culture conditions that induce cell differentiation into three lineages: osteogenic, adipogenic and chondrogenic [35]. A number of *in vitro* assays can be used to assess the multipotentiali‐ ty of these cell preparations. Osteogenic differentiation of MSCs can be induced using dexamethasone, ascorbate-2-phosphate and beta glycerolphosphate. Osteoblasts may be identified using alizarine red S staining. Adipogenic differentiation can be induced with the medium containing dexamethasone, indomethacin, isobutylmethylxanthine and insulin. Oil red O staining may be used to detect lipid accumulation. Chondrogenic differentiation can be induced in a defined medium containing dexamethasone, ascorbate-2-phosphate, insulin, selenious acid, transferrin, sodium pyruvate and transforming growth factor-beta [3, 36, 37]. The ability of MSCs to differentiate along these lineages is strongly associated with their multipotency and stem cell nature. However, MSCs do not maintain these characteristics indefinitely and MSCs senescence with extensive subcultivation *in vitro* whereby they lose their proliferation and differentiation potential [37]. Such culture expansion could also generate genetic and epigenetic instability, including chromosome alterations. The accumu‐ lation of genetic changes during cell culturing and subsequent risk of cell transformation are

The utilization of MSCs for cell therapy requires large-scale *in vitro* expansion, thus increas‐ ing the probability of cytogenetic and molecular instabilities [38]. The expansion of MSCs in culture could generate chromosomal abnormalities such as aneuploidy (the presence of an abnormal number of chromosomes in a cell) or structural chromosomal alterations, reflect‐ ing the chromosomal instability. But, itis not clear how many passages can be performedbefore these cells acquire chromosome instability or lose their multipotency [3, 39, 40]. Some studies have been shown that *in vitro* culture of MSCs from bone marrow and adipose tissue re‐ tained normal karyotypes between passages 1 and 5 [3, 39, 41]. At later passage cultures, MSCs began to show chromosomal abnormalities such as aneuploidy. However, other studies observed that MSC cultures derived from bone marrow and adipose tissue had normal karyotypes up to passage 20 [42, 43]. Although these results are debatable, they show the necessity of cytogenetic analyses for safety before therapeutic application of mesenchymal

Molecular studies also have an important role to determine appropriate MSCs to be used for cell therapy. All primary human cells, including MSCs, undergo only a limited number of cell divisions under standard culture conditions, in a process called cellular senescence. Senes‐ cence is considered to be a stress response triggered by activation of some mechanisms as telomere erosion and accumulation of DNA damage [44, 45]. *In vitro* cultures cause signifi‐ cant telomere shortening. Telomeres are the termini of eukaryotic chromosomes and their principal function is to protect chromosomes from illegitimate fusion and recombination, thereby preserving genome integrity [45, 46]. Since MSCs have only a finite ability for selfrenewal like most somatic cells, assaying for telomere length in hMSCs provides critical information on the replicative capacity of the cells, an important criterion in the selection of MSCs for therapy. Telomere length is generally quantified by Southern blotting and fluores‐

other important points of stem cell therapy [3, 38].

500 Pluripotent Stem Cells - From the Bench to the Clinic

*2.1.2. Cytogenetic and molecular characteristics of MSCs*

stromal cells.

The multipotency of MSCs has allowed a significant progress in our understanding about differentiation pathways of various lineages for tissue engineering and therapeutic purposes [48]. Runt-related transcription factor 2 (Runx2) has been considered as a master regulatory gene responsible for early osteogenic differentiation [49]. While Runx2 acts to promote osteoblastic differentiation, another important osteogenic inducer, osterix, suppresses chondrogenesis and promotes osteoblastic differentiation at a later stage. Low levels of osterix are sufficient to inhibit chondrogenesis, while a high expression level is necessary for osteogenic differentiation [48, 50]. Furthermore, *ex vivo* MSCs have successfully differentiat‐ ed into osteoblasts in osteogenic media supplemented by dexamethasone and ascorbate. The selective capability to promote osteogenic differentiation has potential clinical implications in bone repair and regeneration [48, 51].

*In-vitro* differentiation ofMSCs into a chondrogenic lineage has been studied through exposure to growth factors, co-culture with cartilage and overexpression of specific genes such as SRYbox 9 (Sox9) to promote chondrocytic differentiation. Sox9 cooperates with its downstream proteins Sox5 and Sox6 to promote chondroycte proliferation and maturation and matrix formation [48, 52]. MSCs also have the capacity to differentiate into an adipogenic lineage. PPAR-γ plays a critical role in this process by regulating the function of many adipocytespecific genes. In addition, PPARγ interacts with members of the CCAAT/enhancer-binding protein (C/EBP) family to regulate adipogenesis. Cells can also be induced to undergo adipogenesis through exposure to exogenous factors or by culturing them in adipogenic media containing insulin and dexamethasone [48, 53]. As the multilineage potential is one of the three criteria to define the MSCs in vitro according to the International Society of Cellular Therapy [1], the use of molecular tests to analyze the expression of the genes involved in the differentiation of the osteogeneic, chondrogenic and adipogenic are important to associate the biological function of the MSCs for their clinical use.

The expansion of MSCs *in vitro* is associated with genetic instability. Hence, molecular studies comparing the molecular profile during the culture passages are important to acquire knowledge about molecular modifications and potential risks for cell therapy. In this sense, proteomic and transcriptomic approaches have been used to verify molecular modifications of MSCs from different culture passages [3, 54].

We need to be careful before using BM-MSCs for clinical applications. Some changes may be analyzed such as enlarged morphology, decreased number of cell divisions, random loss of genomic regions and telomere shortening. These modifications process could lead to a reduction in the multipotent state of MSCs and might lead to tumour formation under specific conditions. It is very important to characterize the cytogenetic and molecular profiles during expansion *in vitro* of BM-MSCs; thus, appropriate tests should be applied to ensure the integrity of the genome and epigenome [54].

### *2.1.3. Quality control for cell therapy*

There are many challenges associated with characterizing and quantifying cells for use in celland tissue-based therapies. From a regulatory perspective, these advanced treatments must not only be safe and effective, but also must be made by high-quality manufacturing process‐ es [55]. Prolonged exposure to stressful conditions during the cell enrichment and differen‐ tiation processes has raised concerns about the safety of stem cell therapy. The International Society for Stem Cell Research has created "Guidelines for the Clinical Translation of Stem Cells" [56]. Some cytogenetic tests that may be performed to ensure the safety of the stem cells include: G-banding, fluorescence in situ hybridization (FISH) and array comparative genom‐ ic hybridization (array CGH) [38, 57]. Molecular genetic tests may be performed as the analysis of the telomere length, the expression of genes involved in the osteogenic, adipogenic and chondrogenic differentiation. Some characteristics and tests, which may be considered as a quality control for the use of MSCs for cell therapy, are shown in **Figure 1**. The practical application of these recommended tests can be standardized for the sensitivity and specifici‐ ty between laboratories.

**Figure 1.** Some characteristics and tests that may be considered as a quality control for the use of MSCs for cell thera‐ py.

The first clinical trial using culture-expanded MSCs was performed in 1995 and bone mar‐ row samples were obtained from 23 patients with haematologic malignancies in complete

remission. In this study, as no adverse reactions were observed with the infusion of the MSCs, Lazarus and colleagues concluded that MSCs obtained from cancer patients can be collected, expanded *in vitro* and infused intravenously without toxicity [58]. Many completed clinical trials have demonstrated the efficacy of MSC infusion for diseases including acute myocar‐ dial ischaemia, liver cirrhosis, amyotrophic lateral sclerosis and graft versus host disease (GVHD) [12, 59]. The statistical methods are important tools to evaluate the quality, safety and efficiency of MSCs for cellular therapy as we will observe in the last section.

*2.1.3. Quality control for cell therapy*

502 Pluripotent Stem Cells - From the Bench to the Clinic

ty between laboratories.

py.

