scholarly journals Development, Validation, and Application of HPLC Method for Quantification of Antihyperuricemic Compounds from Lippia nodiflora in Rat Plasma

2019 ◽  
Vol 6 (01) ◽  
pp. e28-e35
Author(s):  
Lee-Chuen Cheng ◽  
Vikneswaran Murugaiyah ◽  
Kit-Lam Chan
Keyword(s):  
Limit Of Detection ◽  
Peak Plasma ◽  
Signal To Noise Ratio ◽  
Rat Plasma ◽  
Hplc Method ◽  
The Body ◽  
The Other ◽  
Aqueous Acetic Acid ◽  
Signal To Noise ◽  
Noise Ratio

AbstractAn HPLC method for simultaneous determination of arenarioside (1 ), verbascoside (2), 6-hydroxyluteolin (3), 6-hydroxyluteolin-7-O-glycoside (4), and nodifloretin (5) from Lippia nodiflora in rat plasma was developed and validated. The optimal chromatographic separation was achieved with a gradient mobile phase comprising 0.1% aqueous acetic acid and acetonitrile. The limit of detection was 78.1 ng/mL for 3 and 39.1 ng/mL for the other compounds (signal-to-noise ratio=3), whereas the limit of quantification was 312.5 ng/mL for 3 and 156.3 ng/mL for the other compounds (signal-to-noise ratio=12). The recovery values of compounds 1–5 ranged from 89.37–100.92%. Their accuracy values were between 96.48 and 105.81%, while their corresponding precision values were in the range of 0.75–9.06% for both intraday and inter-day analysis. The method was then applied in the first pharmacokinetic study of 1–5. Following intravenous administration, 1–5 were eliminated slowly from the body with a mean clearance value of 0.11, 0.13, 0.30, 0.09, and 0.23 L/kg h, respectively. Meanwhile, their peak plasma concentration upon oral administration was 8.97, 1.07, 1.06, 0.65, and 0.38 µg/mL, respectively. Compound 3 (5.97%) exhibited the highest absolute oral bioavailability value, followed by 1 (5.22%), 4 (3.13%), 2 (2.10%), and 5 (0.93%).

scholarly journals HPLC Estimation of New Impurity Methyl Ezetimibe in Ezetimibe Drug

2020 ◽  
Vol 32 (6) ◽  
pp. 1309-1313
Author(s):  
Duggirala Parvatha Venkata Vardhani Devi ◽  
Kapavarapu Maruthi Venkata Narayanarao ◽  
Pulipaka Shyamala ◽  
Rallabhandi Murali Krishna ◽  
Komali Siva Prasad
Keyword(s):  
Limit Of Detection ◽  
Signal To Noise Ratio ◽  
Gradient Elution ◽  
Hplc Method ◽  
The Novel ◽  
Signal To Noise ◽  
Drug Substances ◽  
Chromatographic Detection ◽  
Noise Ratio ◽  
Gradient Elution Mode

A new gradient elution mode HPLC method was developed and validated to detect and monitor the novel impurity namely methyl ezitimibe in ezetimibe drug substances. Chromatographic detection and analysis of methyl ezetimibe was performed on XBridge C18 column with mobile phase consisting of 0.02 M phosphate buffer (pH 5) and acetonitrile with 1 mL/min flow rate in gradient elution mode. Methyl ezetimibe was detected and monitored at 248 nm. The calibration curve was linear over range of 0.015 to 0.219% concentration. The limit of detection and quantification were computed as 0.005% (signal to noise ratio 3.60) and 0.015% (signal to noise ratio 15.96), respectively. The precision was 0.97% (%RSD) and accuracy was 93.2 to 98.2% (recovery). The developed method was proved suitable to detect and monitor methyl ezetimibe impurity in ezetimibe drug substances.

scholarly journals Geometric Approach to Analytic Marginalisation of the Likelihood Ratio for Continuous Gravitational Wave Searches

Universe ◽  
2021 ◽  
Vol 7 (6) ◽  
pp. 174
Author(s):  
Karl Wette
Keyword(s):  
Gravitational Wave ◽  
Likelihood Ratio ◽  
Signal To Noise Ratio ◽  
Geometric Approach ◽  
The Other ◽  
Efficient Computation ◽  
Likelihood Ratios ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Optimal Signal

