Quantification Techniques Research Articles

The clinical application value of 3.0T magnetic resonance T2 mapping imaging in evaluating the degree of acetabular cartilage degeneration in joint replacement surgery running title: MRI and acetabular cartilage degeneration

BackgroundTo explore and compare the values of 3.0T magnetic resonance imaging (MRI) T2 mapping in evaluating the degree of acetabular cartilage degeneration in hip replacement surgery.MethodsA total of 26 elderly patients with femoral neck fractures who were scanned in 3.0T MRI T2 mapping quantification technique were included. Basing on MRI images, the degree of acetabular cartilage degeneration was classified into Grade 0, 1, 2, 3 and 4, according to the International Cartilage Repair Society (ICRS) scores. In addition, 8 healthy volunteers were included for control group.ResultsBy comparison with health population, T2 relaxation values in the anterior, superior, and posterior regions of acetabular cartilage in patients with femoral neck fracture were obviously increased (P < 0.001). Among the patients with femoral neck fractures, there were 16 hip joint with Grade 1–2 (mild degeneration subgroup) and 10 hip joints with Grade 3–4 (severe degeneration subgroup), accounting for 61.54% and 38.46%, respectively. Additionally, T2 relaxation values in the anterior and superior bands of articular cartilage were positively related to the MRI-based grading (P < 0.05); while there was no significant difference of T2 relaxation values in the posterior areas of articular cartilage between severe degeneration subgroup and mild degeneration subgroup (P > 0.05). Importantly, acetabular cartilage degeneration can be detected through signal changes of T2 mapping pseudo-color images.Conclusion3.0T MRI T2 mapping technology can be used to determine the degree of acetabular cartilage degeneration, which can effectively monitor the disease course.

Phenomics in Livestock Research: Bottlenecks and Promises of Digital Phenotyping and Other Quantification Techniques on a Global Scale.

Bottlenecks in moving genomics to real-life applications also include phenomics. This is true not only for genomics medicine and public health genomics but also in ecology and livestock phenomics. This expert narrative review explores the intricate relationship between genetic makeup and observable phenotypic traits across various biological levels in the context of livestock research. We unpack and emphasize the significance of precise phenotypic data in selective breeding outcomes and examine the multifaceted applications of phenomics, ranging from improvement to assessing welfare, reproductive traits, and environmental adaptation in livestock. As phenotypic traits exhibit strong correlations, their measurement alongside specific biological outcomes provides insights into performance, overall health, and clinical endpoints like morbidity and disease. In addition, automated assessment of livestock holds potential for monitoring the dynamic phenotypic traits across various species, facilitating a deeper comprehension of how they adapt to their environment and attendant stressors. A key challenge in genetic improvement in livestock is predicting individuals with optimal fitness without direct measurement. Temporal predictions from unmanned aerial systems can surpass genomic predictions, offering in-depth data on livestock. In the near future, digital phenotyping and digital biomarkers may further unravel the genetic intricacies of stress tolerance, adaptation and welfare aspects of animals enabling the selection of climate-resilient and productive livestock. This expert review thus delves into challenges associated with phenotyping and discusses technological advancements shaping the future of biological research concerning livestock.

A New Latin Hypercube Sampling with Maximum Diversity Factor for Reliability-Based Design Optimization of HLM

This research paper presents a new Latin hypercube sampling method, aimed at enhancing its performance in quantifying uncertainty and reducing computation time. The new Latin hypercube sampling (LHS) method serves as a tool in reliability-based design optimization (RBDO). The quantification technique is termed LHSMDF (LHS with maximum diversity factor). The quantification techniques, such as Latin hypercube sampling (LHS), optimum Latin hypercube sampling (OLHS), and Latin hypercube sampling with maximum diversity factor (LHSMDF), are tested against mechanical components, including a circular shaft housing, a connecting rod, and a cantilever beam, to evaluate its comparative performance. Subsequently, the new method is employed as the basis of RBDO in the synthesis of a six-bar high-lift mechanism (HLM) example to enhance the reliability of the resulting mechanism compared to Monte Carlo simulation (MCS). The design problem of this mechanism is classified as a motion generation problem, incorporating angle and position of the flap as an objective function. The six-bar linkage is first adapted to be a high-lift mechanism (HLM), which is a symmetrical device of the aircraft. Furthermore, a deterministic design, without consideration of uncertainty, may lead to unacceptable performance during the manufacturing step due to link length tolerances. The techniques are combined with an efficient metaheuristic known as teaching–learning-based optimization with a diversity archive (ATLBO-DA) to identify a reliable HLM. Performance testing of the new LHSMDF reveals that it outperforms the original LHS and OLHS. The HLM problem test results demonstrate that achieving optimum HLM with high reliability necessitates precision without sacrificing accuracy in the manufacturing process. Moreover, it is suggested that the six-bar HLM could emerge as a viable option for developing a new high-lift device in aircraft mechanisms for the future.