There are many challenges associated with characterizing and quantifying cells for use in celland tissue-based therapies. From a regulatory perspective, these advanced treatments must not only be safe and effective, but also must be made by high-quality manufacturing process‐ es [55]. Prolonged exposure to stressful conditions during the cell enrichment and differen‐ tiation processes has raised concerns about the safety of stem cell therapy. The International Society for Stem Cell Research has created "Guidelines for the Clinical Translation of Stem Cells" [56]. Some cytogenetic tests that may be performed to ensure the safety of the stem cells include: G-banding, fluorescence in situ hybridization (FISH) and array comparative genom‐ ic hybridization (array CGH) [38, 57]. Molecular genetic tests may be performed as the analysis of the telomere length, the expression of genes involved in the osteogenic, adipogenic and chondrogenic differentiation. Some characteristics and tests, which may be considered as a quality control for the use of MSCs for cell therapy, are shown in **Figure 1**. The practical application of these recommended tests can be standardized for the sensitivity and specifici‐

**Figure 1.** Some characteristics and tests that may be considered as a quality control for the use of MSCs for cell thera‐

The first clinical trial using culture-expanded MSCs was performed in 1995 and bone mar‐ row samples were obtained from 23 patients with haematologic malignancies in complete

## **3. Potential clinical applications of MSCs in haematopoietic stem cell transplantation**

Haematopoietic stem cell transplantation (HSCT) is the first field wherein human stem cell therapy was successful. Allogeneic HSCT has been an important modality to cure various diseases, including haematologic malignancies, various non-malignant haematologic diseas‐ es and primary immunodeficiency diseases. However, autologous HSCT is generally per‐ formed to rescue bone marrow aplasia following high-dose chemotherapy for solid tumour or multiple myeloma [60].

The first successful HSCT using bone marrow from a relative donor was performed in 1968 in a boy with X-linked severe combined immunodeficiency disease (SCID). Since the first successful achievement in human, numerous trials and errors were repeated until 1980, when allogeneic HSCT began to be actively performed under a better recognition of transplant immunology. Dr. Donnall Thomas received Nobel Prize for his pioneering work in bone marrow transplantation to cure leukaemia and other haematologic malignancies [60].

Results from basic and clinicalresearch have allowed the improvement of HSCT. Some ofthese improvements were the use of haematopoietic stem cells from peripheral blood or cord blood, which promoted a change in the terminology from bone marrow transplantation to haemato‐ poietic stem cell transplantation [61]; a better understanding of the complexities of the human leukocyte (HLA) system, which has allowed selecting compatible sibling donors and the establishment of larger registries of HLA-typed volunteers; advances in the immunogenetics of HLA, especially typing of molecular techniques; the development of the preparative regimen of cyclophosphamide and busulfan, which avoids the use of irradiation for some diseases [62]; the development of non-myeloblative conditioning regimens for allogeneic HSCT, avoiding regimen-related toxicity and death and that opened the way to include elderly patients [61, 63].

These advances make HSCT one curative treatment modality for many patients mainly with malignant haematological diseases. But, remaining challenges include further advances in the prevention and treatment of both infections and graft-versus-host disease (GVHD). Despite significant progress in HSCT, GVHD remains a significant cause of morbidity and mortality after allogeneic HSCT [63–67].

Homeostasis of the haematopoietic system, which is a balance between self-renewal and differentiation, is thought to be tightly regulated by interactions among haematopoietic stem

cells (HSCs) and the specialized microenvironment where they reside, the haematopoietic niche. The haematopoietic niche consists of a heterogeneous cellular population of nonhaematopoietic and haematopoietic origin as well as of extracellular matrix, which collective‐ ly provide the structural scaffold, the spatial framework and the appropriate physiological and trophic cues to control HSC maintenance and function [68]. The key component of the haematopoietic microenvironment is bone marrow mesenchymal stromal cells (BM-MSCs).

Many studies have reported that MSCs can promote HSC expansion *in vitro*. Koc and colleagues (2000) first reported rapid haematopoietic recovery after co-infusion of autolo‐ gous BM-MSCs at the time of HSCT without significant side effects [69]. Lazarus and collea‐ gues (2005) showed, in a multicentertrial with 46 patients receiving allogeneic HSCTandMSCs from HLA-identical siblings, a rapid haematopoietic recovery in most patients [58]. These studies showed the beneficial effects of MSC on engraftment after HSCT [2].

BM-MSCs have potential clinical applications in HSCT as an adjuvant cellular therapy for promoting the rapid reconstitution of haematopoiesis after HSCT, to prevent and treat of graft failure, in graft versus tumour effect and in GVHD [2, 70].

#### **3.1. Pathophysiology of graft-versus-host disease (GVHD) and clinical use of MSC for GVHD**

Allogeneic HSCT is an effective treatment for many haematologic and genetic diseases. However, donor-derived cells may also recognize recipient organs as foreign and mount an immune attack against the patient's own tissues, knows as graft-versus-host disease (GVHD) [71].

Graft-versus-host disease (GVHD) is the major cause of morbidity and mortality after an allogeneic HSCT. GVHD has traditionally been classified by the time of its clinical manifesta‐ tions. Acute GVHD occurs within the first 100 days after haematopoietic stem cell transplan‐ tation, whereas chronic GVHD occurs after day 100. This simple classification is increasingly unsatisfactory particularly as reduced-intensity condition regimens gain wider acceptance. The clinical manifestations of acute GVHD after such conditioning often occur much later, sometimes coinciding with the day 100, the demarcation of chronic GVHD [72]. New recom‐ mendations that emphasize the importance of qualitative differences, as opposed to the time of onset after HSCT, are being used to standardize the diagnosis and clinical assessment of chronic GVHD [73].

The pathophysiology of acute GVHD after HSCT can be considered as a three-step process where the innate and adaptive immune system interacts. These three-step processes are as follows: (1) initiation of tissue damage; (2) activation and proliferation of donor T cells; and (3) the effector phase involving cellular and inflammatory factors. The pathophysiology of GVHD is a complex process. Chemotherapy and radiation cause tissue damage, producing pro-inflammatory cytokines, resulting in donor T-cell activation through the host antigenpresenting cell (APC) interaction via MHC-T cell receptor binding and co-stimulatory signals. This leads to T cell expansion and differetiation into various subtypes which traffic through blood vessels to target organs, where they cause tissue destruction and recruitment of other inflammatory cells through pathways such as perforin/grazyme and cytokine release. Moreover, these inflammatory cells and cytokines can propagate the cycle of GVHD [71, 72, 74]. The main step of GVHD reaction is step 2, where donor T cells are activated by the host antigen-presenting cells (APCs). The GVHD reaction is amplified by the intensity of the recipient-conditioning regimen, which can resultin an intensive tissue damage with the release of various cytokines and augment of inflammatory response [75].

cells (HSCs) and the specialized microenvironment where they reside, the haematopoietic niche. The haematopoietic niche consists of a heterogeneous cellular population of nonhaematopoietic and haematopoietic origin as well as of extracellular matrix, which collective‐ ly provide the structural scaffold, the spatial framework and the appropriate physiological and trophic cues to control HSC maintenance and function [68]. The key component of the haematopoietic microenvironment is bone marrow mesenchymal stromal cells (BM-MSCs).

