Gandouling induces GSK3ß promoter methylation to improve cognitive impairment in Wilson's disease.

ETHNOPHARMACOLOGIC SIGNIFICANCE: Cognitive impairment is a serious clinical manifestation of Wilson's disease (WD) in the nervous system. Gandouling (GDL) is a hospital preparation of the First Affiliated Hospital of Anhui University of Chinese Medicine. Previous studies have found that GDL has an ameliorative effect on cognitive impairment in WD. AIM OF THE STUDY: We aimed to explore the molecular-level regulatory mechanisms underlying cognitive impairment in WD, and provide evidence supporting GDL as a promising candidate drug for the treatment of cognitive impairment in WD. We found that GSK3ß was significantly up-regulated in the brain tissue of C3He-Atp7Btx-J/J (tx-j) mice in the WD gene mutant model, and the monomer components of GDL could combine well with GSK3ß. Therefore, in this work, we used Behavioral tests, Hematoxylin and eosin (H&E), Nissl and Terminal deoxynucleotidyl transferase dUTP-biotin nick end labeling(TUNEL) staining, Ultrastructural morphological observation by Transmission electron microscopy (TEM), bisulfite sequencing (BSP), Quantitative real-time polymerase chain reaction (RT-qPCR), Western blot, immunofluorescence, network pharmacology, molecular docking, and related methods to study the effects of GDL in tx-j mice and HT22 cell to clarify the effect of GDL on cognitive impairment in WD. RESULTS: In this study, MWM, NOR, H&E, Nissl TUNEL and TEM results showed that GDL could promote the repair of learning and memory function, improve the morphological damage to hippocampal neurons, and maintain mitochondria integrity. In the HT22 cell experiment, the CCK-8 method showed that GDL increased the viability of copper-overloaded cell models. The study found that GSK3ß may be a target of GDL for the treatment of WD cognitive impairment through network pharmacology. Western blot and qRT-PCR results confirmed that GDL significantly increased the expression of proteins and mRNA in DNMT1, Nrf2, and HO-1. BSP showed that GSK3ß promoter methylation was lower in the Model group than in the control group, and the promoter methylation of GSK3ß was further reduced after intraperitoneal injection with decitabine, and GDL could ameliorate this pathology. CONCLUSION: GDL demonstrates a protective role by inducing GSK3ß promoter methylatio, regulating the GSK3ß/Nrf2 signaling pathway in WD.

Direct shear behaviors of excavated clay reinforced with geocomposite drainage layer encapsulated in thin sand layers.

The objective of this study is to assess the effectiveness of a novel structure comprising a geocomposite drainage layer and a thin sand layer (GDL + sand) in mitigating the rapid dumping of excavated clay and its associated issues, such as landslides. Two sets of direct shear tests were conducted to investigate the influence of sand layer thickness and compaction degree on the interface shear behavior of the GDL + sand structure. As the sand layer thickness increased, both the interface shear strength and friction angle gradually increased, first more sharply and then at a slower rate toward stability, while the interface cohesion decreased gradually. The optimal sand layer thickness for achieving the most effective reinforcement in stabilizing the clay was identified as 10 mm. A higher sand layer compaction degree was found to result in increased interface shear strength, interface friction angle, and interface cohesion. Building on these findings, the reinforcing efficiency of the GDL + sand structure was investigated through mechanism analysis in comparison to that of a geogrid + sand structure and GDL structure as per the interface friction coefficient. The ranking of interface friction coefficients among the three structures emerged as: geogrid + sand > GDL + sand > GDL. These results suggests that the GDL + sand structure exhibits superior reinforcement efficiency compared to the GDL structure and offers better drainage efficiency than the geogrid + sand structure.

Manufacturing protocol and post processing of ultra-thin gas diffusion layer using advanced scanning techniques.

