Imprecise Values Research Articles

Computational Fuzzy Modelling Approach to Analyze Neuronal Calcium Dynamics With Intracellular Fluxes.

Mathematical neuroscience investigates how calcium distribution in nerve cells affects the neurological system. The interaction of numerous systems is necessary for the operation of several cellular processes in neuron cells, such as calcium, buffer, ER etc. The dynamics of interacting parameters give useful information on neural cell function. This work uses a mathematical model to analyze the dynamic interactions of buffer and ER inside neurons, considering their spatial properties. While buffers bind to calcium ions and lower their concentration, the endoplasmic reticulum (ER) serves as a reservoir, holding a significant number of free calcium ions. The uncertainty of initial values of calcium concentration poses challenges for researchers to develop calcium signaling models. In this article, we examined the exact solution and approximate solution of the mathematical model that was analyzed using the fuzzy undetermined coefficient approach. MATLAB is being used to perform the simulation. Endoplasmic reticulum and buffer have been found to have a substantial impact on calcium signaling. Fuzzy differential equation Provides a useful tool for evaluating complicated processes with imprecise values when ordinary differential equations perform not precisely. They allow for the examination of dynamic processes under fuzzy settings, which contributes to advances research.

Nlmixr2 Versus NONMEM: An Evaluation of Maximum A Posteriori Bayesian Estimates Following External Evaluation of Gentamicin and Tobramycin Population Pharmacokinetic Models.

The objective of this project is to compare the results of the same study carried out on NONMEM and nlmixr2. This analysis consists of evaluating previously published population pharmacokinetic models of gentamicin and tobramycin in our population of interest with sparse concentrations. A literature review was performed to determine the gentamicin and tobramycin models in critically ill adult patients. In parallel, gentamicin and tobramycin dosing data, information on the treatment, the patient, and the bacteria were collected retrospectively in 2 Quebec establishments. The external evaluations were previously performed using NONMEM Version 7.5. Model equations were rewritten with R, and external evaluations were performed using nlmixr2. Predictive performance was assessed based on the estimation of bias and imprecision of the prediction error for maximum a posteriori (MAP) Bayesian PK parameter and observed concentrations. Comparison between nlmixr2 and NONMEM was performed on 4 gentamicin and 3 tobramycin population pharmacokinetic models. Compared to NONMEM, for gentamicin and tobramycin clearance and central volume of distribution, nlmixr2 produced individual pharmacokinetic parameters with bias values ranging from -32.5% to 5.67% and imprecision values ranging from 6.33% to 32.5%. Despite these differences, population bias and imprecision for sparse concentrations were low and ranged from 0% to 5.3% and 0.2% to 6.5%, respectively. The external evaluations performed with both software packages resulted in the same interpretation in terms of population predictive performance for all 7 models. Nlmxir2 showed comparable predictive performance with NONMEM with sparse concentrations that are, at most, sampled twice within a single dose administration (peak and trough).

Robustness analysis for imprecise additive value efficiency analysis with an application to evaluation of special economic zones in Poland

We introduce an algorithmic framework for investigating the robustness of efficiency analysis results in the presence of imprecise information about data and preferences. We employ an additive value efficiency model accepting ordinal and interval information about the performances of Decision Making Units and imprecision in the specification of input and output weights and the shapes of marginal value functions. We verify the stability of efficiency measures using a combination of mathematical programming and Monte Carlo simulations. The results capture various certainty levels, emphasizing the necessary, possible, extreme, and expected outcomes and the distribution of outcomes in the space of feasible weights, performances, and marginal functions. The practical usefulness of the proposed framework is demonstrated in a real-world problem concerning the functioning of Special Economic Zones in Poland. We discuss results that increase the discrimination power, indicate overall good performances, and provide hints on the required improvements.

