External Calibration Curve Research Articles

B-297 Tacrolimus Concentrations in Envarsus vs Prograf Patients: An Evaluation of the Clinical Impact of LC-MS/MS and Immunoassay Methods on Tacrolimus measurement

Abstract Background Tacrolimus is a calcineurin inhibitor used as part of an immunosuppressive regimen in kidney and liver transplant patients to prevent graft-versus-host disease (GvHD) and requires therapeutic drug monitoring due to its narrow therapeutic index. Tacrolimus levels are commonly measured using two different methodologies - immunoassay and liquid chromatography tandem mass spectrometry (LC-MS/MS). A global proficiency study to assess and compare the measurement of trough levels by these two methodologies noted a positive bias with the ARCHITECT immunoassay which is likely due to the presence of tacrolimus metabolites. The aim of our study is to assess the impact of two different formulations of tacrolimus on the accuracy of its analytical measurement. While differences between immunoassay and LC-MS/MS results have been evaluated for immediate-release tacrolimus (Prograf), it has yet to be characterized for extended-release formulations such as Envarsus. The discrepancy between immunoassay and LC-MS/MS has clinical implications, as dosage modifications are based on measured concentrations, analytical variation may cause intervals of subtherapeutic or supratherapeutic exposure, increasing the risk of graft rejection or toxicity. Methods Development of an LC-MS/MS method for the simultaneous quantification of tacrolimus and its major metabolite, desmethyl tacrolimus, was performed in whole blood. Traceable calibrators and quality control material for tacrolimus and specimens supplemented with desmethyl tacrolimus were used to determine accuracy and precision. Tacrolimus concentrations were measured by LC-MS/MS and ARCHITECT immunoassay methods in excess whole blood specimens from patients treated with either Prograf or Envarsus. Measurement biases between LC-MS/MS and immunoassay methodologies were determined for both formulations of tacrolimus. Results External calibration curves for both tacrolimus and desmethyl tacrolimus were linear (R2>0.995), and the analytical measurement range (AMR) for tacrolimus spanned from 1.1 - 31.6 ng/ml. Calibrator and Quality control (QC) biases were within 15% of their target values throughout the AMR and within-run imprecision was less than 10% (n=25) for all calibrators and QCs. Between-run imprecision for Low, Mid, and High QC levels over a period of 2 weeks (n=5 days) was less than 10%. Comparative bias of tacrolimus concentrations between immunoassay and LC-MS/MS was significantly lower (P value=0.0074) for Envarsus (n=20 specimens) relative to Prograf (n=32 specimens). Conclusions The differential bias between immunoassay and LC-MS/MS in the measurement of tacrolimus in patients dosed with immediate-release versus extended-release formulations suggests that their distinct pharmacokinetic profile may impact the accuracy of the measurement. Therefore, applying observed measurement biases from different assays derived from Prograf patients to Envarsus patients and vice versa may result in erroneous estimation of tacrolimus concentrations.

Synthetic Colorants in Traditional Sweetmeat (Laddu) available in Dhaka City, Bangladesh

Laddus are very popular sweets all over the South Asian countries including Bangladesh. Commercially accessible sweets (Laddus) contain a variety of Yellow 5 and Yellow 6 dyes, which are hazardous when consumed in excess. In this study, the amount of Yellow 5 and Yellow 6 in twenty-five Yellow-colored Laddus from various sweet shops in Dhaka, Bangladesh, was determined using a UV-visible spectrophotometer at 425 and 482 nm, respectively. Quantification was done using external calibration curves of the standard Yellow 5 and Yellow 6 and the calibration curves showed excellent linear correlation co-efficient (R2) of 0.999 and 0.998 for them, respectively. Of the twenty-five samples, eleven had Yellow 5 in the range of 0.915-6.790 mgkg-1, and eight had Yellow 6 in the range of 0.223-4.315 mgkg-1. The rest of the samples did not contain any Yellow 6 as well as Yellow 5. Dhaka Univ. J. Sci. 72(2): 45-51, 2024 (July)

Development of a nomogram for predicting malnutrition in elderly hospitalized cancer patients: a cross-sectional study in China.

