Search results for: artificial cell

Authors: A. Jéglot, F. Plauborg, M. K. Schnorr, R. S. Sørensen, L. Elsgaard

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Artificial wetlands such as woodchip bioreactors are efficient tools to remove nitrate from agricultural wastewater with a minimized environmental impact. However, the temperature dependence of the microbiological nitrate removal prevents the woodchip bioreactors from being an efficient system when the water temperature drops below 8℃. To quantify and describe the temperature effects on nitrate removal efficiency, we studied nitrate-reducing enrichments from a woodchip bioreactor in Denmark based on samples collected in Spring and Fall. Growth was quantified as optical density, and nitrate and nitrous oxide concentrations were measured in time-course experiments to compare the growth of the microbial population and the nitrate conversion efficiencies at different temperatures. Ammonia was measured to indicate the importance of dissimilatory nitrate reduction to ammonia (DNRA) in nitrate conversion for the given denitrifying community. The temperature responses observed followed the increasing trend proposed by the Arrhenius equation, indicating higher nitrate removal efficiencies at higher temperatures. However, the growth and the nitrous oxide production observed at low temperature provided evidence of the psychrotolerance of the microbial community under study. The assays conducted showed higher nitrate removal from the microbial community extracted from the woodchip bioreactor at the cold season compared to the ones extracted during the warmer season. This indicated the ability of the bacterial populations in the bioreactor to evolve and adapt to different seasonal temperatures.

Keywords: agricultural waste water treatment, artificial wetland, denitrification, psychrophilic conditions

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Authors: Ferreira RC, Simons HZ, Thompson WS, Cutler AJ, Dopico XC, Smyth DJ, Mashar M, Schuilenburg H, Walker NM, Dunger DB, Wallace C, Todd JA, Wicker LS, Pekalski ML

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Type 1 diabetes is caused by autoimmune destruction of insulin-secreting beta cells in the pancreas. T cells are known to play an important role in this immune-mediated destruction; however, there is no general consensus regarding alterations in cytokine production or T cell subsets in peripheral blood of patients with type 1 diabetes. Using polychromatic flow cytometry of peripheral blood mononuclear cells (PBMCs), we assessed production of the proinflammatory cytokines IL-21, IFN-γ and IL-17 by memory CD4 T effector (Teff) cells in 69 patients with type 1 diabetes and 61 healthy donors. We found a 21.9% (95% CI 5.8, 40.2; p = 3.9 × 10(-3)) higher frequency of IL-21(+) CD45RA(-) memory CD4(+) Teffs in patients with type 1 diabetes (geometric mean 5.92% [95% CI 5.44, 6.44]) compared with healthy donors (geometric mean 4.88% [95% CI 4.33, 5.50]). In a separate cohort of 30 patients with type 1 diabetes and 32 healthy donors, we assessed the frequency of circulating T follicular helper (Tfh) cells in whole blood. Consistent with the increased production of IL-21, we also found a 14.9% increase in circulating Tfh cells in the patients with type 1 diabetes (95% CI 2.9, 26.9; p = 0.016). Analysis of IL-21 production by PBMCs from a subset of 46 of the 62 donors immunophenotyped for Tfh showed that frequency of Tfh cells was associated with the frequency of IL-21+ cells (r2 = 0.174, p = 0.004). These results indicate that increased IL-21 production is likely to be an aetiological factor in the pathogenesis of type 1 diabetes that could be considered as a potential therapeutic target.

Keywords: T follicular helper cell, IL-21, IL-17, type 1 diabetes

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Authors: Ashit Syngle, Inderjit Verma, Pawan Krishan

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Aim: Endothelial progenitor cells (EPCs) are unique populations which have reparative potential in overcoming the endothelial damage and reducing cardiovascular risk. Patients with ankylosing spondylitis (AS) have increased risk of cardiovascular morbidity and mortality. The aim of this study was to investigate the endothelial progenitor cell population in AS patients and its potential relationships with disease variables. Methods: Endothelial progenitor cells were measured in peripheral blood samples from 20 AS and 20 healthy controls by flow cytometry on the basis of CD34 and CD133 expression. Disease activity was evaluated by using Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). Functional ability was monitored by using Bath Ankylosing Spondylitis Functional Index (BASFI). Results: EPCs were depleted in AS patients as compared to the healthy controls (CD34+/CD133+: 0.027 ± 0.010 % vs. 0.044 ± 0.011 %, p<0.001). EPCs depletion were significantly associated with disease duration (r=-0.52, p=0.01) and BASDAI (r=-0.45, p=0.04). Conclusion: This is the first study to demonstrate endothelial progenitor cells depletion in AS patients. EPCs depletion inversely correlates with disease duration and disease activity, suggesting the pivotal role of inflammation in depletion of EPCs. EPC would possibly also serve as a therapeutic target for preventing cardiovascular disease in AS.

Keywords: ankylosing spondylitis, endothelial progenitor cells, inflammation, vascular damage

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Authors: Renata De Toledo Cintra, Bruno Ramos, Douglas Gouvêa

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There is a huge global concern due to the emission of greenhouse gases, consequent environmental problems, and the increase in the average temperature of the planet, caused mainly by fossil fuels, petroleum derivatives represent a big part. One of the main greenhouse gases, in terms of volume, is CO₂. Recovering a part of this product through chemical reactions that use sunlight as an energy source and even producing renewable fuel (such as ethane, methane, ethanol, among others) is a great opportunity. The process of artificial photosynthesis, through the conversion of CO₂ and H₂O into organic products and oxygen using a metallic oxide catalyst, and incidence of sunlight, is one of the promising solutions. Therefore, this research is of great relevance. To this reaction take place efficiently, an optimized reactor was developed through simulation and prior analysis so that the geometry of the internal channel is an efficient route and allows the reaction to happen, in a controlled and optimized way, in flow continuously and offering the least possible resistance. The design of this reactor prototype can be made in different materials, such as polymers, ceramics and metals, and made through different processes, such as additive manufacturing (3D printer), CNC, among others. To carry out the photocatalysis in the reactors, different types of catalysts will be used, such as ZnO deposited by spray pyrolysis in the lighting window, probably modified ZnO, TiO₂ and modified TiO₂, among others, aiming to increase the production of organic molecules, with the lowest possible energy.

