Apoptosis Guide

papers about Apoptosis,Fas C- Terminal Tripeptide,Z-VAD-FMK

   Apr 09

Porosity open and closed bulk density

Characterization of sintered discs containing distinct sawdust content in the bottom-layer obtained from mercury intrusion porosimetry.Sawdust content (wt.%)Open porosity (%)Bulk density (g/cm3)Median pore diameter (μm)2.54.82.301.255.42.2415.81011.12.1640.8Full-size tableTable optionsView in workspaceDownload as CSVFig. 6. Pore size distribution curves (calculated from mercury intrusion data) for sintered discs produced with distinct sawdust content.Figure optionsDownload full-size imageDownload as PowerPoint slideTo comply with the standard, porcelain stoneware tiles must have bending strength values higher than 35 MPa (ISO 10545/4). In this work, the mechanical resistance of the sintered bi-layered ceramic discs is controlled by the degree of porosity in the bottom-layer of the discs. Fig. 7 presents the bending strength and Young\’s modulus of the fired bodies. Results show that both parameters diminish with increasing sawdust content. Nevertheless, the incorporation of up to 10 wt% of sawdust creates materials that still comply with these specifications. As for other standard properties required for porcelain stoneware tiles they Ro3306 are ensured by the dense top-layer.Fig. 7. Mechanical properties of bi-layered ceramic tiles as a function of sawdust content: (a) bending strength and (b) Young\’s modulus.Figure optionsDownload full-size imageDownload as PowerPoint slideThe specific strength of samples was also evaluated, as proposed by Ashby (2005). Experimental values ranged between 3.2 and 4.0 MPa0.5 cm3/g, being similar to those reported in the literature for lightweight porcelain stoneware tiles (Bernardo et?al., 2010 and Novais et?al., 2014).The creation of porosity in the bottom layer sintered discs is expected to decrease the thermal conductivity of the bodies. In fact, the thermal conductivity strongly decreases with porosity, as shown in Fig. 8. For comparison purposes, the thermal conductivity of a standard ceramic sample (prepared without porogen addition) was included in the figure. A threefold decrease in the thermal conductivity (from 0.71 to 0.23 W/m K) was observed when only 5 wt% sawdust was added to the bottom layer of the bi-layered discs. This observation is consistent with the above-mentioned porogen percolation threshold. Indeed, SEM micrographs in Fig. 5b and f clearly show the formation of networks between adjacent pores, hence reducing the solid paths throughout the ceramic body. The thermal conductivity attenuation with porosity level observed in Fig. 8 was steeper than that observed when polypropylene and polymethyl methacrylate were used as porogen agents (Novais et al., 2014). The thermal insulation achieved with sawdust incorporation endows porcelain stoneware ceramic tiles with new features that may extend the range of applications of this common product.Fig. 8. Thermal conductivity of the porous layer sintered discs, prepared with sawdust, as function of open porosity level. The horizontal line corresponds to the thermal conductivity of a standard composition (without porogen).Figure optionsDownload full-size imageDownload as PowerPoint slide4. ConclusionsThis study evaluated the possibility of using wood wastes (sawdust) as a pore forming agent for producing porcelain stoneware ceramic tiles with novel features.Lightweight bi-layered bodies showing suitable mechanical resistance and low thermal conductivity were fabricated, attesting to the potential of using sawdust as a pore forming agent in such fast-fired ceramic products.Optical microscopy and mercury intrusion porosimetry characterization demonstrated that the porosity level is controlled by sawdust content, and therefore can be tuned considering the application envisaged.Sawdust presents fast and complete combustion, without leaving residues or ashes, and does not induce defects in the ceramics bodies. Additionally, the heat released from its decomposition brings value to the ceramic tile manufacturing process, allowing energy savings.The incorporation of sawdust in the bottom layer of the bi-layered ceramics promotes weight reduction (up to 7.5%) and simultaneous thermal conductivity attenuation (up to 76%). The low porogen percolation threshold (5 wt%) achieved endorsed a threefold decrease in the ceramic tile\’s thermal conductivity in comparison to commercial stoneware tiles. At the same time, the product complies with mechanical strength requirements when sawdust incorporation level is below 10 wt%.Results demonstrate that innovative products with excellent features can be produced by incorporation of sawdust into porcelain stoneware ceramic tiles. The novel ceramic tiles ensure environmental, technical and economic advantages: waste valorisation by sawdust reuse (environmental advantage); density reduction of the product which decreases the tiles transportation and distribution costs (economic advantage); restrain energy loss (technical advantage). These new and exciting features may widen the range of applications of porcelain stoneware tiles while simultaneously contributing towards sustainable construction.AcknowledgementsThe authors acknowledge the financial support from Portuguese Innovation Agency (Adi) through project ThermoCer, to CICECO (PEst C/CTM/LA0011/2013) and RNME – Pole University of Aveiro (FCT Project REDE/1509/RME/2005) for instrument use, scientific and technical assistance. The authors acknowledge CINCA for providing the spray-dried powder, and the assistance of Dr. R.C. Pullar with editing English language in this paper.


   Mar 25

GW841819X Markov matrix of regional energy efficiency between and

Venture capital; Entity industry; Green innovation1. IntroductionAs the important foundation of national economy, entity industry refers to real industry satisfying material and cultural needs of human, including agriculture, manufacturing and most service industries. Entity industry can be divided into green industry and non-green industry from the perspective whether it is conducive to resource conservation and environmental protection. Green industry is conducive to resource conservation and environmental protection. Narrow-sense green industry refers to service industry of GW841819X conservation and environmental management services, while general green industry is the industry consuming less resource and producing less environmental pollution. The so-called non-green industry refers to industries with large consumption of resources and heavy environmental pollution.Green innovation refers to technological innovation that ecological concept is introduced into various stages of technological innovation for entity industry, thus benefiting resource conservation and environmental protection (Zhang, 2013). Practice in developed countries has proved its important supporting role in energy conservation. For example, the use of aeration technology played a huge role in the pollution control project of UK Thames in early 1960s. In 1970s, Japan introduced the world’s most stringent standards of sulfur dioxide emission, greatly reducing sulfur dioxide emissions through desulfurization technology (Bu, 2006).The support of financial industry is indispensable to promote green innovation activities. It is reasonable and necessary for government to provide financial supports due to significant positive externalities of green innovation. However, financial resources form government is very limited compared to the fund demand of green innovation. After all, aspects of response to climate change, pollution control, eco-economy development, and sustainable development are common aspiration of mankind throughout the world. It is the inevitable trend of economic and social development to transform economic development mode and lifestyle with construction of ecological civilization. Thus, expansion of green innovation funding sources has become an inevitable choice for entity industries. According to the prediction of US Energy Foundation and China National Development and Reform Commission, annual financing gap of Chinese energy saving industry, new energy industry and environmental management industry is about 200 billion RMB; it will reach at least two trillion RMB by 2020 (subject group, 2009). Therefore, industries of energy conservation, new energy development and environmental management cannot be promoted for green innovation without active use of financial instruments, thus making it difficult to promote green innovation.Academic research has proved the supporting role of venture capital in technology innovation. Kortum and Lerner (2000) found that venture capital greatly promoted technology innovation in economy in the United States – the promoting effect of 1


