Curcumin Extract Loaded with Chitosan Nanocomposite for Cancer Treatment

Medicine has become one of the huge fields which has its own growth each and every day. Few diseases are still a threat to medical field. Because of its destructive nature, cancer is a disease that causes fear in many people throughout the world. However, most cancers are curable if detected early and treated with the right medical care and computer-assisted diagnosis. These days, diagnosis is more popular because it works well as a primary screening test for many diseases, particularly cancer. Deep learning is a method of artificial intelligence where the computer is given intelligence that attempts to emulate the way a human brain thinks. This research focuses on creating a deep neural network that can accurately predict the malignancy of breast cancer up to 98%, allowing medical personnel to diagnose patients more quickly and administer therapy more effectively.

Section: Softmaxmentioning

confidence: 99%

Novel Deep Learning Neural Networks for Breast Cancer Malignancy Estimation

G. Nivedhitha,

P. Kalpana,

Rajagopal R

et al. 2024

“…The challenges mentioned in these works involve noise resiliency, selecting the best scale, meeting patient differences, and being practical in real time [26][27]. Gaining understanding of these difficulties is essential to improve continuous wavelet transform-based methods and widening the area of FoG prediction in Parkinson's disease research [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Time Orient Acceleration Gait Pattern Based FOG Prediction on Parkinson Patients Using Deep Learning and Wearable Sensors

Ezhilarasi Jegadeesan,

Senthil Kiumar Thillaigovindhan

2024

The problem of predicting Freeze of Gait (FoG) on Parkinson diseased patients has been well studied. There exists number of approaches in predicting FoG, which uses sensory features, EEG data and so on. However, the methods suffer to achieve higher performance. To handle this issue, an efficient Time Orient Acceleration Gait pattern based FoG prediction model (TOAGP-FoG) is presented in this paper. The model designed to attach accelerometer sensors at different ankle and joints of the body. The sensor signals are recorded at different gait movement in long term. The sensory signals are passed to the central data server which tracks the movement signals. With the time variant signals stored by the model, the method generates Acceleration Gait Pattern with number of features. At each movement, the method analyses the patterns to compute FOG Risk Support (FoGRS) towards various gait movement. The FoG Risk Support is measured according to the movement forces produced by the patient for various gait movement in different time stamp and computes minimum gait force to be produced. Based on the FoGRS value, the method performs FoG prediction. The proposed method improves the performance of FoG prediction with higher accuracy. Other notable aspects of the suggested model include comparable performance, resiliency, real-time prediction capabilities, FOG-specific integration of data, and advanced deep learning methodologies for accurate prediction. The Special Features of TOAGP-FoG include the Multi-Sensor Configuration, Temporal Analysis, Adaptive Thresholding, Dynamic FoG Risk Support (FoGRS), and Enriched Feature Extraction. The TOAGP-FoG model offers an important breakthrough in the predictive modelling of Parkinson's disease since it integrates several features such as temporal flexibility, dynamic FoGRS computation, adaptive thresholding, enriched feature extraction, and multi-sensor configurations.

“…Simplisia was extracted as described in [20] (Alam et al, 2014) 4 g of simplicia powder were mixed with 40 ml of 70% ethanol in a closed flask. Chemicals extracted from natural sources with ethanol or propanol are preferable for culinary applications [21]. The mixture will be allowed to stand for 24 hours at room temperature, stirring occasionally.…”

Section: Preparation Of Extract Curcuma Caesia Roxbmentioning

confidence: 99%

Phytochemical Properties, Antioxidant Activity and α- Amilase Inhibitory of Curcuma Caesia

Kartini

Juariah

Mardhiyani

et al. 2023

Diabetes mellitus (DM) is a metabolic disease characterized by hyperglycemia, which produces free radicals, particularly reactive oxygen species (ROS), through glucose auto-oxidation and glycosylation. Administration of antioxidants and polyphenolic compound components can capture free radicals and reduce oxidative stress. Polyphenols are also able to reduce blood glucose levels by inhibiting the action of amylase enzymes as carbohydrate catabolism so that glucose absorption does not occur. One of the plants that contain polyphenols is Black Turmeric (Curcuma caesia). Some studies on the rhizomes of this plant include methanol extracts containing phenolics, Flavonoids, alkaloids, triterpenoids, saponins, and tannins. Dry powders and wet rhizome powders were studied to have antioxidant activity. Research on the inhibition of the enzyme alfa amylase is still lacking. This study aims to determine the content of chemical compounds using 70% ethanol solvent. The total phenolic and flavonoid assay with spectrophotometry, determine antioxidant activity using DPPH (2,2-diphenyl-1-picrylhydrazyl) at 490 nm and inhibition of α-amylase enzyme using 3,5- dinitro salicylic acid (DNS) at 529 nm. The results showed that phenolic levels were 235 mgGAE/100gr, flavonoids were 61 mg QE/100gr, Alkaloids 30 mg Quinine /100gr and saponin 158 mg Quillaja bark/100gr, dan tannin 535mg tannic acid/100gr. DPPH antioxidant activity obtained IC50 value of 299,18 µg/mLµg/mL and α- amylase inhibitory 50,26 µg/mLµg/mL. The research showed that 70% ethanol extract Curcuma caesia roxb contains phenolic, flavonoids, alkaloids, quinine, and saponins constituents and have been antioxidant activity and high α-amylase inhibitory