Keywords: artificial photosynthesis, CO₂ reduction, photocatalysis, photoreactor design, 3D printed reactors, solar fuels

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Authors: Esther Ekundayo

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The nature of AI-generated works makes it difficult to identify an author. Although, some scholars have suggested that all the players involved in its creation should be allocated authorship according to their respective contribution. From the programmer who creates and designs the AI to the investor who finances the AI and to the user of the AI who most likely ends up creating the work in question. While others suggested that this issue may be resolved by the UK computer-generated works (CGW) provision under Section 9(3) of the Copyright Designs and Patents Act 1988. However, under the UK and Nigerian copyright law, only human-created works are recognised. This is usually assessed based on their originality. This simply means that the work must have been created as a result of its author’s creative and intellectual abilities and not copied. Such works are literary, dramatic, musical and artistic works and are those that have recently been a topic of discussion with regards to generative artificial intelligence (Generative AI). Unlike Nigeria, the UK CDPA recognises computer-generated works and vests its authorship with the human who made the necessary arrangement for its creation . However, making necessary arrangement in the case of Nova Productions Ltd v Mazooma Games Ltd was interpreted similarly to the traditional authorship principle, which requires the skills of the creator to prove originality. Although, some recommend that computer-generated works complicates this issue, and AI-generated works should enter the public domain as authorship cannot be allocated to AI itself. Additionally, the UKIPO recognising these issues in line with the growing AI trend in a public consultation launched in the year 2022, considered whether computer-generated works should be protected at all and why. If not, whether a new right with a different scope and term of protection should be introduced. However, it concluded that the issue of computer-generated works would be revisited as AI was still in its early stages. Conversely, due to the recent developments in this area with regards to Generative AI systems such as ChatGPT, Midjourney, DALL-E and AIVA, amongst others, which can produce human-like copyright creations, it is therefore important to examine the relevant issues which have the possibility of altering traditional copyright principles as we know it. Considering that the UK and Nigeria are both common law jurisdictions but with slightly differing approaches to this area, this research, therefore, seeks to answer the following questions by comparative analysis: 1)Who is the author of an AI-generated work? 2)Is the UK’s CGW provision worthy of emulation by the Nigerian law? 3) Would a sui generis law be capable of protecting AI-generated works and its author under both jurisdictions? This research further examines the possible barriers to the implementation of the new law in Nigeria, such as limited technical expertise and lack of awareness by the policymakers, amongst others.

Keywords: authorship, artificial intelligence (AI), generative ai, computer-generated works, copyright, technology

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Authors: Michael P. Mannino, Gerald W. Hart

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The majority of glucose metabolism proceeds through glycolytic pathways such as glycolysis or pentose phosphate pathway, however, about 5% is shunted through the hexosamine biosynthetic pathway, producing uridine diphosphate N-acetyl glucosamine (UDP-GlcNAc). This precursor can then be incorporated into complex oligosaccharides decorating the cell surface or remain as an intracellular post-translational-modification (PTM) of serine/threonine residues (O-GlcNAcylation, OGN), which has been identified on over 4,000 cytosolic or nuclear proteins. Intracellular OGN has major implications on cellularprocesses, typically by modulating protein localization, protein-protein interactions, protein degradation, and gene expression. Additionally, OGN is known to have an extensive cross-talk with phosphorylation, be in a competitive or cooperative manner. Unlike other PTMs there are only two cycling enzymes that are capable of adding or removing the GlcNAc moiety, O-linked N-aceytl glucosamine Transferase (OGT) and O-linked N-acetyl glucoamidase (OGA), respectively. The activity of OGT has been shown to be sensitive to cellular UDP-GlcNAc levels, even changing substrate affinity. Owing to this and that the concentration of UDP-GlcNAc is related to the metabolisms of glucose, amino acid, fatty acid, and nucleotides, O-GlcNAc is often referred to as a nutrient sensing rheostat. Indeed OGN is known to regulate several signaling pathways as a result of nutrient levels, such as insulin signaling. Dysregulation of OGN is associated with several disease states such as cancer, diabetes, and neurodegeneration. Improvements in glycomics over the past 10-15 years has significantly increased the OGT substrate pool, suggesting O-GlcNAc’s involvement in a wide variety of signaling pathways. However, O-GlcNAc’s role at the receptor level has only been identified in a case-by-case basis of known pathways. Examining the OGN of the plasma membrane (PM) may better focus our understanding of O-GlcNAc-effected signaling pathways. In this current study, PM fractions were isolated from several cell types via ultracentrifugation, followed by purification and MS/MS analysis in several cell lines. This process was repeated with or without OGT/OGA inhibitors or with increased/decreased glucose levels in media to ascertain the importance of OGN. Various pathways are followed up on in more detailed studies employing methods to localize OGN at the PM specifically.

Keywords: GlcNAc, nutrient sensitive, post-translational-modification, receptor

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Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: Sirus Mohammadi, Rohala Moghimi

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The main advantages of microgrids are high energy efficiency through the application of Combined Heat and Power (CHP), high quality and reliability of the delivered electric energy and environmental and economic advantages. This study presents an energy management system (EMS) to optimize the operation of the microgrid (MG). In this paper an Adaptive Modified Firefly Algorithm (AMFA) is presented for optimal operation of a typical MG with renewable energy sources (RESs) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the energy surplus when it’s needed. The problem is formulated as a nonlinear constraint problem to minimize the total operating cost. The management of Energy storage system (ESS), economic load dispatch and operation optimization of distributed generation (DG) are simplified into a single-object optimization problem in the EMS. The proposed algorithm is tested on a typical grid-connected MG including WT/PV/Micro Turbine/Fuel Cell and Energy Storage Devices (ESDs) then its superior performance is compared with those from other evolutionary algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Self Adaptive PSO (FSAPSO), Chaotic Particle PSO (CPSO), Adaptive Modified PSO (AMPSO), and Firefly Algorithm (FA).

