Accurate Quantitative Results Research Articles

Technical considerations regarding saliva sample collection to achieve comparable protein identification and detection via one- and two-dimensional gel electrophoresis among humans

Background and AimsRecently, demands towards identifying various molecules in support of stress detection and potential clinical utilization are dramatically increasing. Moreover, the accuracy with which researchers quantify these informative molecules is now far more improved when compared to the past. As RNA or protein markers are conventionally detected via repeated invasive procedures from blood, it is critical to develop secure technologies to obtain the desired information via less stressful methodologies, such as saliva collection. Moreover, for superb interpretation, it became equally significant to obtain the information from the same exact specimen. RNA is easily degradable, thus it is paramount to supplement the samples with protective agents, such as RNAlater, to achieve accurate quantitative results. MethodsIn our research we investigated whether and how this commonly applied RNA protection procedure influences protein and peptide separation of the human saliva via quantitative two-dimensional protein electrophoresis. ResultsOur results revealed, in contrary to previously published data regarding plasma, the addition of RNAlater to saliva samples negatively influences isoelectric focusing and protein detection. We equally found the application oftentimes employed referred to as selective precipitation and reduction-alkylation, partially rescued separation, however, with a significant loss in protein yield and quality when compared to untreated samples. ConclusionOur results suggest collection of human saliva for biomarker identification must be performed with extreme diligence. We propose application of RNAlater should be avoided and snap freezing of the collected saliva is recommended when joint protein and RNA quantification is the ultimate goal.

Intelligent Identification and Quantitative Evaluation Method of Fluid Components from T2-T1 2D NMR Logging

Summary T2-T1 2D nuclear magnetic resonance (NMR) logging plays an important role in fluid identification and reservoir evaluation. When the signal/noise ratio (SNR) is low, the response of the fluid component overlaps on the NMR 2D spectrum, and the fluid characteristic map obtained in the laboratory could not be directly applied to 2D NMR logging data. In this work, we proposed a T2-T1 2D NMR fluid identification and quantitative evaluation method based on blind source separation (BSS) and hierarchical clustering. This method fixes the number of fluid components of BSS as eight and utilizes a hierarchical clustering method to further process the 2D spectra obtained from BSS, so that each spectrum contains only one fluid component response. Subsequently, the fluid types are determined according to the response characteristics, and the fluid volumes are quantitatively calculated. Based on the synthetic data of T2-T1 2D NMR logging, this method was used to calculate the volumes of the fluid components, and the accuracy of the method was verified by comparing with the model and other four methods. This method was applied to 2D NMR logging data from Well Z and Well G and verified by oil test results. The results show that the relative error between the fluid volumes calculated by the proposed method and the model is smaller than other methods. The data processing results of Well Z and Well G are consistent with those of the oil test. Both synthetic data and actual logging data show that this method can provide the accurate fluid identification and quantitative evaluation results of T2-T1 2D NMR logging data.

Determination of 2-MIB and rancid-related volatile lipid oxidation products in hybrid catfish (Clarias macrocephalus × Clarias gariepinus) with an automated HS-SPME-GC–MS-QTOF-arrow technique

A headspace solid–phase microextraction (HS-SPME) method coupled with gas chromatography/mass spectrometry with quadrupole time-of-flight (GC/MS-QTOF) was developed for analysis of volatile off-odor compounds, i.e., earthy/musty (2-methylisoborneol, 2-MIB) and rancid (aldehydes and alcohols), from farmed hybrid catfish (Clarias macrocephalus × Clarias gariepinus). The most efficient extraction of targeted volatiles was provided by 50 min at 70 °C with a CWR-PDMS fiber and 3 g of fish diluted to 5 mL with 1.5 g NaCl (30 % saturated NaCl). The maximum time-delay before extraction was 8 h to avoid spoilage and lipid oxidation during analysis. The final method showed good linearity, intraday repeatability of 5–9 %, interday reproducibility of 5–12 % and recoveries of 94–112 %. The implementation part proved that the developed method gave accurate quantitative results for oxidation-derived volatiles, several with high correlation to thiobarbituric acid reactive substances (TBARS). Altogether, our study provided an effective SPME-GC–MS method for the extraction and analysis of important off-odor compounds in catfish mince.