The typical commercial size of a Gas Diffusion Layer (GDL) for Proton Exchange Membrane Fuel Cell (PEMFC) application is around 180 µm up to 290 µm. GDL facilitates the diffusion of reactants to the catalyst layers and liquid removal from the membrane to the flow field. In this regard, GDL should be a porous region with conductive materials as thin as possible to reduce the size and the costs. Lowering the thickness of the GDL also results in better performance of the stack since it increases the speed of reactants to reach the catalysts. However, the main obstacle is the formation of ultra-thin porous GDL, which can be also named as standalone microporous layer (MPL). The novelty of this study is the manufacturing process and production of ultra-thin porous GDL with carbon and Polytetrafluoroethylene (PTFE) as the main materials. The produced GDL has the thickness of 28.9 µm, which has been measured using microscope imaging. This novel GDL can be used as the conductive diffusive region inside the PEM fuel cells, Alkaline fuel cells, and the cathode of PEM and Alkaline electrolyzers. Additionally, the novel invention can be considered as a 2D membrane for carbon capture purposes after being functionalized.

Legume milk-based yogurt mimetics structured using glucono-δ-lactone.

The potential to produce protein-structured vegan yogurts with legumes was explored to offer an alternative to conventional polysaccharide-based varieties. Glucono-δ-lactone (GDL) was employed as a slow acidifying agent and was investigated for its ability to generate cold-set, yogurt-like gels using soy and lentil milks made using minimal processing steps. Soy (5.3 % protein) and lentil (6.1 % protein) milks were successfully gelled by GDL at concentrations of 0.5 % and 1 % w/w. Soy and lentil milks experienced similar acidification profiles and demonstrated good fits with double-exponential decay models. The physical properties of these legume gels were evaluated and compared to a commercial stirred dairy yogurt. Penetration tests were carried out on intact gels, then repeated after stirring. All intact soy samples demonstrated significantly stronger gel structures compared to the commercial yogurt, and most experienced greater amounts of brittleness. Results showed that the stirring of gels caused a notable decrease in firmness and brittleness in the soy gels, making them more similar to the control. Power-law modelling of viscosity curves demonstrated that all samples experienced non-Newtonian flow behavior (n < 0.29). Susceptibility to syneresis was measured by the degree of liquid loss following centrifugation. The optimization of protein type and GDL concentration to replicate the physical properties of dairy-based yogurts can enhance their consumer acceptance and provide a more customizable and controlled approach alternative to traditional fermentation methods.

Quantitative measurement and comparison of breakthroughs inside the gas diffusion layer using lattice Boltzmann method and computed tomography scan.

In Proton Exchange Membrane Fuel Cells (PEMFCs), the presence of residual water within the Gas Diffusion Layer (GDL) poses challenges during cold starts and accelerates degradation. A computational model based on the Lattice Boltzmann Method (LBM) was developed to consider the capillary pressure inside the PEMFC and to analyze the exact geometries of the GDLs, which were obtained using the Computed Tomography scan. The novelty of this study is to suggest a methodology to compare the quantitative water removal performance of the GDLs without long-term experimental testing. Two different samples of GDLs were considered, pristine and aged. The results of quantitative measurements revealed the amount of water columns (breakthroughs) inside each sample. Considering the volume of 12,250,000 µm3 for each sample, the pristine and the aged samples are prone to have 774,200 µm3 (6.32%) and 1,239,700 µm3 (10.12%) as water columns in their porous domain. Micro-structural properties such as connectivity, mean diameter, effective diffusivity, etc. were also compared to observe the impacts of aging on the properties of the GDL.

Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data.

Drug repurposing has gained the attention of many in the recent years. The practice of repurposing existing drugs for new therapeutic uses helps to simplify the drug discovery process, which in turn reduces the costs and risks that are associated with de novo development. Representing biomedical data in the form of a graph is a simple and effective method to depict the underlying structure of the information. Using deep neural networks in combination with this data represents a promising approach to address drug repurposing. This paper presents BEHOR a more comprehensive version of the REDIRECTION model, which was previously presented. Both versions utilize the DISNET biomedical graph as the primary source of information, providing the model with extensive and intricate data to tackle the drug repurposing challenge. This new version's results for the reported metrics in the RepoDB test are 0.9604 for AUROC and 0.9518 for AUPRC. Additionally, a discussion is provided regarding some of the novel predictions to demonstrate the reliability of the model. The authors believe that BEHOR holds promise for generating drug repurposing hypotheses and could greatly benefit the field.

