC approach, which needs confirmation by further studies and clinical trials [4].
Contribution of genetic factors in CRC development combines with action of epigenetic ones at cell level.
However important the role of genetic features, most CRC may rather be the outcome of chronic intestinal inflammation preceding tumour development, gut microbiota and such environmental factors as life style (diet included) and food and environmental-borne mutagens [5, 6].
Among chronic intestinal inflammations responsible for CRC risk, inflammatory bowel disease (under 2% of CRC cases every year [7]), Crohn’s disease and ulcerative colitis need special mention for being the frequent cause of tumour growth [8], the risk growing with the severity of inflammation and the duration of the disorder [9]. Statistically, in that respect, 10 years’ duration of Crohn’s disease results in CRC in 2% of patients, and the risk increases four times and nine times for 20 and 30 years’ durations, respectively [7]. On the other hand, a history of over 30 years of ulcerative colitis results in development of some type of cancer precursor or CRC in ca. 16% patients [7].
As regards CRC development, it typically originates in benign, premalignant or malignant polyps occurring at the level of the colon or rectum epithelial lining (as, for instance, hyperplastic polyps, tubular adenoma or colorectal adenocarcinoma, respectively). Such abnormalities are the result of inherited or acquired oncogenic and inactivating mutations revealed by complex genome scale analysis, which has shown the existence of hypermutated and non-hypermutated CRC tumour categories [10].
Among non-hypermutated types, one commonly occurring mutation affects the Wnt signalling pathway, leading to increased signalling activity and emerging at the level of the intestinal crypt stem cell [11].
Most frequently, the mutated CRC-related gene is the
In addition to the absence of the APC protein, high β-catenin-related CRC may also be determined by β-catenin (CTNNB1) mutation, blocking its very own breakdown, or occurrence of mutations in other APC similarly operating genes (e.g., AXINI, AXIN2, NKDI, TCF71.2) [12].
Besides deficient Wnt signalling pathways, realization of the cancer potential requires additional mutations. Usually, action of Wnt pathway defects is prevented by intervention of the cell division monitoring p53 protein, a product of the TP53 gene, which normally eliminates flawed cells. Thus, a mutation arising in the TP53 gene may reverse the potential from benign epithelial tumour cell into invasive epithelial cell.
If not affecting the p53-encoding gene, mutations may instead target a different protein playing a protective role, i.e., the BAX12 but also ARDI A, CTNNB I, SOX9, FAM123B and ATM.
On the other hand, hypermutated tumours progress through specific genetic events and display MSH3, MSH6, TGFBR2, ACVR2A, SLC9A9, BRAF and TCF71.2 mutated forms.
Whichever the tumour type, all these genes are involved in the Wnt and TGF-β signalling pathways, leading to higher MYC activity, as major CRC factor [13]. Role of “field defects”/“field cancerisation”, the concept first emerged in the early 1950s to refer to an area of the epithelium featuring a preconditioning responsible for cancer predisposition of the area in question [14]. Despite unclear origins at the time of their introduction, the terms define premalignant tissue as potential sites for new cancer.
In time, research has progressively emphasized the importance of “field defects” in advance to CRC, and the assumption was confirmed by studies showing almost the exclusive use of discrete neoplastic foci for in vitro research and well-defined tumours for in vivo studies [15] in all cancer research.
In addition, as research further indicated, the majority of somatic mutations occurring at tumour level emerged during development of apparently normal cells [16], at the site of “field defects” (and therefore in preneoplastic stage).
A further addition to terminology refers to the term “aetiologic field effect”, based on the “field defect” concept and referring to molecular and pathologic changes in preneoplastic cells at molecular level. The term also covers the extent to and manner in which exogenous environmental factors as well as molecular changes in the local microenvironment influence neoplastic progression throughout [17].
However important mutation-induced genetic alterations, epigenetic alterations are significantly more common in CRC and involve hundreds of genes. As revealed by research, (Vogelstein and colleagues) oncogene mutations and suppressor mutations (both known as “driver mutations”) are rather limited in average CRC forms (1–2 and 1–5, respectively), although accompanied by an estimated 60 additional so-called “passenger” mutations [18].
Common types of epigenetic cancer-related alterations modifying gene expression levels by action on the different types of RNA (miRNAs) may involve abnormal methylation of DNA in tumour suppressor promoters [19], such as reduced expression of miR-137 because of methylation of the miR-137-encoding DNA sequence in the CpG island [20]. Altered miR-137 expression triggers drastic (2- to 20-fold) alteration of mRNA expression of the target genes and related slighter changes in expression of proteins produced by the genes.
There are further microRNAs, of comparable numbers of target genes, which undergo even more frequent epigenetic alterations of field defects in the colon, resulting in specific CRC forms [21].