A 2D rapid quantitative method for wetting and drying process in cementitious materials based on X-ray radiography

The infiltration of water is a crucial factor in the degradation of the durability of concrete structures. It is important to conduct wetting and drying experiments to validate the water transport performance of construction materials during their service life, with precise measurement of both the process and the outcomes being essential. This study proposes a 2D quantitative detection method based on X-ray Radiography to determine the distribution of moisture content. This technique enhances conventional X-ray Radiography methods by utilizing a higher tube voltage to mitigate the impact of beam-hardening effects, as explained by the monochromatic index introduced here, and employs a coefficient matrix for correcting field non-uniformity and a spatial positioning algorithm to rectify discrepancies among multiple measurements. This method offers several benefits due to its capacity to provide in-time, accurate, non-contact, and cost-efficient quantitative results without any contrast agent while maintaining appropriate spatial resolution. The feasibility of this method is verified through a wetting and drying experiment taking cement paste material as an example. When compared with the gravimetric method, experimental results show that the relative error of moisture content obtained from this method is less than 5.76 %.

Miniaturized Near-Infrared spectrophotometers in forensic analytical science − a critical review

The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.

Development of a Sensitive and Specific Quantitative RT-qPCR Method for the Detection of Hepatitis E Virus Genotype 3 in Porcine Liver and Foodstuff

As an international and zoonotic cause of hepatitis, hepatitis E virus (HEV) poses a significant risk to public health. However, the frequency of occurrence and the degree of contamination of food of animal origin require further research. The aim of this study was to develop and validate a highly sensitive quantitative RT-qPCR assay for the detection and quantification of HEV contamination in porcine liver and food. The focus was on genotype 3, which is most common as a food contaminant in developed countries and Europe. The selected assay has its target sequence in the open reading frame 1 (ORF1) of the HEV genome and showed good results in inclusivity testing, especially for HEV genotype 3. The developed assay seems to show high efficiency and a low intercept when compared to other assays, while having a comparable limit of detection (LOD). In addition, a standard curve was generated using artificially spiked liver to provide more accurate quantitative results for contamination assessment and tracking in this matrix. Application of the assay to test 67 pig livers from different origins resulted in a positivity rate of 7.5%, which is consistent with the results of numerous other prevalence studies. Quantitative detection of the viral genome in the food chain, particularly in pig livers, is essential for understanding the presence and evolution of HEV contamination and thus ensures consumer safety.

Anodic dissolution model with diffusion–migration transport for simulating localized corrosion

A one-dimensional model for simulating localized corrosion is developed, incorporating a time-dependent diffusion–migration transport and a moving interface. The model is applied to iron with the Butler–Volmer formula as the dissolution law, and both crevice and pit configurations are simulated. The mathematical procedure for solving this strongly coupled differential equations system and the numerical development for simulations are detailed. A finite-difference ALE scheme is used for the numerical computation of the solutions of this free boundary problem. The results show that the dissolution rate increases with chloride concentration and metal potential, and migration plays, most of the time, a significant role in species transport for both crevice and pit configurations. The evolution of the repassivation potential with pit depth is computed and is in good agreement with experimental results. The time-dependent model under consideration provides more accurate quantitative results for concentration profile and crevice depth than stationary models.

Automatic simultaneous ciliary muscle segmentation and biomarker extraction in AS-OCT images using deep learning-based approaches

Recent clinical studies have emphasized the importance of understanding the morphology and mechanics of the ciliary muscle. The ciliary muscle plays a vital role in various functions related to the anterior segment of the eye, including the regulation of intraocular pressure and the maintenance of the shape of the crystalline lens. To advance research in this area, we propose a fully automated methodology for the segmentation and biomarker measurement of the ciliary muscle in two different scan depths (6mm and 16mm), which are commonly used by clinicians to analyze biomarkers. Our methodology aims to provide repeatable, and immediate results through an exhaustive analysis of different network architectures, encoders, and transfer learning strategies. We also extracted a comprehensive set of relevant biomarkers, including parameters that provide essential information about its behavior during the accommodation process, overall dimensions, and biomechanical properties. These biomarkers can help clinicians and researchers in the diagnoses and monitor of different ocular diseases such as glaucoma, myopia, and presbyopia and develop new therapeutic strategies, potentially leading to more effective treatments and improved patient outcomes. Our methodology achieved accurate qualitative and quantitative results, with high accuracy values of 0.9665±0.1280 and 0.9772±0.0873 for the best combinations for 6mm and 16mm, respectively. Our proposed system provides a valuable and automatic tool for clinicians and researchers in the segmentation and analysis of the ciliary muscle in AS-OCT images.