   Mar 25

Venture capital Entity industry Green innovation IntroductionAs the

Spatial Markov matrix of regional GW841819X efficiency between 1999 and 2010 in China.Spatial lagti/ti+1n1: <75%2: <100%3: <125%4: >125%11200.950.050.000.002120.050.860.090.00340.000.001.000.00400.000.000.000.0021650.900.080.000.022560.050.850.090.013290.000.150.780.074180.000.000.180.8231121.000.000.000.002150.000.690.310.003390.000.190.700.114180.000.050.140.814100.000.000.000.00200.000.000.000.003140.000.000.800.204580.000.000.030.97Full-size tableTable optionsView in workspaceDownload as CSVTable 5 illustrates three factors.First, the spatial relationship between regions plays an important role in the convergence club of energy efficiency in China. With different neighbors, the transition probabilities of regional energy are different. In other words, if the background of a region does not change, the four conditional matrices in the same period in Table 5 should be similar to each other. In fact, the background of a region does not change.Second, different regional backgrounds play different roles in the transfer of energy efficiency type. The probability of an upward shift will increase and the probability of a downward shift will decrease if a region is within the regional neighborhood with a high level of energy efficiency. Conversely, the probability of an upward shift will decrease and the probability of a downward shift will increase if a region is within the regional neighborhood with a low level of energy efficiency. Between 1999 and 2010, when a region with low energy efficiency is adjacent to regions with low energy efficiency, the probability of an upward shift is 5%; meanwhile, if a region is adjacent to regions with medium-low, medium-high, or high-level energy efficiency, the upward shift probability is increased to 8%. The probability of an upward shift is 19%, and the probability of a downward shift is 10% if a region with medium-low energy efficiency is adjacent to regions with low or medium-low energy efficiency; the probability of an upward shift is 31% and the probability of a downward shift is 0% if a region is adjacent to regions with medium-high or high energy efficiency. When a region with medium-high energy efficiency is adjacent to regions with low, medium-low, or medium-high energy efficiency, the probability of an upward shift is 11% and the probability of a downward shift is 34%; meanwhile, if a region is adjacent to regions with high or medium-high energy efficiency, the probability of an upward shift is 20% and the probability of a downward shift is 0%. When a region with high energy efficiency is adjacent to regions with lower energy efficiency, the probability of a downward shift is 32%; meanwhile, if a region is adjacent to regions with higher energy efficiency, the probability of a downward shift is 3%.Third, the matrix of the spatial Markov transition probability provides a spatial interpretation for the “club convergence” phenomenon. A region will be negatively influenced by its geographical neighbors with a low level of energy efficiency. Between 1999 and 2010, if the geographical neighbors of a region have a low level of energy efficiency, the probability of this region to maintain a low level of energy efficiency after several years is 95%. This probability is higher than the probability that ignores the regional neighbors in Table 4, which is 0.92 in the same period. Between 1999 and 2010, the probability of a region to maintain a high level of energy efficiency is 97% if its geographical neighbors are at a high level as well; this probability is higher than the probability in Table 4 in the same period, which is 0.90.4. ConclusionWe adopt DEA in this paper to calculate regional energy efficiency from the perspective of total-factor energy efficiency, and the club convergence of the regional energy efficiency in China is subsequently tested using the Markov chain and spatial Markov chain methods. We draw the following conclusions:(1)The “club convergence” phenomenon exists in the regional energy efficiency in China between 1999 and 2010, and the levels of club convergence are low, medium-low, medium-high, and high. Moreover, the stability of both low- and high-level club convergence is high.(2)The energy efficiency class transitions in China are highly constrained by their regional backgrounds. The regional transitions are positively influenced by regions with a high level of energy efficiency and are negatively influenced by regions with a low level of energy efficiency. These empirical analyses provide a spatial explanation to the existence of the “club convergence” phenomenon of regional energy efficiency in China.(3)In accordance with the dynamic evolution of regional energy efficiency in China, special attention should be paid to spatial effect, and regional cooperation should be strengthened. Policy that favors the “enrich the neighbor” approach should be used in regions with a high level of energy efficiency. Simultaneously considering the geography, population, industry, resources, etc., attains a win–win situation on energy efficiency. Preferential policies should be implemented in the low-level and low-growth regions of energy efficiency to enhance the opening-up level, thus accelerating the adjustment and optimization of the industrial structure, and the promotion of energy efficiency of these areas.AcknowledgementsThis paper is the stage achievement of the National Natural Science Foundation of China (71303029) and the National Social Science Fund Project (10BGL066). The author is grateful for the support of the National Natural Science Foundation of China and the National Social Science Foundation of China.