As common is direct hyper-/hypo-methylation of CpG islands of protein-encoding genes as well as histone alterations or modification of chromosomal architecture, with influence on gene expression [22]. Research has recently outlined the potential of early epigenetic decline in expression of DNA repair enzyme as cause of cancer characteristic genomic and epigenomic instability [18].
Although CRC clinical signs vary with tumour location in the intestine as well as with the presence of metastases, medical practice has outlined certain warning signs and symptoms now considered typical, such as loss of appetite, weight loss, vomiting and/or nausea, rectal bleeding and anaemia in the over 50 age group [23], severe and persistent constipation and modified stools (accompanied by blood elimination and/or diminished thickness) [24]; weight loss and changed bowel habit may be considered a warning only if accompanied by bleeding [22].
Typically, CRC may be diagnosed by the sampling of colon areas suspected of tumour development during procedures suitable for the lesion site, i.e. colonoscopy or sigmoidoscopy.
Once the tumour is confirmed, the level of the disease needs to be determined, which is generally done by a CT scan involving the chest, abdomen and pelvis, but also by position imaging tomography and MRI, for certain cases.
The next diagnostic step is to determine the stage of the tumour, based on the TNM cancer staging system (where T stands for primary tumour stage, N for the presence of regional lymph nodes and M for remote metastasis). Staging criteria include the extent of initial tumour spreading, the presence and site of lymph nodes and the metastasis level [25].
Adenocarcinoma is a malignant tumour of the epithelium, whose source lies in the superficial glandular epithelial cells of the colon and cecum lining. This tumour invades the colon/cecum wall and further progressively permeates the respective layers (first the muscularis mucosae, then the submucosa and lastly the muscularis propria). Tumour cells in question are organized as irregular tubular, multistratified structures, featuring multiple lumens and decreased stroma (in a back-to-back growth pattern). In addition, in some cases, tumour cell lacks of cohesion may be observed, as well as a secretion of mucus pervading the interstitium and resulting in extensive mucus/colloid (optically “empty” spaces) pools (forming the so called “mucinous (colloid)”), poorly differentiated adenocarcinoma. Mucus remaining within the walls of the tumour cell drives the nucleus towards the cell membrane, and the “signet-ring cell” emerges.
In fact, differentiation may vary in adenocarcinoma, contingent on cellular pleomorphism, glandular architecture and muco-secretion of the predominant pattern; thus, three variants of adenocarcinoma may be observed as regards the degree of cell differentiation: well differentiated, moderately differentiated and poorly differentiated [26].
CRC cell characteristics may be determined by analysis of tissue samples harvested by biopsy or during surgery. The pathology report provides data on cell type and grade. In CRC, the most common cell (98% of cases) is adenocarcinoma, but other types may also occur in rare cases (squamous cell carcinoma and lymphoma) [27].
It is generally considered that more than half of CR adenomas and up to 90% of CRC tumours present overexpression of the COX (cyclooxygenase)-2, normally absent from healthy colon tissue but acting as fuel for abnormal cell growth [28].
The cancer variant may be determined by histologic examination.
In order to predict the likely course of tumour progression and adequate management, macroscopic examination looks closely at the site of the tumour in the intestine; thus, tumour development on the ascending colon and cecum (the right side of the large intestine) is most often exophytic (growing outwards from the bowel wall), which may in infrequent cases result in faecal obstruction, accompanied by anaemia.
Tumours growing on the left bowel side are largely peripheral and may result in obstruction of the bowel lumen and thinner stools [27].
One key approach to CRC (as for other cancers, in fact) and unanimously recognized as such is prevention; closer surveillance and healthier lifestyle can essentially contribute to CRC prevention.
Therefore, research has greatly been focused on effective means in that respect, in all areas of intervention.
As regards lifestyle, diets are currently recommended to include more significant amounts of vegetables, fruits and whole grains and decrease consumption of white flour products, sugars and red meat.
As in other areas of healthcare, physical exercise has been proved to be beneficial, though less significant for preventing or reducing colon cancer risk [29, 30]. However, avoiding prolonged sitting as a daily routine is important [31].
Medication has also been the target of research, which has shown the potential of aspirin and celecoxib to reduce CRC danger in high-risk groups as determined by assessment of family medical history and other personal risk factors, though not in average risk ones [32, 33, 34, 35].
Calcium supplementation is currently under study as well, not with sufficient evidence yet.
As for protection factors, in vitro studies have shown that intake and blood levels of vitamin D act as one, as have lactic acid bacteria, due to their antioxidant activity, immunomodulation as well as promotion of programmed cell death, proliferative effects and epigenetic alteration of cancer cells [36].
Screening is an important and effective means for prevention and early detection of cancer in general and CRC in particular, the more so as most CRC cases (>80%) originate in adenomatous polyps [37].
As mentioned above, screening is also a very important means for cancer diagnosis before the emergency of actual symptoms (by 2–3 years) [25].