Keywords: microgrid, operation management, optimization, firefly algorithm (AMFA)

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Authors: Zhilin Wang, Zhihao Zheng, Linxin Liu

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This study proposes a low-code model-building approach that aims to simplify the development and deployment of artificial intelligence (AI) models. With an intuitive way to drag and drop and connect components, users can easily build complex models and integrate multiple algorithms for training. After the training is completed, the system automatically generates a callable model service API. This method not only lowers the technical threshold of AI development and improves development efficiency but also enhances the flexibility of algorithm integration and simplifies the deployment process of models. The core strength of this method lies in its ease of use and efficiency. Users do not need to have a deep programming background and can complete the design and implementation of complex models with a simple drag-and-drop operation. This feature greatly expands the scope of AI technology, allowing more non-technical people to participate in the development of AI models. At the same time, the method performs well in algorithm integration, supporting many different types of algorithms to work together, which further improves the performance and applicability of the model. In the experimental part, we performed several performance tests on the method. The results show that compared with traditional model construction methods, this method can make more efficient use, save computing resources, and greatly shorten the model training time. In addition, the system-generated model service interface has been optimized for high availability and scalability, which can adapt to the needs of different application scenarios.

Keywords: low-code, model building, artificial intelligence, algorithm integration, model deployment

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Authors: Obsi Terfasa, Bhanupriya Das, Mithilish Passawan

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The large-scale application of microbial fuel cell (MFC) technology is significantly hindered by the high cost of the commonly used proton exchange membrane, Nafion. This has led to the recent development of ceramic membranes using various clay minerals. This study evaluates the characteristics and potential use of a new ceramic membrane made from potter’s clay © mixed with different proportions (0, 5, 10 wt%) of fly ash (FA), labeled as CFA0, CFA5, CFA10, for cost-effective and sustainable MFC use. Among these, the CFA10 membrane demonstrated superior quality with a fine pore size distribution (average 0.41 μm), which supports higher water uptake and reduced oxygen diffusion. Its oxygen mass transfer coefficient was 4.13 ± 0.13 × 10⁻⁴ cm/s, about 40% lower than the control. X-ray diffraction analysis revealed that the CFA membrane is rich in quartz, which enhances proton conductance and water retention. Electrochemical kinetics studies, including cyclic voltammetry and electrochemical impedance spectroscopy (EIS), also confirmed the effectiveness of the CFA10 membrane in MFC, showing a peak current output of 15.35 mA and low ohmic resistance (78.2 Ω). The novel CFA10 ceramic membrane, incorporating coal fly ash, a waste material, shows promise for high MFC performance at a significantly reduced cost (96%), making it suitable for sustainable scaling up of the technology.

Keywords: ceramic membrane, Coulombic efficiency, electro-chemical kinetics, fly ash, proton conductivity, microbial fuel cell

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Authors: Okyu Kwon, Dongho Kang, Byungsik Kim, Seungkwon Jung

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We propose a technique to measure the impact of loss of traffic function in a particular area to surrounding areas. The proposed method is applied to the city of Seoul, which is the capital of South Korea, with a population of about ten million. Based on the actual road network in Seoul, we construct an abstract road network between 1kmx1km grid cells. The link weight of the abstract road network is re-adjusted considering traffic volume measured at several survey points. On the modified abstract road network, we evaluate the traffic vulnerability by calculating a network measure of betweenness centrality (BC) for every single grid cells. This study analyzes traffic impacts caused by road dysfunction due to heavy rainfall in urban areas. We could see the change of the BC value in all other grid cells by calculating the BC value once again when the specific grid cell lost its traffic function, that is, when the node disappeared on the grid-based road network. The results show that it is appropriate to use the sum of the BC variation of other cells as the influence index of each lattice cell on traffic. This research was supported by a grant (2017-MOIS31-004) from Fundamental Technology Development Program for Extreme Disaster Response funded by Korean Ministry of Interior and Safety (MOIS).

Keywords: vulnerability, road network, beweenness centrality, heavy rainfall, road impact

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Authors: Khobaib Khobaib, Alexander Mikkelsen, Zbigniew Rozynek

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Electric fields have proven useful for inducing droplet deformation and to structure particles adsorbed at droplet interfaces. In this experimental research, direct current electric fields were applied to deform particle-covered droplets made out of silicone oil and immersed in castor oil. The viscosity of the drop and surrounding fluid were changed by external heating. We designed an experimental system in such a way that electric field-induced electrohydrodynamic (EHD) flows were asymmetric and only present on one side of the drop, i.e., the droplet adjoined a washer and adhered to one of the electrodes constituting the sample cell. The study investigated the influence of viscosity on the steady-state deformation magnitude of particle-laden droplets, droplet compression, and relaxation, as well as particle arrangements at drop interfaces. Initially, before the application of an electric field, we changed the viscosity of the fluids by heating the sample cell at different temperatures. The viscosity of the fluids was varied by changing the temperature of the fluids from 25 to 50°C. Under the application of a uniform electric field of strength 290 Vmm⁻¹, electric stress was induced at the drop interface, yielding drop deformation. In our study, we found that by lowering the fluid viscosity, the velocity of the EHD flows was increased, which also increases the deformation of the drop.

Keywords: drop deformation and relaxation, electric field, electrohydrodynamic flow, particle assembly, viscosity

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Authors: Ádám Vass, István Bakos, Irina Borbáth, Zoltán Pászti, István Sajó, András Tompos

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Important requirements for the anode side electrocatalysts of polymer electrolyte membrane (PEM) fuel cells are CO-tolerance, stability and corrosion resistance. Carbon is still the most common material for electrocatalyst supports due to its low cost, high electrical conductivity and high surface area, which can ensure good dispersion of the Pt. However, carbon becomes degraded at higher potentials and it causes problem during application. Therefore it is important to explore alternative materials with improved stability. Molybdenum-oxide can improve the CO-tolerance of the Pt/C catalysts, but it is prone to leach in acidic electrolyte. The Mo was stabilized by isovalent substitution of molybdenum into the rutile phase titanium-dioxide lattice, achieved by a modified multistep sol-gel synthesis method optimized for preparation of Ti0.7Mo.3O2-C composite. High degree of Mo incorporation into the rutile lattice was developed. The conductivity and corrosion resistance across the anticipated potential/pH window was ensured by mixed oxide – activated carbon composite. Platinum loading was carried out using NaBH4 and ethylene glycol; platinum content was 40 wt%. The electrocatalyst was characterized by both material investigating methods (i.e. XRD, TEM, EDS, XPS techniques) and electrochemical methods (cyclic-voltammetry, COads stripping voltammetry, hydrogen oxidation reaction on rotating disc electrode). The electrochemical activity of the sample was compared to commercial 40 wt% Pt/C (Quintech) and PtRu/C (Quintech, Pt= 20 wt%, Ru= 10 wt%) references. Enhanced CO tolerance of the electrocatalyst prepared using the Ti0.7Mo.3O2-C composite material was evidenced by the appearance of a CO-oxidation related 'pre-peak' and by the pronounced shift of the maximum of the main CO oxidation peak towards less positive potential compared to Pt/C. Fuel cell polarization measurements were also carried out using Bio-Logic and Paxitech FCT-150S test device. All details on the design, preparation, characterization and testing by both electrochemical measurements and fuel cell test device of electrocatalyst supported on Ti0.7Mo.3O2-C composite material will be presented and discussed.