Application of uncertainty quantification techniques in the framework of process safety studies: Advanced dispersion simulations

AbstractIn the framework of process safety studies, consequence assessment of accidental scenarios is a crucial step affecting the eventual risk profile associated with the facilities under analysis. Conventional models used for consequence assessment are based on integral models, and may not be adequate to cope with the dynamic evolution of accidental scenarios and their three‐dimensional features. On the other hand, consequence assessment models based on computational fluid dynamics (CFD) approaches are promising to cope with complex scenarios and environments, but setting the simulation introduces relevant uncertainties associated with both the input data, assumptions, and with the modelling of physical effects involved. In the present study, uncertainty quantification (UQ) techniques are applied to support advanced safety studies based on CFD simulations of hazardous gas dispersion. Firstly, the accidental scenarios are characterized by defining release scenarios and conditions and quantifying source terms using integral models. At the same time, input meteorological data are gathered. This enables the development of high‐fidelity CFD simulations of gas dispersion based on different input sets and eventually the implementation of UQ techniques. The generalized polynomial chaos (gPC) expansion is employed to obtain hazardous gas concentration based on the variation of wind direction and speed. The present method is applied for the analysis of a real plant featuring a complex layout. The results show the advantages of the present approach by quantifying the influence of meteorological conditions and providing indications for supporting the development of protection systems and emergency measures.

Near-infrared indocyanine green angiography in recognizing bowel ischemia in emergency surgery: game changer or overrated?

Assessing bowel perfusion in emergency intestinal surgery can prove challenging even for experienced surgeons. The necessity of a technological tool assisting clinicians is undisputed. Near-infrared indocyanine green (NIR-ICG) angiography has been increasingly used in elective colorectal surgery to evaluate intestinal perfusion with promising results. This review aims to answer whether a similar outcome can be observed in acute cases of bowel ischemia. We conducted online research of the literature using keywords such as "indocyanine green", "bowel", "emergency" and "ischemia", to identify articles concerning the use of ICG-angiography in evaluating bowel perfusion during emergency operations. PubMed was the primary database. 11 articles were included in this systematic review with a total of 358 patients. Most papers showed a positive effect after using NIR-ICG-angiography, whereas one study indicated the limitations of the method by exhibiting increased reoperation and mortality rates. Moreover, a significant variation in indocyanine green (ICG) dose and fluorescence identification systems was observed. NIR-ICG-angiography has the potential to become a fundamental tool in emergency intestinal operations. Nevertheless, additional research, especially high-quality, randomized studies, as well as quantification techniques are still needed to support these preliminary observations.

Measuring Residual Stresses with Crack Compliance Methods: An Ill-Posed Inverse Problem with a Closed-Form Kernel