The Patient-Generated Subjective Global Assessment (PG-SGA) serves as a specialized nutritional assessment instrument designed for cancer patients. Despite its specificity, the complexity and time requirements of this tool, along with the necessity for administration by trained professionals, limit its practicality in clinical settings. Our objective is to identify a straightforward, efficient, and dependable nutritional assessment tool to promote broader adoption in clinical practice. This study encompassed a total of 450 patients diagnosed with cancer. Of these, 315 individuals constituted the training set, and the remaining 135 were allocated to the external validation set. The model variables were identified through the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. Binary logistic regression outcomes facilitated the development of a nomogram, offering a visual depiction of the predicted probabilities. The predictive accuracy of the nomogram model was evaluated by calculating the area under the Receiver Operating Characteristic (ROC) curve. The LASSO method detected four variables that were included in the final prediction model: age, serum albumin levels (ALB), body mass index (BMI), and activities of daily living (ADL). The area under the curve (AUC) for this prediction model was 0.905. Both the internal and external calibration curves for malnutrition showed that the predictive nomogram model was highly accurate. The study has developed a prediction model that demonstrates remarkable accuracy in forecasting malnutrition. Furthermore, it presents a streamlined nutritional assessment tool aimed at swiftly identifying cancer patients at nutritional risk, thereby facilitating oncologists in delivering targeted nutritional support to these individuals.

Py–GC–MS analysis for microplastics: Unlocking matrix challenges and sample recovery when analyzing wastewater for polypropylene and polystyrene

Matrix interference and recovery when using pyrolysis gas chromatography (Py–GC–MS) to analyze wastewater for polystyrene (PS) and polypropylene (PP) microplastics (MP) was studied. Raw wastewater underwent a sample preparation train commonly applied for such matrix. The train consisted of six discrete steps to reduce the organic matter content without affecting MP in the sample. One large wastewater sample was collected, homogenized, and subdivided into 21 subsamples. Three samples were analyzed without further sample preparation. The remaining samples were divided in sets of three, and each set underwent an increasing number of steps of the procedure, up to the last set, which underwent the full treatment. The matrix effect on the determination of PS and PP was statistically evaluated by comparing in-matrix and external calibration curves at each step. Recovery of MP was assessed after each step by adding deuterated PS to the samples. A main finding was that there was no significant matrix effect for these polymers throughout the preparation train, suggesting that matrix components did not interfere with the analytical method. However, a significant loss of polymer mass was found between the steps, which may result in MPs falling below detection limits. Therefore, Py–GC–MS could be used for MP quantification before analysis by other techniques which require more extensive matrix removal. A downside of this approach is that analyzing such samples without matrix reduction will increase the need for instrumental maintenance.

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.

A novel surgical scheme for hepatectomy in hepatocellular carcinoma patients with clinically significant portal hypertension

ObjectiveClinically significant portal hypertension (CSPH) seriously affects the feasibility and safety of surgical treatment for hepatocellular carcinoma (HCC) patients. The aim of this study was to establish a new surgical scheme defining risk classification of post-hepatectomy liver failure (PHLF) to facilitate the surgical decision-making and identify suitable candidates for individual hepatectomy among HCC patients with CSPH.BackgroundsHepatectomy is the preferred treatment for HCC. Surgeons must maintain a balance between the expected oncological outcomes of HCC removal and short-term risks of severe PHLF and morbidity. CSPH aggravates liver decompensation and increases the risk of severe PHLF thus complicating hepatectomy for HCC.MethodsMultivariate logistic regression and stochastic forest algorithm were performed, then the independent risk factors of severe PHLF were included in a nomogram to determine the risk of severe PHLF. Further, a conditional inference tree (CTREE) through recursive partitioning analysis validated supplement the misdiagnostic threshold of the nomogram.ResultsThis study included 924 patients, of whom 137 patients (14.8%) suffered from mild-CSPH and 66 patients suffered from (7.1%) with severe-CSPH confirmed preoperatively. Our data showed that preoperative prolonged prothrombin time, total bilirubin, indocyanine green retention rate at 15 min, CSPH grade, and standard future liver remnant volume were independent predictors of severe PHLF. By incorporating these factors, the nomogram achieved good prediction performance in assessing severe PHLF risk, and its concordance statistic was 0.891, 0.850 and 0.872 in the training cohort, internal validation cohort and external validation cohort, respectively, and good calibration curves were obtained. Moreover, the calculations of total points of diagnostic errors with 95% CI were concentrated in 110.5 (range 76.9-178.5). It showed a low risk of severe PHLF (2.3%), indicating hepatectomy is feasible when the points fall below 76.9, while the risk of severe PHLF is extremely high (93.8%) and hepatectomy should be rigorously restricted at scores over 178.5. Patients with points within the misdiagnosis threshold were further examined using CTREE according to a hierarchic order of factors represented by the presence of CSPH grade, ICG-R15, and sFLR.ConclusionThis new surgical scheme established in our study is practical to stratify risk classification in assessing severe PHLF, thereby facilitating surgical decision-making and identifying suitable candidates for individual hepatectomy.

