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Samia Siddique,Quratulain Syed,Runbina Nelofer,Ahmad Adnan,Habiba Mansoor,Fahim Ashraf Qureshi 한국응용생명화학회 2014 Applied Biological Chemistry (Appl Biol Chem) Vol.57 No.3
Present study was conducted to optimize avermectinB1b production from S.avermitilis 41445 UV45(m)3 usingartificial neural network and response surface methodology. Threevariables NaCl, KCl, and pH were used for optimization. Coefficientof determination and adjusted coefficient of determination havevery poor values for RSM. Values predicted by RSM for experimentswere also much different from the observed avermectin production. Comparatively predicted avermectin levels by ANN were veryclose to observed values with much higher R2 and adjusted R2. Optimum levels of NaCl, KCl, and pH predicted by ANN were1.0 g/L, 0.5 g/L, and 7.46 respectively. Sensitivity analysis predictedhighest effect being shown was by pH followed by NaCl and KCl. About 37.89 folds increase in avermectin B1b production wasobserved at optimum levels of three variables envisage by ANN. Optimum levels, ranking order of variables, and the predictedavermectin on the optimum levels by the RSM was muchdifferent from ANN values. Results revealed that ANN is a betteroptimization tool for given strain than RSM.
Siddique, Samia,Syed, Quratulain,Nelofer, Runbina,Adnan, Ahmad,Mansoor, Habiba,Qureshi, Fahim Ashraf The Korean Society for Applied Biological Chemistr 2014 Applied Biological Chemistry (Appl Biol Chem) Vol.57 No.3
Present study was conducted to optimize avermectin B1b production from S.avermitilis 41445 UV45(m)3 using artificial neural network and response surface methodology. Three variables NaCl, KCl, and pH were used for optimization. Coefficient of determination and adjusted coefficient of determination have very poor values for RSM. Values predicted by RSM for experiments were also much different from the observed avermectin production. Comparatively predicted avermectin levels by ANN were very close to observed values with much higher $R^2$ and adjusted $R^2$. Optimum levels of NaCl, KCl, and pH predicted by ANN were 1.0 g/L, 0.5 g/L, and 7.46 respectively. Sensitivity analysis predicted highest effect being shown was by pH followed by NaCl and KCl. About 37.89 folds increase in avermectin B1b production was observed at optimum levels of three variables envisage by ANN. Optimum levels, ranking order of variables, and the predicted avermectin on the optimum levels by the RSM was much different from ANN values. Results revealed that ANN is a better optimization tool for given strain than RSM.
Reaz Uddin,Quratulain Nehal Siddiqui,Muhammad Sufian,Syed Sikander Azam,Abdul Wadood 한국유전학회 2019 Genes & Genomics Vol.41 No.11
Background Among the resistant isolates of MTB, multidrug resistant tuberculosis (MDR-TB) and extensively drug resistant tuberculosis (XDR-TB) have been the areas of growing concern. The genomic analysis showed that the respective genomic pool of the XDR-MTB proteome contains more than 30% of the hypothetical proteins for which no functions have been annotated yet. This class of proteins presumably have their own importance to complete genome and proteome information. The bioinformatics advancements have helped to annotate those hypothetical proteins by using various computational tools and have potential to classify them functionally. Objective The objective of this study was to propose a new and unique drug target against the deadly Mycobacterium tuberculosis using Bioinformatics approaches to characterize the hypothetical proteins. Results We stepwise reduced the hypothetical proteins (total number: 1256) out of the complete proteome to only 26 essential hypothetical proteins. Out of those 26 proteins, the protein WP_003401246.1 was computationally characterized as the druggable target. Conclusion The study proposed a hypothetical protein from complete proteome of the XDR-MTB as a new drug target against which new drug candidates can be proposed. Hence, the study opens up the new avenues in the areas of drug discovery against deadly M. tuberculosis.
The Spectrum of Genetic Mutations in Breast Cancer
Sheikh, Asfandyar,Hussain, Syed Ather,Ghori, Quratulain,Naeem, Nida,Fazil, Abul,Giri, Smith,Sathian, Brijesh,Mainali, Prajeena,Al Tamimi, Dalal M Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.6
Breast cancer is the most common malignancy in women around the world. About one in 12 women in the West develop breast cancer at some point in life. It is estimated that 5%-10% of all breast cancer cases in women are linked to hereditary susceptibility due to mutations in autosomal dominant genes. The two key players associated with high breast cancer risk are mutations in BRCA 1 and BRCA 2. Another highly important mutation can occur in TP53 resulting in a triple negative breast cancer. However, the great majority of breast cancer cases are not related to a mutated gene of high penetrance, but to genes of low penetrance such as CHEK2, CDH1, NBS1, RAD50, BRIP1 and PALB2, which are frequently mutated in the general population. In this review, we discuss the entire spectrum of mutations which are associated with breast cancer.
