Pros and cons of dilution modes.
\r\n\t
",isbn:"978-1-80356-678-8",printIsbn:"978-1-80356-677-1",pdfIsbn:"978-1-80356-679-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"6dcb071a2e978694b6b1cb9c20afc1a3",bookSignature:"Prof. Hai-Zhi Song",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11494.jpg",keywords:"Electric Field Effect, Nano-Materials, Electric Field Design, Antenna, Microelectronics, Optoelectronics, Electric Field Stimulation, Brain and Nerve, Electric Field Imaging, Atomic Electric Field, Space Science, Climate",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 22nd 2022",dateEndSecondStepPublish:"May 26th 2022",dateEndThirdStepPublish:"July 25th 2022",dateEndFourthStepPublish:"October 13th 2022",dateEndFifthStepPublish:"December 12th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"A pioneering researcher in the fields of new materials, optoelectronic devices, and quantum information processing, appointed vice director of the Science and Technology Committee of SWITP, author/co-author of more than 170 research papers, and holder of 40 patents.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"196114",title:"Prof.",name:"Hai-Zhi",middleName:null,surname:"Song",slug:"hai-zhi-song",fullName:"Hai-Zhi Song",profilePictureURL:"https://mts.intechopen.com/storage/users/196114/images/system/196114.jpg",biography:"Curriculum Vitae\n\nName: Hai-Zhi Song \nGender: male\nDate of Birth: Oct. 20, 1968\nPlace of Birth: Shanxi, China\nAffiliation and Address: \nSouthwest Institute of Technical Physics\nNo.7, Section 4, Renminnan Road, Chengdu 610041, China\nAnd\nInstitute of Fundamental and Frontier Sciences,\nUniversity of Electronic Science and Technology of China,\nNo. 4, Section 2, Jianshebei Road, Chengdu 610054, China\n\nWork Phone: +86-28-68180751, +86-28-83208728\nMobile Phone: +86-158-28239155\nFax: +86-28-83201896\nE-mail: hzsong1296@163.com, hzsong@uestc.edu.cn\n \nEducation \nSept, 1990 – July, 1995:Peking University, PhD, Thesis “Visible luminescence of porous silicon and its mechanism”, Researches on hydrogen-influenced Schottky diodes and silicon-based light-emitting materials. \nSept, 1986 – July, 1990:Nanjing University, Bachelor of Science, Thesis “Study of refractory metal silicides”, Research on Ohmic contact of semiconductors.\n\nWork Experience \nJuly, 1995 – Sept. 1997: Nanjing University, Nanjing, China, Postdoctoral Researcher, Research on silicon-based light-emitting materials. \nOct, 1997 – Sept. 1998: Catholic University Leuven, Leuven, Belgium, Visiting free Researcher, Research on amorphous semiconductors. \nOct, 1998 – Sept. 2001: Tsukuba University, Tsukuba, Japan, Assistant Professor, Research on semiconductor quantum dots. \nOct, 2001 – March 2012: Fujitsu Lab. Ltd., Atsugi, Japan, Researcher/Senior Researcher, Researches on Semiconductor Quantum Dots for Quantum Information, Semiconductor Optoelectronic Materials and Devices. \nApril, 2012 – March 2014: University of Tokyo, Tokyo, Japan, Senior Researcher, Researches on Quantum Information Processing Devices. \nApril, 2014 – now: Southwest Institute of Technical Physics, Chengdu, China, Professor, Researches on Semiconductor Optoelectronic Materials and Devices. \nJune, 2015 – now: University of Electronic Science and Technology, Chengdu, China, Professor, Researches on Nanoscaled Semiconductors and Quantum Information Processing Devices.\n \nAchievements\nSystematically studied the property of porous silicon materials and verified their mechanism; found green and ultraviolet luminescence, and clarified the multiple luminescence mechanisms of nanocrystalline-silicon embedded in SiO2, which is valuable to silicon-based optoelectronic integration; realized enhanced hole mobility in amorphous silicon, verified the existence of deep trap states in amorphous selenium, providing ways to improve amorphous optoelectronic materials. \nDiscovered lateral coupling between self-assembled quantum dots (QDs) and their tuning effect to 2D electron gas; illustrated and deeply explained the metal-insulator transition in 2D ordered QD arrays, all of which are worth in optoelectronic application of semiconductor QDs. \nDeveloped Sb-free technique to double the InAs/GaAs QD density and suppress the atomic interdiffusion, helped producing 1.3 um QD lasers, which won Japanese national prizes and had been merchandized; developed 1.06 um quantum-well lasers, which have been used to produce pure-green lasers robust against high temperature. \nFound a way to access buried QDs by scanning tunneling microscope; achieved a way to prepare diluted QDs by post-annealing and clarified its mechanisms; invented a technique to control the size and site of QDs by atomic-force microscopy lithography, and an apparatus to detect single electron spin states by optically-detected magnetic resonance; designed a few types of micropillar cavities applicable to realize 1.55 um highly-efficient, even coherent (strongly coupled) InAs/InP QD single photon sources; produced fiber-integrated photon-entangled sources, all of which are very useful to the applications of QDs in quantum information processing. \nDeveloped focal-plane single-photon avalanche detectors, providing central devices for 3D laser detecting and ranging system; explored antimonide middle- and long-wavelength infrared detectors and the surface plasmon enhancement effect in such detectors; advanced the acetone-sensing function of Eu-doped SnO2 nano-belt; found Nickle Phosphide serving as a good catalyst in hydrogen-producing. Realized a series of optoelectronic quantum devices for quantum information processing, such as fiber-integrated photon-pair-entangler, chiplet heralded single photon emitter, fiber quantum memories, quantum number generator, etc.