Specific Performance Research Articles

Within- and between-subject biological variation estimates for the enumeration of lymphocyte deep immunophenotyping and monocyte subsets.

This study aimed to deliver biological variation (BV) estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping (memory T/B cells, regulatory T cells, etc.) and classical, intermediate, and nonclassical monocyte subsets based on the full spectrum flow cytometry (FS-FCM) and a Biological Variation Data Critical Appraisal Checklist (BIVAC) design. Samples were collected biweekly from 60 healthy Chinese adults over 10 consecutive two-week periods. Each sample was measured in duplicate within a single run for lymphocyte deep immunophenotyping and monocyte subset determination using FS-FCM, including the percentage (%) and absolute count (cells/μL). After trend adjustment, a Bayesian model was applied to deliver the within-subject BV (CVI) and between-subject BV (CVG) estimates with 95 % credibility intervals. Enumeration (% and cells/μL) for 25 types of lymphocyte deep immunophenotyping and three types of monocyte subset percentages showed considerable variability in terms of CVI and CVG. CVI ranged from 4.23 to 47.47 %. Additionally, CVG ranged between 10.32 and 101.30 %, except for CD4+ effector memory T cells re-expressing CD45RA. No significant differences were found between males and females for CVI and CVG estimates. Nevertheless, the CVGs of PD-1+ T cells (%) may be higher in females than males. Based on the desired analytical performance specification, the maximum allowable imprecision immune parameter was the CD8+PD-1+ T cell (cells/μL), with 23.7 %. This is the first study delivering BV estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping, along with classical, intermediate, and nonclassical monocyte subsets, using FS-FCM and adhering to the BIVAC design.

A Review on Alzheimer Disease Classification using different ML and DL Models

In this comprehensive review, various machine learning (ML) and deep learning (DL) models are evaluated for their effectiveness in classifying Alzheimer's disease. The study examines a range of methodologies and techniques employed in the classification process, encompassing diverse ML algorithms such as Support Vector Machines (SVM), Random Forests, and k-Nearest Neighbors (k-NN), as well as DL architectures like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Evaluating these models' performance metrics, including accuracy, sensitivity, and specificity, sheds light on their comparative strengths and weaknesses in accurately diagnosing Alzheimer's disease. By synthesizing findings from multiple studies, this review provides valuable insights into the state-of-the-art approaches and identifies promising directions for future research aimed at enhancing Alzheimer's disease classification accuracy and clinical applicability.

The synthesis and application of L-cysteine graphene quantum dots for quantitative analysis of nitrite content in water

The limit standard of nitrite content in drinking water is an internationally recognized safety indicator. Some fluorescence analysis methods used for quantitative analysis of nitrite suffered from low anti-interference performance and inadequate specificity. In this article, L-cysteine graphene quantum dots have been synthesized and used to built a sensor system of ratiometric fluorescence with CdTe quantum dots. The L-cysteine graphene quantum dots were characterized by TEM and XPS. Results shown that the size of product is about 2 nm with homogeneous dispersion and a interplanar distance 0.235 nm, and there were hydroxyl, carboxyl, amino and sulfhydryl groups on its surface. Also, according to the linear association between nitrite concentrations and the ratio of fluorescence intensities at 421 and 570 nm (F421 nm/F570 nm), the quantitative analysis of nitrite in water has been realized, of which the linear range was 7.25–72.5 nmol/l, and the lowest detection limit was 3.012 nmol/l. This method with high sensitivity, selectivity, and anti-interference ability might be applied to analyze of nitrite in water quantitatively.

Applying the Milan models to setting analytical performance specifications- considering all the information.

Analytical performance specifications (APS) are used for decisions about the required analytical quality of pathology tests to meet clinical needs. The Milan models, based on clinical outcome, biological variation, or state of the art, were developed to provide a framework for setting APS. An approach has been proposed to assign each measurand to one of the models based on a defined clinical use, physiological control, or an absence of quality information about these factors. In this paper we propose that in addition to such assignment, available information from all models should be considered using a risk-based approach that considers the purpose and role of the actual test in a clinical pathway and its impact on medical decisions and clinical outcomes in addition to biological variation and the state-of-the-art. Consideration of APS already in use and the use of results in calculations may also need to be considered to determine the most appropriate APS for use in a specific setting.