Insights into the Acid-Induced Gelation of Original Pectin from Potato Cell Walls by Gluconic Acid-δ-Lactone.

The acid-induced gelation of pectin in potato cell walls has been gradually recognized to be related to the improvement in the cell wall integrity after heat processing. The aim of this study was to characterize the acid-induced gelation of original pectin from a potato cell wall (OPP). Rheological analyses showed a typical solution-sol-gel transition process of OPP with different additions of gluconic acid-δ-lactone (GDL). The gelation time (Gt) of OPP was significantly shortened from 7424 s to 2286 s. The complex viscosity (η*) of OPP gradually increased after 4000 s when the pH was lower than 3.13 and increased from 0.15 to a range of 0.20~6.3 Pa·s at 9000 s. The increase in shear rate caused a decrease in η, indicating that OPP belongs to a typical non-Newtonian fluid. Furthermore, a decrease in ζ-potential (from -21.5 mV to -11.3 mV) and an increase in particle size distribution (from a nano to micro scale) was observed in OPP after gelation, as well as a more complex (fractal dimension increased from 1.78 to 1.86) and compact (cores observed by cryo-SEM became smaller and denser) structure. The crystallinity of OPP also increased from 8.61% to 26.44%~38.11% with the addition of GDL. The above results call for an investigation of the role of acid-induced OPP gelation on potato cell walls after heat processing.

Effect of atmospheric cold plasma treatment on acid gelation properties of skim milk: Rheology and textural studies.

Cold plasma processing is a non-thermal food processing technique that has been shown to improve the gelling properties of plant proteins by altering their structure through oxidation and crosslinking. This study aimed to investigate the effects of cold plasma treatment on the rheological properties of skim milk under different conditions, focusing on the impact of feed gas and treatment time on skim milk's sulfhydryl content, flow properties, and acid gelling behavior. Results showed that free sulfhydryl content decreased with treatment time, with a notable reduction observed after 2 min of N2-O2 plasma treatment. Skim milk treated with N2 plasma experienced a more gradual decrease in free SH content. Cold plasma increased skim milk viscosity over time. N2-O2 plasma treatment significantly affected G'40 and G'4 storage moduli, with an increase observed after 2 min of exposure but no change beyond that time. Acid gels' greenness (a* value) decreased with increasing treatment time compared to the control. Acid gel firmness of milk treated with N2-O2 plasma for 1 min significantly increased from 1.804 N to 1.912 N, and further to 2.072 N after 2 min of treatment. However, longer exposure times led to lower firmness in gels. N2 plasma treatment also significantly impacted acid gel firmness. Syneresis in acid gels decreased from 63.4 % to 57.7 % and 58.7 % after 1 and 2 min of N2-O2 plasma treatment, respectively, but increased to about 70 % after 4 min. Acid gels made from milk treated with N2 plasma experienced considerably less syneresis. The cold plasma treatment under different conditions significantly affected the properties of skim milk, with various impacts on sulfhydryl content, flow properties, and acid gelling behavior. These findings demonstrate the potential applications of cold plasma processing in the food industry to improve product properties.

Porous Material (Titanium Gas Diffusion Layer) in Proton Exchange Membrane Fuel Cell/Electrolyzer: Fabrication Methods & GeoDict: A Critical Review.

Proton exchange membrane fuel cell (PEMFC) is a renewable energy source rapidly approaching commercial viability. The performance is significantly affected by the transfer of fluid, charges, and heat; gas diffusion layer (GDL) is primarily concerned with the consistent transfer of these components, which are heavily influenced by the material and design. High-efficiency GDL must have excellent thermal conductivity, electrical conductivity, permeability, corrosion resistance, and high mechanical characteristics. The first step in creating a high-performance GDL is selecting the appropriate material. Therefore, titanium is a suitable substitute for steel or carbon due to its high strength-to-weight and superior corrosion resistance. The second crucial parameter is the fabrication method that governs all the properties. This review seeks to comprehend numerous fabrication methods such as tape casting, 3D printing, freeze casting, phase separation technique, and lithography, along with the porosity controller in each process such as partial sintering, input design, ice structure, pore agent, etching time, and mask width. Moreover, other GDL properties are being studied, including microstructure and morphology. In the future, GeoDict simulation is highly recommended for optimizing various GDL properties, as it is frequently used for other porous materials. The approach can save time and energy compared to intensive experimental work.