By means of relaxation methods, residual stresses can be obtained by introducing a progressive cut or a hole in a specimen and by measuring and elaborating the strains or displacements that are consequently produced. If the cut can be considered a controlled crack-like defect, by leveraging Bueckner’s superposition principle, the relaxed strains can be modeled through a weighted integral of the residual stress relieved by the cut. To evaluate residual stresses, an integral equation must be solved. From a practical point of view, the solution is usually based on a discretization technique that transforms the integral equation into a linear system of algebraic equations, whose solutions can be easily obtained, at least from a computational point of view. However, the linear system is often significantly ill-conditioned. In this paper, it is shown that its ill-conditioning is actually a consequence of a much deeper property of the underlying integral equation, which is reflected also in the discretized setting. In fact, the original problem is ill-posed. The ill-posedness is anything but a mathematical sophistry; indeed, it profoundly affects the properties of the discretized system too. In particular, it induces the so-called bias–variance tradeoff, a property that affects many experimental procedures, in which the analyst is forced to introduce some bias in order to obtain a solution that is not overwhelmed by measurement noise. In turn, unless it is backed up by sound and reasonable physical assumptions on some properties of the solution, the introduced bias is potentially infinite and impairs every uncertainty quantification technique. To support these topics, an illustrative numerical example using the crack compliance (also known as slitting) method is presented. The availability of the Linear Elastic Fracture Mechanics Weight Function for the problem allows for a completely analytical formulation of the original integral equation by which bias due to the numerical approximation of the physical model is prevented.

Unsupervised stochastic learning and reduced order modeling for global sensitivity analysis in cardiac electrophysiology models

Background and Objective:Numerical simulations in electrocardiology are often affected by various uncertainties inherited from the lack of precise knowledge regarding input values including those related to the cardiac cell model, domain geometry, and boundary or initial conditions used in the mathematical modeling. Conventional techniques for uncertainty quantification in modeling electrical activities of the heart encounter significant challenges, primarily due to the high computational costs associated with fine temporal and spatial scales. Additionally, the need for numerous model evaluations to quantify ubiquitous uncertainties increases the computational challenges even further. Methods:In the present study, we propose a non-intrusive surrogate model to perform uncertainty quantification and global sensitivity analysis in cardiac electrophysiology models. The proposed method combines an unsupervised machine learning technique with the polynomial chaos expansion to reconstruct a surrogate model for the propagation and quantification of uncertainties in the electrical activity of the heart. The proposed methodology not only accurately quantifies uncertainties at a very low computational cost but more importantly, it captures the targeted quantity of interest as either the whole spatial field or the whole temporal period. In order to perform sensitivity analysis, aggregated Sobol indices are estimated directly from the spectral mode of the polynomial chaos expansion. Results:We conduct Uncertainty Quantification (UQ) and global Sensitivity Analysis (SA) considering both spatial and temporal variations, rather than limiting the analysis to specific Quantities of Interest (QoIs). To assess the comprehensive performance of our methodology in simulating cardiac electrical activity, we utilize the monodomain model. Additionally, sensitivity analysis is performed on the parameters of the Mitchell-Schaeffer cell model. Conclusions:Unlike conventional techniques for uncertainty quantification in modeling electrical activities, the proposed methodology performs at a low computational cost the sensitivity analysis on the cardiac electrical activity parameters. The results are fully reproducible and easily accessible, while the proposed reduced-order model represents a significant contribution to enhancing global sensitivity analysis in cardiac electrophysiology.

Machine learning techniques for predictive modelling and uncertainty quantification of the mechanical properties of woven carbon fibre composites

Carbon fibre composites owe their exceptional mechanical properties to the characteristics of their constituents; fibres and resin. However, uncertainty in these individual constituent properties can introduce significant variations in the macroscale mechanical performance of composites. These variations are amplified in the presence of various plies in the composite and pose a challenge to quantify during manufacturing stages. In this study, uncertain constituent material properties are propagated to the macroscale composite through multiscale homogenisation to compute the varied resultant thin-shell stiffness matrices. An in-house-built automated simulation environment has been used to create a physics-conforming trainable database for model training, testing, validation and uncertainty quantification using Gaussian Process Regression (GPR), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR). Through rigorous comparative analyses, the superior performance of GPR is demonstrated when compared to alternative techniques with R² ≥ 0.99 and NRMSE < 10⁻⁷. Further, the findings approve the validity of GPR for extrapolatory predictions. For instance, a randomly sampled [-5, 5] % variation of constituent material properties leads to extreme uncertainties in macroscale ± 0.067 %, for axial stiffness with excellent GPR predictive accuracy of R² = 0.99. This slightly increases to ± 0.14 % for a variation of [-10, 10] %. Moreover, the extreme uncertainties indicate that the direct axial and bending stiffnesses are more sensitive to variations in constituent properties than others. Additional investigations presented in the paper produce predictive models and uncertainty bounds for all stiffness entries that are of high importance for macroscale property control during manufacturing.