The likelihood ratio for a continuous gravitational wave signal is viewed geometrically as a function of the orientation of two vectors; one representing the optimal signal-to-noise ratio, and the other representing the maximised likelihood ratio or F-statistic. Analytic marginalisation over the angle between the vectors yields a marginalised likelihood ratio, which is a function of the F-statistic. Further analytic marginalisation over the optimal signal-to-noise ratio is explored using different choices of prior. Monte-Carlo simulations show that the marginalised likelihood ratios had identical detection power to the F-statistic. This approach demonstrates a route to viewing the F-statistic in a Bayesian context, while retaining the advantages of its efficient computation.

scholarly journals Eddy-covariance data with low signal-to-noise ratio: time-lag determination, uncertainties and limit of detection

2015 ◽  
Vol 8 (3) ◽  
pp. 2913-2955 ◽  
Author(s):  
B. Langford ◽  
W. Acton ◽  
C. Ammann ◽  
A. Valach ◽  
E. Nemitz
Keyword(s):  
Eddy Covariance ◽  
Random Error ◽  
Limit Of Detection ◽  
Sampling Error ◽  
Signal To Noise Ratio ◽  
Time Lag ◽  
Systematic Bias ◽  
Signal To Noise ◽  
Cross Covariance ◽  
Noise Ratio

Abstract. All eddy-covariance flux measurements are associated with random uncertainties which are a combination of sampling error due to natural variability in turbulence and sensor noise. The former is the principal error for systems where the signal-to-noise ratio of the analyser is high, as is usually the case when measuring fluxes of heat, CO2 or H2O. Where signal is limited, which is often the case for measurements of other trace gases and aerosols, instrument uncertainties dominate. We are here applying a consistent approach based on auto- and cross-covariance functions to quantifying the total random flux error and the random error due to instrument noise separately. As with previous approaches, the random error quantification assumes that the time-lag between wind and concentration measurement is known. However, if combined with commonly used automated methods that identify the individual time-lag by looking for the maximum in the cross-covariance function of the two entities, analyser noise additionally leads to a systematic bias in the fluxes. Combining datasets from several analysers and using simulations we show that the method of time-lag determination becomes increasingly important as the magnitude of the instrument error approaches that of the sampling error. The flux bias can be particularly significant for disjunct data, whereas using a prescribed time-lag eliminates these effects (provided the time-lag does not fluctuate unduly over time). We also demonstrate that when sampling at higher elevations, where low frequency turbulence dominates and covariance peaks are broader, both the probability and magnitude of bias are magnified. We show that the statistical significance of noisy flux data can be increased (limit of detection can be decreased) by appropriate averaging of individual fluxes, but only if systematic biases are avoided by using a prescribed time-lag. Finally, we make recommendations for the analysis and reporting of data with low signal-to-noise and their associated errors.

Determination of trans-phylloquinone in children's serum.

1989 ◽  
Vol 35 (5) ◽  
pp. 874-878 ◽  
Author(s):  
F Moussa ◽  
L Dufour ◽  
J R Didry ◽  
P Aymard
Keyword(s):  
Limit Of Detection ◽  
Signal To Noise Ratio ◽  
Fluorometric Detection ◽  
Vitamin K1 ◽  
Signal To Noise ◽  
Détection Signal ◽  
Coagulation Tests ◽  
Noise Ratio ◽  
Detection Signal

Abstract By optimizing the conditions for determining trans-phylloquinone and its metabolite, K-2,3-epoxide, in serum through a two-step HPLC process combined with fluorometric detection after coulometric reduction, we have been able to develop a method applicable to small volumes of serum (200 to 500 microL). The limit of detection (signal-to-noise ratio of 3) was 15 ng/L for trans-phylloquinone, 30 ng/L for K-2,3-epoxide. The trans-phylloquinone concentrations measured by this method in serum from 82 children, ages one to six years, whose results were normal for overall coagulation tests, ranged from 40 to 880 ng/L (median 175 ng/L). We discuss these findings and compare them with vitamin K1(20) values reported for adults.