Experimental and Computational Study of Optimized Gas Diffusion Layer for Polymer Electrolyte Membrane Electrolyzer.

Polymer electrolyte membrane fuel cells (PEMFCs) and PEM electrolyzer are emerging technologies that produce energy with zero carbon emissions. However, the commercial feasibility of these technologies mostly relies on their efficiency, which is determined by individual parts, including the gas diffusion layer (GDL). GDL transfers fluid and charges while protecting other components form flooding and corrosion. As there is a very limited attention toward the simulation work, in this work, a novel approach was utilized that combines simulation and experimental techniques to optimize the sintering temperature of GDL. Ti64 GDL was produced through tape casting, a commercial method famous for producing precise thickness, uniform, and high-quality films and parameters such as slurry composition and rheology, casting parameters, drying, and debinding were optimized. The porosity and mechanical properties of the samples were tested experimentally at various sintering temperatures. The experimental results were compared with the simulated results achieved from the GeoDict simulation tool, showing around 96% accuracy, indicating that employing GeoDict to optimize the properties of Ti64 GDL produced via tape casting is a critical step towards the commercial feasibility of PEMFCs and electrolyzer. These findings significantly contribute to the development of sustainable energy solutions.

Integrated Metabolomics and Network Pharmacology to Reveal the Mechanisms of Gandouling Tablets Against Copper-Overload-Induced Neuronal Injury in Rats with Wilson's Disease.

Purpose: Gandouling Tablets (GDL), a proprietary Chinese medicine, have shown a preventive effect against Wilson's disease (WD)-induced neuronal damage in previous studies. However, the potential mechanisms need additional investigation. Combining metabonomics and network pharmacology revealed the GDL pathway against WD-induced neuronal damage. Methods: The WD rat model with a high copper load was developed, and nerve damage was assessed. Total metabonomics was used to identify distinct hippocampus metabolites and enriched metabolic pathways in MetaboAnalyst. The GDL's possible targets against WD neuron damage were then determined by network pharmacology. Cytoscape constructed compound metabonomics and pharmacology networks. Moreover, molecular docking and Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) validated key targets. Results: GDL reduced WD-induced neuronal injury. Twenty-nine GDL-induced metabolites may protect against WD neuron injury. According to network pharmacology, we identified three essential gene clusters, of which genes in cluster 2 had the most significant impact on the metabolic pathway. A comprehensive investigation identified six crucial targets, including UGT1A1, CYP3A4, CYP2E1, CYP1A2, PIK3CB, and LPL, and their associated core metabolites and processes. Four targets reacted strongly with GDL active components. GDL therapy improved five targets' expression. Conclusion: This collaborative effort revealed the mechanisms of GDL against WD neuron damage and a way to investigate the potential pharmacological mechanisms of other Traditional Chinese Medicine (TCM).

Effects of Cathode GDL Gradient Porosity Distribution along the Flow Channel Direction on Gas-Liquid Transport and Performance of PEMFC.

The gas diffusion layer (GDL) is an important component of proton exchange membrane fuel cells (PEMFCs), and its porosity distribution has considerable effects on the transport properties and durability of PEMFCs. A 3-D two-phase flow computation fluid dynamics model was developed in this study, to numerically investigate the effects of three different porosity distributions in a cathode GDL: gradient-increasing (Case 1), gradient-decreasing (Case 3), and uniform constant (Case 2), on the gas-liquid transport and performance of PEMFCs; the novelty lies in the porosity gradient being along the channel direction, and the physical properties of the GDL related to porosity were modified accordingly. The results showed that at a high current density (2400 mA·cm-2), the GDL of Case 1 had a gas velocity of up to 0.5 cm·s-1 along the channel direction. The liquid water in the membrane electrode assembly could be easily removed because of the larger gas velocity and capillary pressure, resulting in a higher oxygen concentration in the GDL and the catalyst layer. Therefore, the cell performance increased. The voltage in Case 1 increased by 8% and 71% compared to Cases 2 and 3, respectively. In addition, this could ameliorate the distribution uniformity of the dissolved water and the current density in the membrane along the channel direction, which was beneficial for the durability of the PEMFC. The distribution of the GDL porosity at lower current densities had a less significant effect on the cell performance. The findings of this study may provide significant guidance for the design and optimization of the GDL in PEMFCs.