A novel method for quantitative evaluation of oil content and mobility in shale oil reservoirs by NMR logging: a case study of the inter-salt shale oil in the Jianghan Basin

The evaluation of oil content and mobility in shale oil reservoirs is the key to the optimization of the "sweet spot" of shale reservoirs and the evaluation of available resources. In order to address the challenge of quantitative evaluation of oil content and mobility in shale oil reservoirs, a combined multi-temperature pyrolysis and NMR experimental platform and experimental procedure are innovatively designed and implemented in this paper. The experiments reveal the NMR T2 distribution characteristics of hydrocarbon components in different reserve states. Accordingly, the NMR T2 cutoff values of different hydrocarbon components are determined, and the quantitative evaluation method based on NMR logging for total oil content and movable hydrocarbon content is established. The method has been applied to the inter-salt shale oil BYY2 well in Jianghan Oilfield, and the results show that the total porosity of the shale reservoir in the Qian 4 sub-Member is similar, but the total hydrocarbon content (0.04 g/kg∼16.0 g/kg) and the movable oil content (0.02 g/kg∼4.9 g/kg) are significantly different. Compared with the preferred scheme of reservoir based on porosity, accurate quantitative evaluation results of oil content and mobility based on logging provide a more straightforward indication of the resource potential of the reservoir that is available for exploitation. The experimental scheme of multi-temperature pyrolysis-NMR and the quantitative evaluation method for oil content and motility based on NMR logging will provide an important basis for resource evaluation, optimal formation selection and efficient development of shale oil reservoirs.

Profiling Analysis of Tryptophan Metabolites in the Urine of Patients with Parkinson's Disease Using LC-MS/MS.

Although Parkinson's disease (PD) is a representative neurodegenerative disorder and shows characteristic motor impediments, the pathophysiological mechanisms and treatment targets for PD have not yet been clearly identified. Since several tryptophan metabolites produced by gut microbiota could pass the blood-brain barrier and, furthermore, might influence the central nervous system, tryptophan metabolites within the indole, kynurenine, and serotonin metabolic pathways might be the most potent targets for PD development. Furthermore, most metabolites are circulated via the blood, play roles in and/or are metabolized via the host organs, and finally are excreted into the urine. Therefore, profiling the overall tryptophan metabolic pathways in urine samples of patients with PD is important to understanding the pathological mechanisms, finding biomarkers, and discovering therapeutic targets for PD. However, the development of profiling analysis based on tryptophan metabolism pathways in human urine samples is still challenging due to the wide physiological ranges, the varied signal response, and the structural diversity of tryptophan metabolites in complicated urine matrices. In this study, an LC-MS/MS method was developed to profile 21 tryptophan metabolites within the indole, kynurenine, and serotonin metabolic pathways in human urine samples using ion-pairing chromatography and multiple reaction monitoring determination. The developed method was successfully applied to urine samples of PD patients (n = 41) and controls (n = 20). Further, we investigated aberrant metabolites to find biomarkers for PD development and therapeutic targets based on the quantitative results. Unfortunately, most tryptophan metabolites in the urine samples did not present significant differences between control and PD patients, except for indole-3-acetic acid. Nonetheless, indole-3-acetic acid was reported for the first time for its aberrant urinary levels in PD patients and tentatively selected as a potential biomarker for PD. This study provides accurate quantitative results for 21 tryptophan metabolites in biological samples and will be helpful in revealing the pathological mechanisms of PD development, discovering biomarkers for PD, and further providing therapeutic targets for various PD symptoms. In the near future, to further investigate the relationship between gut microbial metabolites and PD, we will employ studies on microbial metabolites using plasma and stool samples from control and PD patients.

Precise Control of Trypsin Immobilization by a Programmable DNA Tetrahedron Designed for Ultrafast Proteome Digestion and Accurate Protein Quantification.

