Bioderived cellulose fibre-guar gum grafted poly (N, N'-dimethylacrylamide) polymer network for controlled release of metformin hydrochloride.

An interpenetrating polymer network (IPN) of areca cellulose and guar gum grafted with poly (N, N'-dimethylacrylamide) was made by microwave irradiation technique. N, N-methylenebisacrylamide (MBA) was used as the crosslinking agent. The network polymer was characterised using Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetric Analysis (TGA), Powder X-ray Diffraction (XRD) and Field Emission Scanning Electron Microscopy (FESEM). The chemical interaction between the drug and the polymer was studied using Differential Scanning Calorimetry (DSC). The swelling of the gel was measured under different pH conditions and the swelling parameters were evaluated. The gel was loaded with an anti-diabetic drug, Metformin Hydrochloride, and the in vitro drug release was studied in gastric and intestinal conditions. The results indicated complete release of the drug in 6 h under pH 1.2 and in 10 h under pH 7.4. The kinetic analysis of release data indicated the drug release to follow Higuchi's model. The release exponent "n" of Korsmeyer-Peppas model was found to be >0.45 indicating the drug diffusion to be a non-Fickian process.

A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders.

The human voice production system is an intricate biological device capable of modulating pitch and loudness. Inherent internal and/or external factors often damage the vocal folds and result in some change of voice. The consequences are reflected in body functioning and emotional standing. Hence, it is paramount to identify voice changes at an early stage and provide the patient with an opportunity to overcome any ramification and enhance their quality of life. In this line of work, automatic detection of voice disorders using machine learning techniques plays a key role, as it is proven to help ease the process of understanding the voice disorder. In recent years, many researchers have investigated techniques for an automated system that helps clinicians with early diagnosis of voice disorders. In this paper, we present a survey of research work conducted on automatic detection of voice disorders and explore how it is able to identify the different types of voice disorders. We also analyze different databases, feature extraction techniques, and machine learning approaches used in these research works.