\r\n\tApplied and basic studies - Field studies and lab assays of fungicides can be discussed. We also look for examples of application methods, which may include timing of application, tools for application, fungicide compatibility, phytotoxicity, etc. Field trials have to have at least two years of data;
\r\n\tAdaptation of Integrated Plant Disease Management - How the IPM practice has been adapted in the field. Application of disease risk models, or use of fungicide application aids, which can be hardware or software. The introduction of a new tool for growers can also be included;
\r\n\tNovel fungicides - In addition to the traditional chemical approach, alternative materials (enzymes, oils, extracts, etc.), biological control agents, or plant defense activators can be discussed;
\r\n\tAdaptation of new technologies - Examples will be the use of unmanned vehicles, sensor technologies, advanced sprayers, or disease forecast systems for precision agriculture;
\r\n\tFungicide resistance - Unfortunately, we cannot ignore the fact that fungicide-resistant strains are widespread. Documentation of fungicide-resistant strains, the introduction of new technologies and methods can be discussed.
Most problems arising from mathematical epidemiology are often described in terms of differential equations. However, it is often very difficult to obtain closed form solutions of such equations, especially those that are nonlinear. In most cases, attempts are made to obtain only approximate or numerical solutions. In this work, we revisit the SIR epidemic model with constant vaccination strategy that was considered in [11], where the Adomian decomposition method was used to solve the governing system of nonlinear initial value differential equations.
In this work we develop new accurate iterative schemes which are based on extending Taylor series based linearization method to obtain accurate and fast converging sequence of hybrid iteration schemes. At first order, the hybrid iteration scheme reduces to quasilinearization method (QLM) which was originally developed in [1]. More recently Mandelzweig and his co-workers [8–10] have extended the application of the QLM to a wide variety of nonlinear BVPs and established that the method converges quadratically. In this work we demonstrate that the proposed hybrid iteration schemes are more accurate and converge faster than the QLM approach.
To implement the method we consider the SIR model that describes the temporal dynamics of a childhood disease in the presence of a preventive vaccine. In SIR models the population is assumed to be divided into the standard three classes namely, the susceptibles (
The governing equations for the problem are described [11] by
where
The total population is denoted by
To simplify the formulation of the solution, equations (1) - (3) are scaled by dividing by
where
Previous studies [4–7, 12] have shown that the long term behaviour of systems like (4) - (5) can be classified into two categories namely, endemic or eradication. From the long term behaviour of
Here
It was shown in [11] that the DFE is locally stable if
To develop the method of solution, we assume that the true solution of (4 - 5) is
where
This idea of introducing the coupled equations of the form (9-10) have previously been used in [3] the construction of Newton-like iteration formulae for the computation of the solutions of nonlinear equations of the form
We write equation (9) as
where
We use the quasilinearization method (QLM) of Bellman and Kalaba [1] to solve equation (13). The QLM determines the (
which can be written as
subject to
We assume that
which yields the iteration scheme
We note that equation (19) is the standard QLM iteration scheme for solving (4 - 5).
When
Thus, setting
which yields the iteration scheme
where
The general iteration scheme obtained by setting
where
The initial approximation for solving the iteration algorithms, scheme-
The iteration schemes (19),(24 - 25) can be solved numerically using standard methods such as finite difference, finite elements, spline collocation methods,etc. In this study we use the Chebyshev spectral collocation method to solve the iteration schemes. For brevity, we omit the details of the spectralmethods, and refer interested readers to ([2, 13]). Before applying the spectral method, it is convenient to transform the domain on which the governing equation is defined to the interval [-1,1] on which the spectral method can be implemented. We use the transformation
where
Applying the Chebyshev spectral method to (19), for instance, gives
where
and
In this section we present the results of solving the governing equations (4-5) using the iteration scheme-m. For illustration purposeswe present the results for
1. Case 1:
In this case we observe that
2. Case 2:
In this case we observe that
3. Case 3:
In this case
4. Case 4:
In this case
The results for Case 1 are shown on Figs. 1 - 2. In this case, the initial guess and the first few iterations match the numerical solution all the iterative schemes in the plots of
Case 1: Comparison of the numerical solution of the population fractions
Case 1: Comparison of the numerical solution of the population fractions
Figs. 3 - 5 show the numerical approximation of the profiles of the different classes for Case 2. Again, all the iterative schemes rapidly converge to the numerical solution. The population of the susceptibles decreases with time and that of the removed (those recovered with immunity) increases with time. The infected population initially increases and reaches a maximum, then gradually decreases to zero as
Case 2: Comparison of the numerical solution of the population fractions
Case 2: Comparison of the numerical solution of the population fractions
Case 2: Comparison of the numerical solution of the population fractions
Figs. 6 - 8 show the numerical approximation of the profiles of the different classes for Case 3. It can be noted from the graphs that the Scheme-2 converges fastest towards the numerical results. Only 10 iterations are required for full convergence in Scheme-2 compared to 14 iterations in Scheme-1 and 28 iterations in Scheme-1.