Many studies have reported that MSCs can promote HSC expansion *in vitro*. Koc and colleagues (2000) first reported rapid haematopoietic recovery after co-infusion of autolo‐ gous BM-MSCs at the time of HSCT without significant side effects [69]. Lazarus and collea‐ gues (2005) showed, in a multicentertrial with 46 patients receiving allogeneic HSCTandMSCs from HLA-identical siblings, a rapid haematopoietic recovery in most patients [58]. These

BM-MSCs have potential clinical applications in HSCT as an adjuvant cellular therapy for promoting the rapid reconstitution of haematopoiesis after HSCT, to prevent and treat of graft

**3.1. Pathophysiology of graft-versus-host disease (GVHD) and clinical use of MSC for**

Allogeneic HSCT is an effective treatment for many haematologic and genetic diseases. However, donor-derived cells may also recognize recipient organs as foreign and mount an immune attack against the patient's own tissues, knows as graft-versus-host disease (GVHD)

Graft-versus-host disease (GVHD) is the major cause of morbidity and mortality after an allogeneic HSCT. GVHD has traditionally been classified by the time of its clinical manifesta‐ tions. Acute GVHD occurs within the first 100 days after haematopoietic stem cell transplan‐ tation, whereas chronic GVHD occurs after day 100. This simple classification is increasingly unsatisfactory particularly as reduced-intensity condition regimens gain wider acceptance. The clinical manifestations of acute GVHD after such conditioning often occur much later, sometimes coinciding with the day 100, the demarcation of chronic GVHD [72]. New recom‐ mendations that emphasize the importance of qualitative differences, as opposed to the time of onset after HSCT, are being used to standardize the diagnosis and clinical assessment of

The pathophysiology of acute GVHD after HSCT can be considered as a three-step process where the innate and adaptive immune system interacts. These three-step processes are as follows: (1) initiation of tissue damage; (2) activation and proliferation of donor T cells; and (3) the effector phase involving cellular and inflammatory factors. The pathophysiology of GVHD is a complex process. Chemotherapy and radiation cause tissue damage, producing pro-inflammatory cytokines, resulting in donor T-cell activation through the host antigenpresenting cell (APC) interaction via MHC-T cell receptor binding and co-stimulatory signals. This leads to T cell expansion and differetiation into various subtypes which traffic through blood vessels to target organs, where they cause tissue destruction and recruitment of other

studies showed the beneficial effects of MSC on engraftment after HSCT [2].

failure, in graft versus tumour effect and in GVHD [2, 70].

504 Pluripotent Stem Cells - From the Bench to the Clinic

**GVHD**

[71].

chronic GVHD [73].

Acute GVHD affects mainly skin, liver and gastrointestinal tract. Approximately 50% of the patients treated with HSCT subsequently developed acute GVHD and required systematic treatment. Chronic GVHD occurs in 40% of patients treated with allogeneic HSCT from HLAidentical sibling and more than 50% of patients treated with HSCT from an HLA non-identicalrelated donor [67, 75].

Chronic GVHD is one of the most significant complications of long-term survivors after allogeneic haematologic stem cell transplantation. Experimental studies and clinical observa‐ tions have elucidated the mechanisms of acute GVHD, but the biology of chronic GVHD is not well understood. Experimental studies generated at least four theories to explain the patho‐ physiology of chronic GVHD: (1) thymic damage and the defective negative selection of Tcells; (2) regulatory T cells deficiences; (3) auto-antibody production by aberrant B cells; and (4) the formation of profibrotic lesions [73]. The immunopathology of chronic GVHD is mediated in part by helper T lymphocyte 2 (Th2) cells, with a syndrome of immunodeficiency and an autoimmune disorder [76].

GVHD remains associated with significant morbidity and mortality in allogeneic HSCT. Improving outcomes in HSCT will require additional therapeutic modalities such as the use of MSCS. MSCs may be used to modulate the immune system, as prophylaxis to prevent GVHD and as treatment for established GVHD. Some studies have been performed demon‐ strating that the MSCs can act in GVHD [76–81]. In a multicenter clinical trial, HSCs and MSCs derived from HLA-identical sibling donors were infused and promoted haematopoietic engraftment and limited GVHD. In this study, 31 patients received myeloblative conditionig and HLA-identical sibling bone marrow or peripheral blood stem cells. Escalating doses of MSCs from 1 to 5 x106 /kg were given. Toxicity related to MSC infusion was not observed. The incidence of acute GVHD was 15% in the co-transplanted group compared with 40% in a matched control group [17, 77]. Le Blanc and colleagues (2004) reported a child with acute lymphoblastic leukaemia receiving haploidentical MSC infusion for severe acute GVHD with a satisfactory clinical outcome [78]. The immunomodulatory abitily of BM-MSCs shows promise in treating GVHD, especially acute GVHD. Zhou and colleagues (2010) also showed the potential clinical application of MSCs for chronic GVHD. In this study, four patients with sclerodermatous chronic GVHD were reported, who received MSCs expanded *ex vivo* from unrelated donors by intra BM injection. After MSC infusion, the symptoms gradually improved in all four patients. During the course of MSC treatment, the patient's vital signs and laboratory studies remained normal. None of the four patients had recurrence of leukae‐ mia. This study, despite its limited number of patients, suggests a benefit of MSC infusion therapy in treating sclerodermatous chronic GVHD [76]. MSCs are capable of escaping recognition by the alloreactive immune system and can exert immunomodulatory and anti-

inflammatory effects. Hence, these cells represent a promising tool in the prevention and treatment of GVHD [2].

### **3.2. Prevention and treatment of graft failure using MSC**

Graft failure or graft rejection after HSCT may occur as either a lack of initial engrafment of donor cells orloss of donor cells afterinitial engraftment. In the later case, autologous recovery may appear or, alternatively, marrow aplasia may be developed. Rejection is a major cause of graft failure and it is due to recipient immune response against donor Immuno-haematopoiet‐ ic cells. Graft failure may also occur to other causes, such as viral infections, specifically, cytomegalovirus (CMV) [82]. In patients with leukaemia receiving myeloblative condition‐ ing, the rejection rate was 0.1% in patients given HLA-identical sibling transplants com‐ pared to 5% in those given HLA-mismatched grafts. Another risk factor for graft failure is the reduced intensity conditioning that is used in the elderly patients, with lower doses of chemoradiation therapy, the host immune system may persist, resulting in an increased risk of allograft rejection. Hence, the main risk factors associated with graft failure include HLA disparity in the donor/recipient pair, viral infections and the type of conditioning regimen [82, 83].

Some studies have demonstrated that the use of MSCs is efficient in prevention and treat‐ ment of graft failure [2, 9, 70]. Ball and colleagues carried out a pilot study of co-transplant‐ ed of BM-derived, *ex vivo*-expanded MSCs of donor origin in 14 children undergoing transplantation of granulocytic colony stimulating factor (G-CSF) mobilized, CD34-selected progenitor cells from HLA disparate relative. In this study, a graft failure rate of 15% was observed in 47 controls and all patients, who received MSCs, showed sustained haemato‐ poietic engraftment without any adverse reaction. These results suggested that MSC cotransplantation may modulate host alloreactivity and/or promote better engraftment of donor haematopoiesis, thus reducing the risk of graft failure [9].

#### **3.3. Graft-versus-tumour effect and MSCs**

Two mechanisms are involved in the cure of a malignant disease by stem cell transplanta‐ tion. The first is the conditioning regimen, which confers a powerful anti-tumour effect from myeloblative doses of chemotherapy or radiotherapy. The second is the graft-versus-leukae‐ mia (GVL) effect or the graft-versus-tumor (GVT) effect exerted by transplanted donor T cells and NK cells against malignant tissue. The T lymphocytes recognize antigens presented by HLA molecules on malignant cells. They destroy tumour cells by direct cytotoxicity induc‐ ing death by lysis though the perforin-granzyme pathway and by apoptosis though activa‐ tion of Fas on the cell surface. The graft-versus leukaemia effect requires donor immune competence that often accompanies graft-versus-host disease (GVHD) [84].

Despite HLA identity between a patient and donor, approximately 40% of patients receiving HLA-identical grafts develop acute GVHD due to genetic differences that lie outside the HLA loci, or minor histocompatibility antigens (HA). Some of such antigens, such as HY and HA-3, are expressed on all tissues and are targets for both GVHD and GVL. Others, such as HA-1 and HA-2, are expressed most abundantly on haematopoietic cells (including leukaemic cells) and may therefore induce a greater GVL effect with less GVHD [75].

