A review on host-pathogen interactions: classification and prediction.

The research on host-pathogen interactions is an ever-emerging and evolving field. Every other day a new pathogen gets discovered, along with comes the challenge of its prevention and cure. As the intelligent human always vies for prevention, which is better than cure, understanding the mechanisms of host-pathogen interactions gets prior importance. There are many mechanisms involved from the pathogen as well as the host sides while an interaction happens. It is a vis-a-vis fight of the counter genes and proteins from both sides. Who wins depends on whether a host gets an infection or not. Moreover, a higher level of complexity arises when the pathogens evolve and become resistant to a host's defense mechanisms. Such pathogens pose serious challenges for treatment. The entire human population is in danger of such long-lasting persistent infections. Some of these infections even increase the rate of mortality. Hence there is an immediate emergency to understand how the pathogens interact with their host for successful invasion. It may lead to discovery of appropriate preventive measures, and the development of rational therapeutic measures and medication against such infections and diseases. This review, a state-of-the-art updated scenario of host-pathogen interaction research, has been done by keeping in mind this urgency. It covers the biological and computational aspects of host-pathogen interactions, classification of the methods by which the pathogens interact with their hosts, different machine learning techniques for prediction of host-pathogen interactions, and future scopes of this research field.

Clinical evaluation of purified Shilajit on testosterone levels in healthy volunteers.

Purified Shilajit, an Ayurvedic rasayana, was evaluated in healthy volunteers of age between 45 and 55 years for its effect on male androgenic hormone viz. testosterone in a randomised, double-blind, placebo-controlled clinical study at a dose of 250 mg twice a day. Treatment with Shilajit for consecutive 90 days revealed that it has significantly (P < 0.05) increased total testosterone, free testosterone and dehydroepiandrosterone (DHEAS) compared with placebo. Gonadotropic hormones (LH and FSH) levels were well maintained.

Unsupervised feature evaluation: a neuro-fuzzy approach.

The present article demonstrates a way of formulating neuro-fuzzy approaches for both feature selection and extraction under unsupervised learning. A fuzzy feature evaluation index for a set of features is defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space. Two new layered networks are designed. The tasks of membership computation and minimization of the evaluation index, through unsupervised learning process, are embedded into them without requiring the information on the number of clusters in the feature space. The network for feature selection results in an optimal order of individual importance of the features. The other one extracts a set of optimum transformed features, by projecting -dimensional original space directly to n'-dimensional (n' < n) transformed space, along with their relative importance. The superiority of the networks to some related ones is established experimentally.

Knowledge-based fuzzy MLP for classification and rule generation.

A new scheme of knowledge-based classification and rule generation using a fuzzy multilayer perceptron (MLP) is proposed. Knowledge collected from a data set is initially encoded among the connection weights in terms of class a priori probabilities. This encoding also includes incorporation of hidden nodes corresponding to both the pattern classes and their complementary regions. The network architecture, in terms of both links and nodes, is then refined during training. Node growing and link pruning are also resorted to. Rules are generated from the trained network using the input, output, and connection weights in order to justify any decision(s) reached. Negative rules corresponding to a pattern not belonging to a class can also be obtained. These are useful for inferencing in ambiguous cases. Results on real life and synthetic data demonstrate that the speed of learning and classification performance of the proposed scheme are better than that obtained with the fuzzy and conventional versions of the MLP (involving no initial knowledge encoding). Both convex and concave decision regions are considered in the process.

Study of different anaesthetic techniques on pulmonary haemodynamics during mitral commissurotomy.

A comparative study of the effects of three different techniques of anaesthesia on pulmonary haemodynamics in 20 patients with pulmonary hypertension secondary to mitral stenosis was undertaken. The anaesthetic techniques employed were: (1) N2O + O2 (60:40), relaxant and IPPV. (2) N2O + O2 (50:50), 0.5% halothane, relaxant and IPPV. (3) 99% O2, 1% halothane, relaxant and IPPV. Identical premedication and induction were carried out in all the patients and maintained with N2O + O2 (70:30), relaxant and IPPV with cuffed endotracheal tube using closed circuit with CO2 absorber, at the beginning. Thereafter, anaesthesia was maintained using the above mentioned 3 different techniques in 4 study stages. The different parameters studied were pulmonary arterial pressure, left arterial pressure, PaO2, PaCO2 and pH in all the 4 stages. The pressure changes and blood gas analysis data were presented in Tables and analysed statistically. It was observed that O2 and halothane anaesthesia caused a fall in pulmonary vascular resistance which was beneficial for patients having pulmonary hypertension.

Neuro-fuzzy feature evaluation with theoretical analysis.

The article provides a fuzzy set theoretic feature evaluation index and a connectionist model for its evaluation along with their theoretical analysis. A concept of weighted membership function is introduced which makes the modeling of the class structures more appropriate. A neuro-fuzzy algorithm is developed for determining the optimum weighting coefficients representing the feature importance. It is shown theoretically that the evaluation index has a fixed upper bound and a varying lower bound, and it monotonically increases with the lower bound. A relation between the evaluation index, interclass distance and weighting coefficients is established. Effectiveness of the algorithms for evaluating features both individually and in a group (considering their independence and dependency) is demonstrated along with comparisons on speech, Iris, medical and mango-leaf data. The results are also validated using scatter diagram and k-NN classifier.