Case 3: Comparison of the numerical solution of the population fractions
Case 3: Comparison of the numerical solution of the population fractions
Case 3: Comparison of the numerical solution of the population fractions
Figs. 8 - 11 shows the variation all the population groups with time for Case 4. Again, we observe that Scheme-2 converges fastest towards the numerical results. Only 5 iterations are required for full convergence in Scheme-2 compared to 6 iterations in Scheme-1 and 12 iterations in Scheme-1.
Case 4: Comparison of the numerical solution of the population fractions
Case 4: Comparison of the numerical solution of the population fractions
Case 4: Comparison of the numerical solution of the population fractions
In this work, a sequence of new iteration schemes for solving nonlinear differential equations is used to solve the SIR epidemic model with constant vaccination strategy. The proposed iteration schemes are derived as an extension to the quasi-linearization method to obtain hybrid iteration schemes which converge very rapidly. The accuracy and validity of the proposed schemes is confirmed by comparing with the ode45 MATLAB routine for solving initial value problems. It is hoped that the proposed method of solution will spawn further interest in computational analysis of differential equations in epidemiology and other areas of science.
Extracellular vesicles (EVs), phospholipid bilayer-enclosed vesicles consisting of proteins, lipids and nucleic acids, were once thought of as merely how cells may discard their waste materials and debris. However, recent discoveries have proven them to be indispensable to cells even in normal physiological functions and as diagnostic biomarkers for various diseases [1]. EVs are secreted by various cells and can be isolated from diverse biological sources like saliva, breast milk and blood serum [2].
Over the years, EVs have been researched as promising diagnostic biomarkers for pathological conditions. This is because their concentration and composition correlate with disease progression, a unique characteristic that sets them apart from other types of paracrine secretions [3, 4]. EVs have also been explored as possible carriers for drug delivery. Recent studies have shown promising results regarding the utilisation of EVs as drug delivery systems (DDSs) to treat various conditions, such as cardiovascular diseases [2, 5], osteoporosis [2, 6] and brain tumours [2, 7]. In light of this, EVs are seen as a more desirable strategy for drug delivery compared to other conventional nanoparticles like liposomes, micelles and polymeric nanoparticles [8, 9]. Conventional DDSs have been extensively used for their ability to protect drugs from inactivation in the external environment. However, plasma proteins risk adsorbing onto the surfaces of these non-EV nanoparticles upon injection into the body, making them an easy target of immune cells and decreasing their uptake by their target cells [10]. Although these nanoparticles may undergo modification to avoid immune cell removal, they still lack biocompatibility due to their non-biological origins. EVs, on the other hand, can evade phagocytosis by immune cells naturally, in addition to being highly selective for designated target sites, due to their biological origins and cell-specific surface properties inherited from the parent cells that secrete them.
Although EVs are promising in their diagnostic and therapeutic applications, it is still unclear whether they can cross membranes like the blood-brain barrier (BBB) naturally or when genetically modified, or only when the membranes become more permeable in certain conditions like injury [11, 12]. Furthermore, the uptake of EVs by target cells is still not fully understood at a microscopic level, be it
Classified by their biogenesis, size, morphology and function, there are three main EV categories—exosomes, microvesicles and apoptotic bodies (Figure 1) [16, 17, 18].
Biogenesis, size, morphology and function of exosomes, microvesicles and apoptotic bodies. (A)
Although exosomes, microvesicles and apoptotic bodies are distinct from one another, there is a partial overlap among their respective size range and composition. Although many different methods have been previously deployed to isolate EVs from their sample sources (a notable example being ultracentrifugation in isolating and purifying exosomes and microvesicles [19, 20, 21, 22]), these methods are unable to provide an accurate attribution of unique characteristics to each EV category. This is due to the complex nature of EVs, such that different size ranges can be derived from the same EV source depending on the isolation technique used [23]. As such, this review will mainly elaborate on EVs in general, unless otherwise stated.