Pulsed DPOAEs in serial measurements : Combined analysis paradigm of simultaneously occurring changes in hearing thresholds and DPOAEs. German version

To date, there is no consensus on how to standardize the assessment of ototoxicity in serial measurements. For the diagnosis of damage to the cochlear amplifier, measurement methods are required that have the highest possible test-retest reliability and validity for detecting persistent damage. Estimated distortion-product thresholds (LEDPT) based on short-pulse distortion-product otoacoustic emission (DPOAE) level maps use individually optimal DPOAE stimulus levels and allow reliable quantitative estimation of cochlea-related hearing loss. Hearing thresholds were estimated objectively using LEDPT and subjectively using modified Békésy tracking audiometry (LTA). Recordings were performed seven times within threemonths at 14frequencies (f2 = 1-14 kHz) in 20ears (PTA4 (0.5-4 kHz) < 20 dB HL). Reconstruction of the DPOAE growth behavior as afunction of the stimulus levels L1, L2 was performed on the basis of 21DPOAE amplitudes. Anumerical fit of anonlinear mathematical function to the three-dimensional DPOAE growth function yielded LEDPT for each stimulus frequency. For the combined analysis, probability distributions of hearing thresholds (LTA, LEDPT), DPOAE levels (LDP), and combinations thereof were determined. LTA and LEDPT each exhibited atest-retest reliability with amedian of absolute differences (AD) of 3.2 dB and 3.3 dB, respectively. Combining LEDPT, LDP, and LTA into asingle parameter yielded asignificantly smaller median AD of 2.0 dB. It is expected that an analysis paradigm based on acombination of LEDPT, suprathreshold LDP, and fine-structure-reduced LTA would achieve higher test performance (sensitivity and specificity), allowing reliable detection of pathological or regenerative changes in the outer hair cells.

Novel GA‐OCEAN Framework for Automatically Designing the Charge‐Pump Circuit

In the realm of analog integrated circuit (IC) design, due to the intricate nonlinear relationships between circuit performance metrics and design variables, as well as the complex interdependencies and trade‐offs between various performance criteria, it is difficult to determine the appropriate geometric width and length of the circuit components to meet all performance specifications. This difficulty makes the task of designing analog IC challenging. To address the challenge of analog IC design, this paper introduces a machine learning methodology—specifically employing the genetic algorithm (GA)—to automate the selection of circuit components' geometrical parameters, aiding the work of analog circuit designers in determining the appropriate dimensions for transistors within the charge‐pump circuit. The GA, implemented in Python and integrated with a script program written in the OCEAN language, synergistically collaborates in the GA‐OCEAN framework. The result, which ensures compliance with all specifications with remarkably low error margins, ranging as low as 0.03% and as high as 1.24%, highlights the proposed GA‐OCEAN framework's remarkable capacity to determine optimal dimensions for components inside the charge‐pump circuit. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Multicenter benchmarking of short and long read wet lab protocols for clinical viral metagenomics

Metagenomics is gradually being implemented for diagnosing infectious diseases. However, in-depth protocol comparisons for viral detection have been limited to individual sets of experimental workflows and laboratories. In this study, we present a benchmark of metagenomics protocols used in clinical diagnostic laboratories initiated by the European Society for Clinical Virology (ESCV) Network on NGS (ENNGS).A mock viral reference panel was designed to mimic low biomass clinical specimens. The panel was used to assess the performance of twelve metagenomic wet lab protocols currently in use in the diagnostic laboratories of participating ENNGS member institutions. Both Illumina and Nanopore, shotgun and targeted capture probe protocols were included. Performance metrics sensitivity, specificity, and quantitative potential were assessed using a central bioinformatics pipeline.Overall, viral pathogens with loads down to 104 copies/ml (corresponding to CT values of 31 in our PCR assays) were detected by all the evaluated metagenomic wet lab protocols. In contrast, lower abundant mixed viruses of CT values of 35 and higher were detected only by a minority of the protocols. Considering the reference panel as the gold standard, optimal thresholds to define a positive result were determined per protocol, based on the horizontal genome coverage. Implementing these thresholds, sensitivity and specificity of the protocols ranged from 67 to 100 % and 87 to 100 %, respectively.A variety of metagenomic protocols are currently in use in clinical diagnostic laboratories. Detection of low abundant viral pathogens and mixed infections remains a challenge, implying the need for standardization of metagenomic analysis for use in clinical settings.