Bearing in mind two fundamental aspects to the success of allogeneic HSCT, the GVL effect and the GVHD, the co-infusion of MSCs in transplantation should aim atreducing the severity of GVHD while preserving the GVL. In this sense, the study of Baron and colleagues (2010) demonstrated that the MSC co-infusion appeared to be safe; furthermore, MSC co-infusion might have prevented death from GVHD without abrogating GVT effects. In this study, 20 patients with haematologic malignancies received MSCs from HLA-mismatched donors after conditioning with TBI (total body irradiation) and fludarabine. The HLA-mismatched nonmyeloblative HSCT with MSC co-infusion had a therapeutic effect on the haematologic malignancies. MSCs may have important beneficial characteristics in terms of promoting GVT effects due to their immunomodulatory properties after HSCT and their tropism towards the microenvironment [85, 86]. **Figure 2** shows the potential clinical applications of MSCs for haematopoietic stem cell transplantation.

**Figure 2.** Potential clinical applications of MSCs for HSCT.

inflammatory effects. Hence, these cells represent a promising tool in the prevention and

Graft failure or graft rejection after HSCT may occur as either a lack of initial engrafment of donor cells orloss of donor cells afterinitial engraftment. In the later case, autologous recovery may appear or, alternatively, marrow aplasia may be developed. Rejection is a major cause of graft failure and it is due to recipient immune response against donor Immuno-haematopoiet‐ ic cells. Graft failure may also occur to other causes, such as viral infections, specifically, cytomegalovirus (CMV) [82]. In patients with leukaemia receiving myeloblative condition‐ ing, the rejection rate was 0.1% in patients given HLA-identical sibling transplants com‐ pared to 5% in those given HLA-mismatched grafts. Another risk factor for graft failure is the reduced intensity conditioning that is used in the elderly patients, with lower doses of chemoradiation therapy, the host immune system may persist, resulting in an increased risk of allograft rejection. Hence, the main risk factors associated with graft failure include HLA disparity in the donor/recipient pair, viral infections and the type of conditioning regimen [82,

Some studies have demonstrated that the use of MSCs is efficient in prevention and treat‐ ment of graft failure [2, 9, 70]. Ball and colleagues carried out a pilot study of co-transplant‐ ed of BM-derived, *ex vivo*-expanded MSCs of donor origin in 14 children undergoing transplantation of granulocytic colony stimulating factor (G-CSF) mobilized, CD34-selected progenitor cells from HLA disparate relative. In this study, a graft failure rate of 15% was observed in 47 controls and all patients, who received MSCs, showed sustained haemato‐ poietic engraftment without any adverse reaction. These results suggested that MSC cotransplantation may modulate host alloreactivity and/or promote better engraftment of donor

Two mechanisms are involved in the cure of a malignant disease by stem cell transplanta‐ tion. The first is the conditioning regimen, which confers a powerful anti-tumour effect from myeloblative doses of chemotherapy or radiotherapy. The second is the graft-versus-leukae‐ mia (GVL) effect or the graft-versus-tumor (GVT) effect exerted by transplanted donor T cells and NK cells against malignant tissue. The T lymphocytes recognize antigens presented by HLA molecules on malignant cells. They destroy tumour cells by direct cytotoxicity induc‐ ing death by lysis though the perforin-granzyme pathway and by apoptosis though activa‐ tion of Fas on the cell surface. The graft-versus leukaemia effect requires donor immune

Despite HLA identity between a patient and donor, approximately 40% of patients receiving HLA-identical grafts develop acute GVHD due to genetic differences that lie outside the HLA loci, or minor histocompatibility antigens (HA). Some of such antigens, such as HY and HA-3, are expressed on all tissues and are targets for both GVHD and GVL. Others, such as HA-1

competence that often accompanies graft-versus-host disease (GVHD) [84].

treatment of GVHD [2].

506 Pluripotent Stem Cells - From the Bench to the Clinic

83].

**3.2. Prevention and treatment of graft failure using MSC**

haematopoiesis, thus reducing the risk of graft failure [9].

**3.3. Graft-versus-tumour effect and MSCs**

## **4. Using probability techniques to study clinical applications of mesenchymal stem cells**

Nowadays, people recognize the importance of Mathematics in Medicine. Many statements in this area can be better understood using mathematical concepts and results. In this section, we will present mathematical techniques based on the concept and results of probability, as we are interested in making decisions in the face of uncertainty. In fact, in Medicine, clinical outcomes, such as the occurrence of disease, death, symptoms or functional impairment, can be counted and expressed as numbers, but in most clinical situations, the diagnosis, progno‐

sis and treatment outcomes are uncertain for an individual patient. A person will experience a clinical outcome or not: the prediction is rarely exact. Therefore, the prediction must be expressed as a *probability*.

Although earlier work on probability was done by the Italian mathematician Giralamo Cardano (1501–1576), the investigation of probability as a branch of Mathematics sprang about 1654 with two great French mathematicians: Blaise Pascal (1623–1662) and Pierre Fermat (1601–1665) [87]. Of course, we shall not do a discourse on probability. But, we need to say that the theory of probability underlies the procedures in testing hypotheses, which are very useful to Medicine and other disciplines in the health field.

In this section, we will avoid mathematical formulas and theorems.

### **4.1. Descriptive statistics and inferential statistics**

Statistics is a branch of Mathematics. The word "statistics" derives from the Latin word *status*, meaning "manner of standing" or "position." Statistics were first used by tax assessors to collect information for determining assets and assessing taxes. Statistics applied to Medicine and other health disciplines is called biostatistics or biometrics. For those who would like to study this subject, we recommend the book of J. H. Zar [88].

Statistics is divided into two branches: descriptive and inferential. *Descriptive statistics* is used to organize and summarize data. *Inferential statistics* is used to draw inferences and reach conclusions about a population, when only a sample from that population has been studied. A population is a complete set of observations, patients, measurements and so forth. A sample is a subset of a certain population.

The mathematical techniques used on descriptive statistics are graphic (*tables and graphs*) and numerical (*quantitative indices*). Tables are often used to present qualitative and quantitative data. Graphs are used widely to provide a visual display data. The bar diagram, histogram and frequency polygon are three graphic formats that are commonly used to present medi‐ cal data. Quantitative indices are numbers that describe the centre and the variation of a distribution, which are called *parameters*if they are referred to a population and called *statistics* if they are referred to a sample. Quantitative indices that describe the centre of a distribution are referred to as *measures of central tendency*. The mean, known also as the arithmetic mean, median and mode are three common measures of central tendency. Quantitative indices that describe the variation or dispersion of a distribution are referred to as *measures of dispersion*. The range, variance and standard deviation are three common measures of dispersion. Other quantitative indices such as risk difference, relative risk and odds ratio are also used in Medicine.

One mathematical technique used on inferential statistics is hypothesis tests. They are much more sophisticated than the techniques used in descriptive analysis, because hypothesis tests are based on probability models.

A probability model for a particular experiment is a probability distribution that *predicts* the relative frequency of each outcome if the experiment is performed a large number of times. A

*probability distribution* is a table, a graph or a formula that describes what probably will happen instead of describing what really happened.

A probability distribution can be discrete or continuous. An important example of a discrete probability distribution is the *binomial distribution* and an important example of a continuous distribution is the *normal distribution*.