Apart from biogenesis, size and morphology, each EV category possesses its own unique set of key proteins, lipids and nucleic acids (Table 1). Being able to differentiate EV categories based on their key components is vital in understanding their specific roles in both normal and pathological conditions. In general, all EVs possess cell adhesion proteins [13, 14, 17, 18, 24, 25, 26, 27, 28], heat-shock proteins [13, 14, 18, 25, 28, 29, 30], biogenesis-associated proteins [13, 14, 17, 18, 24, 25, 28], fusion proteins [13, 14, 18, 25], cell-type specific proteins [13, 14, 18, 27, 28], cytoskeletal proteins [13, 18], signalling molecules [13, 14, 28, 31], enzymes [13, 25, 28], messenger ribonucleic acid (mRNA), micro ribonucleic acid (miRNA), non-coding ribonucleic acid (RNA), phosphatidylethanolamine, sphingolipids and higher levels of phosphatidylserine (PS) than the cell plasma membrane [24, 25, 28, 35].
Components | Exosomes | Microvesicles | Apoptotic bodies | Reference(s) |
---|---|---|---|---|
Proteins | ||||
Tetraspanins | CD9, CD63, CD81, CD37, CD82, CD53, TSPAN 6, TSPAN 8, TSPAN 29, TSPAN 30 | CD40 ligands, CD82 | CD40 ligands, CD82 | [13, 14, 24, 25] |
Cell adhesion proteins | Integrins (integrin-alpha, integrin-beta), selectins (P-selectin), lactadherin, ICAM | integrins, selectins (P-selectin), fibronectin, PECAM-1 | integrins, fibronectin, PECAM-1 | [13, 14, 17, 18, 24, 25, 26, 27, 28] |
Heat shock proteins | Hsc70, Hsp20, Hsp27, Hsp60, Hsp70, Hsp90 | Hsp70, Hsp90 | Hsp70, Hsp90 | [13, 14, 18, 25, 28, 29, 30] |
Biogenesis-associated proteins | ESCRT proteins (Alix, Tsg101), VPS4, clathrin, ubiquitin, syntenin, VPS32, PLD | ESCRT proteins (Alix, Tsg101), VPS4, ERK, PLD | VPS4, ERK, PLD | [13, 14, 17, 18, 24, 25, 28] |
Fusion proteins | Flotillin 1 and 2, annexins, GTPases, Rab GTPases, dynamin, syntaxin | Flotilin-2, Rab GTPases, annexins | Rab GTPases, annexins (Annexin V) | [13, 14, 18, 25] |
Cell-type specific proteins | MHC class I, MHC class II, APP, PMEL, TCR, CXCR4, HSPG, CD86, PrP, WNT | MHC class I, MHC class II, LFA1, CD14 | MHC class I, LFA1, CD14 | [13, 14, 17, 18, 27, 28] |
Actin, tubulin, cofilin | Actin, tubulin | Actin, tubulin | [13, 18] | |
Cytoskeletal proteins | Protein kinases, beta-catenin, 14-3-3, G proteins | ARF6, Rab11, ROCK | ARF6, Rab11, ROCK | [13, 14, 28, 31] |
Signalling molecules | PLA2, peroxidases, pyruvate kinase, enolase, GADPH, ATPases | GADPH | GADPH | [13, 25, 28] |
Other enzymes | Glycoproteins
| Glycoproteins
| [18, 28] | |
Additional proteins | Growth-factors and cytokines
| Growth factors and cytokines | [18, 25] | |
Membrane signalling receptors
| Membrane signalling receptors | |||
Phosphoproteins | High phosphoproteins | [28, 32] | ||
Ribosomal proteins | GTP-binding protein ARF6 | [28, 33] | ||
Lysosomal proteins
| Chemokines | [25] | ||
Lipids | High phosphatidylserine | High phosphatidylserine | [14, 25] | |
Phosphatidylethanolamine | Phosphatidylethanolamine | [24, 25, 28, 32] | ||
Sphingolipids
| Sphingolipids | [18] | ||
High cholesterol | [14, 28, 34] | |||
High diacylglycerol | [14] | |||
Ceramides | [13, 24, 28] | |||
Phosphatidylcholine | [28] | |||
Phosphatidylinositol | [18] | |||
LBPA | [13] | |||
Nucleic acids | mRNA | mRNA | [25, 35] | |
miRNA | miRNA | |||
Non-coding RNA
| Non-coding RNA
| |||
DNA with histones | Chromosomal DNA fragments with histones, chromatin remnants, cytosol portions, degraded proteins, cell organelles |
Classification of key components of EVs by their main categories.