\n\nHonor and Group Memberships \nSelected Scholar of the Recruitment Program of Global Experts, China\nEditorial member of “Laser Technology”\nEditorial member of “Journal of Electronic Science and Technology”\nEditorial member of “Internal J. Mat. Sci. Appl”\nMember of APS (American Physics Society)\nMember of OSA (Optical Society of America)\nPermanent Member of China Physical Science and Technology\nPermanent Member of the Chinese Optical Society\nTechnical committee member of PIERS, organizing a series of “quantum information processing and devices” sessions\nTechnical committee member of ICICM",institutionString:"Southwest University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Southwest University",institutionURL:null,country:{name:"China"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"20",title:"Physics",slug:"physics"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"453623",firstName:"Silvia",lastName:"Sabo",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/453623/images/20396_n.jpg",email:"silvia@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"8356",title:"Metastable, Spintronics Materials and Mechanics of Deformable Bodies",subtitle:"Recent Progress",isOpenForSubmission:!1,hash:"1550f1986ce9bcc0db87d407a8b47078",slug:"solid-state-physics-metastable-spintronics-materials-and-mechanics-of-deformable-bodies-recent-progress",bookSignature:"Subbarayan Sivasankaran, Pramoda Kumar Nayak and Ezgi Günay",coverURL:"https://cdn.intechopen.com/books/images_new/8356.jpg",editedByType:"Edited by",editors:[{id:"190989",title:"Dr.",name:"Subbarayan",surname:"Sivasankaran",slug:"subbarayan-sivasankaran",fullName:"Subbarayan Sivasankaran"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2270",title:"Fourier Transform",subtitle:"Materials Analysis",isOpenForSubmission:!1,hash:"5e094b066da527193e878e160b4772af",slug:"fourier-transform-materials-analysis",bookSignature:"Salih Mohammed Salih",coverURL:"https://cdn.intechopen.com/books/images_new/2270.jpg",editedByType:"Edited by",editors:[{id:"111691",title:"Dr.Ing.",name:"Salih",surname:"Salih",slug:"salih-salih",fullName:"Salih Salih"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"50473",title:"Principles of Haemodiafiltration: Rationale for Improved Patients’ Survival",doi:"10.5772/63067",slug:"principles-of-haemodiafiltration-rationale-for-improved-patients-survival",body:'\nThe uremic syndrome is characterised by an accumulation of uremic toxins due to inadequate kidney function. There have been more than 90 compounds considered to be uremic toxins listed by the European Uremic Toxin Work Group. Sixty-eight have a molecular weight less than 500 Da, 10 have a molecular weight between 500 and 12,000 Da and 12 exceed 12,000 Da. Twenty-five solutes (28%) are protein bound [1]. These figures are further increasing with the adoption of new knowledge and technology of uremic toxins detection.
\nThe clearance of solutes during conventional haemodialysis (HD) depends on their size and the concentration gradient across the dialysis membrane. Solutes weighing less than 500 Da are considered low-molecular-weight solutes and they are removed by passive diffusion down a favourable concentration gradient. Urea is considered a marker of such toxins. Its clearance, as measured by Kt/Vurea, correlates with patient morbidity [2] showing the evidence that such toxins contribute to the uremic syndrome.
\nHowever, despite improvements in technology and patient care, the mortality rate of patients on maintenance dialysis remains high, at approximately 15–20% per year [3]. As specified in the United States Renal Data System report, the expected remaining life span after the initiation of renal replacement therapy was eight years for dialysis patients aged 40–44 and 4.5 years for those 60–64 years of age. In general population the ranges of the expected remaining life span at specified ages are 30–40 years and 17–22 years, respectively. The values in older dialysis patients are only slightly better than those in patients with lung cancer [4]. Therefore, there is an obvious need to change the practices and to attempt to approach the problem differently.
\nIn an attempt to reduce mortality, it was postulated in 1983 that a higher Kt/V than commonly prescribed during conventional short dialysis may increase survival among patients undergoing renal replacement therapies [5]. However, the hemodialysis (HEMO) study failed to show a positive effect on patient survival when dialysis dose per haemodialysis session was increased above the current K/DOQI recommendations [6]. Possible explanation for this unfavourable outcome could be in the kinetics of urea removal which is representative of small solutes, but not of larger sized molecules such as middle molecules, large-molecular-weight proteins or protein-bound solutes, thereby making Kt/V misleading. Clearance of urea accounts for only one-sixth of physiological clearance [1]. In addition, several shortcomings are associated with short dialysis schedules that are not captured by Kt/V index such as extracellular fluid volume control, phosphate control and adequate removal of middle and larger uremic molecules compounds.
\nBeta-2 microglobulin levels are associated with the development of dialysis-related amyloidosis and possibly reduced survival [7]. It seems likely that beta-2 microglobulin is a marker for overall middle-molecule clearance, including more toxic and as yet unidentified uremic compounds [8–11]. Those solutes are better removed by high-flux membranes compared to low-flux membranes due to their more porous characteristics with increased permeability.
\nWhether survival differs upon exposure to randomly assigned high- or low-flux membranes was also evaluated in the HEMO study [6]. No difference was observed. The same result was obtained in the Membrane Permeability Outcome study, in which patients were also randomly assigned to high- or low-flux dialysis membranes [12].