A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma

ObjectivesAt present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT.MethodsA total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model.ResultsBy incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model.ConclusionsLeveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.

Flame-retardant aramid felt-reinforced phenolic coatings for carbon fiber-reinforced polymers (CFRPs)

For over fifty years since carbon fiber-reinforced polymers (CFRPs) were introduced to the automotive industry, enhancing their flame-retardant properties has been studied due to their vulnerability to fire. Coating is one method of flame-retardant enhancement, accomplished by modifying surface characteristic to achieve its goal. Phenolic resin-based coatings, as a candidate material, offer advantages such as good chemical compatibility and cost efficiency. However, to meet the necessary specifications for flame-retardant performance, a thick coating layer with higher brittleness is required. In this study, we developed an aramid felt-reinforced coating layer to address the limitations of phenolic coatings. The effectiveness of the flame-retardant coating was demonstrated through various tests, including limiting oxygen index (LOI), micro-combustion calorimetry, heat exposure test with cone shaped heater, and mechanical property tests including surface-burning test. Consequently, the aramid felt-reinforced phenolic coating exhibited superior flame-retardant enhancement using a cost-effective, easy-to-follow, and lightweight method, achieved with a relatively thin coating fabrication.

Evaluation of consumer digital radon measurement devices: a comparative analysis

Kansas State University (KSU) Engineering Extension conducted an abridged evaluation of eight consumer grade digital radon monitors. Using the KSU secondary radon chamber, these devices were exposed to three different radon concentrations for 7 d in average household temperature and relative humidity conditions. The three different radon concentration ranges used were: 12.8 pCi L−1 to 15.5 pCi L−1 (473.6 Bq m−3–573.5 Bq m−3), 27.7 pCi L−1 to 29.4 pCi L−1 (1024.9–10 857.8 Bq m−3), and ambient room level average radon concentration of 0.6 pCi L−1 (22.2 Bq m−3). The American National Standards Institute/American Academy of Radon Scientists and Technologists Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air (ANSI/AARST MS-PC) (ANSI/AARST MS-PC 2022 Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air (AARST Radon Standards)) minimum performance metrics were used to evaluate the accuracy and precision of each model type for each radon concentration tested. The eight different device models performed within the 0 ± 25% requirement for the individual percent error (IPE) for radon concentrations between 27.7 pCi L−1 and 29.4 pCi L−1 (1024.9–10 857.8 Bq m−3). For radon concentrations between 12.8 pCi L−1 and 15.5 pCi L−1 (444–592 Bq m−3) seven of the eight monitors fell within the IPE requirement and for ambient room radon concentrations six of the eight monitors fell within the IPE requirement for the ANSI/AARST MS-PC minimum performance requirement (ANSI/AARST MS-PC 2022 Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air (AARST Radon Standards)) ranges. All eight device models fell within the ± 15% ANSI/AARST MS-PC minimum performance requirement (ANSI/AARST MS-PC 2022 Performance Specifications for Instrumentation Systems Designed to Measure Radon Gas in Air (AARST Radon Standards)) coefficient of variation (CV) range for radon concentrations between 12.8 pCi L−1 and 15.5 pCi L−1 (444–592 Bq m−3) and for radon concentrations between 27.7 pCi L−1 and 29.4 pCi L−1 (1024.9–10 857.8 Bq m−3). In the future, evaluating the performance of these models over time to observe their long term accuracy and precision is anticipated.