Keywords: anode electrocatalyst, composite material, CO-tolerance, TiMoOx

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Authors: Nkechi V. Enwuru, Bullum Nkeki, Elizabeth A. Adekoya, Olumide A. Adebesin, Rebecca F. Peters, Victoria A. Aikhomu, Mendie E. U.

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Background: Probiotics are live microbial feed supplement beneficial for host. Probiotics and their postbiotic products have been used to prevent or treat various health conditions. However, the products cell viability is often low due to harsh conditions subjected during processing, handling, storage, and gastrointestinal transit. These strongly influence probiotics’ benefits; thus, viability is essential for probiotics to produce health benefits for the host. Microencapsulation is a promising technique with considerable effects on probiotic survival. The study is aimed to formulate a microencapsulated probiotic and evaluate its viability, antimicrobial efficacy, and cytotoxic activity of its postbiotic on the MCF-7 breast cancer cell line. Method: Human and animal raw milk were sampled for lactic acid bacteria. The isolated bacteria were identified using conventional and VITEK 2 systems. The identified lactic acid bacterium was encapsulated using spray-dried and extrusion methods. The free, encapsulated, and chitosan-coated encapsulated probiotics were tested for viability in simulated-gastric intestinal (SGI) fluid and different storage conditions at refrigerated (4oC) and room (25oC) temperatures. The disintegration time and weight uniformity of the spray-dried hard gelatin capsules were tested. The antimicrobial property of free and encapsulated probiotics was tested against enteric pathogenic isolates from antiretroviral therapy (ART) treated HIV-positive patients. The postbiotic of the free cells was extracted, and its cytotoxic effect on the MCF-7 breast cancer cell line was tested through an MTT assay. Result: The Lactobacillus plantarum was isolated from animal raw milk. Zero-size hard gelatin L. plantarum capsules with granules within a size range of 0.71–1.00 mm diameter was formulated. The disintegration time ranges from 2.14±0.045 to 2.91±0.293 minutes, while the average weight is 502.1mg. Simulated gastric solution significantly affected viability of both free and microcapsules. However, the encapsulated cells were more protected and viable due to impermeability in the microcapsules. Furthermore, the viability of free cells stored at 4oC and 25oC were less than 4 log CFU/g and 6 log CFU/g respectively after 12 weeks. However, the microcapsules stored at 4oC achieved the highest viability among the free and microcapsules stored at 25oC and the free cells stored at 4oC. Encapsulated cells were released in the simulated gastric fluid, viable and effective against the enteric pathogens tested. However, chitosan-coated calcium alginate encapsulated probiotics significantly inhibited Shigella flexneri, Candida albicans, and Escherichia coli. The Postbiotic Metabolites (PM) of L. plantarum produced a cytotoxic effect on the MCF-7 breast cancer cell line. The postbiotic showed significant cytotoxic activity similar to 5FU, a standard antineoplastic agent. The inhibition concentration of 50% growth (IC50) of postbiotic metabolite K3 is low and consistent with the IC50 of the positive control (Cisplatin). Conclusions: Lactobacillus plantarum postbiotic exhibited a cytotoxic effect on the MCF-7 breast cancer cell line and could be used as combined adjuvant therapy in breast cancer management. The microencapsulation technique protects the probiotics, improving their viability and delivery to the gastrointestinal tract. Chitosan enhances antibacterial efficacy; thus, chitosan-coated microencapsulated L. plantarum probiotics could be more effective and used as a combined therapy in HIV management of opportunistic enteric infection.

Keywords: probiotics, encapsulation, gastrointestinal conditions, antimicrobial effect, postbiotic, cytotoxicity effect

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Authors: Yanni Chang, Dezhi Dai, Albert Y. Tong

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Piecewise linear interface calculation (PLIC) schemes are widely used in the volume-of-fluid (VOF) method to capture interfaces in numerical simulations of multiphase flows. Dynamic overset meshes can be especially useful in applications involving component motions and complex geometric shapes. In the present study, the VOF value of an acceptor cell is evaluated in a geometric way that transfers the fraction field between the meshes precisely with reconstructed interfaces from the corresponding donor elements. The acceptor cell value is evaluated by using a weighted average of its donors for most of the overset interpolation schemes for continuous flow variables. The weighting factors are obtained by different algebraic methods. Unlike the continuous flow variables, the VOF equation is a step function near the interfaces, which ranges from zero to unity rapidly. A geometric interpolation scheme of the VOF field in overset meshes for the PLIC-VOF method has been proposed in the paper. It has been tested successfully in quadrilateral/hexahedral overset meshes by employing several VOF advection tests with imposed solenoidal velocity fields. The proposed algorithm has been shown to yield higher accuracy in mass conservation and interface reconstruction compared with three other algebraic ones.

Keywords: interpolation scheme, multiphase flows, overset meshes, PLIC-VOF method

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Authors: Supannika Klangphukhiew, Roongnapa Srichana, Rina Patramanon

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Cortisol has been used as a well-known commercial stress biomarker. A homeostasis response to psychological stress is indicated by an increased level of cortisol produced in hypothalamus-pituitary-adrenal (HPA) axis. Chronic psychological stress contributing to the high level of cortisol relates to several health problems. In this study, the cortisol biosensor was fabricated that mimicked the natural receptors. The artificial antibodies were prepared using molecular imprinted polymer technique that can imitate the performance of natural anti-cortisol antibody with high stability. Cortisol-molecular imprinted polymer (cortisol-MIP) was obtained using the multi-step swelling and polymerization protocol with cortisol as a target molecule combining methacrylic acid:acrylamide (2:1) with bisacryloyl-1,2-dihydroxy-1,2-ethylenediamine and ethylenedioxy-N-methylamphetamine as cross-linkers. Cortisol-MIP was integrated to the sensor. It was coated on the disposable screen-printed carbon electrode (SPCE) for portable electrochemical analysis. The physical properties of Cortisol-MIP were characterized by means of electron microscope techniques. The binding characteristics were evaluated via covalent patterns changing in FTIR spectra which were related to voltammetry response. The performance of cortisol-MIP modified SPCE was investigated in terms of detection range, high selectivity with a detection limit of 1.28 ng/ml. The disposable cortisol biosensor represented an application of MIP technique to recognize steroids according to their structures with feasibility and cost-effectiveness that can be developed to use in point-of-care.