\nIn an attempt to improve patients’ outcomes, alternative renal replacement therapies have been developed, since removal by diffusion becomes less efficient as the molecular weight of a solute increases. Therefore, the need to mimic the kidney function seems to become mandatory in order to enhance middle-molecule removal because the diffusion in the tubules and loop of Henle follows filtration in glomerulus, which is the principle of convection (Figure 1).
\nMimicking native kidney function to enhance middle-molecule removal.
Haemofiltration (HF) is the treatment modality that employs convection in order to facilitate removal of larger molecular weight solutes while using high-flux dialysers only. In convection the small- and large-molecular-weight solutes are removed in the ultrafiltrate by solvent drag. Although HF is effective in the removal of the larger molecular weight solutes, it is less effective in the removal of small molecules as it is restricted by the magnitude of the ultrafiltration volume achievable. Haemodiafiltration (HDF) is the treatment modality that combines diffusion and enhanced convection in order to facilitate removal of small-molecular-weight solutes. Moreover, small-molecule removal is further increased with the use of high-volume on-line HDF (HV oHDF) (see section “Towards more cardioprotective renal replacement therapy”) and can be higher than haemodialysis, depending upon the volume of the replacement solution [13].
\nThe ratio between treatment techniques, processes and molecular weights of the solutes is shown in Figure 2.
\nThe ratio between treatment techniques, processes and molecular weights of solutes. HD, haemodialysis; HDF, haemodiafiltration; HF, haemofiltration; KoA, mass transfer area coefficient; QB, blood flow; tHD, duration of HD session; SC, sieving coefficient (the proportion of a substance to be removed for a particular filter).
The utilization of convective therapies has been variable. Between 1998 and 2001, about 12% of patients were on HDF in the European countries participating in the Dialysis Outcome and Practice Patterns Study (DOPPS) study [14].
\nThere have not been too many studies to compare the patients’ survival between convective modalities and high-flux HD. One of them was Italian MAMHEBI study, which was a randomised controlled trial (RCT). Significantly higher three-year survival was noted with HF (68% versus 52%) [15]. Dialysis Outcome and Practice Patterns Study (DOPPS) was prospective observational study which showed better survival with HDF, but it compared high-efficiency HDF with composite predictor of high- and low-flux haemodialysis [14]. Vilar et al. conducted retrospective cohort study and showed better survival with on-line HDF than with high-flux haemodialysis [16], as well as Imamović et al. in incident patients’ population [17], but the latter three were epidemiological studies and RCT to show survival benefit of HDF over conventional HD was still missing.
\nUltrafiltrate volume, derived from QUF times treatment time (Figure 3), is removed by the dialysis machine through increased transmembrane pressure (TMP), whereas the replacement solution is infused intravenously at equal volume minus the desired fluid volume removal to preserve extracellular fluid balance and isovolemic state. The replaced solution represents
Filtration fraction (FF) (proportion of ultrafiltration volume obtained compared with total blood volume processed during the HD session) was traditionally supposed to be 25% (Figure 3).
\nFiltration fraction. QB, blood flow rate; QUF, ultrafiltration flow rate.
The fluid can be substituted either after the dialyser as the reference mode (post-dilution mode) or before the dialyser (pre-dilution mode) (Figure 4), or both (mixed dilution mode).
\nDilution modes in haemodiafiltration; HDF, haemodiafiltration.
Choice on which dilution mode to apply depends on patient haemorheology and clinical performance (Table 1).
\nPost-dilution HDF | \nPre-dilution HDF | \nMixed-dilution HDF | \n
---|---|---|
\n | \n\n | \n\n | \n
High solute clearance and removal | \nHaemodilution | \nAvoids the drawbacks of post- and pre-modes | \n
Reduced consumption of substitution volume | \nDecreased viscosity and oncotic pressure | \n\n | \n
\n | \nReduced fibres and membrane fouling | \nRequires specific hardware equipment and software | \n
Haemoconcentration | \nPreserved hydraulic and solute membrane permeability | \n\n |
Increased viscosity and oncotic pressure | \nReduced membrane stress | \n\n |
Fibres and membrane fouling | \n\n | \n\n |
Reduced hydraulic and solute membrane permeability | \nReduced solute clearance and removal | \n\n |
Increased transmembrane pressure | \nIncreased consumption of substitution volume | \n\n |
Reduced sieving coefficient | \n\n | \n |
Fibre clotting | \n\n | \n |
Potential alarms | \n\n | \n |
Increasing membrane stress | \n\n | \n |
Potential albumin leakage | \n\n | \n |
Pros and cons of dilution modes.
HDF – Haemodiafiltration
Possible pyrogenic reactions were considerable threat to patients on HDF since the risk of microbiological contamination with high substitution volumes was increased. Besides, costs were greater with increased substitution volumes used as well as with storage bags for them. Therefore, the need for the production of high purity substitution fluid at lower cost was a challenge, not only because of threatening pyrogenic reactions and financial constraints but also because of higher risk of accelerated atherosclerosis and malnutrition due to ongoing low-grade inflammation [18]. Therefore, the American Association for the Advancement of Medical Instrumentation set the microbiological standards for water for dialysis at <200 colony-forming units (CFU) and <0.5 endotoxin units (EU)/ml. The European Pharmacopoeia was more stringent with <100 CFU and <0.25 EU/ml. Eventually, <0.1 CFU and <0.03 EU/ml were adopted for a solution to be considered “ultra-pure” and it is now widely used even for conventional haemodialysis [19].