Abbreviations: ADP: adenosine diphosphate, APP: amyloid-beta precursor protein, ARF: ADP ribosylation factor, CXCR: CXC chemokine receptor, DNA: deoxyribonucleic acid, ERK: extracellular signal-regulated kinase, ESCRT: endosomal sorting complex required for transport, FasL: Fas ligand, GTP: guanosine triphosphate, HSPG: heparan sulphate proteoglycan, ICAM: intercellular adhesion molecule, Lamp: lysosome-associated membrane protein, LFA: lymphocyte function-associated antigen, MHC: major histocompatibility complex, PECAM: platelet endothelial cell adhesion molecule, piwi: P-element induced wimpy testis, PLA2: phospholipase A2, PLD: phospholipase D, PMEL: premelanosome protein, PrP: prion protein, Rab: Ras-associated binding, TCR: T-cell receptor, RNA: ribonucleic acid, ROCK: Rho-associated protein kinase, TSPAN: tetraspanin, Tsg: tumour suppressor gene, VPS: vacuolar protein sorting-associated protein, WNT: wingless/integrated, GADPH: glyceraldehyde 3-phosphate dehydrogenase, TDP: transactive response DNA-binding protein, TfR: transferrin receptor, TGF: transforming growth factor, TNF: tumour necrosis factor.
The distinct protein, lipid and nucleic acid profiles of each category might be correlated with its formation processes and functions. Both exosomes and microvesicles consist of key protein components which are responsible for cell-to-cell communication [18], such as glycoproteins [18, 28], membrane signalling receptors, growth factors and cytokines [18, 25], while apoptotic bodies do not. This is most likely because exosomes and microvesicles are meant to reach target cells, while apoptotic bodies are merely the means for discarding dead cells. Microvesicles and apoptotic bodies consist of other cytoplasmic proteins which seem to be less prominent in exosomes [13]. This might be due to the similar “budding/bulging” nature of the biogenesis of microvesicles and apoptotic bodies from the cytoplasmic membrane directly, a characteristic that differs from the endocytic-driven biogenesis of exosomes. Unlike exosomes and microvesicles, apoptotic bodies are composed of chromosomal deoxyribonucleic acid (DNA) fragments, chromatin remnants, cytosol portions, degraded proteins and cell organelles from dead cells [25, 35], indicative of their role in removing dead cells.
EVs also possess additional key features according to the specific cell line they originate from (Table 2). In general, cancer cells consist of higher levels of sphingolipids, glycerophospholipids, sterol lipids, ceramide, phosphatidic acid and matrix metalloproteinases like a disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), while non-cancer cells consist of higher levels of prenol lipids, glycerolipids and fatty acids [24, 83].
EV source | EV source subtype | Component(s) | Reference(s) |
---|---|---|---|
Bacteria | Gram-positive | ABC transporters, mobility-related proteins (FliC, PilQ), multidrug efflux pumps, porins (Omps, OprF, PorA, PorB) | [24, 36] |
Gram-negative | Beta-lactamase, coagulation factor, penicillin-binding protein | [24, 37, 38, 39] | |
Myxobacteria | Chaperonin GroEL1, GroEL2, hydrolase, peptidase | [24, 40, 41] | |