Keywords: stress biomarker, cortisol, molecular imprinted polymer, screen-printed carbon electrode

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Authors: F. Kashanian, M. M. Masoudi, A. Akbari, A. Shamloo, M. R. Zand, S. S. Salehi

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Nano-sized materials present new opportunities in biology and medicine and they are used as biomedical tools for investigation, separation of molecules and cells. To achieve more effective cancer therapy, it is essential to select cancer cells exactly. This research suggests that using the antibody-functionalized nontoxic Arginine-doped magnetic nanoparticles (A-MNPs), has been prosperous in detection, capture, and magnetic separation of circulating tumor cells (CTCs) in tumor tissue. In this study, A-MNPs were synthesized via a simple precipitation reaction and directly immobilized Ep-CAM EBA-1 antibodies over superparamagnetic A-MNPs for Mucin BCA-225 in breast cancer cell. The samples were characterized by vibrating sample magnetometer (VSM), FT-IR spectroscopy, Tunneling Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM). These antibody-functionalized nontoxic A-MNPs were used to capture breast cancer cell. Through employing a strong permanent magnet, the magnetic separation was achieved within a few seconds. Antibody-Conjugated nontoxic Arginine-doped Fe₃O₄ nanoparticles have the potential for the future study to capture CTCs which are released from tumor tissue and for drug delivery, and these results demonstrate that the antibody-conjugated A-MNPs can be used in magnetic hyperthermia techniques for cancer treatment.

Keywords: tumor tissue, antibody, magnetic nanoparticle, CTCs capturing

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Authors: Florin G. Borcan, Ramona C. Albulescu, Adela Chirita-Emandi

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pH-responsive drug delivery systems are gaining more importance because these systems deliver the drug at a specific time in regards to pathophysiological necessity, resulting in improved patient therapeutic efficacy and compliance. Polyurethane materials are well-known for industrial applications (elastomers and foams used in different insulations and automotive), but they are versatile biocompatible materials with many applications in medicine, as artificial skin for the premature neonate, membrane in the hybrid artificial pancreas, prosthetic heart valves, etc. This study aimed to obtain the physico-chemical characterization of a drug delivery system based on polyurethane microparticles. The synthesis is based on a polyaddition reaction between an aqueous phase (mixture of polyethylene-glycol M=200, 1,4-butanediol and Tween® 20) and an organic phase (lysin-diisocyanate in acetone) combined with simultaneous emulsification. Different active agents (omeprazole, amoxicillin, metoclopramide) were used to verify the release profile of the macromolecular particles in different pH mediums. Zetasizer measurements were performed using an instrument based on two modules: a Vasco size analyzer and a Wallis Zeta potential analyzer (Cordouan Technol., France) in samples that were kept in various solutions with different pH and the maximum absorbance in UV-Vis spectra were collected on a UVi Line 9,400 Spectrophotometer (SI Analytics, Germany). The results of this investigation have revealed that these particles are proper for a prolonged release in gastric medium where they can assure an almost constant concentration of the active agents for 1-2 weeks, while they can be disassembled faster in a medium with neutral pHs, such as the intestinal fluid.

Keywords: lysin-diisocyanate, nanostructures, polyurethane, Zetasizer

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Authors: Yahya Almurtadha, Mukhtar Ghaleb, Ahmed M. Shamsan Saleh

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The world witnessed a fierce attack from the Covid-19 virus, which affected public life socially, economically, healthily and psychologically. The world's governments tried to confront the pandemic by imposing a number of precautionary measures such as general closure, curfews and social distancing. Scientists have also made strenuous efforts to develop an effective vaccine to train the immune system to develop antibodies to combat the virus, thus reducing its symptoms and limiting its spread. Artificial intelligence, along with researchers and medical authorities, has accelerated the vaccine development process through big data processing and simulation. On the other hand, one of the most important negatives of the impact of Covid 19 was the state of anxiety and fear due to the blowout of rumors through social media, which prompted governments to try to reassure the public with the available means. This study aims to proposed using Sentiment Analysis (AKA Opinion Mining) and deep learning as efficient artificial intelligence techniques to work on retrieving the tweets of the public from Twitter and then analyze it automatically to extract their opinions, expression and feelings, negatively or positively, about the symptoms they may feel after vaccination. Sentiment analysis is characterized by its ability to access what the public post in social media within a record time and at a lower cost than traditional means such as questionnaires and interviews, not to mention the accuracy of the information as it comes from what the public expresses voluntarily.

Keywords: deep learning, opinion mining, natural language processing, sentiment analysis

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Authors: Lynn Lehmann, Dinorah Leyva, Ana Lazic, Stefan Duhr, Philipp Baaske

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Characterization of biomolecular interactions, such as protein-protein, protein-nucleic acid or protein-small molecule, provides critical insights into cellular processes and is essential for the development of drug diagnostics and therapeutics. Here we present a novel, label-free, and tether-free technology to analyze picomolar to millimolar affinities of biomolecular interactions by Microscale Thermophoresis (MST). The entropy of the hydration shell surrounding molecules determines thermophoretic movement. MST exploits this principle by measuring interactions using optically generated temperature gradients. MST detects changes in the size, charge and hydration shell of molecules and measures biomolecule interactions under close-to-native conditions: immobilization-free and in bioliquids of choice, including cell lysates and blood serum. Thus, MST measures interactions under close-to-native conditions, and without laborious sample purification. We demonstrate how MST determines the picomolar affinities of antibody::antigen interactions, and protein::protein interactions measured from directly from cell lysates. MST assays are highly adaptable to fit to the diverse requirements of different and complex biomolecules. NanoTemper´s unique technology is ideal for studies requiring flexibility and sensitivity at the experimental scale, making MST suitable for basic research investigations and pharmaceutical applications.