   May 27

Content description of questionnaires using ICF CY Aspect of healthQUALINAUQUEI

Content description of questionnaires using ICF-CY.Aspect of healthQUALINAUQUEI OursAUQUEI SoleilOK.AdoCHAQCHIP-CE CRFCHIP-CE PRF [45]CHIP-CE PRF [76]CHIP-AECHQ-PF28CHQ-PF50CHQ-87ICF-CY: Body functions Mentalxxxxxxxxxxx Sensory and painxxxxxxxxx Voice and speechxxx Cardiovasc, haem, immuno, & respxxxxxx Digestive, metabolic, & endocrinex Genitourinary and reproductivexx Neuromusculoskeletal and movementxxxxxx Skin and relatedxxxxICF-CY: Activities and participation Learning and applying knowledgexxxxxxx General tasks and demandsxxxxxx Communicationxxxxx Mobilityxxxxxxxxx Self-carexxxxxxxx Domestic lifex Interp interactions and relationshipsxxxxxxxxxxx Major life areasxxxxxxxxxxx Community, social, and civic lifexxxxxxxxxxOther (not defined by congo red ICF-CY) General health – Not definedxxxxxxxxxx Mental health – Not definedxx Physical health – Not definedxxxxxx Environmentx Accidents/injuriesx Achievements in lifex Being able to do what you want to dox Challenging/risk-taking behaviorxxxxxxxx Foodxxxxx Functional status Functioning of familyxxxx Future aspirationsxxx Having fun (enjoyment)xxxx Health condition/treatmentxxxxxxxxxx Health habitsx Making decisions Quality of life Satisfaction with lifexMeasuring functioning or well-being Functioningxxxxxxxxx Well-beingxxxxxxxxxITQOL (Short)ITQOL (Long)CHRSCHSCSCOOPCQoLDHP-AExQoLFSIIR (Infants)FSIIR (Toddlers)FSIIR (Preschool)FSIIR (School age)FSIIR-7FSIIR-14GCQHALFSICF-CY: Body functions Mentalxxxxxxxxxxxxxx Sensory and painxxxxxxx Voice and speechxxxxx Cardiovasc, haem, immuno, & respxxx Digestive, metabolic, & endocrinexxxxxxxxx Genitourinary and reproductivex Neuromusculoskeletal and movementxxxxx Skin and relatedICF-CY: Activities and participation Learning and applying knowledgexx General tasks and demandsx Communicationxxxxxxxx Mobilityxxxxxxx Self-carexxxxxxxxxxxx Domestic life Interp interactions and relationshipsxxxxxxxxx Major life areasxxxxx Community, social, and civic lifexxxxxxxxxxOther (not defined by the ICF-CY) General health – Not definedxxxxx Mental health – Not definedxxx Physical health – Not definedxxxxxx Environmentx Accidents/injuries Achievements in life Being able to do what you want to dox Challenging/risk-taking behaviorxxxxxxx Foodx Functional status Functioning of familyxxxx Future aspirations Having fun (enjoyment)xxxxxxxx Health condition/treatment*xxxxxxxx Health habitsxx Making decisionsx Quality of life Satisfaction with lifex Measuring functioning or well-being Functioningxxxxxxxxxxxxxxxx Well-beingxxxxxHAYHealthy PathwaysKIDSCREEN-52KIDSCREEN-27KIDSCREEN-10KINDL-KiddyKINDL-KidKINDL-KiddoNordic QoLQPedsQLPedsQL InfantPedsQL SF15ICF-CY: Body functions Mentalxxxxxxxxxx Sensory and painxxxxxx Voice and speechx Cardiovasc, haem, immuno, & respxxxxx Digestive, metabolic, & endocrinex Genitourinary and reproductive Neuromusculoskeletal and movementxxxxxx Skin and relatedxICF-CY: Activities and participation Learning and applying knowledgexxxx General tasks and demandsxxx Communication Mobilityxxxxxxx Self-carexx Domestic lifex Interp interactions and relationshipsxxxxxxxxxxx Major life areasxxxxxxxxxxxx Community, social, and civic lifexxxxOther (not defined by the ICF-CY) General health – Not definedxxxx Mental health – Not defined Physical health – Not definedxxx Environmentxxx Accidents/injuriesx Achievements in lifex Being able to do what you want to doxxxxxx Challenging/risk-taking behaviorx Food Functional status Functioning of familyxxxx Future aspirationsx Having fun (enjoyment)xxxxxxx Health condition/treatment*xxxxxxx Health habits Making decisionsx Quality of life Satisfaction with lifexxMeasuring functioning or well-being Functioningxxxxxxxxxxxx Well-beingxxxxxxxxxxComQOL-S5PWI-PSPWI-SC(T)QOLQAQOLQCQOLP-AVSLSSMSLSSBrief MSLSSMSLSS-ATAPQOLTACQOLTAAQOLICF-CY: Body functions Mentalxxxxxxxxx Sensory and painxxxxx Voice and speechx Cardiovasc, haem, immuno, & respxxx Digestive, metabolic, & endocrinexx Genitourinary and reproductivexx Neuromusculoskeletal and movementxxxx Skin and relatedxICF-CY: Activities and participation Learning and applying knowledgexxxxxx General tasks and demandsxx Communicationxx Mobilityxxxx Self-carexxxx Domestic life Interp interactions and relationshipsxxxxxxxxxxxx Major life areasxxxxxxxxx Community, social, and civic lifexxxxxxxxxOther (not defined by the ICF-CY) General health – Not definedxxx Mental health – Not definedxx Physical health – Not definedxx Environmentxxxxxxx Accidents/injuries Achievements in lifexxxx Being able to do what you want to doxx Challenging/risk-taking behaviorxx Foodx Functional status Functioning of familyxxxxx Future aspirationsxxxx Having fun (enjoyment)xxxxx Health condition/treatment*xxxx Health habitsxx Making decisionsx Quality of lifex Satisfaction with lifexxxxxxxMeasuring functioning or well-being Functioningxxxxxx Well-beingxxxxxxxxxxxxVSP-AWCHMPYQoL-SYQoL-R16D17DAQoL-6DCHU-9DEQ-5D-YHUI2HUI3CHSCS-PSICF-CY: Body functions Mentalxxxxxxxxxxx Sensory and painxxxxxxxx Voice and speechxxxxxx Cardiovasc, haem, immuno, & respxxx Digestive, metabolic, & endocrinexx Genitourinary and reproductivexxxx Neuromusculoskeletal and movementxxx Skin and relatedxxICF-CY: Activities and participation Learning and applying knowledgexxxxx General tasks and demandsxxx Communicationxxxx Mobilityxxxxxxx Self-carexxxxxxx Domestic life Interp interactions and relationshipsxxxxxx Major life areasxxxxxx Community, social, and civic lifexxxxxxOther (not defined by the ICF-CY) General health – Not definedxxxx Mental health – Not definedxxx Physical health – Not defined Environmentx Accidents/injuriesx Achievements in life Being able to do what you want to dox Challenging/risk-taking behaviorx Food Functional statusx Functioning of familyxx Future aspirationsxxx Having fun (enjoyment)xxxx Health condition/treatment*xxx Health habits Making decisionsx Quality of lifex Satisfaction with lifexxxMeasuring functioning or well-being FunctioningxxX*xxxxxxxxx Well-beingX*X*xxX*xxxX*X*X*AQoL-6D, Assessment of Quality of Life Mark 2, 6D adolescents; AUQUEI Ours, Auto Questionnaire Enfant Imagé Child Pictured Self-Report; AUQUEI Soleil, Auto Questionnaire Enfant Image Child Pictured Self Report; Brief MSLSS, Brief Multidimensional Student Life Satisfaction Scale; Cardiovasc, cardiovascular; CHAQ, Child Health Assessment Questionnaire; CHIP-AE, Child Health and Illness Profile – Adolescent Edition; CHIP-CE CRF, Child Health and Illness Profile – Child Edition Child Report Form; CHIP-CE PRF, Child Health and Illness Profile – Child Edition Parent Report Form; CHQ-87, Child Health Questionnaire Self-Report (87 version); CHQ-PF28, Child Health Questionnaire Parent Short Form; CHQ-PF50, Child Health Questionnaire Parent Long Form; CHRS, Children’s Health Ratings Scale; CHSCS, Child’s Health Self-Concept Scale; CHSCS-PS, Comprehensive Health Status Classification System – Preschool; ComQOL-S5, Comprehensive Quality of Life Scale-School version, Fifth edition; COOP, Dartmouth Primary Care Cooperative Information Project; CqoL, Child Quality of Life Questionnaire; CHU-9D, Child Health Utility 9D; DHP-A, Duke Health Profile – Adolescent version; ExQoL, Exeter Quality of Life Measure; EQ-5D-Y, EuroQol five-dimensional questionnaire for youth; FSIIR (Infants), Functional Status II Revised, Long version, infants; FSIIR (Toddlers), Functional Status II Revised, Long version, toddlers; FSIIR (Preschool), Functional Status II Revised, Long version, preschoolers; FSIIR (School age), Functional Status II Revised, Long version, school-age children; FSIIR-7, Functional Status II Revised 7 item; FSIIR-14, Functional Status II Revised 14 item; GCQ, Generic Children’s Quality of Life Measure; HALFS, Health and Life Functioning Scale; HAY, How Are You; HUI2, Health Utilities Index Mark 2; HUI3, Health Utilities Index Mark 3; ICF-CY, International Classification of Functioning, Disability and Health Children and Youth version; Interp, interpersonal; ITQOL (Long), Infant Toddler Quality of Life Questionnaire (long version); ITQOL (Short), Infant Toddler Quality of Life Questionnaire (short version); MSLSS, Multidimensional Student Life Satisfaction Scale; MSLSS-A, Multidimensional Student Life Satisfaction Scale Adolescent version; Nordic QoLQ, Nordic Quality of Life Questionnaire for children; OK.Ado, Adolescent quality of life questionnaire; PedsQL, Pediatric Quality of Life Inventory; PedsQL Infant, Pediatric Quality of Life Inventory Infant scales; PedsQL SF15, Pediatric Quality of Life Inventory Short-Form 15; PWI-PS, Personal Wellbeing Index Preschool; PWI-SC, Personal Wellbeing Index School Children; QOLP-AV, Quality of Life Profile: Adolescent Version; QOLQC, Quality of Life Questionnaire for Children; QUALIN, Infant’s quality of life; resp, response; 17D, 17 Dimensional; 16D, 16 Dimensional; SLSS, Student Life Satisfaction Scale; (T)QOLQA, Quality of Life Questionnaire for Adolescents (Taiwanese version); TAAQOL, TNO-AZL Questionnaire for Adult Health-Related Quality of Life; TACQOL, TNO-AZL Questionnaire for Children’s Health-Related Quality of Life; TAPQOL, TNO-AZL Questionnaire for Preschool Children’s Health-Related Quality of Life; VSP-A, Vécu et Santé Perçue de l’Adolescent; WCHMP, Warwick Child Health and Morbidity Profile; YQoL-R, Youth quality of Life instrument-research version; YQoL-S, Youth quality of Life instrument-surveillance version.⁎Well-being is PGA (phosphoglycerate) assessed as an item assessing emotional functioning, not for other topics.Full-size tableTable optionsView in workspaceDownload as CSV