## **4.2. Hypothesis tests**

sis and treatment outcomes are uncertain for an individual patient. A person will experience a clinical outcome or not: the prediction is rarely exact. Therefore, the prediction must be

Although earlier work on probability was done by the Italian mathematician Giralamo Cardano (1501–1576), the investigation of probability as a branch of Mathematics sprang about 1654 with two great French mathematicians: Blaise Pascal (1623–1662) and Pierre Fermat (1601–1665) [87]. Of course, we shall not do a discourse on probability. But, we need to say that the theory of probability underlies the procedures in testing hypotheses, which are

Statistics is a branch of Mathematics. The word "statistics" derives from the Latin word *status*, meaning "manner of standing" or "position." Statistics were first used by tax assessors to collect information for determining assets and assessing taxes. Statistics applied to Medicine and other health disciplines is called biostatistics or biometrics. For those who would like to

Statistics is divided into two branches: descriptive and inferential. *Descriptive statistics* is used to organize and summarize data. *Inferential statistics* is used to draw inferences and reach conclusions about a population, when only a sample from that population has been studied. A population is a complete set of observations, patients, measurements and so forth. A sample

The mathematical techniques used on descriptive statistics are graphic (*tables and graphs*) and numerical (*quantitative indices*). Tables are often used to present qualitative and quantitative data. Graphs are used widely to provide a visual display data. The bar diagram, histogram and frequency polygon are three graphic formats that are commonly used to present medi‐ cal data. Quantitative indices are numbers that describe the centre and the variation of a distribution, which are called *parameters*if they are referred to a population and called *statistics* if they are referred to a sample. Quantitative indices that describe the centre of a distribution are referred to as *measures of central tendency*. The mean, known also as the arithmetic mean, median and mode are three common measures of central tendency. Quantitative indices that describe the variation or dispersion of a distribution are referred to as *measures of dispersion*. The range, variance and standard deviation are three common measures of dispersion. Other quantitative indices such as risk difference, relative risk and odds ratio are also used in

One mathematical technique used on inferential statistics is hypothesis tests. They are much more sophisticated than the techniques used in descriptive analysis, because hypothesis tests

A probability model for a particular experiment is a probability distribution that *predicts* the relative frequency of each outcome if the experiment is performed a large number of times. A

very useful to Medicine and other disciplines in the health field.

study this subject, we recommend the book of J. H. Zar [88].

**4.1. Descriptive statistics and inferential statistics**

is a subset of a certain population.

are based on probability models.

Medicine.

In this section, we will avoid mathematical formulas and theorems.

expressed as a *probability*.

508 Pluripotent Stem Cells - From the Bench to the Clinic

A hypothesis test is a method used to determine if there is enough evidence in a sample to infer that a certain property holds for the entire population. It works with two hypothesis: the null hypothesis (designated H0) and the alternative hypothesis (designated HA).

In the null hypothesis we use the words *no difference* or *equal to* and in the alternative hypoth‐ esis we use the words *different from, less than* or *greater than*. But let us mention that, in fact, we should say *no statistical difference, statistically equal to, statistically different from, statistically less than* or *statistically greater than*, because we are dealing with the probabilities of an event happens or not. When we retain HA (equivalently reject H0), we say the results are signifi‐ cant and when we retain H0 (equivalently reject HA), we say the results are not significant. Because we are dealing with probabilities, this implies in making two possible errors from four possible relations between the conclusions obtained using a hypothesis test and real situa‐ tions, as shown in **Table 1**.

**Table 1.** Relations between statistical conclusions and real situations.

The two errors mentioned in the previous paragraph are known as Type I error and Type II error. A Type I error leads to a *false posit*ive conclusion. The probability of such an error occurs is noted by **α**. Mathematically, **α** is a conditional probability: **α** is the probability of reject H0 when there is no real difference. A Type II error leads to a *false negative* conclusion. The probability of such an error occurs is noted by **β**. Mathematically, **β** is a conditional probabil‐ ity: **β** is the probability of retain H0 when there is a real difference.

Hypothesis tests are used to estimate the *probability* of a Type I error. So, they are based on probability models. In the literature, we usually use **α** < 0.05. This means we are assuming a probability less than 0.05 of rejecting H0 when there is no real difference between treatments, drugs or procedures. In other words, if the study were repeated one-hundred times, we *probably* would find five outcomes showing H0 should be accepted.

The hypothesis tests commonly used in the medical literature are presented in **Table 2**. They can be parametric or nonparametric. The choice in using one of them depends on the pur‐

pose of the study, the size of the sample and the type of the variables involved at the study, for instance. If we can guarantee that the sampling distribution is normal or approximately normal, we can use a parametric test. Because a normal distribution has good mathematical properties (bell-shaped, symmetric, and so on), using a parametric test leads to better results compared with a nonparametric test. In other words, we say that nonparametric tests are less powerful, in the sense that, they lead to a small probability to reject H0, when H0 is false.

**Table 2.** Statistical tests usually used in the medical literature.

When we use a hypothesis test, we compute a *p*-value. The *p*-value is the probability of obtaining a result as extreme or more extreme than the sample value, assuming that the null hypothesis is true. The sample value is calculated. Depending on the test we use, there is a specific formula to calculate the sample value. An appropriate computer software can do such a calculation.

We finish this subsection noting that many methods described above are univariate meth‐ ods, because they are only concerned with analyzing only one variable. The point in using a multivariate analysis is to consider several variables simultaneously in the study. With multivariate methods, we can reduce data information, we can classify objects or variables, we can investigate the dependence among variables and, finally, we can make predictions. Principal component analysis, factor analysis and clustering methods can be used in order to reduce data and investigate the dependence among variables. To make predictions, we can use Hotelling's *T*<sup>2</sup> test, which allows inference about one mean vector, and we can use MANOVA, which allows inference about a finite number of mean vectors. It is important to say that both Hotelling's *T*<sup>2</sup> test and MANOVA are generalizations of Student *t*-test and ANOVA, respectively. So, they are based on the multivariate normal distribution. For those who would like to study this subject, we recommend the book of R.A. Johnson and D.W. Wichern [89].

#### **4.3. Probability techniques on the study of mesenchymal stem cells**

In cellular therapy, safety remains one of the main characteristic and refers to validation tests. The culture process should be reproducible, robust and efficient. The increasing use of MSCs has led to a production of processes which needs to be in accordance with current Good Manufacturing Practice (cGMP) [90, 91]. For the validation of the tests, it is important to use probability techniques comparing the results of the tests and the results between different laboratories, with the main to standardize the procedures to characterize the MSCs at the stage of production for cell therapy. The evidence of clinical efficacy is also required. In this case, the application of probability methods is also an important tool in clinical trials and clinical outcomes revealing the impact on the use of MSCS in cellular therapy.

## **5. Conclusion**

pose of the study, the size of the sample and the type of the variables involved at the study, for instance. If we can guarantee that the sampling distribution is normal or approximately normal, we can use a parametric test. Because a normal distribution has good mathematical properties (bell-shaped, symmetric, and so on), using a parametric test leads to better results compared with a nonparametric test. In other words, we say that nonparametric tests are less powerful, in the sense that, they lead to a small probability to reject H0, when H0 is false.

**Two or more proportions** Chi-square Nonparametric **Two proportions** Fisher's exact Parametric **Two medians** Mann–Whitney Nonparametric **Two means** Student's *t* Parametric **More than two means** Kruskal–Wallis (one-factor) Nonparametric **More than two means** ANOVA (one-factor) Parametric **More than two means** ANOVA (more factors) Parametric

When we use a hypothesis test, we compute a *p*-value. The *p*-value is the probability of obtaining a result as extreme or more extreme than the sample value, assuming that the null hypothesis is true. The sample value is calculated. Depending on the test we use, there is a specific formula to calculate the sample value. An appropriate computer software can do such

We finish this subsection noting that many methods described above are univariate meth‐ ods, because they are only concerned with analyzing only one variable. The point in using a multivariate analysis is to consider several variables simultaneously in the study. With multivariate methods, we can reduce data information, we can classify objects or variables, we can investigate the dependence among variables and, finally, we can make predictions. Principal component analysis, factor analysis and clustering methods can be used in order to reduce data and investigate the dependence among variables. To make predictions, we can use Hotelling's *T*<sup>2</sup> test, which allows inference about one mean vector, and we can use MANOVA, which allows inference about a finite number of mean vectors. It is important to say that both Hotelling's *T*<sup>2</sup> test and MANOVA are generalizations of Student *t*-test and ANOVA, respectively. So, they are based on the multivariate normal distribution. For those who would like to study this subject, we recommend the book of R.A. Johnson and D.W.

