Browsing by Author "Abrar, Sayedul"

Now showing 1 - 2 of 2

Bangladeshi People's Perception of the Homeopathy Medicine System Compared to Allopathy
(Daffodil International University, 23-04-18) Abrar, Sayedul
Homeopathy is a system of alternative medicine that was developed in the late 18th century by Samuel Hahnemann. The fundamental principle of homeopathy is that "like cures like" - meaning that a substance that produces symptoms in a healthy person can be used to treat similar symptoms in a sick person, albeit in extremely diluted doses. Homeopathy practitioners believe that this dilution process makes the remedies more potent, and that the body's innate ability to heal itself can be stimulated by these remedies. On the other hand, allopathy, also known as modern Western medicine, is a system of medicine based on scientific evidence, diagnosis, and treatment using drugs, surgery, and other interventions. Allopathic practitioners diagnose and treat specific diseases and conditions using standardized treatments that have been shown to be effective through rigorous scientific testing. This survey shows that a significant number of people prefer homeopathy over allopathy for various reasons, such as a belief in the effectiveness of natural remedies, dissatisfaction with the side effects of allopathic treatments, and a desire for a more holistic approach to healthcare. However, it is important to note that the efficacy of homeopathy has been widely debated in the medical community, with many studies showing little to no evidence of its effectiveness beyond a placebo effect. Ultimately, the decision between homeopathy and allopathy should be made based on individual circumstances and in consultation with a qualified healthcare professional. Keywords: Homeopathy, allopathy, side effects, chronic disease
Emerging Promise of Computational Techniques in Anti-Cancer Research
(Daffodil International University, 2022-07-25) Rahman, Md. Mominur; Islam, Md. Rezaul; Rahman, Firoza; Rahaman, Md. Saidur; Khan, Md. Shajib; Abrar, Sayedul; Ray, Tanmay Kumar; Uddin, Mohammad Borhan; Kali, Most. Sumaiya Khatun; Dua, Kamal; Kamal, Mohammad Amjad; Chellappan, Dinesh Kumar
Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.