Generalization of stable preference ordering towards ideal solution approach for working with imprecise data

When solving real-world decision-making problems, it is important to deal with imprecise quantitative values modeled by numerical intervals. Although a different extension of the multi-criteria decision-making methods could deal with intervals, many of them are complex and lack such properties as robustness to rank reversal. We present an extension of the stable preference ordering towards ideal solution (SPOTIS) rank reversal free method to deal with imprecise data. This extension of SPOTIS is also rank reversal-free. It offers a new efficient approach for solving multi-criteria decision-analysis problems under imprecision and can use different metrics of distance between intervals. The proposed approach is compared to the popular Interval technique for order preference by similarity to ideal solution) extension and performs very similarly to it. We also show on a practical example that the interval TOPSIS approach is not robust to rank reversal, contrary to our new SPOTIS extension approach, which offers a stable decision-making behaviour.

Greenness and neuropsychiatric symptoms in dementia

ObjectivesIt is acknowledged that living in a green environment may help mental well-being and this may be especially true for vulnerable people. However, the relationship between greenness and neuropsychiatric symptoms in dementia has not been explored yet. MethodsWe collected clinical, neuropsychiatric, and residential data from subjects with dementia living in the province of Modena, Northern Italy. Neuropsychiatric symptoms were measured with the Neuropsychiatry Inventory, a questionnaire administered to the caregiver who assesses the presence and severity of neuropsychiatric symptoms, including delusions, hallucinations, agitation/aggression, dysphoria/depression, anxiety, euphoria/elation, apathy/indifference, disinhibition, irritability/lability, aberrant motor behaviors, sleep disturbances, and appetite/eating changes. Normalized Difference Vegetation Index (NDVI) was used as a proxy of greenness. Regression models were constructed to study the association between greenness and neuropsychiatric features. Results155 patients with dementia were recruited. We found that greenness is variably associated with the risk of having neuropsychiatric symptoms. The risk of apathy was lower with lower levels of greenness (OR = 0.42, 95% CI 0.19–0.91 for NDVI below the median value). The risk of psychosis was higher with lower levels of greenness but with more imprecise values (OR = 1.77, 95% CI 0.84–3.73 for NDVI below the median value). ConclusionOur results suggest a possible association between greenness and neuropsychiatric symptoms in people with dementia. If replicated in larger samples, these findings will pave the road for identifying innovative greening strategies and interventions that can improve mental health in dementia.

Application of local and global interval embedding algorithms in uncertain system processes

The data collected during the fault monitoring of industrial processes are often uncertain because of measurement noise, sensor error, and complex working environments. Currently, most of this uncertainty is transformed into interval-value data for processing. However, the traditional interval fault monitoring method (TIFMM) only considers the global information of the interval-valued data and only rarely considers the local neighborhood information, which can characterize the topological relationship between the interval-valued data points, so that the sample points with some nonlinear relationship still maintain this nonlinear relationship after dimensionality reduction. Therefore, to take into account both local and global information, a new interval-valued data projection algorithm called local and global interval embedding algorithms (LGIEA) is proposed in this paper. First, the measurement error estimation method, based on the principle of reasonable granularity, converts the imprecise variable measurement values into interval-value data that can reflect uncertainty. Then, an interval-matrix feature extraction method based on local and global information fusion, proposed in this paper, extracts process features by minimizing local scattering and maximizing global scattering of interval-valued data. Thereby, it preserves the optimal local neighborhood and global information in low-dimensional space. In addition, four statistical indicators and corresponding variable-contribution graphs have been defined to use online fault monitoring and fault variable identification. Finally, the superiority of the LGIEA was verified using simulation data of the Tennessee Eastman process and actual operating data of a shearer in an inclined-ditch coal mine. The experimental results show that, Compared with TIFMM such as midpoint-radius principal component analysis (PCA), complete information PCA and spectral radius-based interval PCA, proposed method significantly reduces the error alarm rate and the false monitoring rate and improves the accuracy of data classification. It also has better fault identification accuracy and robustness.

