1,721,283 research outputs found
Theoretical modelling of physiologically stretched vessel in magnetisable stent assisted magnetic drug targetingapplication
No full textThe magnetisable stent assisted magnetic targeted drug delivery system in a physiologically stretched vessel is considered theoretically. The changes in the mechanical behaviour of the vessel are analysed under the influence of mechanical forces generated by blood pressure. In this 2D mathematical model a ferromagnetic, coiled wire stent is implanted to aid collection of magnetic drug carrier particles in an elastic tube, which has similar mechanical properties to the blood vessel. A cyclic mechanical force is applied to the elastic tube to mimic the mechanical stress and strain of both the stent and vessel while in the body due to pulsatile blood circulation. The magnetic dipole–dipole and hydrodynamic interactions for multiple particles are included and agglomeration of particles is also modelled. The resulting collection efficiency of the mathematical model shows that the system performance can decrease by as much as 10% due to the effects of the pulsatile blood circulation
Development of artificial neuronal networks for molecular communication
No full textCommunication at the nanoscale can enhance capabilities for nanodevices, and at the same time open new opportunities for numerous healthcare applications. One approach toward enabling communication between nanodevices is through molecular communications. While a number of solutions have been proposed for molecular communication (e.g. calcium signaling, molecular motors, bacteria communication), in this paper, we propose the use of neuronal networks for molecular communication network. In particular, we provide two design aspects of neuron networks, which includes, (i) the design of an interface between nanodevice and neurons that can initiate signaling, and (ii) the design of transmission scheduling to ensure that signals initiated by multiple devices will successfully reach the receiver with minimum interference. The solution for (i) is developed through wet lab experiments, while the solution for (ii) is developed through genetic algorithm optimization technique, and is validated through simulations
Boron Nitride Nanoparticles Loaded with a Boron-Based Hybrid as a Promising Drug Carrier System for Alzheimer’s Disease Treatment
Get PDFThe search for an innovative and effective drug delivery system that can carry and release targeted drugs with enhanced activity to treat Alzheimer’s disease has received much attention in the last decade. In this study, we first designed a boron-based drug delivery system for effective treatment of AD by integrating the folic acid (FA) functional group into hexagonal boron nitride (hBN) nanoparticles (NPs) through an esterification reaction. The hBN-FA drug carrier system was assembled with a new drug candidate and a novel boron-based hybrid containing an antioxidant as BLA, to constitute a self-assembled AD nano transport system. We performed molecular characterization analyses by using UV-vis spectroscopy, Fourier transform infrared spectrophotometer (FTIR), scanning electron microscope (SEM), Energy-dispersive X-ray spectroscopy (EDS) and Zeta potential investigations. Second, we tested the anti-Alzheimer properties of the carrier system on a differentiated neuroblastoma (SHSY5-Y) cell line, which was exposed to beta-amyloid (1–42) peptides to stimulate an experimental in vitro AD model. Next, we performed cytotoxicity analyses of synthesized molecules on the human dermal fibroblast cell line (HDFa) and the experimental AD model. Cytotoxicity analyses showed that even higher concentrations of the carrier system did not enhance the toxicological outcome in HDFa cells. Drug loading analyses reported that uncoated hBN nano conjugate could not load the BLA, whereas the memantine loading capacity of hBN was 84.3%. On the other hand, memantine and the BLA loading capacity of the hBN-FA construct was found to be 95% and 97.5%, respectively. Finally, we investigated the neuroprotective properties of the nano carrier systems in the experimental AD model. According to the results, 25 µg/mL concentrations of hBN-FA+memantine (94% cell viability) and hBN-FA+BLA (99% cell viability) showed ameliorative properties against beta-amyloid (1–42) peptide toxicity (50% cell viability). These results were generated through the use of flow cytometry, acetylcholinesterase (AChE) and antioxidant assays. In conclusion, the developed drug carrier system for AD treatment showed promising potential for further investigations and enlightened neuroprotective capabilities of boron molecules to treat AD and other neurodegenerative diseases. On the other hand, enzyme activity, systematic toxicity analyses, and animal studies should be performed to understand neuroprotective properties of the designed carrier system comprehensively
Integration of host, pathogen and microbiome -omics data for studying infectious diseases
Get PDFIn an ever-growing worldwide population, human infectious diseases are an increasingly serious problem for public health. In particular, more than a million deaths and millions of infectious disease cases per year caused by fungal pathogens have been reported globally in recent years. Hence, more investments must be put into fungal research to overcome the problem. The opportunistic pathogen Candida albicans and the airborne Aspergillus fumigatus are the two most prevalent fungal pathogens causing serious issues in medical care units. Despite the recent advances in fungal research, there is little knowledge about the role of fungal metabolism in developing the infection when coexisting within the human body with microbial community members in different organs. This dissertation applied computational tools, and implemented systems biology approaches to uncover key factors in the colonization of the pathogens, especially C. albicans and A. fumigatus, from a systems biology perspective and unseen by wet-lab experiments alone. Next to multi-omics data analysis, a major effort was put into genome-scale metabolic models (GEMs) generation and analysis as a promising approach to shed light on the role of metabolism in developing the infection. In brief, this thesis sheds light on key factors leading to the inhibition or promotion of fungal growth. This especially includes the first available GEM reconstruction of C. albicans to theoretically study the intricate interaction of the fungus with the human host and the microbial community members. Lastly, a platform of 252 A. fumigatus GEMs at the strain resolution was generated. It revealed the phenotypic diversity of A. fumigatus strains isolated from different hospitals and farms in Germany and explained the contribution of the fungus to the shaping of the metabolic landscape of the lung microbiome in a favorable manner for fungal growth