Assessment of Analytical Techniques for Precise Quantification of Four Antiviral Drugs in Pharmaceutical Research and Development: A Comprehensive Review

: Precise measurement of drug concentration in pharmaceutical research is critical, especially for anti-viral drugs like boceprevir, elvitegravir, indinavir, and saquinavir that combat viral infections. It is well-known that analytical techniques play an imperative role in identifying and characterizing active pharmaceutical ingredients in biological samples and drug formulations. Moreover, precise drug assessment directly influences safety, stability, and efficacy while providing in-depth insight into drug pharmacokinetics. Other than this, analytical techniques also aid in identifying impurities, deteriorated products, and potential pollutants. Thus, reliable analytical methods have become crucial for addressing challenges imposed by complex drug formulations. The most commonly used analytical technique is UV spectrophotometry, which does not have the high sensitivity to detect complex drug formulations. In contrast, Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS) merges two analytical techniques, chromatography and mass spectrometry, to accurately quantify biological samples. Furthermore, Ultra-Performance Liquid Chromatography (UPLC) provides enhanced resolution, faster analysis in short duration, and low solvent consumption in contrast to HPLC. This comprehensive review aims to critically assess each analytical approach's accuracy, applicability, selectivity, and limitation to provide valuable insights for researchers and analysts. Understanding the weaknesses and strengths of these analytical techniques will enable the researchers to select the suitable analytical method based on their needs and requirements for quality assessment, precise drug quantification, and optimal therapeutic efficiency. Eventually, this review intends to advance pharmaceutical research and development, specifically for anti-viral drugs, by ensuring the effective and secure administration of therapies.

Mealybug Population Dynamics: A Comparative Analysis of Sampling Methods for Saccharicoccus sacchari and Heliococcus summervillei in Sugarcane (Saccharum sp. Hybrids).

This research is focused on a comparative field-based study of the population dynamics and sampling methods of two mealybug species, Saccharicoccus sacchari (Cockerell, 1895) (Hemiptera: Coccomorpha, Pseudococcidae) and Heliococcus summervillei (Brookes, 1978) (Hemiptera: Coccomorpha, Pseudococcidae), in sugarcane (Saccharum sp. hybrids) (f. Poaceae) over consecutive growing seasons. The research monitored and compared the above- and belowground populations and seasonal abundance of these two mealybug species in sugarcane fields in Far North Queensland, with non-destructive sampling techniques of yellow sticky traps, pan traps, and stem traps, and destructive sampling of the whole leaf and whole plant. The results indicated that S. sacchari (n = 29,137) was more abundant and detected throughout the growing season, with population peaks in the mid-season, while H. summervillei (n = 2706) showed peaks of the early-season activity. S. sacchari is primarily located on sugarcane stems and roots, compared to H. summervillei, which is located on leaves and roots. The whole-leaf collection and stem trap were the most effective sampling techniques for quantification of H. summervillei and S. sacchari, respectively. This study enhanced the understanding of S. sacchari and the first-ever record of H. summervillei on sugarcane in Australia and will contribute to the development of more effective pest management strategies.

Techniques for quantification of organochlorine pesticides from a validated method by using gas chromatography-electron capture detector

Organochlorine pesticides (OCPs) are persistent organic compounds found in aquatic environments worldwide. A well-validated and well-established analytical method is crucial for detecting OCPs in the environment. In this study, an analytical method for quantifying OCPs in water was developed and evaluated. Here, the range of linearity, reproducibility, uncertainty, specificity, method detection limits (MDL), and special emphasis on detection and quantitation limits were assessed. Recovery studies were performed to measure the accuracy and precision of the method. This method exhibited excellent linearity in the range of 2.5–20 μg/L for all compounds. As none of the targeted compounds was detected in the chromatograms of the blank sample with no baseline noise, the limits of detection (LOD) and limits of quantification (LOQ) were determined using the linear regression method, external calibration curve slope, and laboratory fortified blank-based detection. All compounds showed different LOD and LOQ values, depending on the approach used. In particular, endosulfan sulfate, methoxychlor, endrin ketone, H. epoxide, heptachlor, and 4,4ʹ-DDT exhibited high detection limits. The recovery percentage of the 15 compounds at 5 μg/L spiked concentration was between 50 and 150 %, which is consistent with the accuracy of the APHA method. Except for endosulfan sulfate, the relative standard deviations of all other compounds were below 20 %, indicating good precision. This method has also been applied to real water samples. This validation technique is reliable, sensitive, simple, rapid, easy to comprehend, and reproducible. The application of this method in the real water samples was also conducted. Only α-BHC and γ-Chlordane were detected in the water sample.