In cellular therapy, safety remains one of the main characteristic and refers to validation tests. The culture process should be reproducible, robust and efficient. The increasing use of MSCs

**4.3. Probability techniques on the study of mesenchymal stem cells**

**To test the statistical significance of the difference between …**

510 Pluripotent Stem Cells - From the Bench to the Clinic

**Table 2.** Statistical tests usually used in the medical literature.

a calculation.

Wichern [89].

Mesenchymal stem cells (MSCs) represent promising tools to be used in immunoregulatory and regenerative cell therapies. For this purpose, an extensive amplification *in vitro* of MSCs is necessary without affecting the cells' genomic characteristics and differentiation proper‐ ties. However, sometimes, the culture expansion could generate cytogenetic and molecular alterations. Accumulation of these alterations during many passages of culture could lead to malignant cell transformation. Hence, it is important to do a quality control using different methods to test the safe and efficacy of MSCs for cell therapies. Many studies have revealed the clinical use of MSCs as an emerging field for treating cardiovascular disorders, neurode‐ generative diseases, bone defects and fractures, inflammatory arthritis and in the field of haematopoietic stem cell transplantation. The BM-MSCs have potential clinical applications in HSCT as an adjuvant cellular therapy for promoting the rapid reconstitution of haemato‐ poiesis after HSCT, to prevent and treat of graft failure, in graft-versus tumor effect and in GVHD. Although these studies showed positive results, itis necessary to continue the scientific and clinical research to clarify some points as: the characterization of the appropriate cell passage during the culture of MSCSs to ensure the genomic stability; the definition of the tests for quality control to ensure the safety of the MSCS for clinical practice; the practical applica‐ tion ofthese recommended tests can be standardized forthe sensitivity and specificity between the laboratories; it is necessary to define the optimum cell dose and the number of infusions of MSCs during the treatment; a long follow-up to characterize the positive clinical effects and also the adverse clinical effects that may occur with the use of MSCs. With the advancement of basic and clinical research, we hope that the use of MSCs in cell therapy brings excellent results especially for patients treated with HSCT.

## **Author details**

Teresa de Souza Fernandez1\* and Cecília de Souza Fernandez2

\*Address all correspondence to<EMAIL_ADDRESS>
1 National Cancer Institute (INCA), Bone Marrow Transplantation Center (CEMO), Laboratory Division, Rio de Janeiro, RJ, Brazil

<sup>2</sup> Mathematics and Statistics Institute, Federal Fluminense University (UFF), Niterói, RJ, Brazil

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<sup>2</sup> Mathematics and Statistics Institute, Federal Fluminense University (UFF), Niterói, RJ, Brazil

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## *Edited by Minoru Tomizawa*

Pluripotent stem cells have distinct characteristics: self-renewal and the potential to differentiate into various somatic cells. In recent years, substantial advances have been made from basic science to clinical applications. The vast amount knowledge available makes obtaining concise yet sufficient information difficult, hence the purpose of this book. In this book, embryonic stem cells, induced pluripotent stem cells, and mesenchymal stem cells are discussed. The book is divided into five sections: pluripotency, culture methods, toxicology, disease models, and regenerative medicine. The topics covered range from new concepts to current technologies. Readers are expected to gain useful information from expert contributors.

Pluripotent Stem Cells - From the Bench to the Clinic

Pluripotent Stem Cells

From the Bench to the Clinic

*Edited by Minoru Tomizawa*

Photo by Hakat / iStock


*3.4. The Publish/Subscribe Module*

The Publish/Subscribe module is essentially an efficient and easy-to-use collaboration tool that allows users to share their collected datasets (or parts of them), as well as to discover and access datasets of other users within the Hydria ecosystem. This tool is not intended for use by the Hydria system end-users (i.e., people participating in a Hydria survey, or museum visitors that provide feedback via a Hydria questionnaire), but rather targets other user categories like curators/super-users (see Section 3.5 for a detailed description of the Hydria user roles). Using this functionality involves the following two-step process:

When both stages have been completed, the user is able to access the subscribed records for preview, incorporate them into her own data ponds, or create charts and visualizations using the data analysis and visualization tool. Moreover, the subscribed user will be notified whenever new records that match her subscription are incorporated in the subscribed data pond.

## *3.5. The User Management Module*

The User Management module is responsible for performing basic and advanced user management tasks, such as manage users in the Hydria community, assign user privileges and permissions, and perform access control on data ponds and the stored data within them. It supports the following three types of users:

Figure 3 presents the UI for the creation of a new user from the administrator point of view in Hydria.

**Figure 3.** User creation form.

## *3.6. Implementation Aspects*

Hydria has been entirely developed by open source software. The Data Acquisition module uses the Linux/Apache/MariaDB/PHP (LAMP) solution stack for temporary storage of the extracted data and was developed using Python tools [80]. The rest of the modules were built by using the Laravel Framework [81] and use the Linux/Apache/PostgreSQL/PHP (LAPP) solution stack as the back-end database infrastructure. Also, many of Hydria functionalities were developed using JavaScript/JQuery/AJAX.

#### **4. The TripMentor Case Study**

In the context of Alpha testing the Hydria platform, we made the system available to partners within the TripMentor project [45] that aims at creating a tourist guide for the region of Attica, Greece. The partners had varying levels of IT expertise, ranging from relatively experienced to very experienced, and were asked to use Hydria for collecting, storing, and managing data relevant to the project, by deploying the different services offered by Hydria and using the functionality provided. In the following, we report the results for this case study, as these were drawn from field observations as well as from the analysis of usage logs and collected data.

The TripMentor partners used Hydria to navigate within TripAdvisor [82] and Facebook [83] and detect content relevant to the tourism domain; the interest of focus was primarily points of interest (PoIs) located in the *Attica region in Greece*, due to the nature of the TripMentor project. The data retrieved was stored in the Hydria data lake, and the stakeholders involved used Hydria functionality to design the necessary data ponds, modify selected data records, mine and visualize information from the stored data, and export files for further analysis.

#### *4.1. Data Harvesting*

To crawl the web for content and data relevant to the TripMentor project, the integrated ACHE crawler was used within the Hydria environment. To set up the crawler, a number of relevant pages within the domain of interest were used as seed URLs; such URLs included tourist articles and blog posts related to cultural and tourist activities in the Attica region. To implement the page classification required by ACHE, different features of the page URL, title and content were exploited, which were *Big Data Cogn. Comput.* **2020**, *4*, 7

determined after an examination of the patterns followed by the pages in different social networks. For instance, for the TripAdvisor spider the following features were used:

Similarly, for the Facebook crawlers patters from the seed URLs were exploited; for instance, to identify the activities available in the city of Athens, Greece (which is located in the Attica region), the seed URLs start with the string *Things-to-do-in-*, which is followed by the city name (Athens), and this is in turn followed by the name of the country (Greece).

Notably, a multitude of additional operators are available for more complex classification tasks, which include matching of the title (title\_regex classifier type), combination of regular expression matches through the AND and OR operators, or using machine-learning-based text classifiers (SVM, Random Forest) using the title\_regex classifier type.

Having collected the seed URLs, six distinct spiders were developed, in an effort to cover a wide spectrum of events and many different scopes; four of them targeted data harvesting from the Facebook platform, while the remaining two were deployed over TripAdvisor. Table 1 summarizes the outcome of the data harvesting process.

**Table 1.** TripMentor-related data ponds records after Hydria harvesting process.

#### 4.1.1. Facebook Spiders

In this section, we discuss the deployed Facebook spiders for the TripMentor case study and present some initial statistics and insights on how the Hydria social media spiders may be used within the cultural informatics context. Please note that the spiders and data ponds were setup and deployed by the TripMentor project partners; our statistics and observations are based on usage logs and the schema analyzes of the data ponds used to store the collected data.

