Amit Kumar

Bio: Amit Kumar is an academic researcher from Aryabhatta Research Institute of Observational Sciences. The author has contributed to research in topics: Medicine & Population. The author has an hindex of 25, co-authored 800 publications receiving 4223 citations. Previous affiliations of Amit Kumar include Pandit Ravishankar Shukla University.

Anticancer Potential of Plants and Natural Products: A Review

Abstract: Cancer is one of the leading causes of death and globally the numbers of cases of cancer are increasing gradually. There are several medicines available in the market to treat the various types of cancer but no drug is found to be fully effective and safe. The major problem in the cancer chemotherapy is the toxicity of the established drugs. However plants and plant derived products have proved effective and safe in the treatment and management of cancers. These days most of the research work on cancer drugs is targeted on plants and plants derived natural products. Many natural products and their analogues have been identified as potent anti-cancer agents and day by day the anti-cancer property of various plants is being identified. Here an attempt is being made through this review to highlights the natural products and their analogues established as anti-cancer agents and the new plant species identified with anti-cancer properties either in vivo or in vitro.

Immunomodulatory and therapeutic potentials of herbal, traditional/indigenous and ethnoveterinary medicines.

Abstract: Herbs/Botanical plants are considered as God's gift to human beings in the form of natural medicines, like the one well known "Sanjeevani booti" described in Hindu Mythology. The traditional and ethno-veterinary practices have been in use for centuries, transferring the knowledge from generation to generation and they are accessible, easy to prepare and administer, with little or no cost at all. Even though the modern developments in therapeutic field brought about a rapid decline in traditional medicine, the plant-based remedies are still having a crucial role as potential source of therapeutic aids in health systems all over the world for both humans and animals. Among the 21,000 medicinal plants listed by the World Health Organization (WHO), 2500 species are native to India, which stands first in the production of medicinal herbs. This innumerable treasure of medicinal herbs brings India the distinction of 'the botanical garden of the world'. Nowadays immune-based therapies are gaining more importance than monovalent approaches which are having limited benefits. Apart from the actions like treating diseases, control of ecto- and endo-parasites, fertility enhancement, bone setting and poor mothering management, an array of herbal medicines have been reported which are having immunomodulatory effects like modulation of cytokine secretion, histamine release, immunoglobulin secretion, class switching, cellular co-receptor expression, lymphocyte expression, phagocytosis and so on. The present article describes in brief few of these important ones viz., ashwagandha, amla, tulsi, arjuna, aloe vera, garlic, turmeric, ginger, shatavari, neem, guduchi, kiwifruit, tut, kamala, palashlata, kokilaksha etc. being used for human and animal health benefits.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to