A-075 Development and Validation of Image-based Cell Counter Microscanner C3 for CD34-positive Hematopoietic Stem Cell Enumeration

Abstract Background Accurate measurement of CD34-positive hematopoietic stem cells (HSCs) is critical for successful peripheral blood stem cell transplantation (PBSCT). Flow cytometry is by far the gold standard for measuring CD34-positive stem cells. However, its complicated procedure requires time, cost and highly-trained operators, thus the necessity of simpler and cheaper alternative method is emerging. We have developed a new image-based cell counter, Microscanner C3 (MSC3, Biozentech, South Korea) as an alternative method for enumeration of CD34-positive HSCs and evaluated its performance. Methods 100 µl of apheresis product was stained with CD45-BB515 and CD34-PE antibodies (Becton Dickinson, USA) at room temperature in the dark. Red blood cell lysis with 500 µl of OptiLyze C solution (Beckman Coulter, USA) was followed by centrifugation. After the removal of supernatants, the sample was resuspended with 500 µL PBS, which then infused to filter paper imbedded BZ-1 microchip (Biozentech, South Korea). Thirty-fields of the loaded microchip was auto-scanned by MSC3 and its dedicated software analyzed the images and yielded CD34-positive cell fraction out of CD45-positive cells as percentage. Results of thirty-six samples were compared to that of FacsLyric (Beckton Dickinson, USA) flow cytometry and accuracy and imprecision (pentaplicate measurements for three samples), repeatability (two experimenters performed triplicate measurements for three samples each), linearity (triplicate measurements for five levels), limit of detection (hexaplicate measurements for five levels) and specificity (using whole blood specimen from three donors with monoclonal gammopathy, thrombocytosis or monocytosis) was evaluated. Results CV% values of inaccuracy, imprecision and repeatability did not exceed 15%. The method was found to be linear across the measuring range. LOD was evaluated to be 7 cells/µL and no background interference was detected. The difference between the measured fraction of MSC3 and FacsLyric was within allowable limit, with an R-value greater than 0.98. Conclusion We developed a reliable method for quantifying CD34-positive HSCs based on image analysis. The limitation involved in MSC3 applications in CD34-positive HSCs is that, like dual-platform flow cytometry, the results should be combined with the results of conventional hematology analyzer in order to define absolute HSC numbers. However, with its simpler and faster procedures, MSC3 exhibited a great potential to be used as an alternative to conventional flow cytometry in CD34-positive HSCs enumeration and other applications as well.

A-161 Analytical Performance of Reagents for Determination of Activated Partial Thromboplastin Time: A Sigma Metric Perspective

