REVIEW ON CHAKRADATTA - A GREAT TREATISE BY CHAKRAPANIDATTA

Rasashastra (Indian Alchemy) forms the basis of Ayurvedic pharmaceutics. In ancient Ayurveda, the emphasis has been over the herbs and their therapeutic usages. Later on the animal products, metals and minerals started to find favor of the Ayurvedic practitioners. Ample of literature is available on Rasashastra. Chakradatta is one of the great treatise composed by Shri Chakrapanidatta, the well known Tikakarta of Charak Samhita. Chakradatta holds an important place amongst academicians, researchers and students of Rasashastra. Many verses of Chakradatta are derived from 50 sources. The principle source of this book as stated by the author himself at the end of the work is Vrndss Siddhayoga. Chakradattas main aim was to present it as an up to date text of Ayurveda medicine for the benefit of ailing mankind. The uniqueness of Chakradatta is in the description of several metallic and mercurial preparations for the purpose of internal use as medications. Therapeutic principles specific for the disease recommended by Chakradatta are beneficial even today for the practitioners of Ayurveda. Present paper will highlight the framework of Chakradatta, providing information about the author, text, characteristics & innovations. It will also throw a light towards the contribution of Chakradatta in the field of Ayurveda.

AYURVEDIC APPROACH IN NUTRACEUTICALS W. S. R. TO RASAYANA

The roots of the concept of Nutraceuticals and functional foods can be traced to the ancient Ayurvedic system of health care. The concept of Rasayana or Rejuvenation therapy is a part of eight clinical specialities of Ayurveda. The concept of promotion of health through Rasayana is based on Ayurveda pathophysiology and its understanding of health and physiological system imbalances that lead to disease development. The word Rasayana is composed of two words ‘Ras’ + ‘Ayan’. The means by which one gets the excellence of Rasa (the nourishing fluid which is produced immediately after digestion) is Rasayana. These Rasa nourishes our body and stimulates the immunity of the body and keeps us healthy. The Ayurvedic texts describe a set of rejuvenative measures i.e. Rasayana which are claimed to act as micronutrients offering protection from external and internal stressors. This ancient understanding is being reintroduced as Nutraceuticals. Nutraceuticals are natural, bioactive materials that provide demonstrated physiological benefits or reduce the risk of chronic diseases, above and beyond their basic nutritional function. This concept of promoting health and preventing disease through nutrition and special nutritional preparations has been used for millennia in Ayurveda.The term ‘Nutraceutical’ was coined by Stephen L. De. Felice, in 1989. The word is a portmanteau of ‘Nutrition’ and ‘Pharmaceutical’ and refers to extracts of foods claimed to have a medicinal effect on human health. Consumers’ demand for quality of life has fueled the nutraceutical revolution and seeking complementary or alternative beneficial products. The association of nutraceuticals with traditional medicine brings the long standing consumer acceptance. In this review article Nutraceuticals and Rasayana in Ayurveda have been correlated and discussed

ROLE OF LEPAKALPANA FOR IMPROVING BEAUTY OF SKIN W. S. R. TO MUKHALEPA

Skin (Twacha) is the largest organ of the body and has a great cosmetic value. It is one of the five Jnanendriyas (sense organs) i.e., Sparsha jnyaan (touch sensation) gets from it. So it has ample of importance in physical and mental well being of an individual. It is most vulnerable to disease as it exposes to external environment. There are lots of reasons behind the skin problems and most striking is the todays busy and stressful life. Skin disorders affects all ages from the neonates to the elderly persons.Ayurveda an ancient science has vast description of the topical applications in the name of Lepa kalpana described both as the Chikitsa (treatment) of various skin diseases and for beauty. The medicines that are in the form of a paste and used for external application are called as Lepas. A great demand from Ayurveda in the field of cosmetology has been established due to its unique concept about beauty and effective, cheaper and long lasting therapy without any side effects. The herbal paste which is applied on face to treat acne, pimples, scars, marks and pigments are known as Mukha Lepa (face pack) in Ayurveda. Mukhalepa is a simple and effective skin care treatment in Ayurveda, which is done using variety of herbal pastes. The herbal face pack mainly consists of different rejuvenating herbs that help in nourishment of the skin and also aid in skin cleansing and make it soft and clear. Mukhalepas also helps to increase the elasticity of the skin and thereby prevents the ageing of the skin, avoids wrinkles on the face, and gives a good skin tone.The present article is aimed to provide the details about the Lepa kalpana from the classics of Ayurveda, the concept of Mukha lepa and the drugs mentioned in classics which are useful for improving complexion and for the common skin ailments.

Knowledge and Perceptions of Generic Drugs: A Cross Sectional Study

Generic medicines are pharmaceutical products usually intended to be interchangeable with the originator brand product, manufactured without a license from the originator manufacturer and marketed after the expiry of patent or other exclusivity rights. The current potential economic benefits associated with their use have been exponentially rising and thus, generic medicines are a significant approach to reduce the massive expenditure on medicines. Governments are thus emphasizing on and promoting the usage of generic medicines. However, negative perceptions of their quality influence use and raise issues of certainty and trust in medications. The objective of this study is to identify consumers in terms of their perceptions towards branded and generic medicines in the country. A cross - sectional study was conducted with consumers of various age demographics and location factors. Using convenience sampling, survey forms were distributed to them via social media platforms. For a period of 30 days, 236 respondents had participated in the survey. Assessments of the results were conducted on qualitative data to develop an insight into the perception of medicines amongst the masses. The survey confirmed that generic drug awareness was considerably high among younger age groups than older respondents. An overwhelming 78% respondent understood that generic medicines are cheaper than branded medicines. Regardless, 34.8% of respondents who were aware of generic drugs did not realize that they were less expensive than brand-name drugs. The survey conducted showed a high support for generic substitution. Furthermore, it indicates that programs should be implemented in order to boost generic drug prescriptions by medical practitioners. Keywords: generic, branded, awareness, healthcare, surve

Learning from positive and unlabelled examples using maximum margin clustering

Learning from Positive and Unlabelled examples (LPU) has emerged as an important problem in data mining and information retrieval applications. Existing techniques are not ideally suited for real world scenarios where the datasets are linearly inseparable, as they either build linear classifiers or the non-linear classifiers fail to achieve the desired performance. In this work, we propose to extend maximum margin clustering ideas and present an iterative procedure to design a non-linear classifier for LPU. In particular, we build a least squares support vector classifier, suitable for handling this problem due to symmetry of its loss function. Further, we present techniques for appropriately initializing the labels of unlabelled examples and for enforcing the ratio of positive to negative examples while obtaining these labels. Experiments on real-world datasets demonstrate that the non-linear classifier designed using the proposed approach gives significantly better generalization performance than the existing relevant approaches for LPU

Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques

Improving Distant Supervision for Information Extraction Using Label Propagation Through Lists

Because of polysemy, distant labeling for information extraction leads to noisy train-ing data. We describe a procedure for re-ducing this noise by using label propaga-tion on a graph in which the nodes are entity mentions, and mentions are cou-pled when they occur in coordinate list structures. We show that this labeling ap-proach leads to good performance even when off-the-shelf classifiers are used on the distantly-labeled data.