Evaluation of fetal cardiac morphology and function by fetal heart quantification technique in the normal second and third trimesters.

The study of fetal heart is receiving increasing attention. Fetal heart quantification (Fetal HQ) technology is a new speckle tracking technology that can analyze the 24-segment morphology and function of fetal ventricles. This study aims to use Fetal HQ to assess the changes in the structure and function of the fetal heart in normal mid to late pregnancy, providing a foundation for the clinical application of fetal cardiac speckle tracking technology. The heart size, global sphericity index (GSI), left ventricular [stroke volume (SV)], cardiac output (CO), ejection fraction (EF), global longitudinal strain (GLS), fractional area change (FAC), 24-segment end-diastolic diameter (EDD), sphericity index (SI) and fractional shortening (FS) of the two ventricles of 500 normal pregnant fetuses were evaluated by Fetal HQ. The subjects were divided into 5 groups according to gestational weeks (GA), and the changes of fetal heart morphology and function were observed. P<0.05 indicated the statistically significant difference. The fetal heart rate decreased gradually with the increase of GA (P<0.05). The size parameters of the fetal heart and two ventricles gradually increased with increasing GA (P<0.05). The 24 segments EDD of both ventricles increased with increasing GA (P<0.05), while the EDD increased first and then decreased from the ventricle base to the apex. The GSI and the 24 segments SI of two ventricles were basically not significantly different among the groups (P>0.05). The EF, GLS, FAC of the left ventricle and the GLS, FAC of the right ventricle decreased with the increase of GA (P<0.05), and SV and CO increased with increasing GA (P<0.05). The 24 segments FS of the left ventricle showed a downward trend with the increase of GA and gradually increased from the base to the apex. The FS of most segments of the right ventricle decreased with the increase of the GA and increased first and then decreased from the base to the apex. The whole and segmental size parameters of fetal heart can quantitatively evaluate the growth and development of fetal heart; the GSI and segmental SI are reliable morphological indexes for evaluating fetal heart; fetal ventricular function parameters EF, FAC, GLS and segmental FS can evaluate fetal cardiac function. The Fetal HQ technique can help us to evaluate the heart growth and development of normal fetuses in the second and third trimester of pregnancy.

Utility of the novel fetal heart quantification (fetal HQ) technique in diagnosing ventricular interdependence and biventricular dysfunction in a case of prenatally diagnosed Uhl's anomaly

AbstractUhl's anomaly is characterized by complete or partial absence of right ventricular myocardium. We describe a case of prenatally diagnosed Uhl's anomaly with biventricular dysfunction which was quantified with speckle tracking echocardiography using a novel fetal heart quantification (fetal HQ) technique.

Nuclear safety Enhanced: A Deep dive into current and future RAVEN applications

As the horizon of nuclear energy expands with the advent of small modular reactors, Generation IV reactors, and fusion reactors, there is a growing perspective that the licensing process could benefit from a more comprehensive approach. Moving beyond traditional deterministic and probabilistic risk assessment analyses might pave the way for a novel safety analysis paradigm propelled by the increasing computational power at our disposal.This paper explores different methodologies that can improve the outcomes of nuclear safety analysis. These range from uncertainty quantification techniques, aimed at enhancing the precision of safety margins, to deploying dynamic event trees by driving system code simulations, capturing the potential evolutions of severe accidents. These methodologies offer a better understanding of the management and consequences of nuclear accident scenarios, significantly improving the accuracy and efficiency of safety predictions compared to traditional methods.Specific case studies illustrate the practical application of these advanced techniques, demonstrating substantial improvements in predicting and managing the dynamics of severe accidents. These findings underscore the effectiveness of these methodologies in enhancing risk assessment capabilities and informing decision-making processes for nuclear safety management. The paper also emphasizes the importance of adaptability and continuous evolution, a call for action to address emerging nuclear safety concerns and highlights the utility of advanced tools like RAVEN.

