Jamahussein0544

Taking the beneficial factor into account, they are being used for the treatment of disorders like Cerebral palsy, Parkinson's disorder, Aplastic anemia, Multiple sclerosis and many more. However, it is still illegal to use stem cells in the abovementioned disorders. This review will encompass different stem cells and would emphasize PMSCs for its uniqueness in therapy that no other previously published literature reviews have taken into consideration. Later in the review, we also discuss current regulatory aspects related to stem cells. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.RATIONALE AND OBJECTIVE Proton pump inhibitor (PPI) is one of the most widely prescribed medicines and commonly used in gastric related disorder and there is huge need of addressing the irrational use of PPI in a country like India. The present study was designed to describe the rational drug use and cost comparison analysis of PPI in a rural tertiary care hospital. METHODOLOGY A prospective observational study was performed among 253 inpatients for a period of 9 months after getting ethical approval. Those who received the PPIs for any of its indications were included in the study without any gender or age restriction. US FDA guidelines were used to analyse the appropriateness of the drug use and cost comparison analysis of the branded versus generic PPIs also performed. FINDINGS Among the 253 inpatients, the majority (62%) were male and the mean age was 46±19 years. Mean hospital stay and the number of drugs in prescription were found to be 4.0 ± 1days 4.39 ±1.16 items, respectively. Pantoprazole (76%) was the most prescribed PPI even though the majority (57%) of the patients treated outside the FDA approved indication. Drug interaction has been reported in 14% and ADR in 9% of the population. The average cost of PPI during the hospital stay estimated at 207.96±149.57 INR, and the potential cost saving of INR 41582 was observed with generic replacement. CONCLUSION The study inferred that irrational drug use of PPI still prevalent, that too without considering the economic impact of it on general populations. Healthcare practitioners should be aware and cautious while prescribing the PPI to identify the actual need and to choose with the most cost-effective alternative. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.AIMS Selecting natural inhibitors toward CK2 enzyme using database mining. BACKGROUND Casein Kinase 2 (CK2) is a ubiquitous cellular serine-threonine kinase with broad spectrum of substrates. This enzyme is widely expressed in eukaryotic cells and is overexpressed in different human cancers. Thus, inhibition of CK2 can induce the physiological process of apoptosis leading to tumor cell death. OBJECTIVE Selecting natural inhibitors toward the target enzyme using database mining. METHOD With our continuous effort to discover new compounds with CK2 inhibitory effect, several commercial natural databases were searched using molecular modeling approach and the selected compounds were evaluated in vitro. RESULT Three compounds were selected as candidates and evaluated in vitro using holoenzyme and their effect on three cancer cell lines was performed. Unfortunately, the selected candidates were weak inhibitors toward the target enzyme, and only one compound showed moderate cell viability effect on cancer cells. CONCLUSION Several natural databases were screened and compounds were selected and tested in vitro, despite of the unexpected low activity of the compounds, this study can help in directing the search of potent CK2 inhibitors and better understand the binding requirements of the ATP competitive inhibitors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND Detection of brain tumor is a complicated task which requires specialized skills and interpretation techniques. Accurate brain tumor classification and segmentation from MR images provide an essential choice for medical treatments. The different objects within an MR image have similar size, shape, and density which makes the tumor classification and segmentation even more complex. OBJECTIVES Classification of the brain MR images into tumorous and non-tumorous using deep features and different classifiers to get higher accuracy. METHODS In this study, a novel four-step process is proposed; pre-processing for image enhancement and compression, feature extraction using convolutional neural networks (CNN), classification using the multilayer perceptron and finally, tumor segmentation using enhanced fuzzy c-means method. RESULTS The system is tested on 65 cases in four modalities consisting of 40,300 MR Images obtained from the BRATS-2015 dataset. These include images of 26 Low-Grade Glioma (LGG) tumor cases and 39 High-Grade Glioma (HGG) tumor cases. selleck compound The proposed CNN features-based classification technique outperforms the existing methods by achieving an average accuracy of 98.77% and a noticeable improvement in the segmentation results are measured. CONCLUSION The proposed method for brain MR image classification to detect Glioma Tumor detection can be adopted as it gives better results with high accuracies. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.Diseases are often caused by mutant proteins. Many drugs have limited effectiveness and/or toxic side effects because of a failure to selectively target the disease-causing mutant variant, rather than the functional wild type protein. Otherwise, the drugs may even target different proteins with similar structural features. Designing drugs that successfully target mutant proteins selectively represents a major challenge. Decades of cancer research have led to an abundance of potential therapeutic targets, often touted to be "master regulators". For many of these proteins, there are no FDA-approved drugs available; for others, off-target effects result in dose-limiting toxicity. Cancer-related proteins are an excellent medium to carry the story of mutant-specific targeting, as the disease is both initiated by and sustained by mutant proteins; furthermore, current chemotherapies generally fail at adequate selective distinction. This review discusses some of the challenges associated with selective targeting from a structural biology perspective, as well as some of the developments in algorithm approach and computational workflow that can be applied to address those issues.