Keywords: biochemistry, biophysics, molecular interactions, quantitative techniques

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Authors: Salina Yahya Saddick

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Ovarian cancer remains the most lethal gynecological malignancy due to the lack of highly sensitive and specific screening tools for detection of early-stage disease. The OSE provides the progenitor cells for 90% of human ovarian cancers. Recent morphologic, immunohistochemical and molecular genetic studies have led to the development of a new paradigm for the pathogenesis and origin of epithelial ovarian cancer (EOC) based on a ualistic model of carcinogenesis that divides EOC into two broad categories designated Types I and II which are characterized by specific mutations, including KRAS, BRAF, ERBB2, CTNNB1, PTEN PIK3CA, ARID1A, and PPPR1A, which target specific cell signaling pathways. Type 1 tumors rarely harbor TP53. type I tumors are relatively genetically stable and typically display a variety of somatic sequence mutations that include KRAS, BRAF, PTEN, PIK3CA CTNNB1 (the gene encoding beta catenin), ARID1A and PPP2R1A but very rarely TP53 . The cancer stem cell (CSC) hypothesis postulates that the tumorigenic potential of CSCs is confined to a very small subset of tumor cells and is defined by their ability to self-renew and differentiate leading to the formation of a tumor mass. Potential protein biomarker miRNA, are promising biomarkers as they are remarkably stable to allow isolation and analysis from tissues and from blood in which they can be found as free circulating nucleic acids and in mononuclear cells. Recently, genomic anaylsis have identified biomarkers and potential therapeutic targets for ovarian cancer namely, FGF18 which plays an active role in controlling migration, invasion, and tumorigenicity of ovarian cancer cells through NF-κB activation, which increased the production of oncogenic cytokines and chemokines. This review summarizes update information on epithelial ovarian cancers and point out to the most recent ongoing research.

Keywords: epithelial ovarian cancers, somatic sequence mutations, cancer stem cell (CSC), potential protein, biomarker, genomic analysis, FGF18 biomarker

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Authors: E. Nakamachi, S. Tanaka, K. Yamamoto, Y. Morita

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In this study, we developed a three-dimensional (3D) direct current electric field (DCEF) stimulation bio-reactor for axonal outgrowth enhancement to generate the neural network of the central nervous system (CNS). By using our newly developed 3D DCEF stimulation bio-reactor, we cultured the rat pheochromocytoma cells (PC12) and investigated the effects on the axonal extension enhancement and network generation. Firstly, we designed and fabricated a 3D bio-reactor, which can load DCEF stimulation on PC12 cells embedded in the collagen gel as extracellular environment. The connection between the electrolyte and the medium using salt bridges for DCEF stimulation was introduced to avoid the cell death by the toxicity of metal ion. The distance between the salt bridges was adopted as the design variable to optimize a structure for uniform DCEF stimulation, where the finite element (FE) analyses results were used. Uniform DCEF strength and electric flux vector direction in the PC12 cells embedded in collagen gel were examined through measurements of the fabricated 3D bio-reactor chamber. Measurement results of DCEF strength in the bio-reactor showed a good agreement with FE results. In addition, the perfusion system was attached to maintain pH 7.2 ~ 7.6 of the medium because pH change was caused by DCEF stimulation loading. Secondly, we disseminated PC12 cells in collagen gel and carried out 3D culture. Finally, we measured the morphology of PC12 cell bodies and neurites by the multiphoton excitation fluorescence microscope (MPM). The effectiveness of DCEF stimulation to enhance the axonal outgrowth and the neural network generation was investigated. We confirmed that both an increase of mean axonal length and axogenesis rate of PC12, which have been exposed 5 mV/mm for 6 hours a day for 4 days in the bioreactor. We found following conclusions in our study. 1) Design and fabrication of DCEF stimulation bio-reactor capable of 3D culture nerve cell were completed. A uniform electric field strength of average value of 17 mV/mm within the 1.2% error range was confirmed by using FE analyses, after the structure determination through the optimization process. In addition, we attached a perfusion system capable of suppressing the pH change of the culture solution due to DCEF stimulation loading. 2) Evaluation of DCEF stimulation effects on PC12 cell activity was executed. The 3D culture of PC 12 was carried out adopting the embedding culture method using collagen gel as a scaffold for four days under the condition of 5.0 mV/mm and 10mV/mm. There was a significant effect on the enhancement of axonal extension, as 11.3% increase in an average length, and the increase of axogenesis rate. On the other hand, no effects on the orientation of axon against the DCEF flux direction was observed. Further, the network generation was enhanced to connect longer distance between the target neighbor cells by DCEF stimulation.

Keywords: PC12, DCEF stimulation, 3D bio-reactor, axonal extension, neural network generation

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Authors: Chang-Hui Shen, Paulina Konarzewska

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Cell homeostasis is regulated by vacuolar activity and it has been shown that lipid composition of the vacuole plays an important role in vacuolar function. The major phosphoinositide species present in the vacuolar membrane include phosphatidylinositol 3,5-biphosphate (PI(3,5)P₂) which is generated from PI(3)P controlled by Fab1p. Deletion of FAB1 gene reduce the synthesis of PI(3,5)P₂ and thus result in enlarged or fragmented vacuoles, with neutral vacuolar pH due to reduced vacuolar H⁺-ATPase activity. These mutants also exhibited poor growth at high extracellular pH and in the presence of CaCl₂. Conversely, VPS34 regulates the synthesis of PI(3)P from phosphatidylinositol (PI), and the lack of Vps34p results in the reduction of vacuolar activity. Although the cellular observations are clear, it is still unknown about the molecular mechanism between the phospholipid biosynthesis pathway and vacuolar activity. Since both VPS34 and FAB1 are important in vacuolar activity, we hypothesize that the molecular mechanism of vacuolar function might be regulated by the transcriptional regulators of phospholipid biosynthesis. In this study, we study the role of the major phospholipid biosynthesis transcription factor, INO2, in the regulation of vacuolar activity. We first performed qRT-PCR to examine the effect of Ino2p on the expression of VPS34 and FAB1. Our results showed that VPS34 was upregulated in the presence of inositol for both WT and ino2Δ cells. However, FAB1 was only upregulated significantly in ino2Δ cells. This indicated that Ino2p might be the negative regulator for FAB1 expression. Next, growth sensitivity experiment showed that WT, vma3Δ, and ino2Δ grew well in growth medium buffered to pH 5.5 containing 10 mM CaCl₂. As cells were switched to growth medium buffered to pH 7 containing CaCl₂ WT, ino2Δ and opi1Δ showed growth reduction, whereas vma3Δ was completely nonviable. As the concentration of CaCl₂ was increased to 60 mM, ino2Δ cells showed moderate growth reduction compared to WT. This result suggests that ino2Δ cells have better vacuolar activity. Microscopic analysis and vacuolar acidification were employed to further elucidate the importance of INO2 in vacuolar homeostasis. Analysis of vacuolar morphology indicated that WT and vma3Δ cells displayed vacuoles that occupied a small area of the cell when grown in media buffered to pH 5.5. Whereas, ino2Δ displayed fragmented vacuoles. On the other hand, all strains grown in media buffered to pH 7, exhibited enlarged vacuoles that occupied most of the cell’s surface. This indicated that the presence of INO2 may play negative effect in vacuolar morphology when cells are grown in media buffered to pH 5.5. Furthermore, vacuolar acidification assay showed that only vma3Δ cells displayed notably less acidic vacuoles as cells were grown in media buffered to pH 5.5 and pH 7. Whereas, ino2Δ cells displayed more acidic pH compared to WT at pH7. Taken together, our results demonstrated the molecular mechanism of the vacuolar activity regulated by the phospholipid biosynthesis transcription factors Ino2p. Ino2p negatively regulates vacuolar activity through the expression of FAB1.