\n“On-line” fluid production has enabled the concept of on-line HDF (oHDF). It facilitates the provision of an unlimited volume of sterile, non-pyrogenic substitution fluid not requiring storage, which is an efficient approach to prevent bacterial contamination and growth at a cost close to that of dialysate for conventional haemodialysis [20, 21]. The first step of the process includes the filtration of the water after it is produced using the reverse osmosis technique. The water is then used for the production of dialysate. This step has also been adopted in several haemodialysis machines to produce dialysate of improved purity. The second step includes further filtration of the dialysate. Finally, a third filtration by a disposable microfilter completes the creation of the substitution solution. The disposable microfilter is replaced at the end of the dialysis session. The dialysate prior to the last filtration is used for the diffusive element of HDF. The purity achieved using this approach has been repeatedly confirmed [21, 22] (Figure 5).
\nOne of the examples of water treatment system in the dialysis centre in Hemodialysis Unit of Lapeyronie Hospital in Montpellier (France) with microfilters depicted plus two microfilters within HDF machines (not depicted) showing the water for dialysis passing through HDF machines. The amount used ends up in the sewage; RO, reverse osmosis; HDF, haemodiafiltration.
So, an additional step to ensure full safety of dialysis and substitution fluids is to implement two sterilising ultrafilters built in within HDF machines on the path after the dialysate was prepared. They are disinfected regularly with the HDF machine and are replaced after a certain time of use as defined by manufacturer.
\nFrom an economic perspective, the added cost of the ultrafilters used to prepare the substitution solution has been nullified because of growing tendency of using ultra-pure dialysate even in conventional haemodialysis, thereby leaving the only difference in cost in the amount of water consumed per treatment. Above all, the remaining cost has been balanced by the biochemical and clinical benefits of HDF [20].
\nThe European Dialysis Working Group (EUDIAL) performed a systematic review and meta-analysis of randomised controlled trials on haemodiafiltration in 2014 and found the beneficial effect of post-dilution oHDF over HD in reducing all-cause and cardiovascular mortality (pooled RR-0.84; 95% CI, 0.73–0.96) and recommended wide acceptance of this treatment modality [23]. EUDIAL recommends the adoption of effective convective volume as a key quantifier for HDF. Providing time is constant and anticoagulation is adequate, limiting factors for high-volume haemodiafiltration (i.e. the amount of substitution fluid produced) are blood viscosity, filter performance and blood flow rate.
\nIt is not only that FF from Figure 3 accounts for the proportion of UF volume in the blood volume, but in reality, FF is even higher, because UF volume comes from plasma water, not from the whole blood. Figure 6 shows the example of a patient with FF of 0.30 (UF rate 120 ml/min and blood flow rate 400 ml/min). However, if the haematocrit of this patient is 30%, it means that his/her plasma water flow is only 280 ml/min, which increases FF to 0.43 (Figure 6).
\nIncreased filtration fraction in plasma water.
Consequently, too much convective volume increases the risk of haemoconcentration, thereby compromising the fine balance between the two by interfering with membrane permeability both on hydraulic and solute fluxes.
\nThe efficiency of HDF might be improved by increasing the size of the surface area of the membranes (provided optimal blood flow was achieved), so that high efficiency might be achieved with a surface area of 2.2 m2, as opposed to the standard surface of 1.4 m2, thereby allowing much more substitution fluid to be replaced at a rate of 120 ml/min in post-dilution mode, in contrast to 60 ml/min which was achieved with standard surface [24].
\nThe size of a membrane pore dictates the sieving coefficient (SC) of a substance to be removed. The higher the sc, the higher the UF and clearance of a particular solute. The reduction ratios of beta-2 microglobulin with low-flux, high-flux haemodialysis and HDF are 20, 60 and 75% per session, respectively [25]. The EUDIAL group, nominated by the European Renal Association – European Dialysis and Transplant Association (ERA–EDTA), set the SC for beta-2 microglobulin at minimum of 0.6 [26] in high-flux filters (which are mandatory with HDF). However, in order to achieve the optimal outcome for the patients, filters are nowadays designed with even higher SC for beta-2 microglobulin of 0.8 for efficient elimination, but still retention of albumin (Figure 7).
\nSolute membrane permeability: higher sieving coefficient for beta-2 microglobulin.
Correlation between ultrafiltered volume and beta-2 microglobulin elimination is linear, as specified by Lornoy et al. (Figure 8) [27].
\nA linear function of ultrafiltered volume and beta-2 microglobulin elimination. HDF, haemodiafiltration (with permission of authors).
Enhanced clearances of middle molecules such as beta-2 microglobulin [28] and phosphate [29] and other small molecules, such as homocysteine and complement D factor [30] are the main biochemical benefits of convective-based treatment over conventional HD.
\nOn-line monitoring of blood parameters allows adjustment of ultrafiltration rate to identify the patient-specific exchange rate possible at any given point in time while enabling haemodynamic stability. The substitution rate is adjusted to blood flow rate, thereby controlling haemoconcentration, whereas blood flow rate is adjusted to dialysis flow rate to control diffusion [31] (as for the diffusion component on-line clearance monitoring is used and the goal is to achieve spKt/V of 1.4). Therefore, if the blood flow rate drops from 100 L to 70 L per session, and the total UF volume remains at 25 L, the filtration fraction rises from 25% to 37% (Figure 9), which may generate a lot of difficulty during the session, lot of alarms and lot of critical TMP notifications, and the session may not end with a volume that was targeted.
\nThe impact of blood flow on filtration fraction.