Sultan, Sadia,Irfan, Syed Mohammed,Tanveer, Quratulain,Ali, Navaira Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.17
Background: Essential thrombocythemia (ET) is a Philadelphia chromosome-negative myeloproliferative neoplasm characterized by sustained thrombocytosis and megakaryocytic hyperplasia. It is an uncommon hematological malignancy which primarily affects elderly individuals. The rational of this study was to determine its clinico-hematological profile along with risk stratification in Pakistan patients. Materials and Methods: In this retrospective cross sectional study, 21 patients with ET were enrolled from January 2011 to December 2014. Data was analyzed with SPSS version 21. Results: The mean age was $56.7{\pm}19.0years$ (range 18-87) and the male to female ratio was 1:1.1. Of the total, 62% of patients were above 50 years of age. Overall 61.9% were diagnosed incidentally and were asymptomatic. In symptomatic patients, major complaints were weakness (19%); erythromelalgia (14.2%), transit ischemic attack (9.5%) and gastrointestinal bleed (4.7%). The mean hemoglobin count was $11.7{\pm}2.4g/dl$ with a total leukocyte count of $13.3{\pm}8.1{\times}10^9/l$ and platelets count of $1188.8{\pm}522.2{\times}10^9/l$. Serum lactate dehydrogenase, serum creatinine and uric acid were $454.3{\pm}127.8$, $1.2{\pm}0.5$ and $7.4{\pm}3.4$ respectively. According to risk stratification, 57.1% were in high risk; 23.8% in intermediate risk while 19.1% in low risk group. Conclusions: ET in our patients in Pakistan, unlike in the West, is seen in a relatively young population. Primarily patients were asymptomatic and risk stratification revealed predominance of high risk disease in our setting.
Siddique, Samia,Nelofer, Rubina,Syed, Quratulain,Adnan, Ahmad,Qureshi, Fahim Ashraf 한국응용생명화학회 2014 Applied Biological Chemistry (Appl Biol Chem) Vol.57 No.5
Avermectin is an environment friendly bio-insecticide. Optimization of the culture conditions for avermectin B1b production has not been carried out before using Artificial Neural Network (ANN) method. The present work is therefore conducted to optimize some important factors including yeast extract, $MgSO_4.7H_2O$, and temperature for the avermectin B1b production using ANN methodology from Streptomyces avermitilis DSM 41445. The optimum levels for the yeast extract, $MgSO_4.7H_2O$, and temperature were 16.0 (g/L), 5.0 (g/L) and $32^{\circ}C$ respectively. Maximum effect was observed by yeast extract. Avermectin B1b yield was increased up to 150% after optimization. ANN was found to be a powerful technique for the optimization and prediction of avermectin B1b production from Streptomyces avermitilis DSM 41445.
Samia Siddique,RubinaNelofer,Quratulain Syed,Ahmad Adnan,Fahim Ashraf Qureshi 한국응용생명화학회 2014 Applied Biological Chemistry (Appl Biol Chem) Vol.57 No.5
Avermectin is an environment friendly bio-insecticide. Optimization of the culture conditions for avermectin B1bproduction has not been carried out before using Artificial NeuralNetwork (ANN) method. The present work is therefore conductedto optimize some important factors including yeast extract,MgSO4.7H2O, and temperature for the avermectin B1b productionusing ANN methodology from Streptomyces avermitilis DSM41445. The optimum levels for the yeast extract, MgSO4.7H2O,and temperature were 16.0 (g/L), 5.0 (g/L) and 32oC respectively. Maximum effect was observed by yeast extract. Avermectin B1byield was increased up to 150% after optimization. ANN wasfound to be a powerful technique for the optimization andprediction of avermectin B1b production from Streptomycesavermitilis DSM 41445.
Application of lipase bearing dead mycelia as biocatalyst for octyloctanoate synthesis
Ayoub Rashid,Muhammad Mushtaq,Quratulain Syed,Nergis Naz,Ahmad Adnan 한국식품과학회 2018 Food Science and Biotechnology Vol.27 No.6
The present study scrutinizes lipases of different origins, immobilization methods, carriers, and reaction solvents to accelerate the octyl octanoate synthesis. The acylation reaction parameters including temperature, moisture level, shaking speed and enzyme dose were subsequently investigated and optimized following fully rotatable central composite design. The initial screening revealed that lipases of Rhizopus arrhizus, when applied as a biocatalyst (lipase bearing dead mycelia) furnished the highest acylation activity (147 lM L-1 min-1). Validation of reaction conditions disclosed that 250 I.U. of lipase based biocatalyst when incubated with 850 mM of acylating agent and 750 mM of the substrate at 35 C, 3% moisture level and 150 RPM shaking speed produced 70% acylation yield with an acylation activity higher than 147 lM L-1 min-1. The observed results certify that lipase bearing dead mycelia of R. arrhizus might be an intelligent biocatalyst to manipulate the yield of acylation reactions encountered in the food industry.