In proteomics research, with advantages including short digestion times and reusable applications, immobilized enzyme reactors (IMERs) have been paid increasing attention. However, traditional IMERs ignore the reasonable spatial arrangement of trypsin on the supporting matrixes, resulting in the partial overlapping of the active domain on trypsin and reducing digesting efficiency. In this work, a DNA tetrahedron (DNA TET)-based IMER Fe3O4-GO-AuNPs-DNA TET-Trypsin was designed and prepared. The distance between vertices of DNA TETs effectively controls the distribution of trypsin on the nanomaterials; thus, highly efficient protein digestion and accurate quantitative results can be achieved. Compared to the in-solution digestion (12-16 h), the sequence coverage of bovine serum albumin was up to 91% after a 2-min digestion by the new IMER. In addition, 3328 proteins and 18,488 peptides can be identified from HeLa cell protein extract after a 20-min digestion. For the first time, human growth hormone reference material was rapidly and accurately quantified after a 4-h digestion by IMER. Therefore, this new IMER has great application potential in proteomics research and SI traceable quantification.

Research on the steam power system operation condition assessment method based on PCA-AE

The traditional steam power system operation condition assessment method relies on expert knowledge and is difficult to achieve complete objectivity. Most data-driven methods require training models with system anomaly data. In response to the above issues, this article proposes the A Novel PCA-AE Steam Power System Operation Condition Assessment Method. This method utilizes the PCA model to obtain assessment indicator parameters, and uses the Autoencoder to automatically assign weights and reconstruct parameters. Finally, the vector similarity principle is used for quantitative assessment. Based on the simulation data of marine nuclear power system steam turbine and the experimental data of supercharged boiler, the feasibility and generalization ability of this method are verified. The new method can meet the requirements of different steam power systems, including the nuclear power system secondary circuits, and provide accurate quantitative assessment results of operating conditions to guide maintenance work. Meanwhile, this method does not rely on expert knowledge and abnormal data training sets, and has fewer limitations compared to traditional methods.

A Review on Measurement Techniques of Deformation‐Induced Transformation Kinetics in Transformation‐Induced Plasticity and Transformation‐Induced Plasticity‐Assisted Steels

Deformation‐induced austenite‐to‐martensite transformation is an important phenomenon that controls the flow behavior of transformation‐induced‐plasticity (TRIP) and TRIP‐assisted steels. Transformation kinetics and triggering strain are important parameters that need to be known during transformation. Both these parameters can be correctly determined by an accurate quantitative assessment of the austenite during deformation. Herein, both in situ and ex situ measurement techniques used for quantitative assessment of austenite during/after tensile deformation of the TRIP and TRIP‐assisted steels are reviewed. A comparison of various techniques such as X‐ray diffraction, neutron diffraction, Mossbauer spectroscopy, and DC/AC magnetic permeability is presented. Due to limited penetration power of the radiations, difficult access, and difficulty in setting up the measurement facility for the in situ measurements, X‐ray diffraction, neutron diffraction, and Mossbauer spectroscopy techniques do not yield accurate quantitative results and are time‐consuming. It is envisaged that an accurate quantitative assessment of austenite from the bulk of TRIP and TRIP‐assisted steel specimens is possible with magnetic permeability measurement techniques. Real‐time quantitative assessments can be made by measuring changes in the magnetic permeability due to changes in the ferromagnetic content during deformation. magnetic permeability measurements require extensive calibration for the accurate measurement of the phases during deformation of the steel.

MADM-Q, an efficient multi-attribute decision-making support system for offshore decommissioning

With decades of development, some offshore facilities are reaching their design lives and need to be decommissioned. However, due to the limitation of regulations, technologies, and other aspects, which decommissioning options should be chosen is a challenging task. This paper introduces a multi-attribute quantitative evaluation system called MADM-Q (Multi-Attribute Decision Making-Quantitative), which can show decision makers with decommissioning options’ cost, risk, and environmental impact assessment results. The system consists of an input, output, and three processing modules. The Engineering Cost Evaluation System (ECES) is the first bottom-up approach cost assessment model in offshore facility decommissioning research. The Hierarchical Analyst Domino Evaluation System (HADES) acts as the risk assessment module, considering Domino Effect Accidents (DEAs) and providing accurate and rapid quantitative risk assessment results. The third sub-model, Composite Impact Evaluation System (CIES) assesses the environmental and socioeconomic impacts of the project. The system can obtain the basic cost range and the Individual Risk Per Annum (IRPA) value of the project quickly by using some simple inputs. The results of ECES and HADES reflects excellent accuracy - 12% cost evaluation average deviation and 1.43E-04 IRPA evaluation bias. Although currently, the HADES is a quasi-dynamic risk assessment method, it would be easy to develop it into a real-time dynamic risk monitoring and assessment system by combining advanced technologies such as digital twin technology and sensor technology.

