Role of aromatic pi-stacking on the aggregation of human islet amyloid polypeptide (hIAPP)

Human islet amyloid polypeptide (hIAPP) is secreted in the β-cells of the pancreas, which also secretes insulin. In type 2 diabetes mellitus, hIAPP undergoes self-aggregation, forming fibrils. This self-aggregation is cytotoxic and is thought to be linked to type 2 diabetes mellitus by causing β-cell membrane destruction. The N-terminus of hIAPP (1-19) contains a binding site (residues 14-18) for self-aggregation. Aggregation is thought to be mediated by pistacking interactions between phenylalanine residues of hIAPP. In this study, the hIAPP 1-19 sequence was modified by replacing phenylalanine with alanine and naphthylalanine, to study if the modification of the aromatic side chain alters aggregation and membrane destructive activity. The activity of the modified hIAPP sequences was tested against differently charged lipids, using a fluorescent dye leakage assay. Both modified hIAPP 1-19 sequences were found to be less active when compared to the original hIAPP 1-19 sequence. This might be due to lack of aromatic pi-stacking interactions at the 15th position or due to inappropriate structural conformation of the naphthylalanine analog

Development of a flexible image-based approach for studying signal transduction in isolated arterioles

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on May 11, 2009)Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2007.Dissertations, Academic -- University of Missouri--Columbia -- Biological engineering.Myogenic constriction in arterioles occurs when membrane tension leads to smooth muscle depolarization and voltage-gated calcium channel activation. A barrier to understanding the exact signaling events is the limited ability of glass electrodes to follow temporal changes in membrane potential (Em). This study aimed to develop an imaging station to measure Em, [Ca²+]i and diameter in isolated arterioles. A Forster Resonance Enery Transfer (FRET) approach was proposed for Em meaurement due to enhanced sensitivity and temporal responsiveness. FRET indicators used were CC2-DMPE (Coumarin-labelled Phospholipid, Donor, ex: 405nm) and DisBAC₄(3) (Oxonol, acceptor). For measurement of [Ca²+]i Fluo-4 AM (ex 488nm) was used. Initial studies examined toxicity of the multiple fluorophores. Isolated cells remained viable after loading with the fluorophores. Arterioles maintained myogenic, and agonist, responsiveness pre and post dye loading suggesting a lack of toxicity. Spectral characteristics of the fluorophores used in the project required specific optical filter units and development of a. custom built optical system. Using the system, FRET was demonstrated, as was the ability to detect changes in fluorescence signal to known depolarizing stimuli. The developed system is now ready for translation to the study of myogenic signaling in intact and functional arterioles

DESIGN AND EVALUATION OF SELF-NANOEMULSIFYING DRUG DELIVERY SYSTEMS OF MANIDIPINE FOR ENHANCEMENT OF SOLUBILITY

Objective: The present work is aimed at developing liquid self-nanoemulsifying drug delivery system (liquid-SNEDDS) of manidipine. Methods: The manidipine SNEDDS is formulated with excipients comprising Capmul MCM as oil phase, Transcutol P as surfactant, and Lutrol L 300 as cosurfactant. The prepared fifteen formulations of manidipine SNEDDS were performed for emulsification time, percentage transmittance, particle size, drug release, in vitro dissolution and stability studies. Ternary phase diagram plotted using Chemix software. Results: The optimized manidipine liquid SNEDDS formulation (F14) subjected to drug-excipient compatibility studies by Fourier-transform infrared spectroscopy and characterized for particle size, zeta potential, scanning electron microscopy, and stability studies. The morphology of manidipine SNEDDS indicates spherical shape with uniform particle distribution. The percentage drug release from optimized formulation F14 (98.24±5.14%) was higher than that of pure drug (39.17±2.98%). The stability data indicated no noticeable change in drug content, emulsifying properties, drug release, and appearance. Conclusion: Hence, a potential SNEDDS formulation of manidipine developed with enhanced solubility, dissolution rate, and bioavailability

Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security