Structure Design for Ultrahigh Power Density Proton Exchange Membrane Fuel Cell.

Next-generation ultrahigh power density proton exchange membrane fuel cells rely not only on high-performance membrane electrode assembly (MEA) but also on an optimal cell structure. To this end, this work comprehensively investigates the cell performance under various structures, and it is revealed that there is unexploited performance improvement in structure design because its positive effect enhancing gas supply is often inhibited by worse proton/electron conduction. Utilizing fine channel/rib or the porous flow field is feasible to eliminate the gas diffusion layer (GDL) and hence increase the power density significantly due to the decrease of cell thickness and gas/electron transfer resistances. The cell structure combining fine channel/rib, GDL elimination and double-cell structure is believed to increase the power density from 4.4 to 6.52 kW L-1 with the existing MEA, showing nearly equal importance with the new MEA development in achieving the target of 9.0 kW L-1 .

Fabrication of WPI-EGCG covalent conjugates/gellangum double network emulsion gels by duo-induction of GDL and CaCl2 for colon-controlled Lactobacillus Plantarum delivery.

In this study, we demonstrated the role of whey protein isolate (WPI)/(-)-epigallocatechin-3-gallate (EGCG) covalent conjugates/gellangum double network emulsion gels by duo-induction of glucono-δ-lactone (GDL) and CaCl2 on the digestion viability of Lactobacillus Plantarum powders. We employed a novel one-step continuous cold gelation strategy to product double network emulsion gels. The gellan gum in continuous phase was first induced by CaCl2 to form a gel in a short time. Besides, the GDL was improved covalent cross-linking between WPI-EGCG covalent conjugate Pickering particles or particle-coated oil droplets because of the slowly pH reduction. The first cross-linked gellan gum gels could be more closely integrated into the GDL-induced second gel networks with synergistic role, which promoted the formation of compact and homogeneous networks through hydrophobic interactions and disulfide bonds. Thus, the results provide a promising strategy for probiotic powders encapsulation of double network emulsion gels in oral applications of gel type foods.

Plasma-induced nanoparticle aggregation for stratifying COVID-19 patients according to disease severity.

Stratifying patients according to disease severity has been a major hurdle during the COVID-19 pandemic. This usually requires evaluating the levels of several biomarkers, which may be cumbersome when rapid decisions are required. In this manuscript we show that a single nanoparticle aggregation test can be used to distinguish patients that require intensive care from those that have already been discharged from the intensive care unit (ICU). It consists of diluting a platelet-free plasma sample and then adding gold nanoparticles. The nanoparticles aggregate to a larger extent when the samples are obtained from a patient in the ICU. This changes the color of the colloidal suspension, which can be evaluated by measuring the pixel intensity of a photograph. Although the exact factor or combination of factors behind the different aggregation behavior is unknown, control experiments demonstrate that the presence of proteins in the samples is crucial for the test to work. Principal component analysis demonstrates that the test result is highly correlated to biomarkers of prognosis and inflammation that are commonly used to evaluate the severity of COVID-19 patients. The results shown here pave the way to develop nanoparticle aggregation assays that classify COVID-19 patients according to disease severity, which could be useful to de-escalate care safely and make a better use of hospital resources.

pamlr: A toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic or light data in R.

