No abstract

Aims:To study the demographic factors associated with alcohol dependence syndrome so that the problems of alcohol related co morbidities can be prevented with appropriate preventive measures.Materials and Methods:The study was conducted in De-Addiction Clinic of the Department of Psychiatry, Mamata Medical College, Khammam, Andhra Pradesh from July 2008 to February 2009. Patient who fulfills criteria for alcohol dependence, according to diagnostic and statistical manual of mental disorders, fourth edition were included.Results:Mean age (standard deviation) at first drink was 18.93 (3.81) years and at onset of Alcohol dependence was 28.28 (6.55) years. The most common reason being given by the patients was financial strain (70% of the patients) due to alcohol use and its consequences. Educational qualification of 12th standard or above was seen only in 7.5%. Alcohol dependence syndrome was more common in unemployed, unskilled and semi-skilled patients. Majority of patients (80%) belonged to lower socio-economic class.Conclusion:Alcohol dependence syndrome and its related co morbidities can be minimized to a great extent if the educational and socio-economic standards are improved in countries like India where there is increase in alcohol consumption as a life style choice.

Objective: The objective of the present study was to develop sustained release (SR) matrix tablets of levosulpiride using natural polymers. Methods:The tablets were prepared with different ratios of chitosan, xanthan gum, and guar gum by wet granulation technique. The solubility study of the levosulpiride was conducted to select a suitable dissolution media for in vitro drug release studies.Results: Fourier transform infrared (IR) study revealed no considerable changes in IR peak of levosulpiride and hence no interaction between drug and the excipients. Differential scanning calorimetry thermograms showed that no drug interaction occurred during the manufacturing process. In vitro dissolution study was carried out for all the formulation and the results compared with marketed SR tablet. In vitro release from the formulation LF3 was found to be 94.53%. Hardness of LF3 was found to be 7.96±0.06 kg/cm 2 . Higher hardness tablets contain a compact mass of polymer with relatively less pore, resulting in slower release. The drug release from matrix tablets was found to decrease with increase in polymer ratio of chitosan, xanthan gum, and guar gum. Conclusion:Formulation LF3 exhibited almost similar drug release profile in dissolution media as that of marketed tablets. From the results of dissolution data fitted to various drug release kinetic equations, it was observed that highest correlation was found for first order, Higuchi's and Korsmeyer equation, which indicate that the drug release occurred through diffusion mechanism.

Generative Adversarial Networks (GANs) have swiftly evolved to imitate increasingly complex image distributions. However, majority of the developments focus on performance of GANs on balanced datasets. We find that the existing GANs and their training regimes which work well on balanced datasets fail to be effective in case of imbalanced (i.e. long-tailed) datasets. In this work we introduce a novel theoretically motivated Class Balancing regularizer for training GANs. Our regularizer makes use of the knowledge from a pretrained classifier to ensure balanced learning of all the classes in the dataset. This is achieved via modelling the effective class frequency based on the exponential forgetting observed in neural networks and encouraging the GAN to focus on underrepresented classes. We demonstrate the utility of our regularizer in learning representations for long-tailed distributions via achieving better performance than existing approaches over multiple datasets. Specifically, when applied to an unconditional GAN, it improves the FID from 13.03 to 9.01 on the long-tailed iNaturalist-2019 dataset.

No abstract