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Analyzing Peptidoglycan Elasticity of E. coli Using Hyperosmotic Shock Studies
No full textThis study investigated the physical properties of Escherichia coli cells grown in different media and subjected to various hyperosmotic shock conditions. Image analysis at 100X was used to measure changes in cell length, and the results showed a statistically significant difference between the media types, conditions, and their interaction. The study found that as the growth rate increases, the cell becomes relatively less stiff, and conversely, as the growth rate decreases, the cell becomes relatively stiffer. The study suggests that the cross-link density and relative stiffness of the cell are closely related to the growth rate and can influence the cell\u27s response to osmotic stress. The findings provide valuable insight into the mechanisms underlying bacterial cell wall elasticity, osmoregulation, and antibiotic resistance. Future studies could increase the concentration of hyperosmotic shock conditions and develop the Zetasizer method to accurately reflect the average cell length of bacteria present in the samples
Studazon
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Liposome Synthesis and Evaluation in the HEK-293 Cell Line
Get PDFLiposomes were synthesized using the thin film method. A lyophilized power of egg-derived phosphatidylcholine, stearylamine, and cholesterol were added to ethanol and dried under argon to form a lipid cake. The lipid cake was rehydrated with dPBS and sonicated at 60°C forming a heterogenous batch of liposomes. Our results revealed the average size of the liposomes, determined by Dynamic Light Scattering, was approximately 223.1nm, while demonstrating a weakly positive zeta-potential of 1.9± 8.07mv. Next, we tested the antitumor action of the liposomes in the HEK-293 cell line via an MTT assay. We observed that the liposomes were able to inhibit cell proliferation in the HEK-293 cell line in comparison to controls. Future studies will focus on encapsulating hydrophobic and hydrophilic molecules within the liposome formulation to improve delivery to cancer cells
Medical Biotechnology: Stem Cell Based Therapy for Diabetic Polyneuropathy
No full textThis project is to explore the concept of Stem Cell Therapy and its application to diabetic polyneuropathy or DPN, a side effect of diabetes in which nerve damage occurs within multiple parts of the body. The research will go over the general process of how Stem Cell Therapy can be implemented with patients who have DPN and see if it is a possible and efficient treatment option
Statistics project: Screen time within different Genders
Get PDFA statistics project studying how different demographics affect screen time specifically with cell phone
A Novel, Minimally Invasive, Paper-based Biomarker Assay for Molecular Diagnosis of Preeclampsia
Get PDFPreeclampsia is a severe pregnancy condition marked by high blood pressure and proteinuria that can strike at any point throughout pregnancy or immediately after birth. Although the exact cause of this disease is unknown, there are several symptoms associated. The goal of this study is focused on the evaluation of biomarker-based assays for molecular diagnosis of the condition in a sensitive and timely manner. This project involved the use of an immunoassay, fabricated serum samples, and a paper-based assay to assess the strength of the presence of the placental growth factor (PlGF). This was done to facilitate a proof-of-concept mechanism that can be translated into a molecular diagnostic biosenso
Biofilm Formation in Bath Toys
Get PDFBiofilms are a collective of microorganisms and matrixes that can grow on a variety of surfaces. Depending on the environment, the growth of biofilm formation can vary. These environmental factors may include temperature or access to nutrients. Overtime unkept bath toys can form a biofilm layer, which can be hard to remove and hazardous to children. It is important to understand biofilms so that a variety of methods can be successfully used against them
Effects of GLP-1 mRNA Half-life and Stability on Its Potential in Protein Therapeutics – A Review
No full textNumerous scientific research studies have focused on the significance of mRNA stability and its influence on gene expression and translation. The stability of mRNA has been extensively studied in relation to gene expression, with recent research and advancements in high-throughput RNA sequencing methods indicating that mutations and modifications to the nucleotide sequence of mRNA can significantly impact its regulation. The molecular mechanisms regulating the stability of human GLP-1 mRNA through nucleotide modifications and their effects on GLP-1 protein therapeutics have been studied and characterized in various in-vitro and in-vivo studies to screen various GLP-1 receptor agonists (GLP-1 RAs) that have shown to increase GLP-1 mRNA stability by achieving greater mRNA half-life, prolonged expression, and decreased endogenous cleavage by DPP-IV protease. This review paper seeks to provide a comprehensive understanding of few GLP-1 receptor agonists - their half-life and stability, in-vitro and in-vivo characterization studies, various clinical studies and case studies, which could inform the development of more effective GLP-1 protein therapeutics for Type 2 diabetes mellitus (T2DM) therapy
An Analysis of Medication Adherence in a Large Outpatient Population During the COVID-19 Pandemic Using a Novel Value-Based Pharmacy System
Get PDFBackground:Adherence to a medication regimen is defined as taking the medication as directed by the prescriber. Adherence is critical to achieve the desired therapeutic outcomes. Medication adherence has not been examined in large outpatient populations since the onset of the COVID-19 pandemic. A novel outpatient value-based pharmacy system (VPS) was used to collect adherence data from a large, outpatient population. The aim of this descriptive study was to analyze the reasons, medication classes, and diagnoses associated with nonadherence.
Materials and Methods:Telepharmacist-documented adherence data from a large (n = 6,479) outpatient population that received remote consultation during the COVID-19 pandemic (August 1, 2020–November 28, 2022) were considered for this study. The adherence data were compiled within the VPS.
Results:The overall rate of patients reporting at least one incident of nonadherence to their medication regimens was 21.5%. Medications used to treat hypertension, type 2 diabetes, and hyperlipidemia were least adhered to. Statins, beta-2 agonists, and corticosteroids were least adhered to. The most common reasons for nonadherence included knowledge gaps regarding therapy, forgetfulness, and side effects.
Discussion:This represents the first descriptive analyses of adherence metrics in a large outpatient population during the COVID-19 pandemic. Polypharmacy, prevalence of diagnosis, and medication side effect profile may have contributed to the results observed. This study demonstrates the ability of a VPS to document key data to better inform the health care team. Elucidating adherence metrics in such populations may allow pharmacists and prescribers to identify subpopulations that require further education and management
Metaheuristic link prediction (MLP) using AI based ACO-GA optimization model for solving vehicle routing problem
No full textDelivering goods is crucial to the supply chain industry because it directly affects package delivery, a crucial aspect of real-time vehicle movement on which most e-commerce businesses rely. By improving the vehicle routing process, package delivery speed could be increased, and especially for medical emergency-related items, this will drastically impact the nature of delivery, cost, and time spent on it. This is being done to prepare an efficient routing model for the vehicle route, which will ultimately result in an improved path. As they have a variety of restrictions, the goal of this article is to identify the various parameters that, when used with a multi-objective optimization-based routing model, will satisfy the limit. The routing route may be made more efficient using ant colony optimization (ACO) in conjunction with an upgraded recurrent model of the genetic algorithm (GA). To achieve this, the ACO-GA optimization method known as metaheuristic link prediction (MLP) was used for parameter prediction. This method offers an evaluation of the relationship between the emission of CO2 (carbon dioxide), the trip region, and the other associated parameters. The authors of this study compare the findings of their prior work, which combined ACO and K-means clustering to get better results. Once the results are established, they will become the primary objective function of the optimization algorithm, which will be responsible for choosing the path that is connected to the parameter values. The complete procedure of the suggested method was simulated and evaluated using the publicly accessible data set of Solomon’s benchmark data set with the property pairs, and then it was compared with the ACO-K-means method. In addition to this, the current algorithm is compared with other vehicle routing algorithms to improve the process