An efficient, amine-specific iTRAQ labeling method improves the peptide and protein identification rates

Isotope tags for relative and absolute quantification (iTRAQ) are among the most widely used proteomics quantification techniques. These tags can be rapidly coupled to the primary amines of proteins/peptides through chemical reactions under mild conditions, making this technique universally applicable to any kind of sample. However, iTRAQ reagents also partially react with the hydroxyl groups of serine, threonine and tyrosine residues, particularly when these residues coexist with a histidine residue in the same peptide. This overlabeling of peptides causes systematic biases and significantly compromises protein/peptide identification rates. In this study, we report a novel iTRAQ labeling method that overcomes the detrimental overlabeling while providing high amine labeling efficiency. The impacts of reaction temperature, reactant concentrations, reaction time, buffer compositions, and pH on iTRAQ labeling performance were investigated in-depth. In a comparison experiment between our method and the standard labeling method provided by the iTRAQ manufacturer, our method reduced the number of overlabeled peptides by 55-fold while achieving comparable amine labeling efficiency. This improvement allowed our method to eliminates the systematic bias against histidyl- and hydroxyl-containing peptides, and more importantly, enabled the identification of 23.9% more peptides and 9.8% more proteins. SignificanceIn addition to amines, the hydroxyl groups in serine, threonine, and tyrosine residues can also partially labeled by iTRAQ reagents, which leads to systematic biases and significantly compromises the analytical sensitivity. To address this issue, we developed a novel iTRAQ labeling method that overcomes the detrimental overlabeling while providing high labeling efficiency of amines. When benchmarking our method against the standard method provided by the reagent manufacturer, our method achieved comparable labeling efficiency but reduced the overlabeled species by 55-fold. This significant improvement eliminated the systematic biases, and more importantly, enabled the identification of 23.9% more peptides and 9.8% more proteins, demonstrating its superior performance and potential to enhance proteome quantification using iTRAQ labeling.

Starvation-induced changes in the proteome and transcriptome of the salivary glands of leech (Hirudo nipponia).

Hirudo nipponia is an important medicinal animal in China. Its salivary gland secretions contain a variety of protein bioactive substances. Investigations of its salivary glands are of great significance in the study of the medicinal value and mechanism of leech secretions. Illumina RNA-Seq technology was used to perform transcriptome sequencing of salivary gland tissue of H. nipponia under starvation (D30) and fed (D0) states. A total of 2,650 differentially expressed genes (DEGs) were screened. Using the label-free protein quantification technique and bioinformatics analysis, the expression of differentially expressed proteins (DEPs) in the salivary gland tissue of H. nipponia was compared. A total of 2,021 proteins were identified, among which 181 proteins were differentially expressed between the starvation and fed states, with 72 significantly upregulated and 109 significantly downregulated. The salivary glands of H. nipponia synthesized protein-based active substances after 30 days of starvation and adapted to the starvation environment by weakening respiratory activity and reducing metabolic activity to reduce energy expenditure. Energy was produced by glycolysis and the tricarboxylic acid cycle for the synthesis of substances such as antibiotics. This study combined transcriptome and proteome sequencing data to provide a data reference for an in-depth study of the regulatory mechanism of salivary gland secretions of H. nipponia under starvation stress by analyzing DEGs and DEPs.

Microsampling and conventional sampling techniques for quantification of tacrolimus in blood samples: A systematic review

Microsampling and conventional sampling techniques for quantification of tacrolimus in blood samples: A systematic review

A new method for quantification of tomatine-enriched extracts.