Using Econometrical Models to Analyze the Consumption Pattern of the Iraqi Individual for the Period 2004-2021

Studying Iraqi consumer behavior and analyzing its consumption pattern provides an excellent opportunity to reach logical and more accurate results. In particular, we are looking for the consumption pattern when the Iraqi per capita income witnessed a significant and continuous increase. Accordingly, the source of this increase is a change in the proportions of consumer spending on commodities to the consumer. He changed his consumer preferences and tendencies because of this subject, the consumption function, of an essential link to the overall economy. This was done by providing accurate quantitative results for the consumption function per capita, which can reach the prevailing economic form in society. Therefore, in this research, we relied on standard models after obtaining the data for the research period. The time series of average per capita expenditure in Iraq was analyzed. To achieve the main objective of the research, we identified the research variables, where the independent variable is represented by the average annual per capita income.

A prediction model for magnetic particle imaging–based magnetic hyperthermia applied to a brain tumor model

Background and objectiveBrain tumor is a global health concern at the moment. Thus far, the only treatments available are radiotherapy and chemotherapy, which have several drawbacks such as low survival rates and low treatment efficacy due to obstruction of the blood-brain barrier. Magnetic hyperthermia (MH) using magnetic nanoparticles (MNPs) is a promising non-invasive approach that has the potential for tumor treatment in deep tissues. Due to the limitations of the current drug-targeting systems, only a small proportion of the injected MNPs can be delivered to the desired area and the rest are distributed throughout the body. Thus, the application of conventional MH can lead to damage to healthy tissues. MethodsMagnetic particle imaging (MPI)-guided treatment platform for MH is an emerging approach that can be used for spatial localization of MH to arbitrarily selected regions by using the MPI magnetic field gradient. Although the feasibility of this method has been demonstrated experimentally, a multidimensional prediction model, which is of crucial importance for treatment planning, has not yet been developed. Hence, in this study, the time dependent magnetization equation derived by Martsenyuk, Raikher, and Shliomis (which is a macroscopic equation of motion derived from the Fokker-Planck equation for particles with Brownian relaxation mechanism) and the bio-heat equations have been used to develop and investigate a three-dimensional model that predicts specific loss power (SLP), its spatio-thermal resolution (temperature distribution), and the fraction of damage in brain tumors. ResultsBased on the simulation results, the spatio-thermal resolution in focused heating depends, in a complex manner, on several parameters ranging from MNPs properties to magnetic fields characteristics, and coils configuration. However, to achieve a high performance in focused heating, the direction and the relative amplitude of the AC magnetic heating field with respect to the magnetic field gradient are among the most important parameters that need to be optimized. The temperature distribution and fraction of the damage in a simple brain model bearing a tumor were also obtained. ConclusionsThe complexity in the relationship between the MNPs properties and fields parameter imposes a trade-off between the heating efficiency of MNPs and the accuracy (resolution) of the focused heating. Therefore, the system configuration and field parameters should be chosen carefully for each specific treatment scenario. In future, the results of the model are expected to lead to the development of an MPI-guided MH treatment platform for brain tumor therapy. However, for more accurate quantitative results in such a platform, a magnetization dynamics model that takes into account coupled Néel-Brownian relaxation mechanism in the MNPs should be developed.

Improved Quantitative Analysis Method for Magnetic Particle Imaging Based on Deblurring and Region Scalable Fitting.

Magnetic particle imaging (MPI) is a technique for imaging magnetic particle concentration distribution. It has the advantages of high sensitivity, no signal attenuation with depth, and no ionizing radiation. Although MPI has been widely used in the biomedical field, accurate image analysis has been challenging due to its anisotropic point spread function (PSF). The purpose of this study is to propose an MPI image restoring and segmentation method to facilitate a more precise quantitative evaluation of the magnetic particle imaging in vivo. We proposed a DeRSF method that combined deblurring and region scalable fitting (RSF)to determine the imaging tracer distribution. Then a uniform erosion and scaling criterion was established based on simulation experiments to correct the segmentation results, which was further validated on phantom imaging. Finally, we imaged the MPI tracer at gradient concentrations to establish the calibration curve between the MPI signal and iron mass for iron quantification in phantom and in vivo imaging. The phantom imaging experiments showed that our method achieved improved segmentation performance. The mean value of the dice coefficients for segmentation was up to 0.86, demonstrating that our method can accurately map and quantify the distribution of the tracer. Moreover, the iron quantification on both phantom and in vivo mouse imaging was realized with the minimal error of 5.50%, by our established calibration curve. Our proposed DeRSF method was successfully used for improved MPI quantitative analysis. More importantly, this method also showed accurate quantitative results on images with different shapes and tracer concentrations in both phantom and in vivo data, which laid the foundation for the biomedical study of MPI.