Keywords: vacuole, phospholipid, homeostasis, Ino2p, FAB1

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Authors: Alireza Karimi

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Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: Ayda Youssef, Tarek Rafaat, Iman zaky

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Purpose: Langerhans’ cell histiocytosis (LCH) is not uncommon pathology that implies aberrant proliferation of a specific dendritic (Langerhans) cell. These atypical but mature cells of monoclonal origin can infiltrate many sites of the body and may occur as localized lesions or as widespread systemic disease. Liver is one of the uncommon sites of affection. The twofold objective of this study is to illustrate the radiological presentation of this disease, and to compare these results with previously reported series. Methods and Materials: Between 2007 and 2012, 150 patients with biopsy-proven LCH were treated in our hospital, a paediatric cancer tertiary care center. A retrospective review of radiographic images and reports was performed. There were 33 patients with liver affection are stratified. All patients underwent imaging studies, mostly US and CT. A chart review was performed to obtain demographic, clinical and radiological data. They were analyzed and compared to other published series. Results: Retrospective assessment of 150 patients with LCH was performed, among them 33 patients were identified who had liver involvement. All these patients developed multisystemic disease; They were 12 females and 21 males with (n= 32), seven of them had marked hepatomegaly. Diffuse hypodense liver parenchyma was encountered in five cases, the periportal location has a certain predilection in cases of focal affection where three cases has a hypodense periportal soft tissue sheets, one of them associated with dilated biliary radicals, only one case has multiple focal lesions unrelated to portal tracts. On follow up of the patients, two cases show abnormal morphology of liver with bossy outline. Conclusion: LCH is a not infrequent disease. A high-index suspicion should be raised in the context of diagnosis of liver affection. A biopsy is recommended in the presence of radiological suspicion. Chemotherapy is the preferred therapeutic modality. Liver histiocytosis are not disease specific features but should be interpreted in conjunction with the clinical history and the results of biopsy. Clinical Relevance/Application: Radiologist should be aware of different patterns of hepatobiliary histiocytosis, Thus early diagnosis and proper management of patient can be conducted.

Keywords: langerhans’ cell histiocytosis, liver, medical and health sciences, radiology

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Authors: Anna Cameron, Chunxia Zhao, Haofei Wang, Yun Liu, Guang Ze Yang

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Since the first publication in 1775, cancer research has built a comprehensive understanding of how cellular components of the tumor niche promote disease development. However, only within the last decade has research begun to establish the impact of non-cellular components of the niche, particularly the extracellular matrix (ECM). The ECM, a three-dimensional scaffold that sustains the tumor microenvironment, plays a crucial role in disease progression. Cancer cells actively deregulate and remodel the ECM to establish a tumor-promoting environment. Recent work has highlighted the need to further our understanding of the complexity of this cancer-ECM relationship. In vitro models use hydrogels to mimic the ECM, as hydrogel matrices offer biological compatibility and stability needed for long term cell culture. However, natural hydrogels are being used in these models verbatim, without tuning their biophysical characteristics to achieve pathophysiological relevance, thus limiting their broad use within cancer research. The biophysical attributes of these gels dictate cancer cell proliferation, invasion, metastasis, and therapeutic response. Evaluating the three most widely used natural hydrogels, Matrigel, collagen, and agarose gel, the permeability, stiffness, and pore-size of each gel were measured and compared to the in vivo environment. The pore size of all three gels fell between 0.5-6 µm, which coincides with the 0.1-5 µm in vivo pore size found in the literature. However, the stiffness for hydrogels able to support cell culture ranged between 0.05 and 0.3 kPa, which falls outside the range of 0.3-20,000 kPa reported in the literature for an in vivo ECM. Permeability was ~100x greater than in vivo measurements, due in large part to the lack of cellular components which impede permeation. Though, these measurements prove important when assessing therapeutic particle delivery, as the ECM permeability decreased with increasing particle size, with 100 nm particles exhibiting a fifth of the permeability of 10 nm particles. This work explores ways of adjusting the biophysical characteristics of hydrogels by changing protein concentration and the trade-off, which occurs due to the interdependence of these factors. The global aim of this work is to produce a more pathophysiologically relevant model for each tumor type.

