Volume 4, Issue 4

In Ayurveda, Kala (Time) is considered as one among the Karana dravya.Whole universe is in the influence of kala(Time). As Ayurveda is the science which is meant for wellness of human beings. The word kala is used for time in general as well as for proper time and for a period as portion of time. Kala (time) is one factor which plays an important role in cause of disease, to maintain health, prevention of disease and to treat the disease also. Present study explains the importance of Kala in Ayurveda.

A sensitive and reliable HPTLC method has been developed for quantitative estimation of 6-gingerol in the soft gelatin capsule containing Ginger oleoresin. Chromatography was performed on silica gel 60 F254 percolated TLC plate using n-hexane: ether (4.0: 6.0, v/v) as solvent system and densitometric determination was carried out by TLC scanner (CAMAG) at 254 nm in reflectance/absorbance mode. The Rf value of 6-gingerol was found to be 0.27 ± 0.01. Linearity was found to be in the concentration range of 200 ng to 1400 ng. The linear regression data for the calibration plots showed a good linear relationship with r²=0.997 for 6-gingerol. The accuracy of the method was checked by conducting recovery studies at three different levels, using the standard addition method. The average recovery of 6-gingerol was found to be 99.96%. The proposed HPTLC method provide a good resolution of 6-gingerol and can be used for quantification of 6-gingerol present in soft gelatin capsule. The method is rapid, simple and precise.

Allergic rhinitis is an allergic inflammation of the nasal membrane. It occurs when an allergen such as pollen, dust or animal dander is inhaled by an individual with a sensitized immune system. The characteristic symptoms of allergic rhinitis are rhinorrhea, itching, nasal congestion and obstruction. The symptoms of vata- kaphaj pratishaya resembles most of allergic rhinitis. In Ayurveda the concept of allergy is widely elaborted under the concept of Ama, concept of Asatmya & concept of viruddha aahara. The term Ama means uncooked, unripe, undigested and immature material. Ama of an type produced at any stage and in any Dhatu stimulates intrinsic factor which are responsible for allergic disorders. Asatmya to an individual in the form of aahara and vihara when consumed may lead to an altered response in the body of that individual. These manifestation in the form of altered response of the body towards that particular Asatmya substance are that of allergy. Viruddha aahara means combination of two or more food material having antagonist properties which may lead to allergic response. Allergic Rhinitis is well known for its recurrence & chronicity. Recurrance of disease happens only when the vitiate dosha have not been eradicated completely. This doshas reside in the body in their latent stage & when they come in contact with aggravating factors give rise to same disease again. Viewing to this concept the Ayurvedic line of treatment of Allergic Rhinitis includes, avoid allergens causing reaction (Nandan Parivarjan). Detoxification (Shodhan), Pacification (Shaman) & rejuvenation (Rasayana).

Medical image analysis plays a crucial role in the diagnosis and treatment of various diseases. With the advent of artificial intelligence, machine learning (ML) techniques have revolutionized this field, offering powerful tools for automated and accurate interpretation of complex medical images. This review provides a comprehensive overview of the application of diverse machine learning techniques, including supervised, unsupervised, and deep learning approaches, in medical image analysis. We discuss their fundamental principles, common architectures, and specific applications across different imaging modalities such as MRI, CT, X-ray, and ultrasound. Key challenges, such as data scarcity, interpretability, and generalization, are highlighted, along with potential solutions and future directions. The aim is to provide researchers and practitioners with a clear understanding of the current state-of-the-art, emerging trends, and the potential of ML to enhance diagnostic accuracy and efficiency in healthcare.