Light-level geolocators have revolutionised the study of animal behaviour. However, lacking spatial precision, their usage has been primary targeted towards the analysis of large-scale movements. Recent technological developments have allowed the integration of magnetometers and accelerometers into geolocator tags in addition to barometers and thermometers, offering new behavioural insights. Here, we introduce an R toolbox for identifying behavioural patterns from multisensor geolocator tags, with functions specifically designed for data visualisation, calibration, classification and error estimation. More specifically, the package allows for the flexible analysis of any combination of sensor data using k-means clustering, expectation maximisation binary clustering, hidden Markov models and changepoint analyses. Furthermore, the package integrates tailored algorithms for identifying periods of prolonged high activity (most commonly used for identifying migratory flapping flight), and pressure changes (most commonly used for identifying dive or flight events). Finally, we highlight some of the limitations, implications and opportunities of using these methods.

Les géolocalisateurs lumineux ont révolutionné l'étude du comportement animal. Toutefois, en raison de leur manque de précision spatiale, leur utilisation a été principalement dirigée vers l'analyse de mouvements à grandes échelles. Les développements technologiques récents ont permis l'intégration de magnétomètres et d'accéléromètres dans les balises de géolocalisation, en plus de baromètres et de thermomètres, permettant de nouvelles analyses du comportement animalier. Nous présentons ici notre R package pour l'identification de modèles comportementaux à partir de balises géolocalisatrices multisensoriels. Le package intègre des fonctions conçues spécifiquement pour la visualisation de données, la calibration des balises, la classification du comportement et l'estimation des erreurs d'analyses. Plus précisément, le package permet l'analyse flexible de n'importe quelle combinaison de capteurs de données en utilisant le k-means clustering, le expectation maximisation binary clustering, les hidden Markov models et les analyses changepoint. En outre, le package intègre des algorithmes adaptés pour identifier les périodes de haute activité prolongée (le plus souvent utilisé pour identifier le vol migratoire d'oiseaux), et les changements de pression (le plus souvent utilisé pour identifier des periodes où l'animal est en plongée ou au vol). Enfin, nous soulignons les limites, les implications et les opportunités d'utilisation de ces méthodes.

Multigraph classification using learnable integration network with application to gender fingerprinting.

Multigraphs with heterogeneous views present one of the most challenging obstacles to classification tasks due to their complexity. Several works based on feature selection have been recently proposed to disentangle the problem of multigraph heterogeneity. However, such techniques have major drawbacks. First, the bulk of such works lies in the vectorization and the flattening operations, failing to preserve and exploit the rich topological properties of the multigraph. Second, they learn the classification process in a dichotomized manner where the cascaded learning steps are pieced in together independently. Hence, such architectures are inherently agnostic to the cumulative estimation error from step to step. To overcome these drawbacks, we introduce MICNet (multigraph integration and classifier network), the first end-to-end graph neural network based model for multigraph classification. First, we learn a single-view graph representation of a heterogeneous multigraph using a GNN based integration model. The integration process in our model helps tease apart the heterogeneity across the different views of the multigraph by generating a subject-specific graph template while preserving its geometrical and topological properties conserving the node-wise information while reducing the size of the graph (i.e., number of views). Second, we classify each integrated template using a geometric deep learning block which enables us to grasp the salient graph features. We train, in end-to-end fashion, these two blocks using a single objective function to optimize the classification performance. We evaluate our MICNet in gender classification using brain multigraphs derived from different cortical measures. We demonstrate that our MICNet significantly outperformed its variants thereby showing its great potential in multigraph classification.

A Mild Method for Encapsulation of Citral in Monodispersed Alginate Microcapsules.

Citral is a typical UV-irritation and acid-sensitive active and here we develop a mild method for the encapsulation of citral in calcium alginate microcapsules, in which UV irritation or acetic acid is avoided. Monodispersed oil-in-water-in-oil (O/W/O) emulsions are generated in a capillary microfluidic device as precursors. The middle aqueous phase of O/W/O emulsions contains sodium alginate, calcium-ethylenediaminetetraacetic acid (EDTA-Ca) complex as the calcium source, and D-(+)-Gluconic acid δ-lactone (GDL) as the acidifier. Hydrolysis of GDL will decrease the pH value of the middle aqueous solution, which will trigger the calcium ions released from the EDTA-Ca complex to cross-link with alginate molecules. After the gelling process, the O/W/O emulsions will convert to alginate microcapsules with a uniform structure and monodispersed size. The preparation conditions for alginate microcapsules are optimized, including the constituent concentration in the middle aqueous phase of O/W/O emulsions and the mixing manner of GDL with the alginate-contained aqueous solution. Citral-containing alginate microcapsules are successfully prepared by this mild method and the sustained-release characteristic of citral from alginate microcapsules is analyzed. Furthermore, a typical application of citral-containing alginate microcapsules to delay the oxidation of oil is also demonstrated. The mild gelling method provides us a chance to encapsulate sensitive hydrophobic actives with alginate, which takes many potential applications in pharmaceutical, food, and cosmetic areas.