Keywords: cancer, extracellular matrix, hydrogel, microfluidic

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Authors: Shanna Swart, Caryn Fenner, Athanasios Kotsiopoulos, Susan Harrison

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Linear alkanes are produced as by-products from the increasing use of gas-to-liquid fuel technologies for synthetic fuel production and offer great potential for value addition. Their current use as low-value fuels and solvents do not maximize this potential. Therefore, attention has been drawn towards direct activation of these aliphatic alkanes to more useful products such as alcohols, aldehydes, carboxylic acids and derivatives. Cytochrome P450 monooxygenases (P450s) can be used for activation of these aliphatic alkanes using whole-cells or cell-free systems. Some limitations of whole-cell systems include reduced mass transfer, stability and possible side reactions. Since the P450 systems are little studied as cell-free systems, they form the focus of this study. Challenges of a cell-free system include co-factor regeneration, substrate availability and enzyme stability. Enzyme immobilization offers a positive outlook on this dilemma, as it may enhance stability of the enzyme. In the present study, 2 different P450s (CYP153A6 and CYP102A1) as well as the relevant accessory enzymes required for electron transfer (ferredoxin and ferredoxin reductase) and co-factor regeneration (glucose dehydrogenase) have been expressed in E. coli and purified by metal affinity chromatography. Glucose dehydrogenase (GDH), was used as a model enzyme to assess the potential of various enzyme immobilization strategies including; surface attachment on MagReSyn® microspheres with various functionalities and on electrospun nanofibers, using self-assembly based methods forming Cross Linked Enzymes (CLE), Cross Linked Enzyme Aggregates (CLEAs) and spherezymes as well as in a sol gel. The nanofibers were synthesized by electrospinning, which required the building of an electrospinning machine. The nanofiber morphology has been analyzed by SEM and binding will be further verified by FT-IR. Covalent attachment based methods showed limitations where only ferredoxin reductase and GDH retained activity after immobilization which were largely attributed to insufficient electron transfer and inactivation caused by the crosslinkers (60% and 90% relative activity loss for the free enzyme when using 0.5% glutaraldehyde and glutaraldehyde/ethylenediamine (1:1 v/v), respectively). So far, initial experiments with GDH have shown the most potential when immobilized via their His-tag onto the surface of MagReSyn® microspheres functionalized with Ni-NTA. It was found that Crude GDH could be simultaneously purified and immobilized with sufficient activity retention. Immobilized pure and crude GDH could be recycled 9 and 10 times, respectively, with approximately 10% activity remaining. The immobilized GDH was also more stable than the free enzyme after storage for 14 days at 4˚C. This immobilization strategy will also be applied to the P450s and optimized with regards to enzyme loading and immobilization time, as well as characterized and compared with the free enzymes. It is anticipated that the proposed immobilization set-up will offer enhanced enzyme stability (as well as reusability and easy recovery), minimal mass transfer limitation, with continuous co-factor regeneration and minimal enzyme leaching. All of which provide a positive outlook on this robust multi-enzyme system for efficient activation of linear alkanes as well as the potential for immobilization of various multiple enzymes, including multimeric enzymes for different bio-catalytic applications beyond alkane activation.

Keywords: alkane activation, cytochrome P450 monooxygenase, enzyme catalysis, enzyme immobilization

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Authors: Mengfei Peng, Serajus Salaheen, Debabrata Biswas

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Lactobacillus casei found in the human intestine and mouth is commonly applied for dairy production. Recently, it was found that some byproducts produced by Lactobacillus exhibited antimicrobial activities against multiple bacteria. Meanwhile, introduction of prebiotic-like foods (e.g. cocoa) or probiotics or both of them as food supplements in human diets as well as in farm animal feeds is believed to be an effective ways in control/reduce the colonization of foodborne bacterial pathogens infection in the gut environment. We hypothesized that cocoa may stimulate the production antimicrobial components of Lactobacillus casei and may potentially inhibit/reduce the colonization and infection of foodborne bacterial pathogens in the gut. Mixed culture of L. casei (LC) with enterohemorrhagic E. coli EDL933 (EHEC), Salmonella Typhimurium LT2 (ST), or Listeria monocytogenes LM2 (LM) showed that LC could competitively exclude (100%) them within 72 h. Further, investigation of cell-free culture supernatant (CFCS) revealed that the antimicrobial effects of LC came from CFCS. CFCS of LC eliminated (100%) EHEC, ST, and LM within 72 h, and 2 h CFCS treatment increased the hydrophobicity of EHEC (5.10 folds), ST (8.48 folds), and LM (2.03 folds). In addition, LC cells exhibited more inhibitive effects than CFCS on cell adhesive and invasive activities of EHEC (52.14% & 90.45%), ST (66.89% & 93.83%), and LM (61.10% & 83.40%). Two clusters of poly-peptides in CFCS were identified by SDS-PAGE, the molecular weights of which are ≈5 KD and 40-45 KD. LC CFCS with overnight growth in the presence of 3% strengthened all of the antimicrobial activities (growth inhibition, outer membrane disruption, and cell infective ability reduction). Liquid chromatography/Mass spectrometry analysis detected 5 unique components in class of flavonoids in LC CFCS with overnight 3% cocoa supplement. Furthermore, qPCR results showed that CFCSs up-regulated the expression level of genes responsible for flagellin synthesis and motility, but down-regulated genes for specific binding and invasion-associated proteins synthesis. The stimulatory effects of cocoa in producing bioactive components of probiotics may aid prevention of foodborne illness caused by major foodborne enteric bacterial pathogens.

Keywords: foodborne pathogens, probiotics, prebiotics, pathogen exclusion

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Xin Chen, Xinyong Li, Qidong Zhao, Dong Wang

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A new coupling system was constructed by combining photo-electrochemical cell with electro-fenton cell (PEC-EF). The electrode material in this system was derived from MnyFe₁₋yCo Prussian-Blue-Analog (PBA). Mn₀.₄Fe₀.₆Co₀.₆₇-N@C spin-coated on carbon paper behaved as the gas diffusion cathode and Mn₀.₄Fe₀.₆Co₀.₆₇O₂.₂ spin-coated on fluorine-tin oxide glass (FTO) as anode. The two separated cells could degrade Sulfamethoxazole (SMX) simultaneously and some coupling mechanisms by PEC and EF enhancing the degradation efficiency were investigated. The continuous on-site generation of H₂O₂ at cathode through an oxygen reduction reaction (ORR) was realized over rotating ring-disk electrode (RRDE). The electron transfer number (n) of the ORR with Mn₀.₄Fe₀.₆Co₀.₆₇-N@C was 2.5 in the selected potential and pH range. The photo-electrochemical properties of Mn₀.₄Fe₀.₆Co₀.₆₇O₂.₂ were systematically studied, which displayed good response towards visible light. The photoinduced electrons at anode can transfer to cathode for further use. Efficient photo-electro-catalytic performance was observed in degrading SMX. Almost 100% SMX removal was achieved in 120 min. This work not only provided a highly effective technique for antibiotic treatment but also revealed the synergic effect between PEC and EF.

Keywords: electro-fenton, photo-electrochemical, synergic effect, sulfamethoxazole

Procedia PDF Downloads 182