Some of the 3- hydroxyl flavones derivatives (1b, 3b, 4b, and 5b) and 3- methyl flavanone derivatives (3a, 1a, 2a and 4a) were found to elicit potent antimicrobial activity. The study revealed that 3-hydroxy flavone derivatives were found to be most active against Gram negative, while 3-methyl flavanone derivatives were active against Gram positive bacteria.
compound (10b) was found to be the most potent as both anti-inflammatory and cytotoxic agents. In silico approach was in good tune with the wet lab experiments. The promising compounds have shown to induce cell cycle arrest at G2/M Phase.
The present work has been undertaken with an objective to develop the biodegradable type composite using Polylactic acid as well as natural fibres (chemically treated jute) as a replacement of PVC insulation from the electrical wires and cables. Further, in the present work a separate study has been carried out for finding out the mechanical as well as thermal behaviour of the composite. Crystalline cellulose was extracted from jute by hydrolysis with 40% H 2 SO 4 to get mixture of PLA CC was cellulose derived from local source and bleached jute fiber by acid hydrolysis to remove the amorphous regions The diameter gradually became thinner from cutting to top portions. Thus the middle portion of jute fiber would be the better choice while being used as composites.
Objectives: The aim of the study is to develop new synthetic anti-leishmanial agents as flavanone analogues, which should have low toxicity with noticeable yield. Methodology: The starting materials for the synthesis of test compounds were 2'-hydroxypropiohenones, 2'-hydroxyacetophenone and substituted benzaldehyde. Test compounds were synthesized by three steps reaction starting from condensation, cyclization and reduction to yield 3-substituted flananone analogues. The synthesized compounds were screened by in vitro anti-leishmanial assay against promastigotes of L. donovani. Result: A series of flavanone analogues have been synthesized using cobalt (II) phthalocyanine and NaBH4 in the equimolar ratio of the reactant with noticeable yield. The structures of the test compounds were elucidated and established by U.V. IR, 1 H-NMR, 13 C-NMR and mass spectrometry. The synthesized compounds were screened by in vitro antileishmanial assay against promastigotes of L. donovani. Conclusion: Most of the compounds exhibited moderate leishmanicidal activity, while some compounds such as 4b, 10b, 5b, and 3a have shown promising antileishmanial activity against promastigotes of L. donovani.
Objectives: The objective of the study was to develop new antidiabetic agents from synthetic route. Methods: An attempt was made to synthesize various flavones. The structures of the compounds were elucidated by UV, IR, 1 H-NMR and mass spectrometry. Furthermore, an in vivo antidiabetic activity study was carried out by streptozotocin induced model. Biochemical parameters were extensively studied to support anti-diabetic potential of synthesized flavones. Results: The study reveals that flavones such as F1, F2, F3, F5 and F8 were potentially considered for in-vivo antidiabetic activity. Fasting blood glucose and biochemical parameters like total protein, urea and creatinine, SGOT, SALP and SGPT were performed for the biological evaluation and compared with that of standard glibenclamide (5 mg/kg). Among the five consolidated flavones, F8 possess high significant (p < 0.01) results and restores the blood glucose level, liver enzymes and renal parameters. Based on these results, a promising potent drug would be developed in the management of diabetes mellitus. Conclusion: In-vivo evaluations of selected compounds were carried out for its antidiabetic activity considering different biochemical parameters. Some of the selected flavones showed excellent and noticeable antidiabetic activity.
In this paper Non differentiable minimax fractional programming and particle swarm optimization is used optimized the complex function variable. Where the non differentialminimax fractional programming is used for minimization and particle swarm optimization is used for maximized the complex function. Problem on those equations which is not too optimized by conventional minima and maxima method can be easy to formulate and find the maxima and minima.
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