In the very beginning of oHDF era, both the physician and the nurse in charge were supposed to do manual interventions in an attempt to prescribe and to keep the desired convective volume for each individual patient, respectively. The physician was able to do manual calculation based on the formula that included optimal blood flow rate, desired ultrafiltration rate to compensate for interdialytic weight gain, haematocrit, total proteins and filter performance, which was frequently producing alarms indicating threatening haemoconcentration and possible membrane fouling due to an excessive ultrafiltration rate. Therefore, the nurse had to carefully monitor the TMP (indicator of threatening problems) and react during the session in three possible ways in order to prevent TMP from reaching critical level: (a) rinse the dialyser with substitution fluid or normal saline, (b) decrease the UF rate or (c) switch to conventional haemodialysis. The latter two interventions were reducing the anticipated convective volume in the dialysis prescription, whereas the former intervention was only temporarily fixing the problem.
\nThe advanced technology of automatic adaptation by the built-in software AutoSub®, Fresenius Medical Care or Ultracontrol®, Baxter, was later developed in order to achieve alarm-free setting and to avoid the related problems, so that no manual calculation was needed any more by the physician in charge, but yet not all parameters with impact on flow conditions and blood viscosity were considered in formula. Therefore a nurse still had to do manual interventions because warning notification used to be received by the built-in software in order to opt for (a) ignoring the warning; (b) accepting the warning and letting the software reduce the UF at a new recommended rate in order to be within a safe range and prevent TMP from reaching critical level, albeit reducing the expected convective volume defined at the beginning of the session; or (c) turning off software monitoring. Besides, the nurse can always turn off the substitution pump and switch to HD.
\nFinally, with the development of the new technology for automatic ultrafiltration control in the dialyser, alarm-free maximisation of substitution volumes has been achieved, which is based not only on information about conditions across the membrane but also along the blood flow pathway in the dialyser, so that calculation of substitution volume based on the parameters specified above has become obsolete [32]. This was shown in a crossover study of patients treated during 240-min sessions on the same day with three different blood flow rates (QB300, QB350 and QB380) and switched after two consecutive weeks from conventional HDF to this new technology. The convective volumes were 24.8 ± 3.1, 27.8 ± 3.0, 28.8 ± 2.4 and 23.9 ± 1.2, 27.2 ± 1.9, 28.5 ± 2.1, respectively (
This innovative technology is known as AutoSub plus mode [32] (Figure 10).
\nSwitch from manual to automatic mode. TMP, transmembrane pressure.
\nFigure 10 shows the red line in manual mode favouring increasing convective volume and efficacy, whereas yellow line represents safety and favours TMP limitations, which means that TMP increases over time during the dialysis session as convective volume increases. However, in automatic mode on the right side of Figure 10, the optimisation of substitution volume has been achieved by setting the safety target range so that the machine itself sets the safe TMP target range and continuously regulates the optimal UF rate at an optimal time throughout the session in order to keep the TMP in optimal range to prevent haemoconcentration and membrane complications specified above (Table 1). However, the nurse can still switch to manual mode or even to conventional HD should extraordinary conditions prevent high-volume oHDF (such as special rheologic properties of a patient\'s blood) [32]. Owing to continuous analysis of haemorheological conditions throughout the dialysis session, continuous adaptation of UF flow takes place (Figure 11).
\nContinuous adaptation of ultrafiltration flow in AutoSub plus mode while keeping UF loss constant; red line within the dialyser illustrates dynamic analysis of pressure pulses along the blood flow pathway; UF, ultrafiltrate to be lost during the dialysis session; HDF, haemodiafiltration; FF, filtration fraction; QUF, ultrafiltrate flow; QB, blood flow.
For this improvement sieving coefficient for beta-2 microglobulin was further increased to 0.9 in a new series of Cordiax© dialysers (Figure 12).
\nNew series of Cordiax© dialysers with sieving coefficient for beta-2 microglobulin of 0.9 achieved by widened pore diameter which permits better molecule removal, but still retention of albumin (simplified graph based on manufacturer’s internal data).
The result is the improved removal of middle molecules (while ensuring the retention of albumin) due to increased filtration fraction, thereby generating increased substitution fluid volumes, with no need to even keep the high blood flow rate, so that equally adequate dialysis can also be delivered to patients with suboptimal blood flow rates (such as inpatients with poor vascular access or inadequate needle size). This technology introduced the concept of high-volume oHDF with substitution volumes >20 L as well as the related concept of cardioprotective haemodialysis due to its beneficial effects of cardiovascular system. It introduced the new era of high-volume cardioprotective renal replacement therapy as the major clinical effect of high-volume oHDF.
\nThe main clinical effects of high-volume oHDF include haemodynamic stability [34, 35], possibly improved quality of life [30], a delay in the development of dialysis-related amyloidosis [36], improvement in anaemia management [17], plasma lipid profiles [37] and inflammation [37].
\nHaemodynamic stability is maintained by salt loading via substitution fluid administration [38]. A higher predialysis plasma sodium concentration in patients with higher frequency of oHDF was reported, thus suggesting reduced sodium removal [39]. Haemodynamic stability is maintained by decreasing core body temperature as a result of the infusion of large amounts of fluid at a lower temperature, leading to vasoconstriction [34]. Imamović et al. demonstrated reduction in both erythropoietin (EPO) consumption and EPO resistance index in patients on HV oHDF compared to high-flux HD [17], which reveals the evidence of an increased haemodynamic stability due to improved anaemia management [40].