Matrix overcompensation calibration: A new strategy to correct matrix effects of carbon origin in multielement analysis by inductively coupled plasma mass spectrometry

BackgroundElemental analysis by inductively coupled plasma mass spectrometry (ICP-MS) may suffer from matrix effects; those caused by organic matrices cannot be corrected by internal standardization. A new strategy, matrix overcompensation calibration (MOC), was developed to correct such matrix effects. ResultsClear fruit juices were diluted 1:50 in 1 % HNO3 (v/v)- 0.5 % HCl (v/v)- 5 % ethanol (v/v). A standard series was treated likewise to construct an external calibration curve. As, Se, Cd, and Pb in juices were determined by dilute-and-shoot ICP-MS based on this MOC strategy. The results agreed with those obtained by standard addition calibration and microwave-aided digestion; data accuracy was validated by spike-recovery studies. SignificanceUnlike standard addition calibration, a single external calibration curve established by MOC can be applicable to juices of diversified fruit, geographical, and manufacturer origins enhancing productivity.

SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data.

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Development of a simple screening method for analyzing cereulide toxin in fried rice using liquid chromatography-tandem mass spectrometry.

The presence of cereulide, an emetic toxin produced by Bacillus cereus, in fried rice samples is critical evidence of food poisoning even in situations where B. cereus could not be detected. This study aims to develop a screening method for analyzing cereulide in fried rice using the QuEChERS procedure and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Cereulide was identified and quantified in fried rice samples using the QuEChERS extraction method and LC-MS/MS. The accuracies of the methods were determined by analyzing fortified blank samples at two concentrations (10 and 50µg/kg) conducted on three samples daily for five days. The QuEChERS procedure removed matrix compounds from fried rice. Characteristic MS/MS spectra enabled the identification of cereulide. As the matrix effects in seven fried rice samples were within ± 6%, an external solvent calibration curve could be used for quantification. This method exhibited good accuracy ranging from 88 to 89%. The relative standard deviations for both repeatability and intra-laboratory reproducibility were < 4%. These standard deviations satisfied the criteria of the Japanese validation guidelines for residues (MHLW 2010, Director Notice, Syoku-An No. 1224-1). The limit of quantification was 2μg/kg. The applicability of this method was confirmed using the analysis of cereulide in fried rice samples incubated with emetic Bacillus cereus. The QuEChERS extraction procedure described herein showed substantial promise as a reliable screening tool for cereulide in fried rice sample.

Advancing High-Throughput MS-Based Protein Quantification: A Case Study on Quantifying 10 Major Food Allergens by LC-MS/MS Using a One-Sample Multipoint External Calibration Curve.

