Chromatographic fractionation of the alcoholic extract of the dried fronds of Adiantum capillus-veneris L. (Adiantaceae) yielded seven compounds: four triterpenoidal compounds belonging to adiantane and filicane groups were isolated from the hexane fraction and identified as isoadiantone (1); isoadiantol-B (2); 3-methoxy-4-hydroxyfilicane (3) and 3,4-dihydroxyfilicane (4) and three flavonoids were isolated from the ethyl acetate fraction and identified as: quercetin (5), quercetin-3-O-glucoside (6) and quercetin-3-O-rutinoside (rutin) (7). The identification of the isolated compounds has been established through their physical, chemical and spectroscopic methods including IR, (1)H NMR, (13)C NMR, HSQC, HMBC, NOESY and MS. Biological studies of the total alcoholic extract, hexane fraction and some of the isolated compounds showed an anti-inflammatory activity while the hypoglycemic study of the total alcoholic extract showed a significant activity.

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Application of KADIS in combination with CGMS and the telemedicine-based communication system TeleDIAB successfully improved outpatient diabetes care and management.

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Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggressive in nature due to its high metastasis rate. Therefore, timely diagnosis is critical for its treatment before the onset of malignancy. To address this problem, medical imaging is used for the analysis and segmentation of lesion boundaries from dermoscopic images. Various methods have been used, ranging from visual inspection to the textural analysis of the images. However, accuracy of these methods is low for proper clinical treatment because of the sensitivity involved in surgical procedures or drug application. This presents an opportunity to develop an automated model with good accuracy so that it may be used in a clinical setting. This paper proposes an automated method for segmenting lesion boundaries that combines two architectures, the U-Net and the ResNet, collectively called Res-Unet. Moreover, we also used image inpainting for hair removal, which improved the segmentation results significantly. We trained our model on the ISIC 2017 dataset and validated it on the ISIC 2017 test set as well as the PH2 dataset. Our proposed model attained a Jaccard Index of 0.772 on the ISIC 2017 test set and 0.854 on the PH2 dataset, which are comparable results to the current available state-of-the-art techniques.

This study was designed to assess the application of some edible plants including cayenne, green pepper, parsley, and dill to Kareish cheese and to evaluate the antimicrobial activity of these plant materials against natural microflora, coliforms, molds, and Staphylococcus aureus. Twelve different concentrations of ethanol extract of the plants were prepared for determination of the minimal inhibitory concentration. Cayenne and green pepper extracts showed highest activity followed by dill and parsley against S. aureus. Addition of cayenne or green pepper to Kareish cheese during manufacture revealed that both plants were able reduce the S. aureus population to undetectable level within the first and second days of storage. To study the effect of combining plant materials on the microbiological quality of ready-to-eat Kareish cheese, the total bacterial count, coliform count, and yeast and molds counts were determined. It has been found that addition of plant materials to Kareish cheese reduced the total bacterial and coliform populations. All concentrations of cayenne, green pepper, dill, and parsley (9%) completely reduced the yeast count within 2 hours. Cayenne and green pepper completely reduced the mold count within 2 days, whereas parsley and dill were found to be less effective. Kareish cheese prepared with 1% cayenne pepper and 3% and 6% each of green pepper, dill, and parsley were found strongly acceptable to the consumer and considered the most preferable type. Therefore, this study revealed that pepper, parsley, and dill exhibited antibacterial activity against natural microflora, coliforms, yeast and molds, and S. aureus in Kareish cheese, and the addition of these plants is acceptable to the consumer and may contribute to the development of new and safe varieties of Kareish cheese.

The Weibull distribution has been observed as one of the most useful distribution, for modelling and analysing lifetime data in engineering, biology, and others. Studies have been done vigorously in the literature to determine the best method in estimating its parameters. Recently, much attention has been given to the Bayesian estimation approach for parameters estimation which is in contention with other estimation methods. In this paper, we examine the performance of maximum likelihood estimator and Bayesian estimator using extension of Jeffreys prior information with three loss functions, namely, the linear exponential loss, general entropy loss, and the square error loss function for estimating the two-parameter Weibull failure time distribution. These methods are compared using mean square error through simulation study with varying sample sizes. The results show that Bayesian estimator using extension of Jeffreys' prior under linear exponential loss function in most cases gives the smallest mean square error and absolute bias for both the scale parameterαand the shape parameterβfor the given values of extension of Jeffreys' prior.

Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.