Abstract Background Sigma Metric (SM) assesses the degree of deviation of lab. tests from established specifications, combining Total Acceptable Error (ETa), imprecision and analytical bias. Quality criteria, defined by models of desirable specification, are used to determine the analytical performance of lab. tests. The concepts of biological variation are used as the preferred approach for establishing specifications. Reagents (RE) for the determination of activated partial thromboplastin time (APTT) use thromboplastin obtained from different tissue extracts and activation factors, being standardized to reduce the variability of the tests, and ensure equivalence of clinical diagnosis. This work evaluated three RE for APTT and the fulfillment of the desirable specifications of analytical quality, using the SM as an analysis tool. Material & methods APTT determinations were performed on the Sysmex®CS-2500 (Siemens Healthineers) with Actin®FS (AFS), Actin®FSL (FSL), and Pathromtin®SL (PSL) RE and bi-level Control Plasma N (CN) and Ci-Trol®2 Control (CI). Two different lots were used for each RE and control (CT), and the results were analyzed in RE-CT combination. We determined (a)analytical bias (Bias), from intra-assay imprecision determined by 30 repetitions in a single analytical run and by comparison with manufacturer's data, and (b)analytical coefficient of variation (CVa), from inter-assay imprecision, by triplicate in 3 periods each 3 h, for the 2 CT levels. Analytical quality specifications for APTT were selected in the literature, adopting desirable ETa values of 4.5% and minimum of 6.7%, obtained from a database for biological variation and acceptable limit of 15%, based on CLIA-CAP. SM was calculated with formula: Sigma = ETa–Bias/CVa, with graphic representation adapted from the Westgard’s model. Results For CVa, AFS and FSL results with CN and CI were <1.0%. PSL presented results above 1.0% for CN and CI. All RE presented results lower than those recommended by the manufacturer, respectively 4.0% for AFS; 3.0% for FSL; and 2.8% for PSL. For bias, a greater variation was observed for the combinations of RE and CT, ranging from 1.16% to 4.07%, being related to the differences between the averages obtained and the values defined in the leaflets. For the desirable specification of ETa (4.5%), 3 combinations of RE and CT level AFS-CN, FSL-CN and PSL-CI presented Sigma values >3 (respectively, 3.61, 3.87 and 3.33). When using the minimum specification (6.7%), the PSL-CN combination presented a Sigma = 2.06. With the CLIA-CAP specification, all RE and CT combinations had Sigma >6. Discussion All RE showed similar analytical performance when compared by SM, demonstrating that different commercial formulations for APTT determination do not show differences in analytical Bias and CVa determinations. The graphical representation by SM allows you to visually compare analytical imprecision and inaccuracy values. Conclusion All RE showed no differences in analytical performance, with SM being a visual tool for analyzing and comparing RE performance.

Circular q-Rung Orthopair Fuzzy Set and Its Algebraic Properties

Circular intuitionistic fuzzy sets (CIFS) are a recent extension of intuitionistic fuzzy sets (IFS) that can handle imprecise membership values effectively. However, its representation is limited to the space under the intuitionistic fuzzy interpretation triangle (IFIT). To address this, a new generalization of CIFS called circular q-rung orthopair fuzzy sets (Cq-ROFS) is proposed, extending the IFIT to cover a larger space of imprecision. Several relations and operations, including algebraic operations for Cq-ROFS are proposed. In addition, modal operators and their properties are then investigated.

Evaluation and Benefit of Imprecise Value Prediction for Certain Types of Instructions

Based on branch prediction, value prediction has emerged as a solution for problems caused by true data dependencies in pipelined processors. While branch predictors have binary outcomes (taken/not taken), value predictors face a challenging task as their outcomes can take any value. Because of that, coverage is reduced to enhance high accuracy and minimise costly recovery from misprediction. This paper evaluates value prediction, focusing on instruction execution with imprecisely predicted operands whose result can still be correct. Two analytical models are introduced to represent instruction execution with value prediction. One model focuses on correctly predicted operands, while the other allows for imprecisely predicted operands as long as the instruction results remain correct. A trace-driven simulator was developed for simulation purposes, implementing well-known predictors and some of the predictors presented at the latest Championship Value Prediction. The gem5 simulator was upgraded to generate program traces of SPEC and EEMBC benchmarks that were used in simulations. Based on the simulation result, proposed analytical models were compared to reveal the conditions under which a model with imprecisely predicted operands, but still correct results, achieved better execution time than a model with correctly predicted operands. Analysis revealed that the accuracy of the correct instruction result based on the predicted operand, even when the predicted operand is imprecise, is higher than the accuracy of the correctly predicted operand. The accuracy improvement ranges from 0.8% to 44%, depending on the specific predictor used.

