Academic literature on the topic '030405 Molecular Medicine'

Trigonometric functions are essential part of digital communication systems such as receivers, synthesizers, and phase locked loop. Implementation of trigonometric functions requires many arithmetic units; multipliers and adders circuits which reduces the speed of operation and consumes much power. In this paper we introduce two approximation methods to represents sine, and cosine functions to achieve fast operation and low power consumption. The simulations indicate a matching between the ideal trigonometric functions and the approximation method with 0.001 error which considered as trivial amount.

Background: Abuse of analgesic and sedative drugs often leads to severe respiratory depression and sometimes death. Approximately 69,000 people worldwide die annually from opioid overdoses. Purpose: This work aimed to investigate whether CX1739 can be used for emergency treatment of acute respiratory depression due to drug abuse. Results: First, the results clarify that CX1739 is a low-impact ampakine that can safely activate α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors without causing excito-neurotoxicity. Second, CX1739 rapidly crossed the blood–brain barrier (Tmax = 2 min), which meets the requirement of rapid onset of action in vivo. Our work provides preliminarily confirmation that high-dose intravenous administration of CX1739 can immediately reverse respiratory depression in animal models of respiratory depression caused by opioid agonist 030418, pentobarbital sodium and ethanol.

Modern Machine learning fusion approaches tend to extract features depending on two techniques (hand-crafted feature and representation learning). Hand-crafted features can waste time and are not sufficient for downstream tasks. Unlike representation learning, we automatically learn features with minimum time and effort and are suitable for downstream tasks. In our paper, we provide work on graph neural network methods with details on classical graph embedding approaches and the different methods in neural graph networks such as graph filtering, graph pooling, and the learning parameter for graph following each technique with a general framework or mathematical proof for customer satisfaction. To satisfy customer's feel, this research employs NLP techniques. We describe the adversarial attacks and defenses on graph representation approaches. Also, advanced application of neural graph networks is reviewed, such as combinational optimization, learning program representation, physical system modeling, and natural language processing. Finally, the challenges in geometric neural networks and future research work have been introduced.

Allergic diseases such as asthma and hay fever cause distress to numerous individuals throughout the world, sometimes with fatal consequences. Many associated costs - physical absences, psychological corollaries or medication - are borne by the community via health agencies or the government. The symptoms of these diseases, such as inflammation of the airways, are initially caused by the apposite allergen crosslinking at least two molecules of IgE bound to their high-affinity cell surface receptor FceRI. This interaction initiates intracellular signalling starting a cascade of events resulting in cellular degranulation and the release of inflammatory mediators. Many present treatments for these conditions act at this stage to inhibit the activity of these chemical intermediaries, after inflammation has been initiated. This thesis specifically addresses the high-affinity receptor for IgE (a chain) - FceRI-a, chimeric receptors of FceRI-a and FcyRIIa, with FceRI-a point mutants, and their interaction with IgE.