A Proposed Approach to De-speckle an Ultrasound Image using Anisotropic Filter by Generating Diffusion Tensor

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Poonam Chauhan ◽  
Vikas Kaushik
Keyword(s):  
Ultrasound Imaging ◽  
Diffusion Tensor ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Speckle Noise ◽  
The Body ◽  
Invasive Technique ◽  
Signal To Noise ◽  
Noise Ratio

Ultrasound imaging is a technique that is used to diagnose the diseases in medical field using radiology. US (ultrasound) imaging is a non -invasive technique and used for imaging of internal structure of the body without any kind of penetration which helps to identify the diseases that have probability and tissues. Many kinds of noises present in US images but the presence of speckle noise is a big challenge since last few years in biomedical field. Sometimes speckle noise becomes the part of information and vice-versa. So it becomes hard to find the disease for doctors. There are many de-speckled filters available for de-noising. This paper gives a proposed approach to de-speckled the US image using anisotropic diffusion filter by calculating the different numerical values like SSIM (structural similarity index), SNR (signal to noise ratio), MSE (mean square error), PSNR (peak signal to noise ratio), which results in coherence enhancement The proposed technique provides better and improved edge and coherence enhancement in ultrasound image data.

scholarly journals Study on Spectrum Allocation and Optimization of Wireless Communication Networks Based on SFOA

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yu Zhang ◽  
Jianying Li
Keyword(s):  
Communication Network ◽  
Wireless Communication ◽  
Signal To Noise Ratio ◽  
Spectrum Allocation ◽  
The Other ◽  
Signal To Noise ◽  
Wireless Communication Network ◽  
Maximum Value ◽  
Noise Ratio ◽  
Stability And Accuracy

Wireless communication network spectrum is a limited resource. With the rapid increase of mobile communication services in recent years, traditional spectrum allocation methods are only based on a fixed spectrum allocation strategy, which often results in uneven and wasteful resource allocation. Therefore, spectrum allocation and the optimization problem need to be solved urgently. The application of semantic mobile computing in the Internet of Things and the research of emerging bionic models provide new ideas for this problem. In order to solve the problem of spectrum optimization and allocation, this paper proposes an optimization algorithm that simulates fisherman fishing to reasonably arrange the allocation and optimization of wireless network spectrum. This paper selects SFOA and the other two algorithms, designs experimental functions to perform calculations separately, obtains relevant data indicators, and uses comparative analysis to analyze. The analysis shows that in terms of performance, the success rate of SFOA is higher than that of PSO, and the success rate of the two function calculations has reached 100%. In the signal-to-noise ratio analysis, when the signal-to-noise ratio is -4 dB, the throughput of GPSO reaches the maximum value of 0.17, the throughput of PSO reaches the maximum value of 0.56, and the throughput of SFOA reaches 1, which shows that SFOA is adopted. The stability and accuracy of the algorithm are higher than the other two algorithms, and in the case of high signal-to-noise ratio, the advantages of the SFOA algorithm are also more obvious. This shows that the use of this algorithm will be very helpful for spectrum allocation and optimization. Because SFOA has high stability and accuracy, through reasonable adjustment and improvement, it can make good use of spectrum allocation and optimization. Chinese wireless communication network and the development of Internet of Things technology are of great significance.

scholarly journals Eddy-covariance data with low signal-to-noise ratio: time-lag determination, uncertainties and limit of detection

2015 ◽  
Vol 8 (10) ◽  
pp. 4197-4213 ◽  
Author(s):  
B. Langford ◽  
W. Acton ◽  
C. Ammann ◽  
A. Valach ◽  
E. Nemitz
Keyword(s):  
Eddy Covariance ◽  
Random Error ◽  
Limit Of Detection ◽  
Sampling Error ◽  
Signal To Noise Ratio ◽  
Time Lag ◽  
Systematic Bias ◽  
Signal To Noise ◽  
Cross Covariance ◽  
Noise Ratio