Influence of host-guest interactions on analytical performance of direct analysis in real-time mass spectrometry.

To systematically study the influence of host-guest interactions on the analytical performance of direct analysis in real time mass spectrometry (DART-MS), the interactions between cyclodextrins (CDs) and different Sudan dyes were investigated. The results showed that the host-guest interaction between CDs and Sudan dyes did not affect qualitative analysis of the target compounds, but led to a lower signal intensity for Sudan dyes, thus affecting quantitative analysis of the target compounds. The stronger the host-guest interaction, the weaker the signal intensity of target compound on DART-MS. The results also show that both in solution and in solid-phase microextraction (SPME), the addition of organic solvents can weaken the host-guest interaction between CDs and Sudan dyes, thus improving the signal intensity in DART-MS. In SPME, adding organic solvents has a certain practical value and can improve the efficiency of Sudan dye analysis. This study suggests that appropriate sample pretreatment is needed to weaken noncovalent interactions prior to DART-MS analysis to obtain more accurate quantitative results. The data provide some insight into the effects of other noncovalent interactions on the efficiency of DART-MSas an analytical tool, as well as the potential to study intermolecular interactions with DART-MS.

COMPARING THE PERFORMANCE OF ROOF SEGMENTATION METHODS IN AN URBAN ENVIRONMENT USING DIGITAL ELEVATION DATA

Abstract. In this paper, we present a novel 3D segmentation approach using digital elevation data. Building detection has been emerging as an important area of research. It has attracted many applications, such as geomatics, architectonics, computer vision, photogrammetry, remote sensing, industry, disaster management, and city planning. Building detection techniques can basically be divided into two categories: the classical approach and the deep learning approach. The main goal of this study is to compare some commonly used detection techniques in photogrammetry, like segmentation-based and classification-based methods using digital elevation data as input. The 4 different methods of roof detection with their detailed analysis and their final results are presented in this paper. This study encourages researchers to further advance research in building detection techniques. Results show that the 2D region growing can successfully segment the building components like the main facades of the complex roof and provide accurate qualitative and quantitative results compared to the other methodologies used in this study.

EVALUATION OF QUALITY CONTROL OF FAVIPIRAVIR FOR ITS TEN RELATED SUBSTANCES USING HIGHPERFORMANCE LIQUID CHROMATOGRAPHY: DEVELOPMENT OF PROCEDURE AND VALIDATION

This study deals with designing and validating an HPLC procedure for the determination of favipiravir and ten favipiravir-related impurities simultaneously in favipiravir bulk forms. The ten favipiravir related substances investigated herein included 6-Bromo-3-hydroxy-amide; 6-Fluoro-3-hydroxy-nitrile; 6-Chloro-3-hydroxy-amide; Favipiravir acid; 3-Hydroxy amide; 3,6-Difluoro amide; 3,6-Dichloro amide; 6-Chloro-3-hydroxy-nitrile; 3,6- Difluoro nitrile; and 3,6-Dichloro nitrile. Column Inert Sustain C18 is used to analyze favipiravir and favipiravirrelated impurities. Mobile phases employed included 20 mM KH2PO4 (system A) and 20 mM KH2PO4: Acetonitrile combined in 700:300 (vol/vol) parts (system B,) and given to column through gradient elution. The system was examined in accordance with the ICH prerequisite and was capable of providing accurate (96.4-114.8%) and precise (0.1-0.4%) quantitative results. The peak purities measures for favipiravir and its ten impurities revealed specificity. Linearity was evidenced from the LOQ amount level to 120% amount of the specification level (0.0015 mg/mL) with R 2 measures ˃ 0.999 for favipiravir and its ten related impurities. This method concluded it was beneficial for the evaluation expected.