Impact of an air bubble within the syringe on test results obtained with a modern blood gas analyzer

Background. Minimizing air aspiration by carefully filling blood gas syringes is crucial to prevent air contamination from causing undesirable variations in gasses and other molecules. While some previous studies investigated this aspect, these are now outdated and only analyzed a limited number of blood gas parameters. Thus, we investigated the effects air contamination in the syringe using a modern blood gas analyzer. Methods. We sampled venous blood from 17 laboratory workers (mean age: 46±11 years; 10 women), filling two consecutive blood gas syringes. The first was filled exactly to its nominal volume (i.e., 1.0 mL), while the second was filled with 0.8 mL of blood and 0.2 mL of ambient air. Blood gas analysis was performed in each syringe using an identical analyzer. Results. In the syringe with the air bubble, we found statistically significant increase in pH (0.1%), pO2 (10.8%), SO2 (11.2%), total hemoglobin (3.0%), and hematocrit (2.7%), while values of pCO2 (-4.8%), sodium (-0.5%), and ionized calcium (-1.3%) were significantly reduced. With exception of pH, all these changes exceeded the performance specifications. Potassium, chloride, glucose, lactate, COHb and MetHb values remained unchanged. Conclusion. These findings confirm that air bubbles must be removed as soon as possible after sampling from blood gas syringes to prevent artifactual test results and misleading clinical judgment and inappropriate treatment. When blood gas syringes are received in the laboratory with air bubbles inside, the most vulnerable parameters (i.e., pO2, SO2, pCO2, sodium, ionized calcium, hematocrit and hemoglobin) should be suppressed.

Value of orthogonal axial MR images in preoperative T staging of gastric cancer.

To assess the value of orthogonal axial images (OAI) of MRI in gastric cancer T staging. This retrospective study enrolled 133 patients (median age, 63 [range, 24-85] years) with gastric adenocarcinoma who underwent both CT and MRI followed by surgery. MRI lacking or incorporating OAI and CT images were evaluated, respectively. Diagnostic performance (accuracy, sensitivity, and specificity) for each T stage, overall diagnostic accuracy and rates of over- and understaging were quantified employing pathological T stage as a reference standard. The McNemar's test was performed to compare the overall accuracy. Among patients with pT1-pT4 disease, MRI with OAI (accuracy: 88.7-94.7%, sensitivity: 66.7-93.0%, specificity: 91.5-100.0%) exhibited superior diagnostic performance compared to MRI without OAI (accuracy: 81.2-88.7%, sensitivity: 46.2-83.1%, specificity: 85.5-99.1%) and CT (accuracy: 88.0-92.5%, sensitivity: 53.3-90.1%, specificity: 88.7-98.1%). The overall accuracy of MRI with OAI was significantly higher (83.5%) than that of MRI without OAI (67.7%) (p < .001). However, there was no significant difference in the overall accuracy of MRI with OAI and CT (78.9%) (p = .35). The over- and understaging rates of MRI with OAI (12.0, 4.5%) were lower than those of MRI without OAI (21.8, 10.5%) and CT (12.8, 8.3%). OAI play a pivotal role in the T staging of gastric cancer. MRI incorporating OAI demonstrated commendable performance for gastric cancer T-staging, with a slight tendency toward its superiority over CT.

Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)

Colleges and universities play a crucial role in nurturing talent and providing highly skilled individuals for various sectors of society. Through modifications over time, the model of music education at colleges and universities has advanced. However, there are still numerous issues that demand careful consideration. This manuscript proposes a hierarchically gated recurrent neural network (HGRNN) optimized with the lyrebird Optimization Algorithm (LOA) for predicting music teaching mode of colleges and universities (MTM-HGRNN-LOA). Initially, the data is collected via real time basis. Afterward, the data is fed to an unscented trainable kalman filter (UTKF) based pre-processing process. In the pre-processing segment, it enhances training rate and eliminates the of batch size dependency. The pre-processing output is given to modified spline-kernelled chirp let transform (MSKCT). The input signal undergoes feature extraction to derive the primary features, which are subsequently combined to yield more comprehensive features in an efficient manner. After that, the extracted features are given to a hierarchically gated recurrent neural network and lyrebird optimization algorithm for effectively classifying the music teaching mode for best, good, normal, satisfactory and poor. The weight parameters of hierarchically gated recurrent neural network are optimized using the lyrebird optimization algorithm. The proposed method is implemented in python and evaluated their performance with existing methods. The performance metrics, like precision, F1-score, accuracy, specificity, sensitivity, and ROC is analysed to the proposed method's performance. The proposed MTM-HGRNN-LOA methods of accuracy are provide 97% best, 98% good, 95% normal, 98% satisfactory and 97% poor music teaching mode. The existing methods MTM-CNN, MTM-BPNN and MTM-GNN, the specificity becomes 90%, 70%, 79% best, 77%, 75%, 65% good, 66%, 85%, 84% normal, 59%, 58%, 70% satisfactory, 61%, 79%, 81% poor music teaching mode. The results show that the proposed MTM-HGRNN-LOA method outperforms other existing techniques, such as online vocal music teaching quality using Back Propagation neural network and convolutional neural network based College-Level Music Teaching Quality Evaluation.

Impact of the Copper Second Coordination Sphere on Catalytic Performance and Substrate Specificity of a Bacterial Lytic Polysaccharide Monooxygenase.

Lytic polysaccharide monooxygenases (LPMOs) catalyze the oxidative cleavage of glycosidic bonds in recalcitrant polysaccharides, such as cellulose and chitin, using a single copper cofactor bound in a conserved histidine brace with a more variable second coordination sphere. Cellulose-active LPMOs in the fungal AA9 family and in a subset of bacterial AA10 enzymes contain a His-Gln-Tyr second sphere motif, whereas other cellulose-active AA10s have an Arg-Glu-Phe motif. To shine a light on the impact of this variation, we generated single, double, and triple mutations changing the His216-Gln219-Tyr221 motif in cellulose- and chitin-oxidizing MaAA10B toward Arg-Glu-Phe. These mutations generally reduced enzyme performance due to rapid inactivation under turnover conditions, showing that catalytic fine-tuning of the histidine brace is complex and that the roles of these second sphere residues are strongly interconnected. Studies of copper reactivity showed remarkable effects, such as an increase in oxidase activity following the Q219E mutation and a strong dependence of this effect on the presence of Tyr at position 221. In reductant-driven reactions, differences in oxidase activity, which lead to different levels of in situ generated H2O2, correlated with differences in polysaccharide-degrading ability. The single Q219E mutant displayed a marked increase in activity on chitin in both reductant-driven reactions and reactions fueled by exogenously added H2O2. Thus, it seems that the evolution of substrate specificity in LPMOs involves both the extended substrate-binding surface and the second coordination sphere.

Stabilization of polytopic discrete-time varying systems with rate and magnitude saturating actuators and bounded disturbances

This study tackles the critical challenge of designing parameter-dependent controllers for the wide class of discrete-time polytopic systems under rate and magnitude saturating actuators. Such a class finds widespread use in diverse applications spanning from medicine to industrial engineering. Our central contribution lies in developing novel convex parameter-dependent state feedback controller synthesis conditions that ensure regional and input-to-state stability, effectively mitigating the adverse effects of rate and magnitude-saturating actuators. Our approach considers the ℓ2/ℓ∞ gain between disturbance input and controlled output, offering performance specifications and retaining validity for specific initial conditions and amplitude-limited input disturbances. Moreover, our methodology is highly adaptable and suitable for a broad spectrum of systems, including LPV/quasi-LPV and T–S fuzzy systems. We leverage the position-type feedback model with speed limitation (PMSL) and the generalized sector condition to derive our controller synthesis method, resulting in a parallel-distributed-compensation strategy under standard assumptions, ensuring practicality and applicability to diverse system requirements. To highlight the effectiveness of our approach, we present numerical examples for comparative evaluation concerning the existing literature. Furthermore, we validate our methodology through real-time experiments conducted on a nonlinear coupled tank system, providing concrete evidence of its efficacy and feasibility for real-world implementation.