Blood cells | Platelets | CD31, CD41, CD42a, CD62, C-type lectin, CXCR4, GPIIb/IIIa, PF4, SDF-1α | [24, 42, 43, 44] |
Erythrocytes | Glycophorin A, stomatin | [24, 34] | |
Reticulocytes | Galectin-5 | [42, 45] | |
Bone cells | Osteoblasts | Cadherin-11 | [42, 46] |
Cancer cell lines | Breast cancer cells (MM231, MM231LN) | Rab-5b, actin, integrin beta 1, cavolin-1 | [47] |
Breast cancer cells (MCF7) | Actin, Rab-5b | [47] | |
Breast cancer cells (MCF10A) | Integrin beta 1 | [47] | |
Cervical cancer cells (HeLa) | EGF | [42, 48, 49] | |
Colon cancer cells (LIM1863—EpCAM apical exosomes) | CD44, CD46, CD59, CLDN7, EpCAM, HMGB2, HMGB3, Muc-13, sucrase isomaltase | [50] | |
Colon cancer cells (LIM1863—A33 basolateral exosomes) | ADP-ribosylation factor, AP1G1, AP1M1, AP1M2, AP3B1, CLSTN1, CLTA, CLTB, COPB2, EEA1, GPA33, HLA-A, HLA-B, HLA-C, HLA-E, HLA-A29.1, Rab-13, REEP6 | [50] | |
Colorectal cancer cells (CRC line SW403, CRC28462) | Carcinoembryonic antigen, class I HLA | [51] | |
Hepatoblastoma cancer cells (HepG2, K562) | TfR1, TfR2 | [42, 52] | |
Hepatocellular cancer cells (HKCI-C3, HKCI-8, MHCC97L, MIHA) | ADAM10, ARHGEF18, BROX, CAV1, CAV2, CD44, CDC42, CLDN3, EDIL3, EIF4A3, GNA11, GNA13, GNAQ, GNAS, GRB2, MET, RHOG, RRAS, SNTA1, TNFRSF21, TNFAIP2 | [53] | |
Myeloma cancer cells (RPMI-8226, CAG) | Fibronectin | [54] | |
Nasopharyngeal cancer cells (C15) | Galectin-9, LMP1 | [55] | |
Nasopharyngeal cancer cells (C17) | Galectin-9 | [55] | |
Ovarian cancer cells (IGROV1, OVCAR-3) | Beta-actin, EpCAM, hnRNPA1, hnRNPK | [56] | |
Prostate cancer cells (PC3) | Rab-5b, integrin beta 1, cavolin-1 | [47] | |
Prostate cancer cells (PC-3 M-luc) | Rab-5b, actin, Integrin beta 1 | [47] | |
Prostate cancer cells (22Rv1) | Rab-5b, actin | [47] | |
Prostate cancer cells (PNT2) | Actin, integrin beta 1 | [47] | |
Endothelial and epithelial cells | C-type lectin, galectin-3, Muc-1 | [42] | |
Immune cells | B-cells | A2,3-linked sialic acid, CD169 | [42, 57] |
T-cells | CXCR4, SDF-1α | [42, 48, 58, 59] | |
Dendritic cells | FLOT1, galectins, Lamp-1, MFG-E8, MHC class I and II, TNFR1, TNFR2 | [24, 42, 60, 61, 62] | |
Macrophages | C-type lectin, LFA-1 | [42, 63, 64] | |
Natural killer cells | Granzyme B, perforin | [65] | |
Mesenchymal stem cells | Alternative splicing and Golgi apparatus component mRNA encoding transcription factors, CD54, CD73, CD86, CD90, CD105, CD166, MHC class I and II, sialic acids | [24, 28, 42, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75] | |
Milk cells | Bovine milk cells | β-casein, β-lactoglobulin mRNA, CD59, MFG-E8, miR-30a, miR-92a, miR-223, Rab-1b, Rab-11a | [24, 76, 77, 78, 79, 80] |
Human breast milk cells | miR-17, miR-181a | [81] | |
Nervous cells | Astrocytes | MCP-1, MMP3, MMP9, TIMP-1 | [65] |
Microglia | CD13, CD107a, CD107b | [65] | |
Placental cells | MHC class I chain-related proteins A and B, placental alkaline phosphatase, placental leucine aminopeptidase, pregnancy specific glycoprotein 3, RAET1 proteins/ULBP1–5, TGFβ1, TRAIL, trophoblast glycoprotein 5 T4 | [82] |
Classification of additional key components of EVs by their specific cell lines.
