Parrottbryant5469
At last, surface modifications of PNPs and PLGA nanocomplexes (NCs) are additionally examined.
Substances present in nature have been a continuous source for the development of drugs for cardiovascular and infectious diseases, cancer, and many other diseases. As the literature concerning these natural products grows, it becomes more difficult for a reader to quickly grasp the essential facts and develop a well-informed impression of this field of research. Until now, it has also been difficult to determine which natural products and research objectives were receiving the most attention as measured by number of citations.
The current study of all published articles concerned with natural products sought to identify which natural products and which research objectives are connected with the major contributors to scientific journals based on the number of relevant publications and the number of times each publication was cited elsewhere.
Bibliometric data, including citation data, were extracted from the Web of Science database using the search string TS=("natural product*)" and analyzed by the VOSviewer software.
The search yielded 63,194 articles, with more than half of the manuscripts published since 2012. The ratio of original articles to reviews was 5.81. The major contributing countries were the United States, China, Germany, Japan, and India. Articles were published mainly in journals focused on chemistry, pharmacology or biochemistry. Curcumin, resveratrol, and terpenoids were the most frequently cited natural products.
The results of the current study provide researchers from different backgrounds and healthcare professionals with a brief overview of the major trends in natural-product research in the form of a citation-based summary of the relevant literature.
The results of the current study provide researchers from different backgrounds and healthcare professionals with a brief overview of the major trends in natural-product research in the form of a citation-based summary of the relevant literature.
Aberrant expression of cell adhesion molecules and matrix metalloproteinase (MMPs) plays a pivotal role in tumor biological processes including progression and metastasis of cancer cells. Targeting these processes and detailed understanding of their underlying molecular mechanism is an essential step in cancer treatment. Dexamethasone (Dex) is a type of synthetic corticosteroid hormone used as adjuvant therapy in combination with current cancer treatments such as chemotherapy in order to alleviate its side effects like acute nausea and vomiting. Recent evidences have suggested that Dex may have antitumor characteristics.
Dex affects the migration and adhesion of T47D breast cancer cells as well as cell adhesion molecules e.g., cadherin and integrin, and MMPs by regulating the expression levels of associated genes.
In this study, we evaluated the cytotoxicity of Dex on the T47D breast cancer cell line through MTT assay. Cell adhesion assay and wound healing assay were performed to determine the impact of Dex on cell adhesion and cell migration, respectively. Moreover, real-time PCR was used to measure the levels of α and β integrin, E-cadherin, N-cadherin, MMP-2, and MMP-9.
Dex decreased the viability of T47D cells in a time and dose-dependent manner. Cell adhesion and migration of T47D cells were reduced upon Dex treatment. Amenamevir order The expression of α and β integrin, E-cadherin, N-cadherin, MMP-2, and MMP-9 were altered in response to the Dex treatment.
Our findings demonstrated that Dex may have a role in the prevention of metastasis in this cell line.
Our findings demonstrated that Dex may have a role in the prevention of metastasis in this cell line.
Reverse transcriptase is an important therapeutic target to treat AIDS caused by the Human Immunodeficiency Virus (HIV). Despite many effective anti-HIV drugs, reverse transcriptase (RT) inhibitors remain the cornerstone of the drug regimen to treat AIDS. In the present work, we have expedited the use of different computational modules and presented an easy, cost-effective and high throughput screening method to identify potential reverse transcriptase inhibitors.
A congeneric series of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one analogs having anti-HIV activity were subjected to structure-based 2D, 3D QSAR, Pharmacophore Modeling, and Molecular Docking to elucidate the structural properties required for the design of potent HIV-RT inhibitors. Prediction of preliminary Pharmacokinetic and the Drug Likeliness profile was performed for these compounds by in silico ADME study.
The 2D and 3D- QSAR models were developed by correlating two and three-dimensional descriptors with activity (pIC50) by spation and important structural insights for the discovery, design of novel and potent reverse transcriptase inhibitors with high therapeutic windows in the future.
The results of the present work provide more useful information and important structural insights for the discovery, design of novel and potent reverse transcriptase inhibitors with high therapeutic windows in the future.Ultrasound elastography has become available in the everyday practice, allowing direct measurement of tissue elasticity with important and expanding clinical applications. Several studies that have evaluated pathological and nonpathological tissues have demonstrated that ultrasound elastography can actually improve diagnostic accuracy of underlying disease process by detecting differences in their elasticity. Ocular and periocular tissues can also be characterized for their elastic properties. In this context, comprehensive review of literature on ultrasound elastography as well as its current applications in Ophthalmology is presented.
Osteoarthritis (OA) is a common degenerative joint inflammation which may lead to disability. Although OA is not lethal, this disease will remarkably affect patient's mobility and their daily lives. Detecting OA at an early stage allows for early intervention and may slow down disease progression.
Magnetic resonance imaging is a useful technique to visualize soft tissues within the knee joint. Cartilage delineation in magnetic resonance (MR) images helps in understanding the disease progressions. Convolutional neural networks (CNNs) have shown promising results in computer vision tasks, and various encoder-decoder-based segmentation neural networks are introduced in the last few years. However, the performances of such networks are unknown in the context of cartilage delineation.
This study trained and compared 10 encoder-decoder-based CNNs in performing cartilage delineation from knee MR images. The knee MR images are obtained from Osteoarthritis Initiative (OAI). The benchmarking process is to compare various CNNs based on the physical specifications and segmentation performances.
