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Development and validation of a simple UV spectrophotometric method for the determination of levofloxacin both in bulk and marketed dosage formulations

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Journal of Pharmaceutical Analysis 2012年第6期2卷 454-457页

作者： Mahfuza Maleque Md. raquibul hasan Farhad Hossen Sanjana Safi Department of Pharmacy School of Health Sciences State University of Bangladesh Dhaka Bangladesh Department of Clinical Pharmacy and Pharmacology Faculty of Pharmacy University of Dhaka Bangladesh

A rapid, specific and economic UV spectrophotometric method has been developed using a solvent composed of water：methanol：acetonitrile （9：0.5：0.5） to determine the levofloxacin content in bulk and pharmaceutical dosage formulations. At a pre-determined 2 of 292 nm, it was proved linear in the range of 1.0 12.0 μg/mL, and exhibited good correlation coefficient （R2=0.9998） and excellent mean recovery （99.0-100.07%）. This method was successfully applied to the determination of levofloxacin content in five marketed brands from Bangladesh and the results were in good agreement with the label claims. The method was validated statistically and by recovery studies for linearity, precision, repeatability, and reproducibility. The obtained results proved that the method can be employed for the routine analysis of levofloxacin in bulks as well as in the commercial formulations.

关键词： Fluoroquinolone Levofloxacin UV spectrophotometricmethod Validation

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Effect of Packing Materials and Other Parameters on the Air Stripping Process for the Removal of Ammonia from the Wastewater of natural Gas Fertilizer Factory

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Journal of Water Resource and Protection 2009年第3期1卷 210-215页

作者： raquibul ALAM Md. Delwar HOSSAIN 不详

Air-stripping method was used to remove ammonia from the wastewater collected from natural gas fertilizer factory. Different materials were used as packing materials for the air stripping system. The effect of pH over 10.5, air-water flow ratio, nature of packing materials, height of materials and initial influent concentration of ammonia on air stripping unit were investigated. An attempt has been made to find out the stripping con-stant. Stripping constant was found to be .001, 0014, .001 and .0009 for coal, plastic ring, stone chips and wood chips, respectively. Best result was found for plastic ring for its higher surface area. Wood chips did not give good result, because the chips amalgamate with each other and hence reduces the surface area.

关键词： Ammonia Air Stripping Packing Materials Stripping Constant

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Targeted chemotherapy via HER2-based chimeric antigen receptor(CAR)engineered T-cell membrane coated polymeric nanoparticles

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Bioactive Materials 2024年第4期34卷 422-435页

作者： Serkan Yaman harish Ramachandramoorthy Priyanka Iyer Uday Chintapula Tam Nguyen Manoj Sabnani Tanviben Kotadia Soroush Ghaffari Laurentiu M.Pop raquibul ha.na. Jon A.Weidanz Kytai T.Nguyen Department of Bioengineering University of Texas at ArlingtonTXUSA Department of Biology University of Texas at ArlingtonTXUSA Joint Bioengineering Program University of Texas Southwestern Medical CenterTXUSA Department of Radiation Oncology University of Texas Southwestern Medical CenterTXUSA Department of Kinesiology University of Texas at ArlingtonArlingtonTXUSA

Cell membrane-derived nanoparticles(NPs)have recently gained popularity due to their desirable features in drug delivery such as mimicking properties of native cells,impeding systemic clearance,and altering foreign body *** NP technology,adoptive immunotherapy has emerged due to its promise in cancer specificity and therapeutic *** this research,we developed a biomimetic drug carrier based on chimeric antigen receptor(CAR)transduced T-cell *** that purpose,anti-HER2 CAR-T cells were engineered via lentiviral transduction of anti-HER2 CAR coding lentiviral ***-HER2 CAR-T cells were characterized by their specific activities against the HER2 antigen and used for cell membrane ***-cancer drug Cisplatin-loaded poly(D,L-lactide-co-glycolic acid)(PLGA)NPs were coated with anti-human epidermal growth factor receptor 2(HER2)-specific CAR engineered T-cell ***-HER2 CAR-T-cell membrane-coated PLGA NPs(CAR-T-MNPs)were characterized and confirmed via fluorescent microscopy and flow ***-coated NPs showed a sustained drug release over the course of 21 days in physiological ***-loaded CAR-T-MNPs also inhibited the growth of multiple HER2+cancer cells in *** addition,in vitro uptake studies revealed that CAR-T-MNPs showed an increased uptake by A549 *** results were also confirmed via in vivo biodistribution and therapeutic studies using a subcutaneous lung cancer model in nude ***-T-MNPs localized preferentially at tumor areas compared to those of other studied groups and consisted of a significant reduction in tumor growth in tumor-bearing *** Conclusion,the new CAR modified cell membrane-coated NP drug-delivery platform has demonstrated its efficacy both in vitro and in ***,CAR engineered membrane-coated NP system could be a promising cell-mimicking drug carrier that could improve therapeutic outcomes of lung cancer treatments.

关键词： nanoparticles CAR-T cells Membrane-based drug delivery Cancer chemotherapy

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A Machine Learning Classification Model for Detecting Prediabetes

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Journal of Data Analysis and Information Processing 2024年第3期12卷 462-478页

作者： A. K. M. raquibul Bashar Mahdi Goudarzi Chris P. Tsokos Department of Mathematics & Computer Science Augustana College Rock Island Illinois USA Independent Researcher San Francisco California USA Department of Mathematics & Statistics University of South Florida Tampa Florida USA

The incidence of prediabetes is in a dangerous condition in the USA. The likelihood of increasing chronic and complex health issues is very high if this stage of prediabetes is ignored. So, early detection of prediabetes conditions is critical to decrease or avoid type 2 diabetes and other health issues that come as a result of untreated and undiagnosed prediabetes condition. This study is done in order to detect the prediabetes condition with an artificial intelligence method. Data used for this study is collected from the Centers for Disease Control and Prevention’s (CDC) survey conducted by the Division of Health and Nutrition Examination Surveys (DhaNES). In this study, several machine learning algorithms are exploited and compared to determine the best algorithm based on Average Squared Error (ASE), Kolmogorov-Smirnov (Youden) scores, areas under the ROC and some other measures of the machine learning algorithm. Based on these scores, the champion model is selected, and Random Forest is the champion model with approximately 89% accuracy.

关键词： Prediabetes Machine Learning SVM Forest Cumulative Lift

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