\nIn the era of convective treatment modalities employed in oHDF, the substitution volume appears to be critical for patient survival in prevalent patients [14, 41–43]. The Dialysis Outcomes and Practice Patterns Study (DOPPS) showed better survival of prevalent patients with high-volume HDF defined as having a substitution fluid volume of >15 L compared to low-flux HD (RR 0.65) [14]. The Turkish RCT and the CONTRAST RCT revealed survival benefits of oHDF over conventional HD in prevalent patients with 17.4 L (RR 0.54) and 21.95 L (RR 0.61) of substitution and convective volumes, respectively, but only in post hoc analyses [41, 42]. The first positive RCT in favour of improved survival of oHDF over conventional HD was the ESHOL study which showed convective volumes cut-offs of 23.1–25.4 L (RR 0.60) and >25.4 L (RR 0.55) in the intermediate and upper tertiles, respectively, also in post hoc analysis of prevalent patients’ data [43]. Eventually, the observational Balkan study in incident HD patients conducted in Bosnia and Herzegovina, Serbia and Slovenia, while using European Clinical Database (EuCliD®), [17] showed the lowest RR for mortality of 0.29 on high-volume oHDF compared to high-flux conventional HD. The substitution volume cut-off of 20.4 L was discriminating between low- and high-volume oHDF, which makes the convective volume at least in the range of the one achieved in ESHOL study. Consequently, overall negative correlation may be observed between increasing ultrafiltration volumes and mortality risks in patients on convective-based treatments in comparison with conventional HD. Figure 13 shows descriptive hazard ratios for mortality based on literature data (not on forest-plot analysis).
\nNegative correlation between increasing ultrafiltration volumes and decreasing relative risk of mortality in patients on haemodiafiltration.
Recently, Canaud et al. aimed at determining optimal convection volume in 2293 international incident dialysis patients treated for 4 h with oHDF in post-dilution mode. Two-year survival rate was found to increase at about 55 L of convection volume per week and to stay increased up to about 75 L/week [44].
\nHV oHDF is a renal replacement treatment modality that is becoming increasingly employed in haemodialysis units worldwide because of a strong body of evidence of its survival benefit over conventional haemodialysis. Optimal substitution volume has become the measure of convective dose, reflecting mainly middle- and large-molecular-weight solutes, and it completed former Kt/V that was mainly dedicated to small-molecular-weight solutes. Both dialysis dose components are intent to act synergistically and provide more precise tools for assessing dialysis adequacy. More specifically, convective dose should be tailored to individual patients’ needs depending on optimal blood flow rate, rheological blood conditions and the condition of vascular access. Studies in this field are still needed to set the cut-offs of substitution volumes to be adjusted based on the residual renal function. An alternative proposal for dialysis dose is serum beta-2 microglobulin clearance or plasma level determination, but those measurements are relatively expensive and confounded by calibration differences and variations in the generation rate. The convective volume must be set for each individual patient and should be normalised to a body size-related factor as a surrogate for the convective dialysis dose [26], but given that they varied a lot between the studies, research is still needed to set the required cut-off values in the years to come [23].
\nThe authors would like to thank Dr. Aileen Grassmann, Director Clinical and Epidemiological Research, Fresenius Medical Care Deutschland GmbH for having the chapter critically reviewed; Prof. Bernard Canaud and Dipl. Ing. Angelica Kneppel for providing the figures; and Sanja Kozlik, NephroCare Manager, Bosnia and Herzegovina for covering the publication fee.
\nVector-borne diseases are infections caused by viruses, bacteria, or parasites that are transmitted to humans by the bite of infected arthropod species, these can be diseases transmitted by mosquitoes (dengue fever, West Nile fever, chikungunya, malaria, Zika, etc.), by sandflies (leishmaniasis), by ticks (encephalitis, Lyme Borreliosis, Crimean-Congo hemorrhagic fever, Human Granulocytic Anaplasmosis) by triatomines (Chagas disease), among others. These diseases account for more than
Vectors are living organisms that can transmit infectious pathogens between humans or from animals to humans. Many of these vectors are insects that ingest disease-causing microorganisms during a blood meal from an infected host and then transmit it to a new host after the pathogen has replicated. Another characteristic of arthropod vectors is that they are cold-blooded (ectothermic) and therefore very sensitive to climatic factors, although the climate is only one of many factors that influence vector distribution, as there are also geographic and sociodemographic factors [1].
In order to interpret the behavior of vector-borne diseases in the most accurate and simplified way possible, statistical models are used. A statistical model is a simplified representation of a phenomenon of interest [3, 4]. With their help, it is possible to model, predict and make inferences about natural phenomenons, biological systems, epidemiological studies, and others [5]. One of the most widely used statistical models is linear regression models, which predict a continuous target based on linear relationships between the target and one or more predictors. But there is another type of model that extends the general linear model, so that the dependent variable is linearly related to the factors and covariates by means of a certain link function, which is known as a generalized linear model [6].
Generalized Linear Models (GLMs) provide a collection of linear regression models including the exponential family, such as the Binomial and Poisson, which are distributions for counting data. The GLMs were introduced by Nelder in
There are three components in GLMs: A response variable distribution, a linear predictor, and a link function. A response variable
GLMs can help in numerous areas such as epidemiology, mining engineering, Earth and environmental sciences, ecology, biology, geography, economics, agronomy, forestry, image processing, and more [15, 16]. For epidemiology in particular, as it is about understanding diseases that affect a population, the most usual thing is to find a binary variable that represents the presence or absence of a disease or to count the events of a disease for certain areas.