Close relatives of HNPCC patients need accurate and structured screening, according to a well-designed programme and schedule [38], as in certain countries such as Canada, the United Kingdom, Australia and the Netherlands [39, 40, 41].
Therefore, these should undergo a first routine colonoscopy at the age of 25, which, as a routine, should be repeated every 3 years, in the case of negative results, and every year should an adenoma be found. In cases where routine colonoscopy reveals the presence of cancer, subtotal colectomy needs to be performed.
In addition, for women, ovarian ultrasound and endometrial biopsy need to be performed as early as 25 years old.
Screening tests have been devised and researched, current practice now relying mostly on colonoscopy (both standard and virtual via CT scan), faecal occult blood testing, multitarget stool DNA screening and flexible sigmoidoscopy [25].
Although with proven efficacy in other respects, sigmoidoscopy is the only procedure able to provide screening of the right side of the colon, the site for almost half (42%) of malignancies [42].
Equally effective, standard colonoscopy is less costly than virtual colonoscopy via CT scan and avoids the additional risk of exposure to radiation and also able to eliminate any potential abnormal growth found [25].
In the 50–75 age group with standard risk factors, screening should include faecal occult blood testing or immunochemical testing every 2 years; an alternative is performance of sigmoidoscopy every 10 years, to the detriment of colonoscopy [43].
For patients with familial adenomatous polyposis, the high-malignancy risk may be offset by total proctocolectomy, ensuring elimination of the risk of both colon and rectal cancers [44].
Given CRC’s incurable character, therapeutic decisions in that respect can only be directed to either cure or as a palliative, largely depending on tumour stage [45] but also on other factors as well, such as the patient’s health status and even preferences.
Surgery can be a means leading to cure but in early stages only, whereas at later stages, when the metastatic disease has also been initiated, the curative potential of surgery decreases, and palliation (alleviation of tumour-related symptoms and patient’s comfort and quality of life) becomes prevalent [25].
For the very first stage, one colonoscopy intervention can suffice to eliminate cancer [46], while the curative potential of surgery decreases with the tumour stage.
Therefore, one stage further, in localized cancers, cure may still be attempted through ample removal associated with ensuring adequate margins, which can be achieved laparoscopically or more often by open laparotomy [25], with colon reconnection, or by colostomy [46].
In the stage of a few emerging metastases, those in the lungs or liver may be eliminated.
In certain cases at this stage, surgery may be preceded by chemotherapy, in an attempt to minimize the tumour before removal.
If recurrence occurs, this mainly involves the lungs and liver [25].
This is administered in cases beyond stage 1 CRC, given the curing potential of surgery. No chemotherapy is also customary in CRC stage II; on condition no such risk factors as threats from negative lymph node sampling or the presence of a T4 tumour are present.
Chemotherapy is also not feasible in patients with identified abnormal mismatched repair genes.
On the contrary, chemotherapy is a must and an integral therapy component in stage II and stage IV CRC25, characterized by cancer spreading to remote organs or the lymph nodes; the use of the chemotherapeutic agents oxaliplatin, fluorouracil or capecitabine is instrumental in increasing life expectancy, with the disadvantage of debatable chemotherapy benefits in the case of cancer-free lymph nodes.
Turn to palliative care becomes necessary where CRC has become extensively metastatic or may not be resected, opening the alternative for several different chemotherapy medications [25], including, oxaliplatin, fluorouracil, capecitabine, irinotecan and tegafur/uracil [47, 48].
Given bowel sensitivity to radiation, patient with colon cancer treatment cannot benefit from addition of radiation to chemotherapy, although this may be effective for rectal cancer [25]. The same was for chemotherapy; radiotherapy may be used as neoadjuvant and adjuvant in certain rectal cancer stages only [49].
For patients with incurable CRC forms, palliative care, though not a promising cure, may bring the benefit of better quality of the patient’s life both directly and indirectly, via the life of their families, lessening symptoms and anxiety and also reducing the need of hospital admission [50].
Palliation is typically symptom directed and consists of procedures designed to improve symptoms or minimize the possibility of complications such as abdominal pain, tumour bleeding and/or bowel obstruction [51], thus contributing to improved quality of life.
Such procedures may include surgery, for elimination of cancer tissue to some extent, without attempting to cure, placement of a stent or performing a bypass of part of the bowel.
Non-surgical palliative care approaches include pain medication and/or radiotherapy aiming to reduce the tumour size [52].
The main purpose of follow-up is to obtain the earliest identification of later metachronous lesions, i.e., metastases or tumours not originating from the initial cancer [53].
As an underlying measure for cancer survivors, exercise as a mainstay of lifestyle may be useful as secondary therapy, as shown by results indicating important reduction in 8-oxo-dG in the urine of patients after taking moderate exercise for 2 weeks of following primary therapy [54].