Abbreviations: ABC: adenosine triphosphate-binding cassette, ADAM: A disintegrin and metalloproteinase domain-containing protein, ADP: adenosine diphosphate, AP: adaptor related protein complex, ARFGEF: Rho/Rac guanine nucleotide exchange factor, BROX: BRO1 domain and CAAX motif containing, CA: carbohydrate antigen, CAV: caveolin, CLDN: claudin, CLSTN: calsyntenin, CLT: clathrin light chain, COP: coatomer protein complex, CXCR: CXC chemokine receptor, EDIL: EGF like repeats and discoidin domains, EEA: early endosome antigen, EGFR: epidermal growth factor receptor, EGF: Epidermal growth factor, EpCAM: epithelial cell adhesion molecule, FLOT: flotillin, GN: guanine nucleotide-binding protein, GP: glycoprotein, GRB: growth factor receptor-bound protein, HLA: human leukocyte antigen, HMG: high-mobility group, HNRNP: heterogeneous nuclear ribonucleoprotein, Lamp: lysosome-associated membrane protein, LDLR: low-density lipoprotein receptor, LDL: low-density lipoprotein, LFA: lymphocyte function-associated antigen, LMP1: Epstein-Barr virus latent membrane protein 1, MAPK: mitogen-activated protein kinase, MCP: membrane cofactor protein, MET: mesenchymal-epithelial transition facror, MFG-E: milk fat globule-EGF factor, MHC: major histocompatibility complex, miRNA: microribonucleic acid, MMP: matrix metalloproteinase, Muc: mucin, PF: platelet factor, RAET: retinoic acid early transcript, Rab: Ras-associated binding, REEP: receptor expression-enhancing protein, RHOG: Ras homolog family member G, RRAS: RAS-related protein R-Ras, SDF: stromal cell-derived factor, SNT: syntrophin, TfR: transferrin receptor, TNF: tumour necrosis factor, TNFR: tumour necrosis factor receptor, TNFRSF: TNF receptor superfamily, TNFAIP: TNF alpha-induced protein, TRAIL: tumour necrosis factor-related apoptosis-inducing ligand, TYRP: tyrosinase-related protein, ULBP: UL16 binding protein.
To reach their target sites, EVs need to overcome various biological barriers (Figure 2). Complementing these barriers are blood vessels (capillaries in particular). EVs can enter and extravasate from these vessels
Biological barriers encountered by extracellular vesicles (EVs). (A)
EVs administered orally need to overcome digestive enzymatic degradation, harsh stomach acidic conditions and the small intestinal barrier before entering the bloodstream for systemic absorption. As milk and plant-derived EVs are delivered into the body naturally
Milk-derived EVs have been shown to withstand acidic and enzymatic conditions [87, 91]. However, their ability to do so might be dependent on the milk source, as EVs from processed milk would have undergone more damage than those from unprocessed milk and hence possess less integrity [87, 92, 93, 94, 95, 96, 97]. Although bovine milk-derived EV surface proteins CD9 and CD81 were found to be partially degraded by acidification at pH 4.6 in one study [98], these findings did not demonstrate whether these EVs can survive stomach acidic conditions, which are usually characterised by a much lower pH. Moreover, the study was focused on evaluating the effectiveness of acidification in ultracentrifugation to isolate EVs. Thus, these conditions would have differed vastly from true gastrointestinal conditions. Although the underlying mechanism is unclear, the ability of both processed and unprocessed milk-derived EVs to withstand harsh conditions might be correlated with milk calcium content [87]. This could be due to the adhering of milk calcium to the surface of EVs, which might strengthen their membrane integrity against acidic and enzymatic degradation. Another hypothesis is that calcium might influence milk-derived EV biogenesis pathways in alveoli cells to increase the expression of certain proteins or transporters in secreted EVs that enable them to withstand gastrointestinal conditions.
Fruit and vegetable-derived EVs have been shown to withstand gastrointestinal conditions and eventually be internalised by rodent intestinal tissue
The placenta supports foetal growth and development while secreting female hormones [106, 107, 108, 109, 110, 111]. The placental barrier (PB) is suggested to be selectively penetrable, given that drugs administered to pregnant women can either cause adverse side effects in both the mother and the fetus or not penetrate the PB at all. It consists of an inner blood-vessel-rich layer with the syncytiotrophoblast facing the bloodstream and an outer layer of trophoblasts [106, 112, 113, 114]. Occurring in large amounts during pregnancy [115, 116], placental exosomes exert their functions during foetal growth and development, being involved in processes like angiogenesis regulation and cell migration [106, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]. This implies that they can overcome the PB, though the underlying mechanism is unclear. Placental exosomes have also been tested as diagnostic biomarkers for foetal development [106, 115] and gestational diabetes [106, 127].