Inventory Management Under Uncertainty Condition with Fuzzy Logic: A Literature Review

Fuzzy set theory, or fuzzy logic, has been in use in inventory systems since the 1980s, with vaguely defined, ill-defined, or imprecise values, or decisions based on individual subjective beliefs. Provides a framework for describing the parameters that can be passed. There are many uncertainties in inventory management that can arise due to many things, such as: Order Changes, Random Supplier Capabilities, or Unexpected Events. Fuzzy logic as a method of inventory control that provides a framework by considering parameters that are vague or poorly defined, or whose values are imprecise and determined based on individual subjective beliefs make it available. The purpose of this white paper is to review previous studies using fuzzy logic in inventory management to see the uncertainty variables used. The method used is a thematic analysis of articles on the application of fuzzy logic in various industries. The results of the review show that variables like fuzzy logic make it easy to obtain results from uncertainty variables that can be applied to industrial activities to enable manufacturing activities to be carried out effectively and efficiently increase.

Short-term modeling of carbon price based on fuel and energy determinants in EU ETS

Accurate price forecasts on the European Union Emissions Trading System (EU ETS) market are of interest to many production and investment entities. The article presents a new approach, which models the most significant determinants by the use of a rule-based model containing facts written in the form of ordered fuzzy numbers, as the description of trends of imprecise values of prices. This approach allows for a qualitative analysis of potential values and directions of EU allowance (EUA) price changes and their trends along with the probability of their occurrence in the future. Article presents a wide set of factors used for the analysis of EUA price conditions and the comparison of the results of various feature selection methods (Hellwig's method, F-test, Neighborhood Component Feature Selection method, RreliefF method). Another scientific value added to the presented research results is a new approach to modeling an influence of the significant determinants for a day-ahead prediction CO2 price in the form of fuzzy rules which contain additional information about the tendencies of value changes. Thanks to a comprehensive approach, improvements to existing methods, and proprietary models, additional information was obtained about the tendencies of EUA price level changes.

An interactive ACO enriched with an eclectic multi-criteria ordinal classifier to address many-objective optimisation problems

Despite the vast research on many-objective optimisation problems, the presence of many objective functions is still a challenge worthy of further study. A way to treat this kind of problem is to incorporate the preferences of the decision maker (DM) into the optimisation process. In this paper, we introduce an interactive ant colony optimisation combined with an ordinal classification method in which classes are described by characteristic profiles. Through several interactions, the DM is supposed to identify some representative solutions of the classes ‘satisfactory’ and ‘dissatisfactory,’ which are used to initialise an ordinal classifier that increases the selective pressure by discriminating in favour of the ‘satisfactory’ class. This method can work with any either asymmetric or symmetric binary preference relation, a feature that confers a very wide generality. As another advantage, the interaction with the DM has minimal cognitive demands, which is an advisable feature for any approach based on interaction. Although the preference model is quite general, the proposal was tested using an eclectic model which combines compensatory preferences, veto conditions, and interval numbers to handle imprecise values; those preferences are aggregated in an asymmetric preference relation. Our approach performs particularly well in 10-objective problems according to the standards in the state-of-the-art literature. Numerical results and tests for statistical significance on the DTLZ and WFG test suites support this claim.

Tractable depth-bounded approximations to FDE and its satellites

Abstract FDE, LP and $\mathbf {K_{3}}$ are closely related to each other and admit of an intuitive informational interpretation. However, all these logics are co-NP complete, and so idealized models of how an agent can think. We address this issue by shifting to signed formulae, where the signs express imprecise values associated with two bipartitions of the corresponding set of standard values. We present proof systems whose operational rules are all linear and have only two structural branching rules that express a generalized Principle of Bivalence. Each of these systems leads to defining an infinite hierarchy of tractable approximations to the respective logic, in terms of the maximum number of allowed nested applications of the two branching rules. Further, each resulting hierarchy admits of an intuitive 5-valued non-deterministic semantics.

LC-MS/MS-based multiplex antibacterial platform for therapeutic drug monitoring in intensive care unit patients.