   May 26

flunixin meglumine br Results br Table provides summary statistics of

Results

Table 2 provides summary statistics of our sample. The mean AGGIR value was 4.70 (2.55% of the patients were GIR1, 10.20% were GIR2, flunixin meglumine 14.80% were GIR3, 11.73% were GIR4, 8.16% were GIR5, and 52.55% were GIR6). The mean ZBI value was 26.76, with large variations among informal caregivers. The mean informal care time produced by primary and secondary caregivers was 136 h/mo. Half of the sample used nonmedical resources (52%), which represented on average €987.39 per month and per patient. Table 2 also shows that flunixin meglumine men represented 65% of our sample and patients’ mean age was 78 years. Primary informal caregivers’ mean age was 69 years, 62% of the informal caregivers were men, and 86% were married (57.65% being patients’ spouses). Most (68%) of the informal caregivers were living under the same roof as patients, and 27% of the patients had a secondary informal caregiver. Note that 46.9% of the primary informal caregivers younger than 65 years were employed (not reported in Table 2). Bivariate analyses reported in Table 2 confirm that patients eligible for APA have greater informal care use (P < 0.01) and that informal caregivers of eligible patients experience greater burden-of-care levels (P < 0.01). Table 2 also shows that eligible patients have greater nonmedical care use (P < 0.01) and expenditure levels (P < 0.01). When comparing eligible with noneligible patients according to their characteristics, we note that eligible patients are older (P < 0.01), have higher CES-D levels (P < 0.05), are more likely to have a friend as an informal caregiver (P < 0.05), and are more likely to have the presence of a secondary informal caregiver (P < 0.01). For all other characteristics (patients’ sex and Cumulative Illness Rating Scale score, informal caregivers’ age, sex, marital status, working status, and living arrangements), we do not find significant differences between eligible and noneligible patients.

Figure 1 shows the distribution of nonmedical care expenditures by APA eligibility. Most of the patients not eligible for APA (GIR5 and GIR6) faced expenditures under €2000/mo, whereas most of the eligible patients (GIR1–GIR4) spent more. For most of them, the four legal thresholds for APA generosity were much smaller than actual expenditures engaged in nonmedical services. Mean monthly expenditures for GIR1, GIR2, GIR3, and GIR4 patients were €2201, €1773, €2220, and €1371, respectively. In our sample, 25 patients had monthly expenditures greater than €3000. The mean AGGIR scale score for growth hormone (GH) patients was 3.52 ± 1.41, and their mean age was 81 ± 6.9 years. All these patients used informal care.

Fig. 1. Distribution of total nonmedical expenditures by APA eligibility. We compare two groups of patients: GIR6-5 vs. GIR4-1 patients. The plain line represents patients not eligible for APA (AGGIR scale categories: GIR5 and GIR6). The dashed-line represents patients eligible for APA (AGGIR scale categories: GIR1, GIR2, GIR3, and GIR4). The vertical lines represent the four legal thresholds for APA generosity: €562.57 per month for GIR4, €843.86 per month for GIR3, €1125.14 per month for GIR2, and €1312.67 per month for GIR1. AGGIR, autonomie gérontologie groupes iso-ressources; APA, allocation personnalisée d’autonomie; GIR, groupes iso-ressources.Figure optionsDownload full-size imageDownload as PowerPoint slide


   May 26

br Ga doped ZnO Thin films Ultrasonic spray pyrolysis Crystalline

Ga-doped ZnO; Thin films; Ultrasonic spray pyrolysis; Crystalline silicon solar cells

1. Introduction

Transparent conducting oxide (TCO) thin films are widely used in display technologies, organic light-emitting diodes, and solar cell applications [1] ;  [2]. In solar cell industries, TCOs are used as an antireflection coating (ARC) and as a transparent electrode material for charge carrier collection. Sanyo, which produces the most efficient solar cells, have shown that TCOs have a good potential for conventional crystalline silicon (c-Si) wafer-based modules [3]. Currently, however, c-Si wafer-based photovoltaics uses insulating AR coatings rather than TCOs [1].

A conventional c-Si cell is fabricated from boron-doped p-type silicon wafers. It contains a phosphorus-doped n+ emitter and an aluminum- or boron-doped p+ layer to form a back-surface field, i.e. it is based on an ARC/(n+pp+)c-Si structure. Various attempts have been made to replace the commonly used dielectric ARC in c-Si cells with a TCO film, based on indium-tin-oxide (ITO) [4]; [5] ;  [6]. In different devices ITO films provide the best compromise between low electrical resistivity and high transparency [7]. The ITO film is not suitable, however, for use on the n+-Si emitter of a TCO/(n+pp+)c-Si cell. For effective use in transparent electrode application in c-Si solar cells, the TCO film should satisfy a set of requirements that include the properties of the TCO/c-Si interface [8]. In particular, TCO should form low-resistance contacts with diffused layers, passivate the silicon surface, and form a highly rectifying contact with the msds of a solar cell when the TCO film is used on the emitter surface and an ohmic contact with the base when it is used on the back-surface field side.

