Protective effects of Moringa oleifera Lam. leaves against arsenic-induced toxicity in mice

<p><b>OBJECTIVE</b>To evaluate the protective role of leaves of Moringa oleifera (M. oleifera) Lam. against arsenic-induced toxicity in mice.</p><p><b>METHODS</b>Swiss albino male mice were divided into four groups. The first group was used as non-treated control group while, the second, third, and fourth groups were treated with M. oleifera leaves (50 mg/kg body weight per day), sodium arsenite (10 mg/kg body weight per day) and sodium arsenite plus M. oleifera leaves, respectively. Serum indices related to cardiac, liver and renal functions were analyzed to evaluate the protective effect of Moringa leaves on arsenic-induced effects in mice.</p><p><b>RESULTS</b>It revealed that food supplementation of M. oleifera leaves abrogated the arsenic-induced elevation of triglyceride, glucose, urea and the activities of alkaline phospatase, aspartate aminotransferase and alanine aminotransferase in serum. M. oleifera leaves also prevented the arsenic-induced perturbation of serum butyryl cholinesterase activity, total cholesterol and high density lipoprotein cholesterol.</p><p><b>CONCLUSIONS</b>The results indicate that the leaves of M. oleifera may be useful in reducing the effects of arsenic-induced toxicity.</p>

Efficient Mining of Interesting Patterns in Large Biological Sequences

Pattern discovery in biological sequences (e.g., DNA sequences) is one of the most challenging tasks in computational biology and bioinformatics. So far, in most approaches, the number of occurrences is a major measure of determining whether a pattern is interesting or not. In computational biology, however, a pattern that is not frequent may still be considered very informative if its actual support frequency exceeds the prior expectation by a large margin. In this paper, we propose a new interesting measure that can provide meaningful biological information. We also propose an efficient index-based method for mining such interesting patterns. Experimental results show that our approach can find interesting patterns within an acceptable computation time.

An Efficient Approach to Mining Maximal Contiguous Frequent Patterns from Large DNA Sequence Databases

Mining interesting patterns from DNA sequences is one of the most challenging tasks in bioinformatics and computational biology. Maximal contiguous frequent patterns are preferable for expressing the function and structure of DNA sequences and hence can capture the common data characteristics among related sequences. Biologists are interested in finding frequent orderly arrangements of motifs that are responsible for similar expression of a group of genes. In order to reduce mining time and complexity, however, most existing sequence mining algorithms either focus on finding short DNA sequences or require explicit specification of sequence lengths in advance. The challenge is to find longer sequences without specifying sequence lengths in advance. In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets. The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.