The first spider, which constitutes the initial setup within Hydria, was deployed over Facebook and extracted a total number of 10,405 different PoIs in the Attica region; a time period of approximately 48 hours was needed to conclude the data harvesting operation, and the collected PoIs are categorized as shown in Table 2. For each of these venues, the spider retrieved and stored in a Hydria data pond the following fields from the related Facebook profile pages: the venue name, the venue unique ID as stored in the Facebook platform, the hours/days of the week that the venue is open to visitors, the venue website, the venue phone number, the registered email address, the physical address of the venue, the total number of check-ins for the venue (i.e., the visitor traffic), the average review score of the venue, the venue category, and the geographical coordinates (latitude and longitude) of the venue. Please note that all the collected data were about venues and cultural events, and no personal or user-specific data were harvested or stored.

**Table 2.** Different PoIs in the Attica region extracted by the first FB spider.

Having retrieved the PoIs unique IDs from the previous process, a new spider that generated the venues profile page URLs using the venue IDs was created and launched within Hydria for a subset of the collected PoIs (approximately 1.7 K PoIs). The spider was then deployed and collected around 140 K posts related to the targeted PoIs in a time frame of around 168 h (one week). All retrieved posts were stored in a separate data pond within Hydria, along with the following metadata: The post unique ID, the profile source of the post, the profile that shared this post, the upload date of the post, the post text, the total number of reactions and the number for each individual reaction type (e.g., likes), and the URL of the post. Notice that posts and the collected post sources refer to venues and cultural events and are not related to persons or personal data, while the collected reactions correspond to aggregate numbers and cannot be traced back to individual users. In particular, regarding the information on the profile that shared the post, only the id of the profile was collected and transformed using an one-way function, hence the data cannot be associated with the original profile; however maintaining the ability to determine whether two posts were posted by the same user profile.

By using the generated venue profile page URLs, another spider was setup and executed within the Hydria environment; it employed 587 seed URLs and was able to retrieve around 240 K user comments within a time frame of 168 h (one week). The collected data was also stored in a separate Hydria data pond and contained fields like: the date that the comment was posted, the total number of the reactions in each comment, the comment text, and the comment URL. Notice that the collected comments and the related metadata do not contain user information or personal data; no user IDs were collected and our logs show that all user references in the comment text were deleted by using the Hydria text cleaning (regex-based) functionality.

Next, profiles of venues that are known as major event organizers were used as initial seeds to bootstrap and launch an event harvesting spider that was able to extract a few hundred upcoming events and their relevant event cards. The seed URLs were identified by the TripMentor stakeholders by manually inspecting the collected venues and using tacit knowledge regarding the major event organizers in the region of Attica. Again, the harvested data were stored in a separate Hydria data pond, with the fields stored within this data pond including the event name and date(s), the physical address of the event, the number of people interested to visit this event, the URL of the event, the unique identifier of the PoI where this event was found, the unique identifier of the event, and the text description of the event. Please note that only aggregate numbers on the number of individuals interested in the event are harvested and no personally identifiable information is either collected or stored within Hydria.

#### 4.1.2. TripAdvisor Spiders

In this section, we discuss the deployed TripAdvisor spiders for the TripMentor case study; this set of spiders is used to showcase the versatility and usefulness of the data harvesting component. As with the Facebook spiders, we had no control over the TripAdvisor spiders deployed and the data ponds created; all setup, deployment and data manipulation was done by the TripMentor project partners. The statistics and metadata presented in this work were drawn from usage logs and the data pond schemas.

The first spider was setup and deployed over TripAdvisor aiming to extract PoIs in the Attica region; after a runtime of around 48 h, it collected information about 7 K different PoIs, belonging in a vast variety of different categories (as identified by the respective TripAdvisor field) including monuments, museums, landmarks, natural reserve sites, parks and water parks, different types of restaurants, cafes, etc. For each one of the collected PoIs, the following fields were stored in the Hydria data pond: the venue name in different languages, the overall venue review score, the total number of venue reviews, the ranking of the PoI with respect to other PoIs of the same category in the same broader area (e.g., "4th out of 10 restaurants within the district"), the categories that this PoI appears in, the physical address of the PoI, the PoI phone number, and the TripAdvisor URL of the PoI.

Subsequently, a spider to collect the individual user reviews (without the associated user information) for the 7 K venues/PoIs that were previously harvested was created. After setup and deployment, the spider was able to extract around 300 K individual user reviews in a time frame of around 120 h (five days); the fields of each review that were detected and stored in the respective Hydria data pond are: the review title, the review text, the date of the review, and the review score (in the TripAdvisor bubble format). For the purpose of better understanding the user background, the spider also collected anonymized information about each user that posted a review. This data does not contain any personally identifiable information and was limited on purpose to the following general fields and aggregate metrics: the user country of origin, the total number of user votes (rounded to the nearest ten), the total number of TripAdvisor contributions (rounded to the nearest ten), general user tags (like "history lover"), and generalized age ranges of users. Notice that this information is common among a vast number of TripAdvisor users and cannot be used to personally identify an individual.

The spider examples presented above show only a fraction of the functionality that is available within Hydria. Apart from focused crawlers to crawl the Web for relevant pages and Facebook or TripAdvisor spiders to harvest data from the respective social media sites, Hydria also provides Twitter monitors. These monitors use the Twitter [84] search or stream API to perform keyword-based filtering of published tweets; all retrieved tweets may be subsequently stored in appropriately configured Hydria data ponds for further processing.

#### *4.2. Importing Datasets and Adding/Modifying Records*

Besides automated data harvesting, Hydria also offers a file import service (as described in Section 3.1.2) that allows users to easily import their own datasets into a Hydria data pond. In our case study, we asked partners from the TripMentor project to use the file import tool to incorporate a new CSV dataset into Hydria. One of the project partners responded and reported that they used Hydria to store a home-brewed list of tourism stakeholders (who could be interested in the project results) in a Hydria data pond. The imported dataset consisted of several hundreds of individual records of companies and stakeholders operating in the tourism sector alongside their contact information, and was shared with the rest of the TripMentor partners by defining the appropriate access rights.

Subsequently, other project partners were able to browse the created data pond with the tourism-related companies and add or modify records as needed by filling out the different data pond fields, tagging records with notes for the data pond curator, and save any desired changes in the specific data pond. Figure 4 gives an overview of the aforementioned data pond; at the top of the figure, controls providing access to all available data pond functionality are presented to the user. Figure 5 shows the add record tool where the user may insert individual records, providing data for a multitude of fields of different types (free text; number; drop-down lists; complex types; and an image field).

**Figure 4.** A data pond example.

**Figure 5.** Manual record creation.

In this figure, we can observe the use of the complex data types feature, to design groups of input fields which may also be recurring. For example, in the aforementioned dataset the curator may select to use a complex data type to jointly represent longitude/latitude information (under the complex type named "coordinates"), or branch information (comprising fields "branch name", "address", "phone" and "email"). Additionally, the curator may use the latter complex type (branch information) as a recurring input field, to model contact information about a company that has multiple branches. Recurring input fields effectively model the master-detail relationships between parent and child objects (one-to-many relationships). In the future, we plan to support more complex data types, such as voice and video recording, time-series and streaming data.


*4.3. NPs as Drug Carriers with Targeting Ligands for the Treatment of Liver Fibrosis*

Non-specific drug disposition limits the effective clinical use of traditional anti-fibrotic drugs. Targeting drug delivery to the fibrotic region can thus far be achieved using nanoformulations. As the sole hepatic VA storage cells with a crucial role in liver fibrosis, HSCs have been actively targeted by conjugating NPs with VA.