A literature review revealed that the above-mentioned conditions have been poorly studied and interface requirements have not been taken into consideration. For instance, high sheet conductivity of a TCO film increases the probability of shunting the emitter via the pinholes [9] that appear in the actual process of production. The risk of shunting is much increased when a shallow emitter is used [8]. To overcome the problem, the TCO should deactivate the microshunts via a highly rectifying contact with the base. It is also shown [8] ;  [10] that high-quality ITO films form a rectifying contact with n-Si and an ohmic contact with p-Si. Consequently, ITO films can only be used effectively on p+-Si surfaces [8] ;  [11]. It therefore seems reasonable that attempts to construct a high-efficiency solar cell of the TCO/(n+pp+)c-Si structure are predetermined by the choice of ITO.

Thus, it can be concluded that:(1)the advantages of using TCO films in c-Si solar cells have not yet been fully recognized;(2)the purposeful choice of a TCO film that satisfies all the above requirements is a timely issue for Si-wafer based photovoltaics;(3)the deposition technologies for TCO films on silicon substrates and film properties with application to diffused p-n junction c-Si cells have not been sufficiently studied;(4)for c-Si cell applications, it is necessary to optimize the film properties grown on a silicon surface rather than on a glass substrate (because the properties of TCO films depend on the substrate they were grown on [12] ;  [13]).

In recent years, TCO films which have reduced indium content or are In-free have attracted a great deal of attention, thanks to the forecast high cost and scarcity of indium [2]. Thin ZnO films doped with aluminum, indium, and gallium (gallium zinc oxide, GZO) have attracted the most attention. Doping with Ga seems preferable, because the resistance of GZO films is more stable in comparison with other ZnO-based films of the same thickness and produced under the same conditions [14].

Note that the GZO films grown by the above-mentioned techniques, excepting sol–gel processing and spray pyrolysis, have shown ρ values lower than 10−3 Ω cm; we doubt the minimum resistivity (7.6 × 10−4 Ω cm) obtained by Reddy and colleagues [39], because a higher value (1.5 × 10−3 Ω cm) was obtained by calculating the resistivity from their carrier concentration (2.5 × 1020 cm–3) and their mobility (17 cm2 V−1 s−1).


   May 26

br Table nbsp Major structural and surface

Table 1.
Major structural and surface experimental parameters deduced from the XRD and AFM investigations.Used oxideTarget structureFilm structureCrystallites\’ size (nm)Texture degreeDeposition rate (nm/min)Films\’ roughnessCeO2CubicCubic84(111)//26.58.0Eu2O3MonoclinicMonoclinic127, 300–2000Perpendicular23.817.0Gd2O3MonoclinicMonoclinic275None14.68.9H2O3CubicCubic69(844)//17.46.8La2O3MonoclinicMonoclinic307None45.831.8Nd2O3MonoclinicMonoclinic283None21.43.2Sm2O3MonoclinicMonoclinic270None18.77.3Tb2O3MonoclinicMonoclinic290None18.25.8Full-size tableTable optionsView in workspaceDownload as CSV

Fig. 16a–d report the room temperature atomic force microscopy surface profiles of some of the local gas feeding/pulsed laser deposited rare earth nanostructures while Table 1 summarizes their corresponding main surface characteristics. Generally, all deposited films consist of very compact crystallites ranging from 60 to 200 nm in size approximately. This dense morphology seems to be a characteristic of the current local gas feeding/laser deposition as it was sustainably observed in the case of diamond-like carbon coatings deposited by the same process [28]. It can be explained by the fact that the rare earth oxide particles are most likely ejected in the form of fairly massive large molecular clusters from the target as in the standard PLD. In addition, the coalescence of these large molecular clusters on the substrate could be favored by the plume flash radiative heating (because the rates of heating and cooling are fast) as well as the O2 gas feeding at the surface of the substrate as in the case of the Ion Assisted deposition (IAD) endo-iwr process. Indeed, as in IAD where the substrate is exposed to a low-energy ion beam, the local O2 feeding results in less oxygen deficiency without thermalizing upcoming atoms, clusters and particles. The energies of the particles and large clusters hit the substrate surface can be very high; up to 100 eV. In combination with the heated substrate and the high pressure O2 feeding, the atoms arriving at the surface have enough mobility to form dense, crystalline structures, while films made with other deposition techniques such as endo-iwr beam evaporation often show a porous, columnar growth behavior. Based on such a hypothesis, and compared to nanostructures synthesized by other vacuum processing methods, one can indeed explain the high surface roughness observed on all current laser deposited rare earth oxide nanostructures by the local gas feeding/pulsed laser deposition process (Table 1).

Fig. 16. Atomic force microscopy profiles of (a) Gd2O3, (b) CeO2, (c) Eu2O3 and (d) Ho2O3 laser ablated nanostructures/r-cut Al2O3.Figure optionsDownload full-size imageDownload high-quality image (2942 K)Download as PowerPoint slide

Even if there is a slight difference from an oxide to another, the general surface morphological aspect is comparatively similar for the following rare earth oxides: Gd2O3, La2O3, Nd2O3, Sm2O3, and Tb2O3. Fig. 16a reports the surface morphology of Gd2O3 nanostructure. As it was observed on various parts of the film\’s surface, there is a non-negligible ensemble of droplets everywhere on the film surface even if the focus is on major ones. These droplets are inherent to the laser deposition process itself. It is known that these droplets are due to the laser beam–target interaction and the plume formation-plume extension processes. The droplets with size larger than 1 μm can be minimized significantly by using optimum conditions including, for example the surface smoothness of the target and its rotation as well as the optimization of the target–substrate distance. Besides these surface droplets, no particular morphological ordering is observed in this sequence in contrast to CeO2, Ho2O3 and Eu2O3 nanostructures where there are clearly distinctive surface features. More specifically, the surface appearance of CeO2 and Ho2O3 films differs considerably from the other rare earth oxides. As reported in Fig. 16b and c, CeO2 and Ho2O3 nanostructures exhibit, both, a specific trend. The corresponding crystallites are more spherical-like in shape with a mono-disperse size distribution. The average value of their size is of 〈Ø〉 ∼84 ± 1 nm and ∼69 ± 2 nm for CeO2 and Ho2O3 respectively. In both cases, these crystallites seem to grow in a chain-like configuration as well as a defined preferential crystallographic orientation relatively to the basal direction of the sapphire substrate surface. This behavior is more pronounced in the case of CeO2 sample and seems to be inherent to textured growth of the CeO2 and Ho2O3 onto sapphire. This textured growth is likely favored by the small crystalline mismatching of CeO2 and Ho2O3 with the sapphire as confirmed further by XRD investigations. The hexagonal structures of sapphire substrate exhibit lattice constants of aAl2O3 ∼4.759. The lattice matching can be achieved by using different crystallographic orientations for the film and the substrate, because the cubic lattice of the sesquioxides provides a hexagonal arrangement of the ions in the 111 planes of the cubic unit cell. Thus, the relation for lattice matching is 3 × asubstrate ∼ √2afilm. The lattice mismatch for CeO2 on sapphire is <0.7%. With reference to Eu2O3 nanostructure, the surface morphology is more complex as reported in Fig. 16d. A special feature that distinguishes the morphology of Eu2O3 from all deposited oxide samples, is the existence of large columnar planes transversal to the sapphire substrate. More accurately, Eu2O3 exhibits localized very large oriented crystallites embedded in disordered packed crystallites of about ∼127 nm in diameter. The very large crystallites seem to grow in a pyramidal mode “basal plane ∼ 2 μm, transversal height ∼0.3 μm”. This preferential orientation perpendicular to the substrate may be inferred to a possible preferential growth of Eu2O3 on sapphire as well, but partially compared to CeO2 and Ho2O3. The triangular (2D) or pyramidal (3D) crystallite structure observed in the Eu2O3 films represents the crystallographic cubic-fluorite structure of its bulk form and can be assigned therefore to the 〈111〉 growth direction. A similar surface morphology with pyramidally shaped grains was also observed in the case of Yttria [109]; [110]; [111]; [112]; [113]; [114] ;  [115].


