Rapid multiplication and in vitro production of leaf biomass in Kaempferia galanga through tissue culture

An efficient protocol has been established for rapid multiplication and in vitro production of leaf biomass in Kaempferia galanga L, a rare medicinal plant. Different plant growth regulators like Benzyladenine (BA), Indoleacetic acid (IAA), Indolebutyric acid (IBA), Napthaleneacetic acid (NAA) and adenine sulphates (Ads) have been tried for induction of multiple shoots using lateral bud of rhizome as explants. The highest rate of shoot multiplication (11.5 \ub1 0.6) shoot/explant as well as leaf biomass production (7.4 \ub1 0.3) gram/explant was observed on Murashige and Skoog medium supplemented with Benzyladenine (1 mg/l) and Indoleacetic acid (0.5 mg/l). Data of shoot multiplication and leaf biomass production were statistically analysed. Upon excission of leaves after 2 months of culture under sterile condition, the base of each plantlet was transferred to fresh media which could produce the same leaf biomass within another 2 months in a 50 ml culture tube containing 20 ml and 250 ml conical flasks containing 30 ml Murashige and Skoog medium. The rate of multiplication and leaf biomass production remained unchanged in subsequent subcultures. The regenerated plantlets were acclimatized in greenhouse and subsequently transferred to the field. Survival rate of the plantlets under ex vitro condition was 95 percent. Genetic fidelity of the regenerants was confirmed using random amplified polymorphic DNA (RAPD) marker. The protocol could be commercially utilized for large scale production of true-to-type plantlets and as an alternative method of leaf biomass production in Kaempferia galanga

Rapid multiplication and in vitro production of leaf biomass in Kaempferia galanga through tissue culture

An efficient protocol has been established for rapid multiplication and in vitro production of leaf biomass in Kaempferia galanga L, a rare medicinal plant. Different plant growth regulators like Benzyladenine (BA), Indoleacetic acid (IAA), Indolebutyric acid (IBA), Napthaleneacetic acid (NAA) and adenine sulphates (Ads) have been tried for induction of multiple shoots using lateral bud of rhizome as explants. The highest rate of shoot multiplication (11.5 ± 0.6) shoot/explant as well as leaf biomass production (7.4 ± 0.3) gram/explant was observed on Murashige and Skoog medium supplemented with Benzyladenine (1 mg/l) and Indoleacetic acid (0.5 mg/l). Data of shoot multiplication and leaf biomass production were statistically analysed. Upon excission of leaves after 2 months of culture under sterile condition, the base of each plantlet was transferred to fresh media which could produce the same leaf biomass within another 2 months in a 50 ml culture tube containing 20 ml and 250 ml conical flasks containing 30 ml Murashige and Skoog medium. The rate of multiplication and leaf biomass production remained unchanged in subsequent subcultures. The regenerated plantlets were acclimatized in greenhouse and subsequently transferred to the field. Survival rate of the plantlets under ex vitro condition was 95 percent. Genetic fidelity of the regenerants was confirmed using random amplified polymorphic DNA (RAPD) marker. The protocol could be commercially utilized for large scale production of true-to-type plantlets and as an alternative method of leaf biomass production in Kaempferia galanga

Chemometric profile & antimicrobial activities of leaf extract of <i>Calotropis procera</i> and <i>Calotropis gigantea</i>

<p><i>Calotropis procera</i> and <i>Calotropis gigantea</i> are medicinal plant having therapeutic value. The leaf extracts of <i>C. procera</i> have been investigated, its pharmacological actions in detail and leaf extracts of <i>C. gigantea</i> were not studied till date. The objective of present work was to find the bioactive constituents present in the ethanolic leaf extract of <i>C. procera</i> and <i>C. gigantea</i> to evaluate their antibacterial and anifungal activities. The major phytochemical groups in <i>C. procera</i> ethanolic leaf extracts were fatty acid ethyl ester (21.36%), palmitic acid ester (10.24%), linoleic acid (7.43%) and amino acid (8.10%) respectively, whereas ethanolic leaf extracts of <i>C. gigantea</i> contain palmitic acid (46.01%), diterpene (26.53%), triterpene (17.39%), linoleic acid (5.13%) as the major phytochemical groups. Ethanol extract of <i>C. procera</i> leaves showed the highest inhibition (11 mm) against <i>Escherichia coli</i>, while ethanolic extract of <i>C. gigantea</i> leaves inhibited Klebsiella (20 mm). These findings will use in new directions in pharmacological investigations.</p

Development of prediction model and experimental validation in predicting the curcumin content of turmeric (Curcuma longa L.)

The drug yielding potential of turmeric (Curcuma longa L.) is largely due to the presence of phyto-constituent ‘curcumin’. Curcumin has been found to possess a myriad of therapeutic activities ranging from anti-inflammatory to neuroprotective. Lack of requisite high curcumin containing genotypes and variation in the curcumin content of turmeric at different agro climatic regions are the major stumbling blocks in commercial production of turmeric. Curcumin content of turmeric is greatly influenced by environmental factors. Hence, a prediction model based on artificial neural network (ANN) was developed to map genome environment interaction basing on curcumin content, soli and climatic factors from different agroclimatic regions for prediction of maximum curcumin content at various sites to facilitate the selection of suitable region for commercial cultivation of turmeric. The ANN model was developed and tested using a data set of 119 generated by collecting samples from 8 different agroclimatic regions of Odisha. The curcumin content from these samples was measured that varied from 7.2% to 0.4%. The ANN model was trained with 11 parameters of soil and climatic factors as input and curcumin content as output. The results showed that feed-forward ANN model with 8 nodes (MLFN-8) was the most suitable one with R2 value of 0.91. Sensitivity analysis revealed that minimum relative humidity, altitude, soil nitrogen content and soil pH had greater effect on curcumin content. This ANN model has shown proven efficiency for predicting and optimizing the curcumin content at a specific site