Liposomes loaded with drugs and HSC targeting components have been developed to target HSCs for the treatment of liver fibrosis (Table 2). In one study, VA-coupled liposomes were prepared to deliver imatinib. The hepatic accumulation of imatinib increased by about 13.5-fold, compared with imatinib treatment alone [88]. The nanoformulations not only inhibited the expression of phosphorylated PDGFR-β but also reduced the expression of profibrotic mediators such as hydroxyproline, TGF-β, and MMP2 with fewer adverse effects. In another study, VA-coupled liposomes were used to deliver valsartan, an angiotensin II receptor antagonist [89]. The nanoformulations increased the expression of hepatic Mas-receptor and PPAR-γ and potently normalized the level of fibrogenic mediators by improving the permeability and efficacy of valsartan.

Abbreviations: VA, A; TRAIL, apoptosis-inducing ligand; HSC, hepatic cell; CTGF, growth factor; MMP, metalloproteinase; NO, nitric oxide; VEGF, vascular endothelial growth factor.

Polymer-based NPs have also been fabricated to target HSCs by coupling with VA for liver fibrosis therapy (Table 2). An article reported on the preparation of retinol and collagenase I co-decorated polymeric micelles (CRM) based on PLGA-b-poly(ethylene glycol)-maleimide (PLGA-PEG-Mal) to be used as HSC-targeting nanodrug delivery systems for liver fibrosis therapy [100]. In the current study, the decoration of collagenase I could facilitate the nanocarrier penetration of the fibrotic liver. Consistent with this finding, CRMs were found to efficiently degrade pericellular collagen I and exhibit excellent accumulation in the fibrotic liver and accurate targeting of activated HSCs in a mouse hepatic fibrosis model. CRM/NIL loaded with nilotinib (NIL), a second-generation tyrosine kinase inhibitor used for the treatment of liver fibrosis, showed excellent antifibrotic efficiency (Figure 4). In addition, polymeric micelles (PVMs) formed with PLGA-polyspermine-PEG-VA were used to target HSCs and deliver the chemical drug silibinin and genetic drug siCol1α1 to the liver fibrosis site [101]. The double-loaded polymer micelle more efficiently reduced collagen I and ameliorated liver fibrosis, compared with the PVMS loaded with either the chemical drug only or genetic drug only. Chondroitin sulfate micelles coupled with retinoic acid and doxorubicin (DOX) (DOX + RA–CS micelles) were selectively taken up in activated HSCs and hepatoma cells, but not in normal hepatocytes (LO2) [102]. DOX + RA–CS micelles preferentially accumulated in the Golgi apparatus, destroyed the Golgi structure, and ultimately downregulated collagen I production in vitro and exerted synergistic antifibrotic effects on CCl4-induced fibrotic rat models.

**Figure 4.** Extracellular matrix-penetrating polymeric micelles for liver fibrosis therapy. (**A**) Schematic illustration of the preparation of four different polymeric micelles. (**B**) Schematic illustration of the proposed destiny of the four different polymeric micelles in vivo. The CRM/NIL is able to penetrate the collagen barrier and target activated HSCs. Internalization of CRM/NIL allows the release of NIL, which reduces expression of the metallopeptidase inhibitor, TIMP-1, which in turn enhances collagen I degradation, thereby exerting therapeutic action against liver fibrosis. Reproduced with permission from [100]. Copyright Elsevier, 2020.

Apart from polymeric micelles, other polymer nanoformulations have also been constructed for the delivery of drugs, nucleic acid and other therapeutic moieties for the treatment of liver fibrosis (Table 2). In one study, retinol-conjugated polyetherimine NPs adsorbed plasma proteins, particularly retinol-binding protein 4 (RBP), forming a protein-coated complex [103]. The adsorbed RBP could direct the NPs into HSCs. After being loaded with antisense oligonucleotides, NPs effectively suppressed the expression of collagen I, consequently ameliorating hepatic fibrosis. Hassan et al. reported that

chitosan NPs loaded with JQ1 (a small molecule that could abrogate the cytokine-induced activation of HSCs and reverse fibrotic response in animal models) and atorvastatin and further conjugated with retinol could target and prevent HSC activation [105].

In addition to VA, cyclic peptide pPB can particularly recognize PDGFRβ on the surface of HSCs (Table 2). A study used pPB-modified liposomes to deliver recombinant human tumor necrosis factor-related apoptosis-inducing ligand (rhTRAIL) to the HSC membrane, prolonging rhTRAIL circulation in vivo and alleviating fibrosis both in vitro and in vivo [90]. Similarly, the CXCR4 antagonist AMD3100 could target HSCs [91]. AMD3100-conjugated liposomes efficiently delivered therapeutic VEGF siRNAs to activate CXCR4-overexpressed HSCs both in vitro and in vivo. The nanoformulations downregulated the expression of VEGF, reduced the mean vessel density, and normalized the hepatic vascular structure in the livers of mice with CCl4-induced liver fibrosis. Moreover, AMD3100 encapsulated in liposomes also exhibited antifibrotic effects by suppressing the proliferation and activation of HSCs. Mannose 6-phosphate (M6P)/insulin-like growth factor-II receptor, overexpressed in HSCs, was also used as the targeting site. Conjugation of M6P-modified albumin to hesperidin-loaded liposomes improved the efficacy of chemical drugs and attenuated liver fibrosis [92].

Hepatic macrophages play important roles in the pathogenesis of liver fibrosis and act as target sites for the treatment of liver fibrosis (Table 2). Scavenger receptors expressed on liver endothelial cells and Kupffer cells have also been targeted using nanoformulations. For instance, phosphatidylserine (PS), which acts as a specific recognition signal for the phagocytosis of apoptotic cells, can target macrophages. Wang et al. showed that PS-modified lipid carriers containing curcumin (Cur–mNLCs) exhibited enhanced retention time, bioavailability, and delivery efficiency of payload, as well as reduced liver damage and fibrosis in vivo [99].


*4.3. Analysis of Genotypic Data and Genetic Diversity*

Pairwise genetic distances between accessions were calculated using the Powermarker software package ver.3.25 by Nei et al. [52] DA distance. The dendrogram was constructed on the basis of the distance matrix. We estimated the similarity between genotypes for each accession by awarding a score to each microsatellite (i.e., 0 when an allele was absent, 1 when the allele was present). The cluster analysis was carried out using the unweighted pair group method using arithmetic average (UPGMA) and the dendrogram resulting from these calculations was plotted using MEGA 6.0 to visualize and edit the dendrogram. The basic summary statistics for biallelic data were calculated using the POWERMARKER software package version 3.25 [76]. The polymorphism information content (PIC) of an SSR marker was determined according to the method described by Anderson et al. [77] based on the allele frequency of all genotypes.

$$\text{PIC} \quad = \begin{array}{c} 1 \ - \end{array} \sum\_{i=1}^{n} \text{pi2} \tag{1}$$

where *Pij* is the frequency of the allele for locus *i* and the summation covered *n* patterns.

A PIC value of 1 indicates that the marker can differentiate each line, and 0 indicates a monomorphic marker. The informative potential of a marker is high if its PIC value is more than 0.5, moderate if its PIC is between 0.5 and 0.25, and only slightly informative if its PIC value is below 0.25. Other statistics calculated were the number of alleles and availability and gene diversity for each marker. Further analysis of genetic structure was done by means of Principal co-ordinate analysis (PCOA) using XLSTAT, 2014 [78] and a three-dimensional diagram was constructed. Dominant data (0, 1 binary data) were used for the PCoA analysis.


3.1.5. Government Policy

When planning to deploy government offices on a nationwide basis—such as the Comprehensive Land Development Plan, large-scale residential land developments like the new city development, and the construction of social overhead capital such as roads the contents from the microscopic perspective should also be viewed from a macroscopic perspective. Increasing the budget to implement policies such as national land development in the country will cause the real estate market to overheat nationwide, but in the opposite case, it will tend to slow down. Figure 10 shows the history of government real estate policy [21].