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Amritarishta (AMR), an ayurvedic preparation, is widely used as a traditional medicine in the treatment of pain & fever in the rural population. Hepatoprotective effect of AMR was investigated through Paracetamol Induced Hepatotoxicity Model. It was administered at 2.5, 5 and 10 ml/kg dose of AMR to Sprague-Dawley rats. Obtained data was analyzed by One-way ANOVA followed by Tukey posthoc analysis. In paracetamol induced hepatotoxicity model, AMR exhibited dose dependent and statistically significant hepatoprotective effect upto 5ml/kg dose evident by its suppressive effect on elevation of different serum hepatic enzymes and lipid peroxidation of liver caused by chronic paracetamol overdose. However, the higher dose (10ml/kg) of AMR failed to be proved better hepatoprotective than that of 5ml/kg dose. AMR, at the dose of 5ml/kg, was able to inhibit serum AST, ALT, Bilirubin, ALP and liver lipid peroxidation was comparable to standard Silymarin at 100mg/kg dose, elicited suppressive activity of 70.1% (p<0.01), 80.72% (p<0.001), 78.57% (p<0.01), 89.55% (p<0.001) and 70.32% (p<0.01) respectively. The results suggest that AMR has good potential as hepatoprotective agent.

This chapter starts with a general introduction to protein folding. We then present a probabilistic method of dealing with multi-class classification, in particular multi-class protein fold prediction, using Stochastic Logic Programs (SLPs). Multi-class prediction attempts to classify an observed datum or example into its proper classification given that it has been tested to have multiple predictions. We apply an SLP parameter estimation algorithm to a previous study in the protein fold prediction area, in which logic programs have been learned by Inductive Logic Programming (ILP) and a large number of multiple predictions have been detected. On the basis of several experiments, we demonstrate that PILP approaches (eg. SLPs) have advantages for solving multi-class (protein fold) prediction problems with the help of learned probabilities. In addition, we show that SLPs outperform ILP plus majority class predictor in both predictive accuracy and result interpretability.

Introduction: Diabetes induces leakage of hepatic enzymes from cytosol into the circulation and increases the plasma levels of these enzymes. In this study, the impacts of ginger (Zingiber Officinale) aqueous extract on the levels of liver enzymes, biochemical parameters, and histological changes in male rats were assessed following induction of diabetes by Streptozotocin.

Materials & Methods: 42 male Wistar rats were divided into 6 groups: the control (left untreated), diabetic control (received 70 mg/kg Streptozotocin once at the beginning of the experiment), the experimental groups 1 and 2 (daily received 250 and 500 mg/kg ginger aqueous extract, respectively), the experimental groups 3 and 4 (first received streptozotocin once, and then a daily dose of 250 and 500 mg/kg extract). Oral administration of aqueous extract continued over a period of two months. 48 hours after the last administration, blood samples were prepared and used for measurement of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and serum levels of albumin and total protein. Also, hepatic histological study was carried out in hematoxylin-eosin staining method.

Findings: The serum levels of AST, ALT, and ALK enzymes in diabetic control group (recipient of 70mg/kg Streptozotocin) and the AST enzyme concentration in experimental group 2 (recipient of 500 mg/kg Ginger extract alone) increased significantly compared to the control group
(p<0.05). In contrast, the levels of ALT and ALP enzymes declined significantly in experimental group 1 (recipient of 250mg/kg extract alone) relative to the control p (< 0.05). Serum concentrations of total protein and albumin reduced significantly in the experimental group 2 in comparison with the control group. Also, In experimental groups 3 and 4 (recipient of 250 and 500 mg/kg ginger extract and streptozotocin) relative to the diabetic control group, the levels of AST , ALT and ALP indicated a significant reduction, while the concentration of total proteins increased significantly in experimental group 3          (p < 0.05). Finally, histological study revealed cellular necrosis, cellular inflammation, and vacuolar fat accumulation, disintegration of portal spaces and formation of large intercellular spaces in diabetic control relative to the control group. Such disorders were less significant in groups receiving streptozotocin along with 500mg/kg doses of extract compared to the group receiving streptozotocin alone.

Discussion & Conclusions: Due to the presence of compounds such as gingerol, shoagol, and anthocyanins, ginger aqueous extract can possibly reduce hepatic toxicity caused by streptozotocin and improve the serum levels of liver enzymes.