The most commonly used prognosis criterion is the 5-year survival rate, which is under 60% for CRC in Europe, whereas this is the cause of death for one third of CRC patients [25] in most developed countries. The reason for these unexpectedly low outcomes despite evident progress in new therapeutic means and their improved availability worldwide is mainly CRC late identification (stage IV already present in 20% of patients seeking medical attention), with potentially resectable isolated liver metastasis in ca. 25% of these patients. Of these 25%, one third of patients undergoing resection achieve 5-year survival [55, 56].
Despite the major advances in cancer therapies, the morbidity and mortality associated with this disease are still enormous. Tumour heterogeneity holds the main responsibility underlying inefficient treatment and failure of current therapeutic strategies, including the targeted therapies. For efficiency reasons, the molecular targeted therapies require constant monitoring of the tumour genome, but harvesting consecutive tissue biopsies is very difficult and inconvenient for medical and economic reasons. Therefore, the lack of real-time information regarding tumour heterogeneity during the disease evolution most commonly results in the treatment failure and requires the development of novel approaches. In this view, liquid biopsies offer a tool for real-time screening of disease particularities, stratify patients for the best treatment and also monitor the response of the treatment. Due to their non-invasive nature, liquid biopsies can be used for repeated sampling to monitor tumour genetic alterations over time, avoiding this way consecutive tissue biopsies. Liquid biopsies analyse circulating tumour cells, cell-free tumour DNA and/or exosomes, known as tumour-circulating markers.
Circulating tumour cells (CTCs) have been identified during the 1800s and presumed responsible for the metastatic process [57]. These cells are of epithelial origin and shed from the tumours in the peripheral blood of patients where they can be enriched, detected and analysed.
The detection of CTCs in the peripheral blood of patients with cancer holds a great promise for the future development of efficient anticancer therapies. However, due to the very low concentrations of CTCs in the peripheral blood (one tumour cell for millions of normal blood cells), their detection and identification still remain challenging and require high analytical sensitivity and specificity methods, which usually consist in a combination of enrichment and detection [58].
CTC enrichment strategies include a wide range of technologies based on those CTC particularities that can discriminate them out of the normal haematopoietic cells. Concrete CTCs can be detected based on physical properties such as size, density, electric charges, deformability or biological properties such as cell surface marker expression and viability. CTC separation based on their physical properties holds the great advantage of being done without labelling the cells. Some of these methods include density gradient centrifugation, filtration, photoacoustic flow cytometry, microfluidics, etc. [59, 60].
Nevertheless, the biological properties of the CTCs hold a major role in their identification, mainly based on immunobead assays. These assays use antibodies targeting tumour-associated antigens (positive selection) or leukocyte-specific antigens such as CD45 (negative selection) in order to detect and separate CTCs from the blood cells. The positive selection usually targets the epithelial cell adhesion molecule (EpCAM). Subsequently, CTCs are confirmed with antibodies against cytokeratins (CKs) [59]. Among the current EpCAM-based technologies, the US Food and Drug Administration approved CellSearch® system (Veridex) which is the current “gold standard” for all new CTC-detection methods. According to this standard, CTCs are nucleated cells that express the epithelial cell adhesion molecule and cytokeratins but lack the expression of the common leukocyte CD45 marker (EpCam+_CK18/19+_DAPI+_CD45− cells).
Interestingly, some CTCs undergo the epithelial to mesenchymal transition (EMT) and loose critical epithelial markers. Capturing CTCs’ lacking EpCAM expression requires the use of antibody cocktails against a panel of epithelial cell surface antigens such as HER2, MUC-1, EGFR and folate-binding protein receptor and against mesenchymal or stem cell antigens such as c-MET, N-cadherin and CD318 [61].
Regardless of the enrichment method, the isolated CTCs still contain a significant number of normal blood cells, and therefore CTCs should be next identified by a method that can discriminate between malignant cells and normal blood cells. The CellSearch® system as well as other assays is based on the fluorescent staining of the cells for the following markers: CKs (positive marker), the common leukocyte antigen CD45 (negative marker) and a nuclear dye (4,6-diamidino-2-phenylindole, DAPI).
Functional EPISPOT (for EPithelial ImmunoSPOT) assay has been introduced for CTC analysis in order to detect only the viable CTCs, able to produce metastases [62].
Other alternatives to immunologic assays of viable CTC-detection target specific mRNAs. A commercially available RNA-based CTC assay is the AdnaTest™ (AdnaGen), which uses nonquantitative RT-PCR to identify cells that express the transcripts of tumour-specific genes after immunomagnetic capture of MUC-1, HER2 and EpCAM cells [63].