Although placental EVs may be used to pass through the PB, the use of non-placental EVs to deliver drugs across the PB is a potential area for exploration. The placenta can respond to signals from immune cells and exert an inflammatory response during infection. An
The body is heavily guarded by immune cells responsible for eliminating pathogens and perceived foreign substances. As such, nanoparticles injected into the bloodstream risk being removed by phagocytes of the mononuclear phagocyte system (including those in the liver and spleen), or the adaptive immune system
A recent study on an
The blood-brain barrier (BBB) is characterized by an innermost layer of endothelial cells (which prevents blood and extracellular fluid from mixing), pericytes surrounding the endothelial cells and astrocyte end-feet acting as a sheath in the outermost layer. Though the movement of substances across the BBB is tightly regulated [136], different EVs have been observed to cross the BBB. One study demonstrated the ability of exosomes to carry miR-193b-3p across the BBB to exert an anti-inflammatory effect on rodent brain cells with subarachnoid haemorrhage [137], although the mechanism of crossing was unclear. Other studies involving the BBB in zebrafish showcased the ability of various human breast cancer cell EVs and brain endothelial cell EVs to cross the BBB
Modifications have also been made to EVs to enhance their ability to cross the BBB. In one experiment, after overexpressing the rabies virus glycoprotein (RVG) peptide on their surface, dendritic exosomes became significantly localized in rodents’ brain cells [139]. Mouse L929 fibroblastic cell exosomes loaded with methotrexate and functionalized with LDL peptide in another experiment showed enhanced BBB exosome extravasation in rodents [140]. When miR-210-loaded mesenchymal stromal cell-derived exosomes were coupled with c(RGDyK) peptide in another experiment, they displayed enhanced targeting of rodent ischaemic brain cells, indicating greater angiogenesis and improving animal survival significantly [141]. Another experiment showed that RGE-Exo EVs demonstrated greater accumulation and duration of accumulation in murine glioma tumour cells than free exosomes [142].
Apart from surface components, the size of EVs might also be a crucial factor in determining whether EVs can cross the BBB, as deduced from another study where intranasal administration of exosomes to rodent microglial cells
The blood-labyrinth barrier (BLaB) and blood-retinal barrier (BRB) are two other neurological barriers pertaining to the ear and eye, respectively. The BLaB consists of five layers, namely, the blood-endolymph barrier, blood-perilymph barrier, cerebrospinal-fluid-perilymph barrier, middle-ear-labyrinth barrier and endolymph-perilymph barrier [106, 144]. The BRB consists of the retinal vascular endothelium and the retinal pigment epithelium (RPE) [106]. These two barriers share similarities with each other and the BBB, though the number of EV studies on these two barriers is smaller than that involving passage across the BBB [106]. Nevertheless, the utilization of EVs as potential drug carriers targeting the ear and eye with negligible side effects is worth further research, especially when current drug treatments have resulted in adverse side effects [106]. EVs from RPE cells are involved in the progression of age-related macular degeneration
The process of crossing the blood-lymph barrier (BLyB) is regulated by various mechanisms including extravasation, overcoming of the interstitium, diffusion and passage through the mucosal barrier [106, 147]. In addition, the collagen reticular network (RN) hinders soluble substances from passing through [106, 148, 149, 150, 151, 152, 153]. However, EVs possess certain characteristics that enable them to cross the BLyB. For instance, human ovarian cancer cell exosomes were found to be able to travel from the periphery to the lymph node in just a matter of minutes in rodents due to their small size [106, 154], and their lipidic rather than soluble nature seemed to enable them to cross the RN [106, 155].
Although EVs already possess intrinsic advantages that enable them to cross the BLyB, methods like microfluidic surface engineering have been conducted on EVs to modify them further as potential drug carriers for lymphoma treatment or other diseases related to the lymphatic system [106, 156, 157]. A recent study explored the modification of exosomes derived from bovine serum. α-D-mannose was added to the exosomes containing immune stimulators to enable them to interact with the mannose receptors on dendritic cells for uptake, and the exosomes were PEGylated. This has been found to enhance the internalisation of the exosomes by murine dendritic cells and to increase their localisation in the lymph nodes, paving the way for efficient delivery of immune stimulators
Located in the lungs, the blood-air barrier (BAB) possesses characteristics that enable it to guard against pathogenic invasion. For instance, the lung mucosa is a rich source of immune cells [106, 159], and lung epithelial cells can sense a wide range of bacteria and viruses
The nephron’s ability to efficiently filter out waste materials from the blood into the urine is attributed to the high pressure in the glomerulus due to high blood flow, as well as the presence of the glomerular filtration barrier consisting of three layers—fenestrated endothelium, glomerular basement membrane and glomerular epithelium [66, 164, 165, 166]. Despite the tiny pores (2.5–2.8 nm) of the glomerular basement membrane [166, 167] which are smaller than the smallest EVs (30 nm [16, 17, 18]), and the presence of filter proteins lining the slits in the glomerular epithelium [165, 166, 168], the urine is surprisingly a rich source of EVs from both renal and non-renal sources. While the majority of EVs found in urine originate from the kidney, urinary bladder, testis, prostate, epididymis and seminal vesicle [169, 170, 171, 172], studies have also identified EVs from outside the urinary tract, such as those carrying biomarkers of acute myocardial infarction [173]. EVs injected into the bloodstream of rodents in one study were found to accumulate in the kidneys, and eventually the urine, without undergoing degradation, as indicated by their ability to be internalized by HEK293 cells after being retrieved from the urine and introduced to the cells [166]. The presence of EVs in urine might imply that EVs can squeeze through the tiny pores of the glomerular filtration barrier due to their fluid membranes, or undergo mechanisms like transcytosis to reach the glomerular filtrate. It is also logical to deduce that EVs can cross the glomerular filtration barrier better in pathological conditions like diabetic nephropathy when the glomerular filtration barrier becomes more porous due to injury [166].
