Anogeissus latifolia : A Comprehensive Review from Ethanobotanical Insights to Future Pharmacological Frontiers.

Anogeissus latifolia, commonly known as Axlewood, Indian Gum Tree, or Gum Ghatti, belongs to the Combretaceae family and is native to India, Nepal, Myanmar, and Sri Lanka. It thrives in most tropical and subtropical regions of India. Different parts of the plant contain various proteins, carbohydrates, sugars (arabinose, galactose, mannose, xylose, rhamnose, and glucuronic acid), minerals (magnesium and calcium salts of ghattic acid), and a wide range of phytochemicals such as alkaloids, flavonoids, phenols, terpenoids, sterols, saponins, tannins, coumarins, quinine, and ellagic acid. Studies have indicated the potential of Anogeissus latifolia in managing various health conditions, including antimicrobial, anti-inflammatory, anti-diabetic, anti-parasitic, antioxidant, hepatoprotective, wound healing, and hypolipidemic effects. However, like many medicinal plants, Anogeissus latifolia demonstrates a dose-dependent toxicity profile, particularly when administered intraperitoneally. Research efforts have been directed towards elucidating its safety profile, and findings suggest that it can be considered safe when administered within prescribed dosages. This paper aims to provide a comprehensive review encompassing the ethnobotanical significance, morphological characteristics, phytochemistry, pharmacological actions, and toxicological aspects of Anogeissus latifolia. Additionally, it explores future research perspectives in the field of Anogeissus latifolia pharmacology and therapeutics.

Precancerous Change Detection Technique on Mammography Breast Cancer Images based on Mean Ratio and Log Ratio using Fuzzy c Mean Classification with Gabor Filter

BACKGROUND: The growing rate of breast cancer necessitates immediate global attention. Mammography images are used to determine the stage of malignancy. Breast cancer stages must be identified in order to save a person's life. OBJECTIVE: This article's main goal is to identify different techniques to obtain the difference between two breast cancer mammography images taken of the same individual at different times. This is the first effort to identify breast cancer in mammography images using change detection techniques. The Mammogram Image Change Detection (ICD) technique is also a recent advancement to prevent breast cancer in the early stage and precancerous level in medical images. METHODS: The main purpose of this work is to observe the changes between breast cancer images in different screening periods using different techniques. Mammogram Breast Cancer Image Change Detection (MBCICD) methods usually start with a Difference Image (DI) and classify the pixels in the DI into changed and unaffected classes using unsupervised fuzzy c means (FCM) clustering methods based on texture features taken from the log and mean ratio difference pictures. Two operators, mean ratio and log ratio, were used to check the changes in the images. The Gabor wavelet is utilized as a feature extraction technique among several standards. Using the Gabor wavelet ratio operators is a useful method for altering the detection of breast cancer in mammography images. Currently, it is challenging to obtain real malignant images of the same person for testing or training. In this study, two images are utilized. To clearly see the changes, one is an image from the MIAS breast cancer mammography images dataset, and the other is a self-generated change image. RESULTS: The research aims to examine the image results and other quantitative analysis results of proposed change detection methods on cancer images. The Mean Ratio Accuracy result is 0.9738, and the Log ratio PCC is 0.9737. The classification results are the Log Ratio + Gabor Filter + FCM is 0.9737, and Mean Ratio +Gabor Filter + FCM is 0.9719. The mean Ratio Accuracy result is 0.9738, Log ratio is 0.9737. Log Ratio + Gabor Filter + FCM is 0.9737, Mean Ratio +Gabor Filter + FCM is 0.9719. Comparing the PCC of proposed change detection methods with the FDA-RMG method on the same dataset, the accuracy is 0.9481 only. CONCLUSION: The study concludes that variations in mammography breast cancer images could be successfully identified using the ratio operators with Gabor wavelet features.

Bioflavonoid Daidzein: Therapeutic Insights, Formulation Advances, and Future Directions.

Bioflavonoids, are a diverse group of phytonutrients that are widely distributed in fruits, vegetables, grains, teas, and certain medicinal herbs. They are characterized by their antioxidant properties and play essential roles in plant biology, such as providing color to fruits and flowers, protecting plants from environmental stresses. Daidzein, a bioflavonoid classified under natural products, is sourced from plants like soybeans and legumes. It exists in forms such as glycosides and aglycones, with equol and trihydroxy isoflavone being key metabolites formed by gut bacteria. Known for its wide-ranging therapeutic potential, daidzein has shown effects on cardiovascular health, cancer, diabetes, skin conditions, osteoporosis, and neurodegenerative disorders. Its mechanisms include interaction with estrogen receptors, antioxidative and anti-inflammatory properties, and modulation of apoptosis and cell cycles. Recent advances in formulation technologies aimed at enhancing daidzein's bioavailability and efficacy are critically evaluated, including nanoparticle-based delivery systems and encapsulation strategies. Researchers have developed advanced formulations like nanoparticles and liposomes to enhance daidzein's solubility, stability, bioavailability, and targeted delivery. Considered a promising nutraceutical, daidzein warrants further exploration into its molecular actions and safety profile to fully realize its clinical potential. This review offers a succinct overview encompassing therapeutic benefits, chemical characteristics, historical uses, toxicology insights, recent advancements in delivery systems, and future directions for daidzein research.