Such is the case of a study conducted by Hashizume et al. [17] in Bangladesh,
An important extension of the GLMs is the Generalized Linear Mixed Models (GLMMs) [18]. GLMMs provide a range of analyses for those data that are correlated in space and belong to the exponential family (Gamma, Poisson, Binomial, among others) [19]. Generalized Linear Spatial Models (GLSMs) are basically GLMMs, since latent variables are derived from a spatial process. In recent years, there has been a growing interest in the analysis of spatial data in epidemiology, in order to predict the incidence of vector-borne diseases.
Using techniques available to epidemiologists and other health professionals, the potential of remote sensing, Geographic Information Systems (GIS), and spatial analysis of epidemiological data has been demonstrated by some authors such as those mentioned below; however, there are still few studies that adequately prove the potential of these tools, since they are still being exploited in the fight against diseases [20].
For instance, a Colombian paper published in
Another example is the work of Estallo et al. [22] in
As can be seen, spatial analysis is a powerful tool for the analysis of georeferenced data, as it can give health research a broader perspective of the occurrence of health events and diseases. Spatial statistical models are useful because they estimate the spatial variance inherent in the data, and can also be used to perform statistical inference throughout the study area. Spatial prediction can be made based entirely on a stochastic model or in combination with a deterministic trend [20, 23].
The aim of this chapter is to show an example of the application of spatial statistics, implementing a Generalized Linear Spatial Model for the prediction of dengue disease in the state of Chiapas. For this, there are considered patient age and the next information of each municipality: garbage disposal service, maximum environmental temperature, average monthly rainfall, and altitude as covariates. For the study of the disease in the
Space models have a simple structure, flexible enough to handle a variety of problems. The data may be continuous or discrete, present spatial aggregations, or be point observations in space. As for the spatial locations can be regular or irregular. A spatial model is usually used to predict sites where the study phenom was not observed.
Let
Structure 1, allows to differentiate and talk about problems with continuous spatial indexes, lattice, and point patterns giving rise to three types of data: geospatial, lattice data, and point patterns. In geospatial data,
Finally, the entangled data or also known as area data,
Knowing the type of variables with which they are working and taking into account their spatial dependence, helps to determine the regression technique that best fits the characteristics of the data [21]. For the study of spatial data Gaussian processes can be used, which are stochastic processes, a collection of variables. This allows any subset of finite random variables to have a multivariate Gaussian distribution. Gaussian processes can thus be thought of as distributions of random vectors or random functions [26]. Gaussian processes began to be studied in the
Gaussian stochastic processes are widely used as models for geostatic data. If a transformation of the original response variable is used, the scope of the Gaussian models can be amplified, and so with this extra flexibility the model provides a good empirical fit to the data.
A Gaussian process, {
Any such process is fully specified by the average function
A spatial Gaussian process is stationary if
Among the parametric functions for the covariance function [29] are the following:
Exponential:
Gaussian:
Matérn:
In these covariance functions (Eqs. (3)–(5))
There are several criteria in the literature to validate the covariance structure of a Gaussian process Eq. (2). Among the most used are: Mean Error (ME), Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Square Normalized Error (MSNE) (Table 1). ME and MSE should tend to zero when the covariance structure of the Gaussian process was correctly estimated. The MAE and RMSE criteria are considered as the most efficient criteria to validate the covariance structure of the Gaussian process. The RMSE is expected to be small like MAE, while the MSNE is expected to be close to
Measurement | Definition |
---|---|
Mean error | ME= |
Mean square error | MSE= |
Mean absolute error | MAE= |
Root mean square error | RMSE= |
Mean square normalized error | MSNE = |
Criteria for evaluating the covariance structure of the Gaussian process.
Spatial Generalized Linear Models were introduced by Diggle et al. in
Where
It is assumed that
To prove the existence of spatial dependence on a variable
Where
Several packages are available in statistical software R [34] to perform spatial modeling.
The
The
The geostatistical model assumes the response variable to be Gaussian, which may be an unrealistic assumption for some data sets. The GLSM provides a framework for analyzing Binomial and Poisson distributed data. The likelihood for such a model, in general, cannot be represented in closed form, since it is a high-dimensional integral
where
This section shows the application of a spatial model taking into account the social, climatic, and geographical characteristics of the municipalities of the state of Chiapas in relation to dengue virus infections registered from January to August of the year
Dengue disease is endemic to the state of Chiapas with scattered case reports, this is due to the different geographic characteristics of the state, such as the altitude of its municipalities and its border condition with the country of Guatemala. It is known that at different altitudes, in the regions, the climatic conditions tend to vary and this can favor the reproduction of the vector. The state of Chiapas is divided into
The data, which were collected at the municipal level, being 36 the municipalities that registered positive cases of dengue and were considered for the analysis, were obtained from different sources that are mentioned below.
The database with dengue cases registered in the state of Chiapas, during the period January–August
The climatic data were obtained from the World Meteorological Organization (WMO) [38], for each municipality of residence where the dengue cases were registered, working with the daily reports of average environmental temperature, maximum and minimum environmental temperature and average monthly rainfall. The climatic data were taken into account for the analysis,
Other factors related to infection were also considered in the analysis. Data on the population density and altitude of each municipality of residence per observed case were obtained from the INEGI, the other variables such as garbage disposal, contact with the mosquito, drinking water service, patient age, and sex were obtained from the original database of registered dengue cases provided by the secretary of health [37].