Empirically prescribed standard dosing regimens of antibacterial agents may result in insufficient or excess plasma concentrations with persistently poor clinical outcomes, especially for patients in intensive care units (ICUs). Therapeutic drug monitoring (TDM) of antibacterial agents can guide dose adjustments to benefit patients. In this study, we developed a robust, sensitive, and simple liquid chromatography-tandem mass spectrometry (LC-MS/MS) platform for the quantification of 14 antibacterial and antifungal agents (beta-lactams piperacillin, cefoperazone, and meropenem; beta-lactamase inhibitors tazobactam and sulbactam; antifungal agents fluconazole, caspofungin, posaconazole, and voriconazole; and daptomycin, vancomycin, teicoplanin, linezolid, and tigecycline) that can be used for patients with severe infection. This assay requires only 100µL of serum with rapid protein precipitation. Chromatographic analysis was performed using a Waters Acquity UPLC C8 column. Three stable isotope-labeled antibacterial agents and one analogue were used as internal standards. Calibration curves ranged from 0.1-100μg/mL, 0.1-50μg/mL, and 0.3-100μg/mL for different drugs, and all correlation coefficients were greater than 0.9085. Intra- and inter-day imprecision and inaccuracy values were below 15%. After validation, this new method was successfully employed for TDM in routine practice.

Tobramycin a Priori Dosing Regimens Based on PopPK Model Simulations in Critically Ill Patients: Are They Transferable?

In recent years, multiple population pharmacokinetic models have been developed for drugs such as tobramycin that need therapeutic drug monitoring. Some of these models have been used to develop a priori dosing regimens for their respective populations. However, these dosing regimens may not apply to other populations. Therefore, this study aimed to evaluate tobramycin population pharmacokinetic models in critically ill patients and establish an adequate dosing regimen. Evaluated models were identified from a literature review of aminoglycoside population pharmacokinetic models in critically ill patients. After retrospective data collection in 2 Quebec hospitals, external evaluation and model re-estimation were performed with NONMEM (v7.5) to assess imprecision and bias values. Dosing regimens were simulated and compared between the best-performing model and its re-estimated counterparts. None of the 3 evaluated models showed acceptable imprecision or bias values in the data sets of the 19 patients. Similar percentages of target attainment were obtained for the original and re-estimated models after the dosing regimen simulations. Although the predictive performance evaluation criteria were inadequate, the original and re-estimated models yielded similar results. This raises the question of what a priori bias and imprecision thresholds should be defined as acceptable for the external evaluation of models to be applied in clinical practice. Studies evaluating the impact of these thresholds are needed.

Integrated risk averse and possibilistic humanitarian logistic model with social costs and material convergence

A stepwise development is proposed for the construction of a model that comprehensively solves the disaster planning and response problem. In addition to traditional logistics costs, the suggested approach incorporates response time sensitivity, social costs related to post-disaster deprivation, the impact of material convergence, and measurable and epistemic uncertainty. Starting from a stochastic model based on previously published scenarios, the global costs of the scenarios are minimized using the expected value of the parameters, in a risk-averse bi-objective function that also optimizes the robustness of the model by minimizing the variability of the parameters whose probabilities are known. Finally, the complexity arising from imprecise parameter values with unknown probabilities is addressed by using chance-constrained fuzzy programming to optimize the risk associated with inter-scenario cost variability and feasibility of the constraints. This formulation is applied to real case study data, demonstrating that both the overall costs and the social cost component of the costs decrease progressively from the base formulation. The model allows the decision-maker to test the sensitivity of the results and thus set the uncertainty parameters and the incorporation of multiple objectives accordingly.