Abstract. All eddy-covariance flux measurements are associated with random uncertainties which are a combination of sampling error due to natural variability in turbulence and sensor noise. The former is the principal error for systems where the signal-to-noise ratio of the analyser is high, as is usually the case when measuring fluxes of heat, CO2 or H2O. Where signal is limited, which is often the case for measurements of other trace gases and aerosols, instrument uncertainties dominate. Here, we are applying a consistent approach based on auto- and cross-covariance functions to quantify the total random flux error and the random error due to instrument noise separately. As with previous approaches, the random error quantification assumes that the time lag between wind and concentration measurement is known. However, if combined with commonly used automated methods that identify the individual time lag by looking for the maximum in the cross-covariance function of the two entities, analyser noise additionally leads to a systematic bias in the fluxes. Combining data sets from several analysers and using simulations, we show that the method of time-lag determination becomes increasingly important as the magnitude of the instrument error approaches that of the sampling error. The flux bias can be particularly significant for disjunct data, whereas using a prescribed time lag eliminates these effects (provided the time lag does not fluctuate unduly over time). We also demonstrate that when sampling at higher elevations, where low frequency turbulence dominates and covariance peaks are broader, both the probability and magnitude of bias are magnified. We show that the statistical significance of noisy flux data can be increased (limit of detection can be decreased) by appropriate averaging of individual fluxes, but only if systematic biases are avoided by using a prescribed time lag. Finally, we make recommendations for the analysis and reporting of data with low signal-to-noise and their associated errors.

scholarly journals Developing a Validated HPLC Method for the Phytochemical Analysis of Antihyperuricemic Phenylethanoid Glycosides and Flavonoids in Lippia nodiflora

2017 ◽  
Vol 12 (11) ◽  
pp. 1934578X1701201
Author(s):  
Lee-Chuen Cheng ◽  
Vikneswaran Murugaiyah ◽  
Kit-Lam Chan
Keyword(s):  
Signal To Noise Ratio ◽  
Water Extract ◽  
Aqueous Acetic Acid ◽  
Phytochemical Analysis ◽  
Bioactive Constituents ◽  
Signal To Noise ◽  
Phenylethanoid Glycosides ◽  
Chromatography Method ◽  
Whole Plant ◽  
Noise Ratio

The phenylethanoid glycosides and flavonoids of Lippia nodiflora were reported as xanthine oxidase inhibitors that possessed antihyperuricemic effects in chemically-induced hyperuricemic rats. The present study developed a validated high performance liquid chromatography method for the simultaneous phytochemical analysis of five isolated antihyperuricemic constituents in L. nodiflora, comprising two phenylethanoid glycosides, arenarioside (1) and verbascoside (2), and three flavonoids, 6-hydroxyluteolin (3), 6-hydroxyluteolin-7- O-glycoside (4) and nodifloretin (5). These compounds were analysed at 340 nm, using gradient elution of 0.1 % aqueous acetic acid and acetonitrile. The limits of detection were 78.1 ng/mL for 3 and 39.1 ng/mL for the other compounds at a signal-to-noise ratio of 3. The limits of quantification were 312.5 ng/mL for 3 and 156.3 ng/mL for the rest at a signal-to-noise ratio of 12. The intra-day and inter-day accuracy for the five bioactive constituents were between 98.60 % and 100.97 % while their precision values were below 2.11 %. The method was then successfully applied for the quantification of 1-5 in the methanolic extract of L. nodiflora whole plant and its fractions (F1-F4), the methanolic plant part extracts (leaves, stems, roots, and flowers), and water extract of the L. nodiflora whole plant at room temperature.

scholarly journals A Simple LC-MS/MS Method for Determination of Vemurafenib in Rat Plasma Fed with High Fat Diet

2021 ◽  
pp. 261-266
Author(s):  
Ayşe Özdemir ◽  
Senem Şanli ◽  
Erten Akbel
Keyword(s):  
High Fat Diet ◽  
Regression Coefficient ◽  
Limit Of Detection ◽  
Signal To Noise Ratio ◽  
Rat Plasma ◽  
Tandem Mass ◽  
High Fat ◽  
Signal To Noise ◽  
Highly Sensitive

In this study, high fat diet was fed to rats and the amount of vemurafenib in rat plasma was determined by the developed liquid chromatography with tandem mass spectrometry. The calibration curve was linear between 0.01 and 0.8 µg mL−1 vemurafenib with 0.999 regression coefficient.  The limit of detection and quantification of the method are estimated from the signal to noise ratio 3:1 and 10:1, respectively. These are 1.10-4 µg mL−1 for LOD and 4. 10-4  µg mL−1 for LOQ. This method has been found to be reproducible and highly sensitive and  provides a combination of faster analysis time and improved limits of detection.

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