Natural products and derivatives thereof have contributed significantly to drug discovery and development and have also been used in traditional medicine for the treatment of various disease states. Sometimes, the use of such traditional medicines may be based upon many hundreds, if not thousands, of years of human experience. An advantage of drugs derived from natural products compared to synthetic drugs is their availability and the likelihood of reduced side effects. Drugs and drug leads derived from natural products may also be less time consuming and expensive to develop and may be more accessible to developing countries. An exciting advance in this area is the application of computational chemistry to potentially bioactive molecules that can be identified in such natural products. Thus, the structural and physicochemical properties of such molecules can be reconciled with current theories on the molecular aspects of a given disease and/or be used to improve upon such theories or to develop new ones. Computed properties may also be benchmarked to experimental data for isolated molecules of interest that can lead to improved molecular design. In this context, two different strategies have been devised and implemented for the identification and development of potentially bioactive compounds from medicinal plant materials whereby reliable molecular structures and experimental data, that have been previously reported in the peer reviewed scientific literature, have been reconciled with carefully designed computational chemistry calculations and/or further experimentation - with a view to (I) developing improved antioxidants as potential anti-inflammatory drugs and (II) to identify small molecule potential metal chelators that may pass through the blood brain barrier and potentially ameliorate neurodegenerative diseases such as Alzheimer’s or Parkinson’s disease. Thus two bioactive products derived from medicinal plants, namely Ribes nigrum (Blackcurrant leaves) and Bacopa Monnieri (Brahmi tea), that are traditionally employed to treat rheumatic disease and neurodegenerative symptoms, respectively, have been chosen for investigation under these strategies. These plant materials have been extensively investigated in the scientific literature in terms of the identification of the structures of their potentially bioactive molecules and also with respect to experimental and biological investigations. For the former material, the purported anti-inflammatory effects of the component poly phenolic molecules, in terms of their reported experimental radical scavenging propensities, have been reconciled with their computed antioxidant capacities. These have also been related to a range of computed qualitative and quantitative structural and physicochemical properties. This is with a view to optimizing their antioxidant potential and possibly designing anti- inflammatory drug candidates. For the latter material, a screening of the reported molecular structures of potentially bioactive components has identified two smaller molecular fragments, namely the isomers jujubogenin and pseudojujubogenin, that may also be present in the plant extract and that are deemed capable of passing through the blood brain barrier and complexing transition elements within the brain, specifically copper and/or zinc, that are associated with stabilizing the amyloid plaque of Alzheimer’s disease, or iron, that may over-load the substantial nigra in Parkinson’s disease. In this thesis, the metal complexes of these two molecules have been modelled utilizing semi-empirical quantum chemistry and density functional calculations and the characteristics of the copper, zinc and iron complexes have been described. These studies clearly show that the diaquo, square planar copper complex of jujubogenin is the preferred structure, revealing that jujubogenin is an excellent bidentate ligand for this particular transition metal. The corresponding zinc complex was also shown to be feasible, but less likely to form; whereas the iron complex was shown not to be accommodated at all. To complement these studies, the Brahmi tea material was extracted with a range of solvents, and fractions were systematically subjected to ESI- MS. Scrutiny of the resultant spectra revealed the presence of the protonated jujubogenin moiety in one of the ethyl acetate fractions. Subsequent spiking of this fraction with copper, zinc and iron revealed the presence in the spectra of the diaquo copper complex of jujubogenin, exactly as predicted from the computer modelling. Notably, no zinc or iron complexes could be detected and competition experiments only revealed the presence of the copper complex - also consistent with the computer modelling. Subsequent MS/MS experiments on the copper complex yielded the free ligand. In summary, for possible anti-inflammatory agents, these investigations show that the computed homolytic bond dissociation energies of the component poly phenolics, by themselves, are not sufficient to explain enhanced antioxidant activity and suggest that other properties such as molecular conformation, steric effects and, in particular, the magnitude and direction of the dipole moment also have important roles to play. In relation to possible drug leads for the treatment of neurological conditions, the discovery of the extraordinary copper specificity of the jujubogenin molecule, both computationally and experimentally, makes this molecule a candidate for a BBB penetrating chelating agent that could be active towards the amelioration of Alzheimer’s disease and possibly other conditions. This is an exciting discovery and warrants the isolation of jujubogenin and its derivatives in for further testing. Thus, the design and implementation of the key strategies devised and described within this thesis and their respective application to two selected traditional medicines relating to specific disease states, is demonstrably useful in the rational design of drug candidates and suggest new avenues for future research.

The interaction of proteins with lipids is an important aspect of research as it plays a main role in various biological responses such as metabolic pathways, signal transduction and in drug discovery. Proteins that take part in the treatment of different diseases act as drug targets and hence research is ongoing to find new series of ligands of medicinally significant proteins. Few such proteins, peroxisome proliferator activated receptors (PPARs), retinoid receptors, cannabinoid receptors (CB1 and CB2), lipoxygenase (LOX), cyclooxygenases (COXs) were selected for the author’s study due to their therapeutic role to act as pharmacological targets. The existing ligands for these protein targets are causing some side effects. For example, thiazolidinediones are the currently used ligands for PPARs. Thiazolidinediones bind to PPARs and used in the treatment of diabetes. However, this treatment results in obesity. Similarly, the use of Nonsteroidal Anti-inflammatory Drugs (NSAIDs) like aspirin and ibuprofen lead to stomach or gastrointestinal ulcers, heartburn, headache and dizziness. Hence, a set of ligands which have a significant role in the treatment of diseases were selected and compared for their binding affinities towards the design of a new series of drugs.