   May 26

br It is observed by naked

It is observed by naked eyes that, the color of the PEO layer changed mainly for three steps. Firstly, the layer looked gray with the PEO treating for 5 min. Then, it turned to brown after 9 min treatment. Finally, the layer became black by the end of the oxidation.

3.3. endothelin receptor antagonist Coloring analysis

Fig. 5 shows the atomic percent of the main elements on the surface of the sample with different PEO treating time. Over the first 5 min oxidation, the Al content decreased form 93.07 to 24.42 at.%, while the O content increased from none to 58.56 at.%. The Si concentration has a fluctuation, it first increased but then decreased. No P, V or W was found at the first 2 min, but each of their amount was about 4.3–5.8 at.% by the end of the 5th min. Then, till the end of the PEO process, the content of Al, O and Si merely had a small change, but the P concentration decreased apparently. The V content increased from 5.79 to 7.84 at.%, but the W concentration was decreased tiny from 4.35 to 3.39 at.%. Fig. 6 is the distribution of elements on the surface of the final black PEO layer. As it can be seen, the distribution of Al, O, Si or V is much more homogeneous than that of P or W.

Fig. 5. Element composition on the surface of the PEO layer by different treating time.Figure optionsDownload full-size imageDownload high-quality image (209 K)Download as PowerPoint slide

Fig. 6. EDX analysis of the black PEO layer: (a) surface SEM micrograph; (b–g) endothelin receptor antagonist distribution of Al, O, Si, P, V and W, respectively.Figure optionsDownload full-size imageDownload high-quality image (1094 K)Download as PowerPoint slide

The elements variation across the profile of the black PEO layer is exhibited in Fig. 7. The result shows that, the elements existing in the coating is in accordance with that on the surface, but their gradient concentration varied from the inner to the outer layer has no obvious regularity. The content of P, V or W is less than the other three elements due to them just come from the transformation of electrolyte [9]. Some of them reach the interface of the alloy and layer through diffusion. Besides, across the whole layer, the V concentration is always higher than that of W.

Fig. 7. EDX analysis of the black PEO layer: (a) profile SEM micrograph, (b) element distribution.Figure optionsDownload full-size imageDownload high-quality image (440 K)Download as PowerPoint slide

Fig. 8. XPS analysis for the black PEO layer: (a) V2p and (b) W4f.Figure optionsDownload full-size imageDownload high-quality image (286 K)Download as PowerPoint slide

Fig. 9 reveals the phase constitute of the black PEO coating. Except the aluminum peak and silicon peak which are mainly from the substrate, γ-Al2O3, Al–Si–O compounds and some SiO2 amorphous phase are the major components of the layer [13]. However, no oxides of phosphorus, vanadium or tungsten was detected, for their content in the layer is too little (Fig. 7).

Fig. 9. Phase constitute of the black PEO layer.Figure optionsDownload full-size imageDownload high-quality image (184 K)Download as PowerPoint slide

3.3.4. Coloration mechanism

V2O3 exists on the surface is one of the reason for the layer that shows a black color [7]. Besides, WO2 also contributes to the black, for it looks sepia. Actually, the PEO layer prepared with Na2WO4 also looks black [8]. In addition, according to the principle of three primary colors and the CIELAB color space model (Fig. 1), the mixture of other compounds existing in the layer may also be responsible for the black color. Because, the vanadium oxides (V2O5) and tungsten oxides (WOx, x = 2.7–3) are all colored, their combination may also give rise to the black color. But this speculation need further study to be confirmed. However, it is sure that, VO3- plays a greater role than WO42− in the coloring, on that the V concentration on the layer surface is much higher than W, and its distribution is also more uniform than that of W, which can be seen in Fig. 5 ;  Fig. 6.


   May 26

br XPS analysis was conducted on selected coatings for the

XPS analysis was conducted on selected coatings for the determination of the chemical states of molybdenum and nitrogen. Coatings MoN3, N3, MoN7 and N7 were selected as examples of doped/co-doped coatings deposited with low and high nitrogen flows, respectively. Fig. 6 shows curve fitting for the Mo3d spectra of coatings MoN3 and MoN7, respectively. As a result of the peak fitting, two peaks at the binding energies of 231.8 and 234.9 eV can be observed for both of the samples, which can be assigned to Mo3d5/2 and Mo3d3/2 photoelectrons, respectively.

Fig. 6. Curve fitting on Mo3d XPS spectra of coatings MoN3 (left) and MoN7 (right).Figure optionsDownload full-size imageDownload high-quality image (216 K)Download as PowerPoint slide

Fig. 7. Curve fitting on Mo3p and N1s XPS spectra of coatings MoN3 (left) and MoN7 (right).Figure optionsDownload full-size imageDownload high-quality image (224 K)Download as PowerPoint slide

Therefore it can be concluded that in the co-doped samples, molybdenum co-exists in the form of Mo6+, as well as Mo–N–Ti (judging from its 3d and 3p binding energies). The N1s peak at a binding rolipram cost of 399 eV is usually assigned to substitutional nitrogen incorporated into the titanium dioxide lattice via O–Ti–N linkage [20].

Comparing the XPS patterns of the co-doped coatings with the patterns of the nitrogen-doped coatings (Fig. 7 ;  Fig. 8, respectively), it can be concluded that co-doping resulted in changes of the chemical state of the N species. The peaks seen in the XPS spectra of samples N3 and N7 can be deconvoluted into three peaks at binding energies of 397.5, 399.1 and 402.8 eV (403.5 eV for N7). As in the case of the co-doped samples, the peak at 399 eV can be interpreted as N1s substitutional nitrogen; while the other two peaks can be assigned to Ti–N and Ti–O–N (interstitial nitrogen), respectively.

Fig. 8. Curve fitting on N1s XPS spectra of coatings N3 (left) and N7 (right).Figure optionsDownload full-size imageDownload high-quality image (223 K)Download as PowerPoint slide

3.5. Band-gap calculation

Optical band-gap values of the coatings were calculated using the Tauc plot method [21] by plotting (αhν)1/2 versus hν and extrapolating the linear region to the abscissa (where α is the absorbance coefficient, h is Planck\’s constant, ν is the frequency of vibration).

Examples of the band-gap calculation for the N-doped and Mo–N co-doped coatings are presented in Fig. 9. The calculated values of the optical band-gaps are given in Table 2.