Effect of exogenous lipids contamination on blood gas analysis.

The purpose of this study was to investigate theeffects of contamination of venous blood with a lipid-containing solution on parameters measured by a modern blood gas analyzer. We collected venous blood from 17 healthcare workers (46±11years; 53 % women) into three blood gas syringes containing 0 , 5 and 10 % lipid-containing solution. Blood gas analysis was performed within 15 min from sample collection on GEM Premier 5000, while triglycerides and serum indices were assays on Roche COBAS C702. Triglycerides concentration increased from 1.0±0.3 mmol/L in the uncontaminated blood gas syringe, to 39.4±7.8 and 65.3±14.4 mmol/L (both p<0.001) in syringes with 5 and 10 % final lipid contamination. The lipemic and hemolysis indices increased accordingly. Statistically significant variation was noted for all analytes except hematocrit and COHb in the syringe with 5 % lipids, while only COHb did not vary in the syringe with 10 % lipids. Significant increases were observed from 5 % lipid contamination for pO2, SO2 and lactate, while the values of pH, pCO2, sodium, potassium, chloride, ionized calcium, glucose, hematocrit (10 % contamination), hemoglobin and MetHB decreased. All these changes except lactate and CoHb exceeded their relative performance specifications. Artifactual hyperlipidemia caused by contamination with exogenous lipids can have a clinically significant impact on blood gas analysis. Manufacturers of blood gas analyzers must be persuaded to develop new instruments equipped with serum indices.

Stable measurement of chemistry, immunochemistry, and hematology analytes in a heparin-based anticoagulant with iloprost additive: A promising candidate for the polyvalent blood collection tube.

A polyvalent blood collection tube could potentially reduce the number and volume of blood samples drawn from patients and reduce the risk of tube mix-ups in a point-of-care setting in the emergency department and the intensive care unit. Four different concentrations of our experimental heparin anticoagulant with iloprost additive (HEP-ILOP 50 nM, 150 nM, 1000 nM, and 10 μM, respectively) were tested for significant differences and bias performance specifications against EDTA for 29 hematology analytes, and the highest concentration (HEP-ILOP 10 μM) against lithium heparin for 14 chemistry and immunochemistry analytes. Samples were drawn from 79 consenting subjects from the Oncology Department (n = 38) and the Intensive and Intermediary Care Unit (n = 41). For hematology analytes, the HEP-ILOP formulation generally provided stable measurement within optimal requirements within 5 h after sampling (mean 104 ± 56 min), with very little difference between the four HEP-ILOP concentrations. Because of differences in platelet and red blood cell swelling between EDTA and HEP-ILOP, all size-dependent analytes required proportional factorization to produce similar results. Platelet count by impedance similarly required factorization, whereas the fluorescent method provided results identical with EDTA. Chemistry and immunochemistry analytes were within optimal requirements except for potassium, lactate dehydrogenase, and glucose, indicating a cytoprotective effect of iloprost reducing cell metabolism and rupture, thereby producing results closer to in vivo conditions. Our novel dry-sprayed anticoagulant formulation, HEP-ILOP, is a promising candidate for a polyvalent blood collection tube, enabling the analysis of hematology, chemistry, and immunochemistry analytes in the same tube.

Evaluation of measurement uncertainty for 19 clinical chemistry analytes and comparison with analytical performance specifications

Evaluation of measurement uncertainty for 19 clinical chemistry analytes and comparison with analytical performance specifications

A practical approach to determining blood ketone performance specifications

A practical approach to determining blood ketone performance specifications

Analytical performance specifications for plasma copeptin based on outcome and biological variation data

Analytical performance specifications for plasma copeptin based on outcome and biological variation data