SDOT: Secure Hash, Semantic Keyword Extraction, and Dynamic Operator Pattern-Based Three-Tier Forensic Classification Framework

Most traditional digital forensic techniques identify irrelevant files in a corpus using keyword search, frequent hashes, frequent paths, and frequent size methods. These methods are based on Message Digest and Secure Hash Algorithm-1, which result in a hash collision. The threshold criteria of files based on frequent sizes will lead to imprecise threshold values that result in an increased evaluation of irrelevant files. The blacklisted keywords used in forensic search are based on literal and non-lexical, thus resulting in increased false-positive search results and failure to disambiguate unstructured text. Due to this, many extraneous files are also being considered for further investigations, exacerbating the time lag. Moreover, the non-availability of standardized forensic labeled data results in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(O(2^{n}))$ </tex-math></inline-formula> time complexity during the file classification process. This research proposes a three-tier Keyword Metadata Pattern framework to overcome these significant concerns. Initially, Secure Hash algorithm-256 hash for the entire corpus is constructed along with custom regex and stop-words module to overcome hash collision, imprecise threshold values, and eliminate recurrent files. Then blacklisted keywords are constructed by identifying vectorized words that have proximity to overcome traditional keyword search’s drawbacks and to overcome false positive results. Dynamic forensic relevant patterns based on massive password datasets are designed to search for unique, relevant patterns to identify the significant files and overcome the time lag. Based on tier-2 results, files are preliminarily classified automatically in O(log n) complexity, and the system is trained with a machine learning model. Finally, when experimentally evaluated, the overall proposed system was found to be very effective, outperforming the existing two-tier model in terms of finding relevant files by automated labeling and classification in O(nlog n) complexity. Our proposed model could eliminate 223K irrelevant files and reduce the corpus by 4.1% in tier-1, identify 16.06% of sensitive files in tier-2, and classify files with 91% precision, 95% sensitivity, 91% accuracy, and 0.11% Hamming loss compared to the two-tier system.

On portfolio analysis using oriented fuzzy numbers for the trade-related sector of the Warsaw Stock Exchange

Oriented fuzzy numbers are useful in portfolio management since they convey information regarding uncertainty and imprecision when considering financial markets. One may apply a fuzzy discount factor and an imprecise present value in the form of a trapezoidal oriented fuzzy number. An investor can obtain recommendations on individual stocks (buy, sell, accumulate, reduce). Analogous recommendations are also issued by experts. In such cases, recommendations are mostly based on available data and expert’s knowledge and experience. The purpose of the paper is to present a procedure for comparing the accuracy of both types of recommendations. Also, the real impact the recommendations have on potential changes in portfolio composition in trading-related industries is considered. The research uses quotations from companies from the trading sector of the Warsaw Stock Exchange (WSE). Theoretical considerations are presented in the form of an empirical case study.

Performance Evaluation of the DxC 700 AU Chemistry Analyzer in Hemoglobin A1c Measurement

Accurate measurement of glycated hemoglobin (HbA1c) is crucial for a diabetes diagnosis and subsequent patient management. The detection method and presence of variant Hb can interfere with HbA1c measurements. We evaluated the HbA1c-measuring performance of the DxC 700 AU (Beckman Coulter, Brea, CA, USA) immunoassay-based device in comparison with another immunoassay device and the reference method. A total of 120 normal and 14 variant Hb samples were analyzed using the Cobas c 513 (Roche Diagnostics, Mannheim, Germany) and DxC 700 AU analyzers. Variant Hb samples were also analyzed using the reference method, along with 20 normal samples. The accuracy, precision, linearity, and carryover were determined. DxC 700 AU results strongly correlated with those of Cobas c 513 and exhibited accuracy in comparison with the reference method. The within-run, between-run, between-day, and total imprecision (%CV) values for the low- and high-concentration control materials were below 2%. The results of DxC 700 AU were linear over a wide HbA1c range (3.39%-18.30%). Although DxC 700 AU performed well in the presence of variant Hb, the HbA1c concentration was underestimated in the presence of fetal Hb. The possibility of interference from a high HbH proportion could not be ruled out. The overall analytical performance of DxC 700 AU was acceptable. The device is accurate, precise, and linear over a wide HbA1c concentration range. Although DxC 700 AU results highly correlated with those of Cobas c 513, caution should be exercised in cases of high HbF and HbH concentrations.