Background: Presently, there is a paucity of effective topical agents for the treatment and management of hyperpigmentary disorders. \nObjective: We aim to compare the lightening efficacy of topical 1% L-cysteamine cream with 4% hydroquinone in a black guinea pig, animal model.\nMethods: Six black guinea pigs were treated once daily for 10 days on one of their ears with 1% L-cysteamine cream and on the contralateral ear with 4% hydroquinone cream. Six control guinea pigs were treated with the vehicle alone. Dermacatch® colorimetric assessments were performed at the beginning and at the end of the study. Biopsies were taken at the end of the trial.\nResults: Our study confirmed the strong depigmenting effect of both formulations. No statistically significant difference was found between the epidermal melanosomes content in skin treated with topical L-cysteamine versus hydroquinone (P>0.05).\nConclusion: L-cysteamine cream is a potent depigmenting cream with an in vivo efficacy that is comparable to hydroquinone and may serve as an alternative agent for patients unable to tolerate the irritant effects of topical hydroquinone therapy

Over the past decade computer networks have grown rapidly but the network security became a critical problem in the computer systems. Thus in the recent years various soft computing technique based methods were proposed to detects the growth of intrusion. Also many researchers have reported that the large set of pattern classifications and machine learning algorithms are trained and testing is made on the knowledge discovery intrusion detection dataset which is unsuccessful in finding the remote-to-local attacks and user-to root attacks. Moreover Hyperbolic Hopfield Neural Network (HHNN) based Intrusion Detection Systems (IDSs) detection stability, detection ratio, particularly low-frequent attacks detection are still required to be improved. Thus this paper proposes a new method known as K-Medoids-HNNN using the technique HHNN and K-Medoids clustering. This system achieves the higher intrusion detection rate, detection stability and less false positive rate. At first the proposed system implements the K-Medoids clustering technique on the various training subsets. Afterwards a mono HHNN model is trained using the different training subsets to detect the intrusion. The experimental results shows the K-Medoids-HHNN approach achieve better results rather than other framework.

One of the major health problem faced today and left unnoticed in the hills and villages is snake envenomation. Millions of people die every year due to snake envenomation. Various medicinal plants are considered to have potency against snake envenomation and experiments using these plants are been carried out to identify their potency against envenomation. In this study the potentialities of Ruta graveolens L. leaf extract was studied against crude venom of Naja naja by in vitro methods. In vitro methods such as procoagulant activity, phospholipase activity and fibrinogenolytic activity were used for testing various solvents of leaf extract of the plant with the venom. In procoagulant activity, 2 mg and 2.49 mg of acetone extract and ethanol extract were required respectively to inhibit the effect of venom completely. In phospholipase activity, 2.25 mg of acetone extract and 4.16 mg of ethanol extract were required to inhibit the venom. In fibrinogenolytic activity, 4.5 mg of acetone extract and 5 mg of ethanol extract were required to inhibit 393.5 µg/µl of venom. Thus this preliminary study shows that the leaf extract of R. graveolens is capable of inhibiting venom and further studies such in vivo methods, compound identification can be done with the leaf extract of R. graveolens for the commercialization against envenomation.