This chapter starts with a general introduction to protein folding. We then present a probabilistic method of dealing with multi-class classification, in particular multi-class protein fold prediction, using Stochastic Logic Programs (SLPs). Multi-class prediction attempts to classify an observed datum or example into its proper classification given that it has been tested to have multiple predictions. We apply an SLP parameter estimation algorithm to a previous study in the protein fold prediction area, in which logic programs have been learned by Inductive Logic Programming (ILP) and a large number of multiple predictions have been detected. On the basis of several experiments, we demonstrate that PILP approaches (eg. SLPs) have advantages for solving multi-class (protein fold) prediction problems with the help of learned probabilities. In addition, we show that SLPs outperform ILP plus majority class predictor in both predictive accuracy and result interpretability.

... This newly emerging re-search field is known under the name of statistical relational ... learning about one entity im-proves the prediction for other entities; it might even ... natural language processing, robotics, bio-and chemo-informatics, electronic games, and activity recognition. ...

Iterative learning is a model of language learning from positive data, due to Wiehagen. When compared to a learner in Gold’s original model of language learning from positive data, an iterative learner can be thought of as memory-limited. However, an iterative learner can memorize some input elements by coding them into the syntax of its hypotheses. A main concern of this paper is: to what extent are such coding tricks necessary? One means of preventing some such coding tricks is to require that the hypothesis space used be free of redundancy, i.e., that it be 1-1. By extending a result of Lange & Zeugmann, we show that many interesting and non-trivial classes of languages can be iteratively identified in this manner. On the other hand, we show that there exists a class of languages that cannot be iteratively identified using any 1-1 effective numbering as the hypothesis space. We also consider an iterative-like learning model in which the computational component of the learner is modeled as an enumeration operator, as opposed to a partial computable function. In this new model, there are no hypotheses, and, thus, no syntax in which the learner can encode what elements it has or has not yet seen. We show that there exists a class of languages that can be identified under this new model, but that cannot be iteratively identified. On the other hand, we show that there exists a class of languages that cannot be identified under this new model, but that can be iteratively identified using a Friedberg numbering as the hypothesis space.

Emerging data indicate that the mortality rate is rising due to liver disorders day-by-day in the developed countries. The present study was conducted to evaluate the potential of the Biofield Energy (The Trivedi Effect®) Treated test item (DMEM) in HepG2 cell-line. The test item was divided into two parts. One part of the test item received Consciousness Energy Healing Treatment by a renowned Biofield Energy Healer, Alice Branton and was labeled as the Biofield Energy Treated DMEM and the other part defined as untreated DMEM, where no Biofield Treatment was provided. Cell viability of the test items using MTT assay showed 113% and 129.9% viable cells in the untreated DMEM and Biofield Energy Treated DMEM groups, respectively suggested that the test items were nontoxic and safe in nature. The Biofield Energy Treated DMEM showed significant (p≤0.001) protection of cells by 15% against oxidative stress induced by t-BHP, while untreated DMEM group showed 0.4% protection. The level of IL-8 was significantly (p≤0.01) reduced by 32.15% in the Biofield Energy Treated DMEM group compared to the untreated DMEM group. Moreover, the level of ALT enzyme activity was significantly (p≤0.01) reduced by 53.2% in the Biofield Energy Treated DMEM group compared to the untreated DMEM group. Cholesterol level was significantly (p≤0.001) reduced by 37.35% in the Biofield Energy Treated DMEM group compared to the untreated DMEM group. Besides, the Biofield Energy Treated DMEM group showed 43.13% increased the level of albumin compared to the untreated DMEM group. Altogether, results suggested that Biofield Treatment significantly improved liver function. Thus, Consciousness Energy Healing (The Trivedi Effect®) Treatment could be utilized as a hepatoprotectant against several hepatic disorders such as Gilbert’s disease, cirrhosis, steatosis, alcohol abuse, hemochromatosis, Budd-Chiari syndrome, Wilson’s disease, cholangiocarcinoma, etc.

A Bounded Example Memory learner is a learner that operates incrementally and maintains a memory of finitely many data items. The paradigm is well-studied and known to coincide with set-driven learning. A hierarchy of stronger and stronger learning criteria is obtained when one considers, for each k ∈ N, iterative learners that can maintain a memory of at most k previously processed data items. We report on recent investigations of extensions of the Bounded Example Memory model where a constructive ordinal notation is used to bound the number of times the learner can ask for proper global memory extensions.