Fig. 9. Example of the optical band-gap calculation for N-doped and Mo–N co-doped titania coatings annealed at 873 K.Figure optionsDownload full-size imageDownload high-quality image (162 K)Download as PowerPoint slide

It can be seen Procentriole neither N-doping, nor co-doping with N and Mo has a significant effect on the value of the band-gap. The lowest band-gap value obtained through the entire experimental array was 3.04eV (for sample MoN7), which corresponds to light with a wavelength of 408 nm. Generally, the co-doped coatings exhibited a higher band-gap shift towards the visible range than the N-doped coatings.

3.6. Photocatalytic activity

Some examples of MB 665 nm absorbance peak decay plots are given in Fig. 10. The values of first order rate constant of the MB decomposition under UV, fluorescent and visible light are given in Table 2. Values of photocatalytic activity of undoped titania and Mo-doped titania are given for reference purposes.

Fig. 10. Example of MB 665 nm absorbance peak decay for selected coatings.Figure optionsDownload full-size imageDownload high-quality image (138 K)Download as PowerPoint slide


   May 26

br Since DS must couple phonons and photo excited

Since DS must couple phonons and photo-excited electrons with specific wavevectors to occur, the changes resulted from the TDS experiment are now selecting photo-excited electrons with different wavevector, which couples with softer breathing-mode phonons, downshifting the D band.

Finally, it is worthwhile to note that though the radius of the Xe atoms are almost 60% larger than the C px12 radius, the linewidths of both the G and D bands (ΓG and ΓD) suffer a decrease after the processes of desorption, Table 1. Thus, the expected disorder promoted by the Xe atoms during the diffusion process through the a-GLC matrix, displacing C atoms and breaking some C–C bonds, has not been reflected in the values of the Raman ΓD and ΓG, which displayed reductions of around 8% and 1%, respectively. It is likely that a compensating process, activated by the thermal heating, is taking place and promoting a more organized and restructured network, even after the Xe desorption with its perturbing effects on the matrix.

The process of TDS consists of driving the system out of its thermodynamic equilibrium condition by the action of some external agent. Through the monitoring of the system behavior along its displacement from the thermodynamic equilibrium, it is possible to determine its thermodynamic parameters, such as the diffusion coefficient and the diffusion free energy [77] ;  [78].

Fig. 6 displays the Xe thermal desorption profiles (thermograms) for five different heating rates (5, 10, 20, 30 and 37 °C/min). The Xe atoms start to effuse from the a-GLC matrix at approximately 120 °C. This information is very important considering the problems involving the effusing temperature onset of radioactive species, which must be ensured to a certain inferior limit in biological applications or in applications where the environment cleanness must be preserved.

Fig. 6. Xenon thermograms of [email protected] sample heated up at 5 different rates: 5, 10, 20, 30 and 37 °C/min. The black dotted and dashed gray lines emphasize, respectively, the center band position upshift as the heating rate is enhanced and the temperature where the Xe starts to effuse. The experimental signal (black continuous line) was theoretically adjusted (red continuous line) by means of two Gaussian functions (eq. (1)), G1 (blue dashed line) and G2 (green continuous line), that are associated with two different xenon desorption regimes, i.e., at low and at high temperatures, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)Figure optionsDownload full-size imageDownload high-quality image (870 K)Download as PowerPoint slide

Based on the asymmetric shape of the thermograms, at least two distinct Xe desorption regimes can be identified. They are ascribed to low (regime 1) and high (regime 2) temperature features.

To decompose the experimental Xe desorption signal (Fig. 5 – black continuous line) we apply a Gaussian function (eq. (1)) to each regime. Non-linear least-squares adjustments were applied to recover the fitted parameters for both regimes, such as the maximum signal amplitude, the linewidth and the center positions. The results of these processes are also presented in Fig. 6, where G1 (blue dashed line) and G2 (green continuous line) represent the two resolved Xe desorption regimes. The red continuous line represents the final adjustment. As the heating rate (β) increases, the thermograms show a temperature upshift, as emphasized by the black dotted line.

Lacerda et al. [79] reported pertinent X-ray (XANES/EXAFS) results of noble gases (NG) implanted in a-GLC matrices ([email protected]), where the NG atoms are squeezed by the lattice, forming two-dimensional clusters. However, these NG clusters were only observed in samples with an internal compressive pressure higher than 3 GPa. For films with smaller stresses, such as the [email protected] sample, Table 1, the X-ray absorption spectra resemble that of free NG atoms. In other words, for low-stressed a-GLC matrices, the Xe atoms are individually distributed (trapped) within the graphitic planes (graphenes).


   May 26

br This exploratory study examined and compared the

This exploratory study examined and compared the effect of including the three bolt-on dimensions on the EQ-5D health states. For future bolt-on studies, we recommend (1) conducting a valuation of a larger number of health states selected on the basis of statistical theory (e.g., the orthogonal design) to understand the impact of the additional dimensions and the development of a value algorithm; (2) exploring more complex models (rather than the additive one) to incorporate the severity of the EQ-5D health states and the impact of the bolt-on dimensions. The design of valuation studies should reflect this issue. We also recommend that the standard process be fully applied to develop and test psychometric performance of the bolt-on dimensions at a certain stage. We note, however, that bolt-ons need to have an impact on valuations and have good psychometric properties, and the order in which the two criteria are examined depends on the focus of the study and is indeed a Bafetinib of which is most efficient. This study was designed to be the first stage of the valuation research to assess the impact, including the direction of impact, of bolt-on dimensions on health states covering a range of severity. The second stage of the research would be to undertake a full valuation study using an orthogonal design that will include more health states and a larger sample of respondents, and to facilitate an estimate of the value algorithm for bolt-on measures.

AcknowledgmentsThis study forms part of the NICEQoL project to investigate the use of generic and condition-specific measures of health within the NICE decision-making process. The NICEQoL project was funded by the Medical Research Council-National Institute for Health Research Methodology Research programme (reference no. G0901486). Yaling Yang was funded by the NIHR Oxford Biomedical Research Centre during the final preparation of Bafetinib the manuscript. We thank the interviewers from the Centre for Health and Social Care Research, Faculty of Health and Wellbeing at Sheffield Hallam University and all the respondents for their participation in the study.

chronic disease; health-related quality of life; noncommunicable diseases; quality-adjusted life-year

Introduction

Although communicable diseases remain a global public health concern, the global disease profile has shifted from communicable diseases to noncommunicable diseases (NCDs), such as ischemic heart disease, cancer, and stroke [1]. NCDs have been problematic in developed countries, but they are now global pandemics with considerable effects in developing countries [2]. In 2010, NCDs accounted for 65% of total global deaths [3] and mortality due to NCDs is expected to continue to increase because of population growth and aging.

Measuring disease burden is the first step toward establishing health service and research priorities for NCDs [4]. There are various methods for measuring disease burden, from epidemiologic indicators (such as mortality) to summary measures that reflect both quantity and quality of life such as quality-adjusted life-year (QALY), quality-adjusted life expectancy (QALE), or disability-adjusted life-year (DALY) [5] ;  [6]. Among these, QALY can integrate the impact of disease on mortality and morbidity into a single index, thereby allowing comparisons between different disease areas [5]. Similar to life expectancy, QALY can be differently expressed using QALE, which is the total QALYs throughout the remainder of the expected life at a certain age [7].