Cell-free DNA (cfDNA) is a powerful tool for its potential use in a wide range of clinical fields such as cancer research [64, 65], non-invasive prenatal testing [66] and transplant rejection diagnostics [67]. Most cfDNA in plasma is highly fragmented (150–180 bp) [68] with a higher prevalence of tumour-associated mutations in the shorter fragments [69]. In patients suffering from cancer, a fraction of the cfDNA is tumour-derived and is known as circulating tumour DNA (ctDNA).
cfDNA reaches the systemic circulation by various pathologic or normal physiologic mechanisms [70]. However, with respect to solid tumours, the ctDNA is usually released as a result of necrosis or autophagy [71]. Notably, unlike apoptosis, necrosis generates larger DNA fragments [72]. Cancer patients generally have much higher levels of cfDNA than healthy individuals [73, 74]. ctDNA carries genomic and epigenomic alterations according to the tumour genomic alterations (copy number variation, point mutations, microsatellite instability, degree of integrity, loss of heterozygosity, rearranged genomic sequences, DNA methylation, etc.) [75]. Only on the basis of these biological characteristics, ctDNA can be discriminated from normal cfDNA. Consequently, after its validation ctDNA could be used as a specific biomarker that provides personalized information to detect residual disease or monitor tumour progression during therapy.
Due to the high degree of fragmentation as well as the small fraction of ctDNA within the cfDNA, the analysis of ctDNA is challenging and requires highly sensitive techniques. Classical methods of analysis include qRT-PCR, fluorescence and spectrophotometric approaches [76, 77, 78]. Digital droplet PCR has been developed as a high sensitive tool to detect ctDNA [79]. This technique consists in a droplet-based system [80, 81], a microfluidic platform [82, 83] and the so-called BEAMing strategy [84, 85]. Additionally, next-generation sequencing technology is currently used in plasma DNA analysis in order to identify ctDNA alterations [86, 87, 88].
There is increasing evidence that circulating tumour markers such as CTCs and ctDNA offer real-time information regarding cancer progression and tumour genotype in the view of a better systemic therapy management with direct impact on patient’s disease prognosis. Additionally, future characterization of these circulating markers could contribute to approach-specific-targeted therapies to a certain population of cancer patients.
This work was done under the project PN-IIIP2-2.1-PTE-2016-0149/19PTE - TUMFLOW, financed by UEFISCDI.
Bitcoin is a digital currency built on a peer-to-peer network and on the blockchain, a public ledger where all transactions are recorded and made available to all nodes. Opposite to traditional banking transactions, based on trust for counterparty, Bitcoin relies on cryptography and on a consensus protocol for the network. The entire system is founded on an open source software created in 2009 by a computer scientist known under the pseudonym Satoshi Nakamoto, whose identity is still unknown (see [1]). Hence, Bitcoin is an independent digital currency, not subject to the control of central authorities and without inflation; furthermore, transactions in the network are pseudonymous and irreversible.
\nBitcoin and the underlying blockchain technology have gained much attention in the last few years. Research on Bitcoin often deals with cybersecurity and legitimacy issues such as the analysis of double spending possibilities and other cyber-threats; recently, high returns and volatility have attracted research toward the analysis of Bitcoin price efficiency as well as its dynamics (see, among others, [2, 3, 4]). Moreover, many contributions claim that Bitcoin price is driven by attention or sentiment about the Bitcoin system itself; see [5, 6, 7, 8]. Possible driving factors for the sentiment about the Bitcoin system are the volume of Google searches or Wikipedia requests as in [5], or more traditional indicators as the number or volume of transactions, as suggested in [6]. In [9], the author suggests a time series model in order to identify the dynamic relation between speculation activity and price.
\nIn this chapter, after having introduced the basic concepts underlying Bitcoin, we sum up and describe to a broader audience the recent outcomes of the research reported in [10], by avoiding unnecessary technicalities. Some new insights are also given by looking at possible extensions in order to take into account the presence of bubble effects or the special feature of Bitcoin being traded in different online platforms (exchanges) that will be further investigated in our future research.
\nWe recall that Bitcoin was first introduced as an electronic payment system between peers by Satoshi Nakamoto (pseudonym) in [1]. Opposite to traditional transactions, which are based on the trust in financial intermediaries, this system relies on the network, on the fixed rules and on cryptography. Bitcoins can be purchased on appropriate websites that allow to change usual currencies in the cryptocurrency.
\nThe Bitcoin network has several attractive properties for its users:
No central bank authority for money supply and no regulator;
Transactions are 24/7 and without any country border;
Transaction cost are almost negligible with respect to traded amount;
Transaction are anonymous;
The security of each transaction is guaranteed by cryptography and digital signature;
The security of the whole network is guaranteed by construction unless more than 50% of the network nodes agree on a deceptive action.
As a digital payment system, Bitcoins may be used to pay for several online services and goods. Special applications have been designed for smartphones and tablets for transactions in Bitcoins and some ATMs have appeared all over the world (see Coin ATM radar) to change traditional currencies in Bitcoins. Accepting Bitcoins as a payment method is also related to an advertisement opportunity for companies. However, the high returns achieved in the last few years have transformed Bitcoin in a speculative asset affecting its use as a form of payment.