EVs that eventually reach the target cell has to overcome the plasma membrane, escape the endosome and evade lysosomal degradation to release their cargo into the cytosol (Figure 3).
Internalisation, lysosomal degradation and lysosomal escape mechanisms of extracellular vesicles (EVs) in target cells. Upon reaching the target cell, EVs may be internalised by the cell
The plasma membrane is an intricate structure consisting of various domains formed
EVs can be internalised by target cells
In general, the CME of EVs is initiated through the mediation of lectins, tetraspanins, cell adhesion proteins and other receptor-ligand interactions [18, 179]. For instance, exosomes from macrophages possess C-type lectin, which interacts with the C-type lectin receptor found on dendritic and brain endothelial cells [179, 180]. Galectin-5 on red blood cell (RBC) EVs enables them to be internalised by macrophages [45, 179]. Integrins on tumour EVs have been associated with the uptake of these EVs by lung fibroblasts and liver macrophages [42, 179]. Exosomes and target cells can exploit the interaction between intercellular adhesion molecule 1 (ICAM-1) and lymphocyte function-associated antigen 1 (LFA-1) in exosomal uptake [179, 180, 181]. Heparan sulphate proteoglycans on target cells bind to EV fibronectin to facilitate uptake of EVs [54, 179, 182]. The high levels of outward-facing PS on the surface of exosomes also enable the recognition and uptake of these exosomes by antigen-presenting cells
EVs internalised
EVs have also been reported to deliver their cargo into target cells
In providing a recommendation to engineer EVs as DDSs, EV engineering methods to overcome specific barriers can be deployed. Natural evasion of immune cells is a highly favourable quality and should mark all engineered EVs regardless of the barrier(s) they target. CD47, CD24, CD31 and PD-L1 are prominent surface proteins that achieve this quality and should be incorporated into engineered EVs in high amounts if not originally present [129, 130, 131, 132, 133, 134]. Fusing EVs with liposomes to create hybrid DDSs can also increase their drug loading capacity without risking cargo aggregation [198, 199].
The choice of the source of EVs depends on its availability and the ability of its EVs to overcome respective biological barriers associated with the disease. Milk and plant-derived EVs, which are highly available in nature and able to overcome gastrointestinal barriers [76, 77, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105], can be engineered for drug delivery
A summary of the recommendation is shown in Figure 4.
Recommendation on how to engineer extracellular vesicles (EVs) to overcome various biological barriers for drug delivery applications. (1)
Through critically analysing the relationship between the key components of EVs and the biological barriers EVs overcome, this review is the first to put together a recommendation in such a manner (Figure 4) to engineer EVs as suitable DDSs based on various studies. The implementation of EV drug carriers would revolutionise the global worldview of therapeutic treatments, as EVs unlock a whole new realm of endless possibilities in achieving the ideal therapeutic for patients, one of maximum efficacy and biocompatibility with negligible side effects. Even now, efforts have been made to transform the notion of personalised medicine into a reality, and having EVs as fully-approved personalised DDSs is worth pursuing. As past findings are limited due to the complex nature of EVs and various biological membranes, it is hoped that the mechanisms of EVs and their interactions with various biological membranes can continue to be more fully delved into, and that EV engineering can be carried out
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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\n\nDisruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
\n\nIntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
\n\nIf this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
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Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,series:{id:"11",title:"Biochemistry"}}},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:318,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/169189",hash:"",query:{},params:{id:"169189"},fullPath:"/profiles/169189",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()