Longitudinal big biological data in the AI era
No full textAbstract Generating longitudinal and multi-layered big biological data is crucial for effectively implementing artificial intelligence (AI) and systems biology approaches in characterising whole-body biological functions in health and complex disease states. Big biological data consists of multi-omics, clinical, wearable device, and imaging data, and information on diet, drugs, toxins, and other environmental factors. Given the significant advancements in omics technologies, human metabologenomics, and computational capabilities, several multi-omics studies are underway. Here, we first review the recent application of AI and systems biology in integrating and interpreting multi-omics data, highlighting their contributions to the creation of digital twins and the discovery of novel biomarkers and drug targets. Next, we review the multi-omics datasets generated worldwide to reveal interactions across multiple biological layers of information over time, which enhance precision health and medicine. Finally, we address the need to incorporate big biological data into clinical practice, supporting the development of a clinical decision support system essential for AI-driven hospitals and creating the foundation for an AI and systems biology-based healthcare model
Calculation of nanoparticle capture efficiency in magnetic drug targeting
No full textThe implant assisted magnetic targeted drug delivery system of Avil\ue9s, Ebner and Ritter, which uses high gradient magnetic separation (HGMS) is considered. In this 2D model large ferromagnetic particles are implanted as seeds to aid collection of multiple domain nanoparticles (radius ). Here, in contrast, single domain magnetic nanoparticles (radius in 20–100 nm) are considered and the Langevin function is used to describe the magnetization. Simulations based on this model were performed using the open source C++ finite volume library OpenFOAM. The simulations indicate that use of the Langevin function predicts greater collection efficiency than might be otherwise expected
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Inclusion of magnetic dipole–dipole and hydrodynamic interactions in implant-assisted magnetic drug targeting
No full textMathematical modelling of the implant-assisted magnetic drug targeting system of Avilés, Ebner and Ritter is performed. In order to model the agglomeration of particles known to occur in this system, the magnetic dipole–dipole and hydrodynamic interactions are included. Such interactions were calculated previously by Mikkelsen et al. under low magnetic fields (~0.05 T) in microfluidic systems. Here, a higher magnetic field (0.7 T) is considered and the effect of interactions on two nanoparticles with a seed implant is calculated. The calculations were performed with the open-source software OpenFOAM. Different initial positions are considered and the system performance is assessed in terms of capture cross section. Inclusion of both interactions was seen to alter the capture cross section of the system by up to 7% in absolute terms
Inclusion of Interactions in Mathematical Modelling of Implant Assisted Magnetic Drug Targeting
Get PDFDrug delivery technologies are an important area within biomedicine. Targeted drug delivery aims to reduce the undesired side effects of drug usage by directing or capturing the active agents near a desired site within the body. This is particularly beneficial in, for instance, cancer chemotherapy, where the side effects of general (systemic) drug administration can be severe.
One approach to targeted drug delivery uses magnetic nanoparticles as the constituents of carriers for the desired active agent. Once injected into the body, the behaviour of these magnetic carriers can be influenced and controlled by magnetic fields. In implant assisted magnetic drug targeting systems a magnetic implant, typically a stent, wire or spherical seed can be used to target sites deep within the body as the implant acts as a focus for the resulting magnetic force. This can be easily understood as the force depends on the gradient of the magnetic field and the gradient near the implant is large.
In designing such a system many factors need to be considered including physical factors such as the size and nature of the implants and carriers, and the fields required. Moreover the range of applicability of these systems in terms of the regions of the vasculature system, from low blood velocity environments, such as capillary beds to higher velocity arteries, must be considered. Furthermore, assessment criteria for these systems are needed. Mathematical modelling and simulation has a valuable role to play in informing in vitro and in vivo experiments, leading to practical system design.
Specifically, the implant assisted magnetic drug targeting systems of Avil´es, Ebner and Ritter are considered within this work, and two dimensional mathematical modelling is performed using the open source C++ finite volume library OpenFOAM. In the first system treated, a large ferromagnetic particle is implanted into a capillary bed as a seed to aid collection of single domain nanoparticles (radius 20-100 nm). The Langevin function is used to calculate the magnetic moment of the particles, and the model is further adapted to treat the agglomeration of particles known to occur in these systems. This agglomeration can be attributed to interparticle interactions and here the magnetic dipole-dipole and hydrodynamic interactions for two mutually interacting nanoparticles are modelled, following Mikkelsen et al. who treated two particle interactions in microfluidic systems, with low magnetic field (0.05 T). The resulting predicted performance is found to both increase and decrease significantly depending on initial positions of the particles. Secondly, a ferromagnetic, coiled wire stent is implanted in a large arterial vessel. The magnetic dipole-dipole and hydrodynamic interactions for multiple (N < 20) particles are included. Different initial positions are considered and the system performance is assessed. Inclusion of these interactions yields predictions that are in closer agreement with the experimental results of Avil´es et al.. We conclude that the discrepancies between the non interacting theoretical predictions and the corresponding experimental results can (as suggested by Avil´es et al.) be largely attributed to interparticle interactions and the consequent agglomeration
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