For the georeferencing of dengue cases registered in the period January–August
The database that is made up of
The spatial distribution of the
Georeferencing of cases
The Moran’s Index obtained, with the number of CDCs in the
After selecting the spatial model and the variables, we proceeded to estimate the covariance structure of the Gaussian process. For this, the Exponential, Gaussian, and Matérn covariance functions were tested, taking CDCs as the response variable, and measures of central tendency of the explanatory variables maximum environmental temperature, altitude, patient age, and average monthly rainfall were taken. Of the three functions, the Matérn covariance function generated the best value for
For the simulation and conditional prediction of the process Eq. (6) MCMC was used, since this provides a solution to the impediment of direct calculation of the predictive distribution due to the high dimensionality of the integral Eq. (8) [36]. For this,
For modeling the number of registered dengue cases in the
In Table 2, it is observed that the variables that have an effect on the cases of dengue observed are maximum environmental temperature, altitude of the municipalities, average monthly rainfall, and patient age. High temperatures and altitudes favor the presence of the disease, while young people will be preferred factors by the vector, as well as low rainfall because in seasons where there is no continuous flow of water in the rivers, stagnation causes an increase in the proliferation of
Parameter | Estimation coeff. | |
---|---|---|
Intercept ( | ||
Maximum temp. ( | ||
Altitude ( | ||
Rainfall ( | ||
Age ( |
Estimation of parameters and their confidence intervals of the selected model.
The projection of the model was carried out on a map of the state of Chiapas which was made based on the municipalities where the cases were registered, as can be seen in Figure 2, the prediction is divided by zones in shades of green to yellow with a contour delimited by contour lines that show the area in which the model predicts the number of cases for that area. As we can see, most of the predicted cases occur within the metropolitan area where the state capital Tuxtla Gutiérrez and the municipalities of Chiapa de Corzo, Berriozábal and Suchiapa are located, this corresponds to the observed data, since most of the cases occurred in the same area. On the other hand, it is observed that the prediction power is diminished in areas where no dengue cases were registered.
Prediction of confirmed dengue cases.
The purpose of this chapter is to present and expand the use of spatial statistics to contribute to public health and the epidemiology of vector-borne diseases, and for this reason, the example of the use of a GLSM was proposed to model the distribution of dengue in Chiapas, since this is one of the endemic diseases that cause numerous infections per year. Climatological, geographic, and sociodemographic variables were used for the modeling, where it was found that the maximum environmental temperature, altitude, patient age, and average monthly rainfall are the variables that best predict the spread of dengue.
Maximum environmental temperature is shown to have a significant effect on dengue cases, as it is an environmental risk factor for dengue transmission, higher temperatures increase viral replication in the vector in a shorter time and thus increase the potential for transmission of dengue viruses. This is described by a study on the extrinsic incubation period. Liu et al. [41] found that the virus remained in the midgut of the vector at
The altitude above sea level of each municipality was also an important variable in the study, which is consistent with the findings of the systematic review by Aswi [42], where this variable was used in different statistical models in order to describe the behavior of the disease, since the spread of the Aedes aegypti mosquitoes is limited by climatic conditions and this will be governed by the location of the geographical area and its altitude. The study of Reinhold et al. [43] alludes that Aedes Aegytpi cannot regulate its body temperature because it is an endothermic arthropod, and that is why its temperature is defined by the climatic conditions of its environment. Thus, geographic location and altitude are important variables for dengue disease.
On the other hand, we have average monthly rainfall, where we see a negative association, since the less rainfall, the more cases of dengue. This coincides with the results of the work of Hashizume et al. [17], where they indicate that dengue cases increase by
Finally, we have the variable patient age, as can be seen in the results, the correlation was negative too, due to the young population being preferred by the vector, since there is a greater number of cases at an average age of
Vector-borne diseases (VBD) are an important public health issue worldwide. The distribution of these diseases as well as their transmission and seasonality are known to be largely determined by environmental, geographic, and socio-demographic factors. GLSMs allow robust analysis of the complex and diverse factors that influence the occurrence of VBD, incorporating spatial dimensions. They can also be a valuable tool for targeting interventions in surveillance and control programs for VBD at the global or regional level. These analytical approaches have recently been used in the field of public health, but in Mexico there are still very few studies that contribute to this knowledge. For this reason, this chapter presents an example of the application of GLSM with a study of dengue, one of the most common VBD in Mexico, finding that the maximum temperature, altitude, and average monthly rainfall of each municipality, as well as patient age, are the factors that best predicted the presence of dengue cases in the state of Chiapas in the period from January to August
The authors declare no conflict of interest.
ARMA | Autoregressive Moving Average |
CAR | Conditional Autoregressive Model |
CDCs | Confirmed Dengue Cases |
DENV | Dengue Virus |
EW | Epidemiological Week |
GLM | Generalized Linear Model |
GLMM | Generalized Linear Mixed Model |
GLSM | Generalized Linear Spatial Model |
INEGI | National Institute of Statistic and Geography |
INLA | Integrated Nested Laplace Approximation |
MAE | Mean Absolute Error |
MCMC | Markov Chain Monte Carlo |
ME | Mean Error |
MI | Moran’s Index |
MSE | Mean Square Error |
MSNE | Mean Square Normalized Error |
RMSE | Root Mean Square Error |
SAR | Simultaneous Autoregressive Model |
VBD | Vector-Borne Diseases |
WMO | World Meteorological Organization |
ZCL | Zoonotic Cutaneous Leishmaniasis |
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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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. 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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. 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