\nThe Bitcoin system has been subject to many cracks but has proven to be very resilient as the value of the cryptocurrency was able to rise again after all the falls. Nevertheless, at the time of writing, Bitcoin was experiencing a fall in its exchange rate with main fiat currencies.
\nTwo of the main crackdowns were China enforcement in December 2013 and Mt. Gox bankruptcy in February 2014.
\nBesides technical and regulation issues, the Bitcoin system also faces reputational concerns.
\nIn fact, the ambiguity of anonymous transactions has blamed the network of allowing several criminal activities such as buying illegal goods, money laundering or the financing of terrorism actions. As a representative example, we recall that
It is worth noticing that while counterparties are represented by secret addresses and are anonymous, all transactions are recorded and might be traced. Investigation is hence favored by this feature of the network.
\nDespite the flaws in the system, Bitcoin has achieved a notwithstanding rise in recent years.
\nIn Figure 1, we report Bitcoin price and returns from January 2012 to December 2017 (source https://blockchain.info/en/charts).
\nBitcoin price (top) and returns (bottom) from January 2012 to December 2017.
The model we suggest in what follows is motivated by findings in [5, 6, 8, 11] where it is showed that Bitcoin price is related to investors’ attention measured by the trading volume and/or the number of searches in engines such as Google and Wikipedia. Bitcoin is treated as a financial stock as suggested in [12] and the suggested model may be applied in principle to other assets that are proven to depend on market attention.
\nConsider a probability space \n
Let us denote the Bitcoin price process as \n
where \n
It is well known that the above dynamics for the attention factor is a geometric Brownian motion, the solution of which is given by \n
We collect in this subsection the properties of the logarithmic returns obtained by the price process defined in Eq. (1).
\nConsider the discrete process \n
Theorem 2.1.
As for the unconditional distribution, it is easy to obtain, for \n
where \n
Proposition 2.2. The joint probability density of the vector \n
where \n
The proof follows from Bayes’ rule and application of Theorem 2.1.
\nIt is worth to remark that the probability density \n
Precisely, we have that \n
We apply the outcomes above in order to estimate model parameters according to the maximum-likelihood method (see for example [14, 15]) where the likelihood is approximated by applying the Levy approximation [13].
\nParameter estimates are obtained as
\nwhere
The first step in our procedure is to identify possible measures of investors’ attention. As already mentioned in the introduction, we consider the total trading volume on Bitcoin available from https://blockchain.info as well as the search volume index (SVI) for Google searches on the topic “bitcoin” provided by https://trends.google.it/trends/.
\nThe trading volume of exchange is a classical measure of the attractiveness of a traded asset for an investor; besides, in [16], the authors find evidence that the latter captures the attention of retail/uniformed investors.
\nWe consider daily data from January 1, 2015, to June 30, 2017, for the total volume and the SVI Index. As for the daily value of the Bitcoin, we have considered the average mean across main exchanges represented by the Index in https://blockchain.info.
\nIn Table 1, the outcomes for parameter estimates, obtained by maximizing the approximate likelihood given the observed time series, are summed up.
\n\n | \n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|
0.9571 | \n1.1346 | \n0.0218 | \n0.0829 | \n|
1.3584 | \n1.0687 | \n0.0743 | \n0.1559 | \n
Parameter estimates for the model in Eq. (1) fitted on daily observations from January 2015 to June 2017.
In this section, we show how to characterize the price of European call options on Bitcoins in the underlying market model. Let us fix a finite time horizon \n
where \n
The proof can be deduced from that of Lemma 1.4 in [10], where they also account for a possible delay between the attention factor and its effect on Bitcoin prices trend. The process \n
is an \n
Equivalently, we can write the discounted Bitcoin price process \n
Clearly, under the minimal martingale measure \n
where \n
Now, we compute the fair price of a Bitcoin European call option via the risk-neutral evaluation approach, so it can be expressed as expected value of the terminal payoff under the selected pricing measure, that is, the minimal martingale measure. Let
where
\nand \n
Here, \n
The following result provides the risk-neutral price of the option under the minimal martingale measure \n
Hence, the resulting risk-neutral pricing formula when evaluated in \n
In order to appreciate the performance of the pricing formula in Eq. (19), we compute model prices for option traded on the online platform http://www.deribit.com on July, 30, 2017, by plugging in the estimated parameters. The outcomes are compared with the Black & Scholes benchmark (see [20]) as a reference price, computed by plugging the volatility parameter estimated on the same time series of the trading volume/SVI index, and with the bid-ask prices provided in the website. Best overall pricing values are obtained when market attention is measured by volume; in the case of the SVI Google index, near-term options are very close to the mid-value of the bid-ask, while next-term options are overpriced. One possible explanation is that investors that get information about Bitcoin on search engines are more likely to be uninformed/retail investors that are self-exciting and may add spurious noise to the Bitcoin price volatility leading to an increase in call option prices (Table 2).