Green tomato extracts, an agro-food industry waste, are rich in the glycoalkaloid tomatine, which presents activity against several diseases. High-performance liquid chromatography (HPLC) with ultraviolet (UV) detection is one of the most used techniques for quantification of bioactive compounds. The aim of this study was to optimize and validate a selective HPLC method with diode array detector (DAD) for the quantitative analysis of tomatine extracted from green tomatoes by subcritical water. Chromatographic runs were performed on a InertSustain Phenyl (250 mm × 4.6 mm, 5 μm) analytical column, at a wavelength of 205 nm. A concentration range of 50-580 μg mL-1 was used. The validation process was performed considering the linearity, precision, trueness, limit of detection (LOD) and limit of quantitation (LOQ) of the method. The selected mobile phase composed of acetonitrile and a solution of 20 mmol L-1 potassium dihydrogen phosphate (KH2PO4) pH 3, resulted in suitable retention times and a standard calibration curve with adequate linearity (R2 = 0.9999). The method trueness was evaluated by the recovery assay, obtaining a mean recovery of 105% and the precisions were 1.4% and 0.9% (percentage relative standard deviation, RSD%) for the tomatine standard and extract samples, respectively. The inter-day variability was 2.7-9.0% (RSD%) for the standards and 6.9% (RSD%) for extract. The LOD and the LOQ of the method were determined at 8.0 and 24.1 μg mL-1, respectively. The herein described method was successfully used for the quantification of tomatine in a tomato-derived extract. Furthermore, the method constitutes a simple and rapid analytical approach able to be used as a routine protocol. © 2024 Society of Chemical Industry.

Coronary artery calcium quantification technique using dual energy material decomposition: a simulation study

Coronary artery calcification is a significant predictor of cardiovascular disease, with current detection methods like Agatston scoring having limitations in sensitivity. This study aimed to evaluate the effectiveness of a novel CAC quantification method using dual-energy material decomposition, particularly its ability to detect low-density calcium and microcalcifications. A simulation study was conducted comparing the dual-energy material decomposition technique against the established Agatston scoring method and the newer volume fraction calcium mass technique. Detection accuracy and calcium mass measurement were the primary evaluation metrics. The dual-energy material decomposition technique demonstrated fewer false negatives than both Agatston scoring and volume fraction calcium mass, indicating higher sensitivity. In low-density phantom measurements, material decomposition resulted in only 7.41% false-negative (CAC = 0) measurements compared to 83.95% for Agatston scoring. For high-density phantoms, false negatives were removed (0.0%) compared to 20.99% in Agatston scoring. The dual-energy material decomposition technique presents a more sensitive and reliable method for CAC quantification.

Screening the capability of vibrational spectroscopic techniques for simultaneous quantification of vitamins B1, B6, and B12 in a powder blend

The rising possibilities of vibrational spectroscopy coupled with multivariate modeling, enables many applications of these well-established analytical techniques, easy-to-use for rapid and non-destructive analysis. Owing to the lack of need for hazardous solvents or reagents in sample preparation, FTIR, NIR and Raman spectroscopy belong to the group of environmentally friendly techniques, known as “green analytical techniques”. Such characteristics re-introduce them into the pharmaceutical industry, especially as part of process-analytical-technology (PAT) tools for both qualitative and quantitative analysis.This research aimed to assess the potential of ATR-FTIR, NIR and Raman as a PAT for the quantification of Vitamins B1 (thiamine), B6 (pyridoxine) and B12 (cyanocobalamin) in a powder blend, using a partial least square (PLS) regression model, as well as to monitor the powder blend homogeneity.Results from each of the vibrational spectroscopy techniques were compared with those obtained by HPLC, which served as the reference analytical technique for quantifying the active substances in complex matrices using multivariate analysis. The developed PLS models were evaluated for their ability to quantify each of the active substances in the powder blend. The ATR-FTIR, NIR and Raman spectrum segments, revealed by the Variable Importance for Projection (VIP) plot for each model, respectively, were linked to the band assignment of each active ingredient.The statistical indicators of the models (interpretation rate (R2X and R2Y), predictive ability (Q2 and RMSEP) and accuracy (RMSEE)) demonstrated their suitability for in-process estimation of vitamin B1, B6 and B12 contents in powder blends. Estimating B12 content proved more challenging, likely due to sampling issues related to its low content in the powder blend. Nevertheless, the observed variability in the models aligns with the variability in results obtained by HPLC, indicating the lack of blend homogeneity. Therefore, the quantification models could be considered for a further upgrade as PAT for monitoring the powder blend homogeneity during the manufacturing of vitamin blends.