Judul: Isolation of Endophytic Actinomycetes in Medicinal Plants and Their Potency as an Antidiabetes Based on α-glucosidase Activity.
Abstrak: Endophytic actimomycetes lives in plant tissues without causing a negative impact on plant. Several medicinal endophytic microbes are able to produce the same bioactive compounds as produced by the host. Inhibition of α-glucosidase is one of antidiabetes mechanism. The potency of endophytic actinomycetes from medicinal plant as α-glucosidase inhibitor needs to be elucidated. Temulawak (Curcuma xanthorrhiza), ciplukan (Physalis minima) and brotowali (Tinospora crispa) were surface sterilized and crushed, then plated on humic acid vitamin B. Actinomycetes colonies were purified and produced on International Streptomyces Project No. 4 followed by α-glucosidase assay. Crude extracts with the highest inhibition of α-glucosidase were freeze dried, then diluted at 0.07%, 0.1%, 0.3%, 0.5% (w/v) with dimethylsulfoxide and assayed. Commercial inhibitor, acarbose (Glucobay; Bayer) were used as a comparation. Fourty six isolates were classified into 29 isolates Gram positive and 17 isolates Gram negative. Fifteen isolates were actinomycetes which 11 isolates obtained from brotowali stem, and 4 isolates from temulawak rhizome. The highest inhibition (61.27%) were shown by Cx-10.1 temulawak isolate at 0.07% (w/v) concentration compared with Glucobay 0.1% (w/v) which was only 39.70%. At 0.07% (b/v) concentration, Tc-2.1 from brotowali produce low activity (24.16%). Endophytic Cx-10.1 isolate could be potential to produce higher activity of α-glucosidase inhibitor compared with Glucobay.
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Judul: Potensi Aktinobakteri Endofit Tabat Barito ('Ficus deltoidea Jack) sebagai Penghasil Inhibitor a-Glukosidase
Abstrak: Diabetes mellitus (DM) is a chronic and serious metabolic disorder that is t revalent in the world. Diabetes mellitus occurs due to insulin deficiency and inswli resistance. This condition causes high blood glucose levels or called hYN.. rglycemia condition. One therapeutic approach to treat diabetes is to retard the abs" tion of glucose via inhibition of enzymes, such as a-glucosidase, in the small tinal. Inhibition of this enzyme slows the elevation of blood glucose following · rl:hohydrate meal. The a-glucosidase inhibitor is a bioactive compounds i8uted widely in plants and microorganisms including actino bacteria. }\ctinobacteria are Gram positive bacteria widely distributed in nature. They produce numerous metabolic products, most of which are bioactive compound with applications in many therapeutic areas. Actinobacteria are predominantly free living and are found in diverse environments including plants tissue. Microbes that live on plant tissues without causing harmfull to the host are called endophytes. Such icrobial endophytes are beginning to pay attention ofresearchers due to their abilit)1 to produce various compounds of pharmacological value similar to their li.osts. Previous reports showed that endophytic actinobacteria have a-glucosidase mhibi or activity such as Tinospora crispa (Brotowali) and Datura stramonim L (Kecubung). lf abat barito (Ficus deltoidea Jack) is a medicinal plant originating from Southeast Asia. In Indonesia, tabat barito has the most distribution in Borneo island. Tabat barito was used traditionally to treat diabetes. On another hand, several studies have shown that tabat barito has an a-glucosidase inhibitor activity. The abilit)1of tabat barito to produce a-glucosidase inhibitors is suggested to be related to the presence of endophytic microbes including actinobacteria. Previous studies showed that the abundance of actinobacteria in the tabat barito plant tissue is quite umer:ous and varied. Based on these reports, it is expected that the capability of abat barito on producing a-glucosidase inhibitors is related to the presence of actinobacteria. In this study, it was proven that endophytic actinobacteria from tabat barito <t:ould produce a-glucosidase inhibitors. Screening using a-glucosidase from rat intestine is recommended for in vitro a-glucosidase inhibitor because the homology sequeroce of catalytic side closer to enzymes in the human intestine. In this study endophytic actinobacterial isolates more inhibited a-glucosidase from rat intestines according to acarbose which only inhibit mammalian a-glucosidase. Screening of 40 enoophytic actinobacteria isolates using yeast a-glucosidase showed that 25 sG).}ates (62%) of isolates had a-glucosidase inhibitors activity. Whereas screening msm rat intestinal enzymes from 40 isolates, 31 isolates (77%) had a-glucosidase lnor activity. Aqueous extract and n-hexane ofTBL 7 isolates were the best extracts for a­ sidase inhibition, IC₅₀ value of 159.25 µg mL-¹ (aqueous extract) and 118.52 mIL-1 (n-hexane extract). Based on the 16S rRNA gene TBL 7 showed was similar to Streptomyces tendae (99.91%). The ISP 2 medium with 10 days incubation period is the best condition to produce a-glucosidase inhibitor compound. After fractionation, AF 2 from aqueous crude extract has ICso value of 58.8 µg mL-I, higher than acarbose as positive control (ICso = 90.4 µg mL-I). The LC-MS analysis showed that the fraction of AF 2 had bioactive compounds similar t phenylpropynal, butyric acid, solketal, p-coumaric
Keyword: Actinobacteria, Diabetes mellitus, Endophytes, Tabat barito

Judul: Pengembangan model probabilistic neural network (PNN) pada pengenalan kisaran usia dan jenis kelamin berbasis suara
Abstrak: Voice signal can be used to represent a speaker. Voice can also be used to recognize the age range and the gender of the speaker, because of the difference in frequency of the voice signal for different age and gender. Age range for this experiment is divided into children (8-10 years old), teenagers (12-17 years old) and adults (30-50 years old). We performed feature extraction to obtain a representation of the speaker using Mel-Frequency Cepstrum Coefficient (MFCC). MFCC is used with 13, 20, 26 coefficients. Probabilistic Neural Network (PNN) is used as the feature matching model. The PNN model is constructed from three different proportions of TRD (Training data) : TSD (Testing data) (25%:75%, 50%:50%, 75%:25%). MFCC with 26 coefficient gives the best accuracy of 93.47%. The proportion of 75% (TRD) : 25% (TSD) also gives the best accuracy of 94.22%. PNN-age and PNN-gender identification result in an accuracy of 91.26%. Specifically, PNN accuracy percentage for male and female are 92.26%, and 95.20%, respectively, while for children, teenagers and adults the accuracies are 99.85%, 92.28% and 95.57%, respectively. In conclusion, the average of accuracy for all categories is 91.26%. The experiment using the voice of male-teenagers gives the worst accuracy, which is caused by the overlapping frequencies of voice in children, teenagers and adults.
Keyword: Voice identification, PNN model, MFCC, Age-identification, Gender-identification