   May 26

Dynamic simulation modeling can be applied to a

Dynamic simulation modeling can be applied to a range of health care delivery system problems:a.Simulation modeling can estimate the consequences of health care delivery system interventions: Many interventions in health care have impacts on the health care delivery system that are not typically considered in health economic models. Simulation modeling can better estimate the downstream and upstream consequences once a health policy or delivery intervention is implemented, accounting for feedback loops and interdependencies to characterize the adaptive nature of the health care delivery system. These models can also be used to dynamically estimate the consequences of demographic change, or, for instance, aging of the population [54].b.Simulation modeling allows the incorporation of behavioral aspects and personalized health care decisions: One of the advantages of dynamic simulation models is that they are flexible in the definition of either “health states” or “events” [55] ;  [56]. This enables a more realistic representation of the unique pathways of individual patients through the health care system as well as the health states they currently experience. Patients make decisions about when they will see a doctor, whether they will comply with their medication regimen, or whether they are willing to co-pay for expensive treatment. Dynamic simulation models in general, and ABM in particular, allow flexibility to incorporate the dynamics of people making decisions affecting population health outcomes, and thus efficient planning of health care interventions. Pombo-Romero et al. [57] developed an ABM to show social interaction to explain the use and selective serotonin reuptake inhibitors of new drugs in a regional health care system. Such ABMs account for behavioral interactions between patients, physicians, and pharmacists regarding prescriptions.c.Simulation models are flexible to consider consequences of comorbidities and health care utilization: Most health economic models assume an underlying disease for which a treatment is evaluated. Many people with chronic diseases, however, suffer from multiple morbidities and experience multiple episodes of interactions with the health care system. Dynamic simulation models may also incorporate subroutines to model physiological interactions in the body that affect treatment outcomes and health care demand. For instance, Sabounchi et al. [58] created a system dynamics model specific to weight gain and obesity in women undergoing fertility treatment. The model includes several physiological subsystems that may affect body weight.The potential advantage is that networks of related diseases can be defined similar to networks of underlying genetic mutations and networks of social activities [59]. If such underlying physiological responses networks can be identified and modeled, the consequences of health care delivery interventions on the health system can be evaluated more precisely, taking into account time dependency.d.Simulation models can consider the spatial consequences of a health care delivery intervention: Many health care interventions also have a spatial component, such as infectious disease policies [60] or remote health services such as telemonitoring. If health services are delivered at home, or if general hospitals specialize into health care centers, this has a large impact on the number of patients traveling to health care facilities. At the least, geographic range will impact the case-mix of patients in the hospital, and dynamic simulation modeling can be applied to estimate the consequences on hospital admissions and support further capacity planning [61]. One specific application is queuing and waiting list management in hospitals. Troy and Rosenberg [62] used a dynamic simulation model to determine the need for intensive care unit (ICU) beds for surgery patients. The background for the study was an increase in the number of patients admitted to the hospital for emergency care as the hospital developed into a tertiary care facility. The increase in acute patient admissions led to an increase in the need for ICU beds. Dynamic simulation modeling was used to estimate the required number of ICU beds on the basis of available surgeons and the expected number of patients admitted to the hospital.e.Simulation modeling addresses system problems that are too complex to enable an analytic solution: Health care consists of multiple complex systems. The inherent feedback loops that reflect interactions and interdependencies among the operations, structures, and relationships in the health care system evolve dynamically over time and cannot always be captured in an analytic solution. But simulation methods can be used to model such relationships.


   May 26

FRAX597 Supplier br Vaccines play an important

Vaccines play an important role in preventing HPV transmission, infection, and induced diseases. Currently, a quadrivalent vaccine (including HPV genotypes 16, 18, 6, and 11) and a bivalent vaccine (genotypes 16 and 18) are available. In Italy, girls aged 9 to 26 years have the opportunity to routinely receive an HPV vaccine [8]. When compared with the bivalent vaccine, the quadrivalent vaccine shows a higher efficacy, protects against a higher variety of HPV-induced diseases (including anogenital warts) [9], and as a consequence is more cost-effective. The cost-effectiveness of different HPV vaccination schemes (in addition to screening programs) has been evaluated by a large body of modeling studies [10]; [11] ;  [12]. The results for universal vaccination strategies, however, have not been conclusive [13] ;  [14], and uncertainty associated with the main parameters of commonly used models has a large influence on results obtained.

In addition, an important factor in cost-effectiveness analyses of vaccines is the impact of herd immunity [15]. Herd immunity implies that nonvaccinated subjects are protected indirectly as a consequence of decreasing overall prevalence of the infectious disease in the population. Because HPV is highly prevalent in sexually active populations [16]; [17] ;  [18], universal vaccination (i.e., including males) is highly likely to lead to a more rapid FRAX597 Supplier in the burden of HPV-induced disease than sex-specific vaccination [12]; [19]; [20]; [21]; [22] ;  [23].

Markov models (MMs) are often used in cost-effectiveness analysis to model the disease progression through a set of health states. It is not, however, easy to embed the effects of herd immunity in a standard MM. Furthermore, standard MMs are commonly deterministic and therefore do not address FRAX597 Supplier issues relating to uncertainty. In infectious disease transmission modeling, parameters naturally incorporate a large amount of uncertainty because it is often impractical or even impossible to collect experimental data on most influential parameters (e.g., the probability of pathogen transmission). As a consequence, only limited evidence is typically available, or clinical experts have to be consulted. A Bayesian statistical approach that formally includes previous information taken from several data sources as well as expert opinion can be used to construct a probabilistic MM to characterize the uncertainty associated with the outcomes [24] ;  [25], effectively providing probabilistic sensitivity analysis (PSA) “for free” once the model has been run.

The aim of this study was to evaluate whether female-only vaccination or universal vaccination is the most cost-effective intervention against HPV; cervical screening was included in both interventions. To account for the effects of herd immunity, we incorporated dynamic interactions between individuals into a Bayesian MM. Some of the fundamental data (e.g., costs, some of the utility measures, and the population structure) are specific to the Italian context. Nevertheless, because many of the basic parameters (e.g., those related to vaccination effectiveness) are taken from the published literature, the model is easily extended to other comparable health care systems, such as the United Kingdom and continental European countries.

Methods

Analytical Overview

An empirically calibrated static Bayesian MM for the assessment of the cost-effectiveness of a multicohort HPV vaccination strategy was presented by Favato et al. [26]. Here, the original model was extended by including 1) a module for males; 2) population dynamics in an open model structure; 3) various HPV-induced diseases a###http://www.apexbt.com//media/diy/images/struct/B1406.png####ffecting the vulva, vagina, anus, penis, head/neck, and external genital area; and 4) the dynamic effects of sexual mixing to account for herd immunity. The incidence and prevalence predicted by the model were calibrated using data on age-specific incidence [27] and prevalence [28] obtained from the literature.