\nT-K | \nMarket bid | \nMarket ask | \nModel volume | \nModel Google SVI | \nBenchmark BS | \n
---|---|---|---|---|---|
Aug-2200 | \n0.1662 | \n0.2318 | \n0.2029 | \n0.2282 | \n0.1967 | \n
Aug-2300 | \n0.1670 | \n0.2072 | \n0.1737 | \n0.2032 | \n0.1655 | \n
Aug-2400 | \n0.1390 | \n0.1845 | \n0.1469 | \n0.1802 | \n0.1369 | \n
Aug-2500 | \n0.1142 | \n0.1638 | \n0.1228 | \n0.1591 | \n0.1112 | \n
Aug-2600 | \n0.0922 | \n0.1376 | \n0.1014 | \n0.1399 | \n0.0887 | \n
Aug-2700 | \n0.0749 | \n0.1202 | \n0.0828 | \n0.1226 | \n0.0695 | \n
Aug-2800 | \n0.0572 | \n0.1047 | \n0.0684 | \n0.107 | \n0.0535 | \n
Aug-2900 | \n0.0442 | \n0.0983 | \n0.0549 | \n0.0931 | \n0.0405 | \n
Sept-2200 | \n0.1991 | \n0.2648 | \n0.2546 | \n0.3204 | \n0.2173 | \n
Sept-2300 | \n0.1766 | \n0.2432 | \n0.2321 | \n0.3019 | \n0.1906 | \n
Sept-2400 | \n0.1890 | \n0.2230 | \n0.2113 | \n0.2844 | \n0.1662 | \n
Sept-2500 | \n0.1375 | \n0.2042 | \n0.1919 | \n0.2679 | \n0.1439 | \n
Sept-2600 | \n0.1207 | \n0.1828 | \n0.1741 | \n0.2523 | \n0.1239 | \n
Sept-2700 | \n0.1120 | \n0.1668 | \n0.1576 | \n0.2377 | \n0.1060 | \n
Sept-2800 | \n0.0953 | \n0.1504 | \n0.1463 | \n0.2239 | \n0.0903 | \n
Sept-2900 | \n0.0848 | \n0.1422 | \n0.1325 | \n0.2109 | \n0.0764 | \n
Comparison between model prices computed according to formula in Eq. (19), Black & Scholes formula in [20], and the bid and ask prices provided in http:\\\\www.deribit.comfor options traded on July, 30, 2017, and expiring on August 25, 2017, and on September 28, 2017.
Motivated by empirical evidences (see for example [21, 22]), we discuss a generalization of the model introduced in Section 3.1, which is capable to describe speculative bubbles in Bitcoin markets.
\nPrecisely, we fix a finite time horizon \n
Without loss of generality, we assume that the interest rate is fixed and equal to zero. In this setting, the discounted Bitcoin price trend and the market attention factor dynamics are described by
\nwhere we have set \n
By simulating trajectories for the asset price \n
Simulated trajectories with
Indeed, we will show formally that the possibility of Bitcoin speculative bubbles is related to the sign of the correlation parameter \n
The mathematical theory of financial bubbles is developed, among others, in [23, 24, 25]. Precisely, we introduce the following definition from [23].
\nThe term strict \n
Recall that the absence of arbitrage opportunities is “essentially” equivalent to the existence of a probability measure \n
Then, to exclude arbitrage opportunities from the market, we define the process \n
where \n
To ensure that \n
and we can consider the corresponding family of equivalent (local) martingale measures \n
where the \n
\n\n
Now, suppose that the risk perception process is zero, that is, \n
is a true \n
The proof is based on the application of some of Sin’s results given in [27], where the existence of risk-neutral measures for the Hull-White stochastic volatility model [19] and for similar frameworks is determined by the possibility of explosion in finite time for solutions of certain auxiliary stochastic differential equations. Precisely, it is possible to show that the martingale property of the discounted stock price \n
Let us generalize the model introduced in Eq. (1) by assuming a possible delay \n
where \n
Analogous results as those in Section 2 can be derived by similar computations, and model parameters, for a fixed delay, can be estimated by means of the maximum likelihood method. In order to estimate the delay parameter, we maximize the profile likelihood as defined in [15]. Details of this procedure can be found in [10]. The estimation results of model in Eq. (27) on the same daily data considered in Section 2 are summed up in Table 3.
\n\n | \n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|---|
1 day | \n0.4881 | \n1.0459 | \n0.0282 | \n0.0924 | \n|
7 days | \n1.0964 | \n0.9946 | \n0.1005 | \n0.1885 | \n
Parameter estimates for model in Eq. (27) fitted on daily observations from January 2015 to June 2017.
In Figure 3, we plot simulated trajectories of the price process in Eq. (27) by letting the delay parameter vary.