Characterization of Commercial Gas Diffusion Layers (GDL) by Liquid Extrusion Porometry (LEP) and Gas Liquid Displacement Porometry (GLDP).

This works aims to study the porous characterization of several commercial Gas Diffusion Layers (GDL). Three carbon-based porous GDL featuring a highly rigid microstructure of interconnected pores of several manufacturers were analyzed. Gas Liquid Displacement Porometry (GLDP) and Liquid Extrusion Porometry (LEP) have been used to obtain their pore size distributions (PSD) and the mean and mode pore diameters (davg and dmod), by means of a gas/liquid and extrusion porometer developed at IFTS. N-dodecane liquid has been used to completely wet the GDL's assuring penetration of the liquid into the carbon fibrous structure. The results demonstrated the accuracy of the setup on characterizing GDL in the Particle Filtration (PF) range by GLDP and LEP, with reasonable agreements of resulting PSD and average sizes between both techniques when GLDP and LEP results are compared. Differences can be explained in terms of the high pore connectivity of these kinds of structures.

Cost-Effectiveness of Ferrous Fumarate-Folic Acid and Ferrous Gluconate-Multivitamins in a High Prevalence Area of Iron Deficiency Anemia in Indonesia.

BACKGROUND: Up to now, the combinations of ferrous fumarate-folic acid (FF-FA) and ferrous gluconate-multivitamins (FG-MV) have been implemented by the local government in the province of Papua. Nevertheless, there is no a specific economic evaluation that has been applied to investigate the cost-effectiveness of FF-FA and FG-MV. OBJECTIVE: This study aimed to investigate the cost-effectiveness of FF-FA and FG-MV to be implemented in Teluk Bintuni, as one of the districts with the highest prevalence of iron deficiency anemia in Papua by taking the healthcare perspective into account. METHODS: A prospective observational study was applied by considering two groups of women (15-49 years old) with iron deficiency anemia who received FF-FA and FG-MV from September to November 2018. Applying a purposive sampling method, respondents were selected from 875 targeted women in six sub-districts, who met inclusion criteria. To estimate the total cost, we applied a healthcare perspective that considered direct medical cost only (eg, the procurement cost of iron tablets, cost of Hb test, and cost of healthcare visit). To estimate the effectiveness of intervention, we applied two major parameters, such as Hb level and utility score in quality-adjusted life year (QALY). The cost-effectiveness values were evaluated by using the criteria on the cost-effectiveness of healthcare intervention according to the threshold of gross domestic product (GDP) per capita (cost per QALY gained). RESULTS: From 875 targeted women in six sub-districts who met inclusion criteria, we found approximately 222 women with moderate-severe iron deficiency anemia and 110 women with complete data in the group of FF-FA (n=69) and FG-MV (n-41). The results showed that there were significant differences (p-value <0.05) on the number of respondents, age, oral iron cost, total healthcare cost and utility score in both intervention groups. Comparing the use of FG-MV with FF-FA, we estimated the incremental cost-effectiveness ratios (ICERs) would be $255.77 per controlled patient, $142.09 per patient with Hb increment >2.00 g/dL, $79.93 per patient with Hb increment >1.00 g/dL, and $11.59 per QALY gained. CONCLUSION: The ICER was estimated to be $11.59 per QALY gained, which was highly cost-effective, according to GDP-based cost-effectiveness threshold. In addition, the utility score of women with iron deficiency anemia was considered to be the most influential factor impacting the cost-effectiveness value.