\nSimulated trajectories of
The different delays result in a shift to the south-east between the faster and slower reacting trajectories; in the picture, this behavior is sharp since the other model parameters are kept constant. By looking at the picture, the idea to model the price of Bitcoin in different exchanges by the same model in Eq. (27) but allowing different parameters naturally arises.
\nIn particular, considering for instance two exchanges, we have
\nwhere \n
Note that within this model, prices for Bitcoin traded in different exchanges are perfectly correlated. Indeed, this is what happens in observed data; considering daily prices from January 2015 to June 2017 for Bitstamp, Kraken, Cex.io, Gdax, and The Rock exchanges we get cross-correlation values larger than 0.999.
\nWe fit model in Eq. (28) for the Bitstamp and Gdax exchanges on daily observations of Bitcoin price from January 2015 to June 2017 obtaining the outcomes reported in Table 4, when the attention is measured by the trading volume, and in Table 5, when attention is measured by the Google SVI index.
\nExchange | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|---|
Bitstamp | \n1 | \n0.4994 | \n1.0461 | \n0.0281 | \n0.0896 | \n
Gdax | \n2 | \n0.4997 | \n1.0420 | \n0.0326 | \n0.1036 | \n
Model fitting with delay parameter: outcomes for Bitstamp and Gdax exchanges when attention is measured by the trading volume.
Exchange | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|---|
Bitstamp | \n7 days | \n1.0934 | \n0.9946 | \n0.0992 | \n0.1782 | \n
Gdax | \n7 days | \n1.0964 | \n0.9946 | \n0.1160 | \n0.2087 | \n
Model fitting with delay parameter: outcomes for Bitstamp and Gdax exchanges when attention is measured by the SVI index.
It is evident from the outcomes in Table 4 that the model parameters are not significantly different while the delay might be quite different as if the reaction to the attention factor is faster for some exchanges and slower for others. On the contrary, when attention is measured by the Google SVI Index, the delay is unchanged, but the difference between estimated parameters for the price dynamics is nonnegligible.
\nBy analyzing the outcomes and considering the shift effect as depicted in Figure 3, it is tempting to conjecture that the faster reaction determines the leader exchanges and that the slower exchange will then follow. If we could forecast that the next day price of the slower exchange will reach the price today for the faster one, we could obtain a profit by suitably investing in the two exchanges. However, it is worth noticing that the estimation of the delay parameter is obtained by maximizing the likelihood over a whole time series and is a product of averaging so arbitrage cannot be achieved in a direct way.
\nNevertheless, in a multivariate setting as ours, the theory guarantees that arbitrage opportunities are ruled out if the market price of risk in the market is unique. Without entering technical details and assuming \n
It is evident that these values are not equal if we plug parameter estimates in Eq. (30); hence, arbitrage opportunities are not ruled out at least from a theoretical point of view. We will address this issue more precisely in future research.
\nIn this chapter, we have introduced a model in continuous time in order to describe the dynamics of Bitcoin price depending on an exogenous stochastic factor, which represents market attention on the Bitcoin system. Market attention is measured either by the total trading volume in Bitcoins or by means of the Google Search Volume Index, which, as suggested in [16], is a direct measure of the revealed attention for uniformed retail investors. More precisely, the attention factor affects directly the instantaneous mean and volatility of logarithmic returns; in addition, it may be also correlated with the price changes. An estimation procedure to fit the model to observed data is also suggested and, under the assumption of no correlation, a closed formula for standard European option prices on Bitcoin is provided.
\nBy applying outcomes within the mathematical theory of bubbles [23, 24, 25, 27], we are able to show that Bitcoin boosts in a bubble if and only if there is a positive correlation between changes in the price and in the attention factor. This finding is reasonable and claims that a stronger positive dependence between the two processes in Eq. (21) may result in an explosion of the price process.
\nFinally, we allow for a delay on the effect of market attention on the Bitcoin price, and, based on this generalized model, we introduce a multivariate setting for our model (Eq. (28)) in order to take into account the special feature of multiple exchanges where it is possible to trade in Bitcoins. Preliminary results indicate that arbitrage opportunities may arise in two exchanges that are characterized by different delays.
\nWe gratefully acknowledge Marco Patacca for having provided the routines in Matlab® to develop the empirical sections of the paper and for useful suggestions and comments. We also acknowledge funding from Fondazione Cassa di Risparmio di Perugia (Grant 2015:0459013) and Bank of Italy (Grant 407660/16).
\nThe authors declare no conflict of interest.
Gianna Figà-Talamanca gratefully acknowledges interesting discussions on the topic of this chapter with colleagues of the Department of Finance and Risk Engineering where she was visiting professor while preparing this research. The authors also wish to thank Stefano Bistarelli for having introduced them to the intriguing and worth to explore world of cryptocurrencies.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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