The LC-MS-based method has emerged as the preferred approach for quantifying food allergens. However, the preparation of a traditional calibration curve (MSCC) is labor-intensive and error-prone. Here, a sensitive and robust LC-MS/MS method for quantifying 10 major food allergens was developed and validated, where the one-sample multipoint external calibration curve (OSCC) was employed instead of MSCC. By employing the multiple isotopologue reaction monitoring (MIRM) technique with only one spiked level in the blank, OSCC can be effectively established. Results demonstrate that the proposed method exhibits excellent performance in selectivity, sensitivity, accuracy, and precision, comparable to that of the traditional MSCC. Additionally, this strategy allows for isotope sample dilution by monitoring the less abundant MIRM channel. Moreover, the developed method was successfully applied to investigate the contamination of 10 food allergens in commercial food products. With its high throughput and robustness, the MIRM-OSCC-LC-MS/MS methodology has many potential applications, especially in the MS-based protein quantification analysis.

Identification and quantification of nanoplastics in different crops using pyrolysis gas chromatography-mass spectrometry

Quantitative studies of nanoplastics (NPs) abundance on agricultural crops are crucial for understanding the environmental impact and potential health risks of NPs. However, the actual extent of NP contamination in different crops remains unclear, and therefore insufficient quantitative data are available for adequate exposure assessments. Herein, a method with nitric acid digestion, multiple organic extraction combined with pyrolysis gas chromatography-mass spectrometry (Py-GC/MS) quantification was used to determine the chemical composition and mass concentration of NPs in different crops (cowpea, flowering cabbage, rutabagas, and chieh-qua). Recoveries of 74.2–109.3% were obtained for different NPs in standard products (N = 6, RSD <9.6%). The limit of detection (LOD) and the limit of quantitation (LOQ) were 0.02–0.5 μg and 0.06–1.5 μg, respectively. The detection method for NPs exhibited good external calibration curves and linearity with 0.99. The results showed that poly (vinylchloride) (PVC), poly (ethylene terephthalate) (PET), polyethylene (PE), and polyadiohexylenediamine (PA66) NPs could be detected in crop samples, although the accumulation levels associated with the various crops varied significantly. PVC (N.D.−954.3 mg kg−1, dry weight (DW)) and PE (101.3−462.9 mg kg−1, DW) NPs were the dominant components in the samples of all four crop species, while high levels of PET (414.3−1430.1 mg kg−1, DW) NPs were detected in cowpea samples. Furthermore, there were notable differences in the accumulation levels of various edible crop parts, such as stems (60.2%) > leaves (39.8%) in flowering cabbage samples and peas (58.8%) > pods (41.2%) in cowpea samples. This study revealed the actual extent of NP contamination in different types of crops and provided crucial reference data for future research.

Integrating a Multiple Isotopologue Reaction-Monitoring Technique and LC-MS/MS for Quantitation of Small Molecules: Ten Mycotoxins in Cereals as an Example.

Accurate quantification of mycotoxin in cereals is crucial for ensuring food safety and human health. However, the preparation of traditional multisample external calibration curves (MSCCs) is labor-intensive and error-prone. Here, a multiple isotopologue reaction-monitoring (MIRM)-LC-MS/MS method for accurate quantitation of ten major mycotoxins in cereals was successfully developed and validated, where a novel one-sample multipoint calibration curve (OSCC) strategy is used instead of MSCCs. The OSCC can be established by examining the correlation between the calculated theoretical isotopic abundances and the measured abundance across various MIRM channels. In comparison to the MSCC, the OSCC strategy exhibits outstanding performance including superior selectivity, accuracy (78.4-108.6%), and precision (<12.5%). Furthermore, the proposed OSCC-MIRM-LC-MS/MS method was successfully applied to investigate mycotoxin contamination in cereal samples in China. Considering the advantages of simplified workflows and improved throughput, the OSCC-MIRM-LC-MS/MS methodology holds great promise for accurately quantifying chemical contaminants in foods.

Tailored risk assessment and forecasting in intermittent claudication.

Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the first line of treatment in intermittent claudication, but implementation challenges and poor patient compliance lead to significant variation in management and therefore outcomes. The development of a precise risk stratification tool is proposed through a machine-learning algorithm that aims to provide personalized outcome predictions for different management strategies. Feature selection was performed using the least absolute shrinkage and selection operator method. The model was developed using a bootstrapped sample based on patients with intermittent claudication from a vascular centre to predict chronic limb-threatening ischaemia, two or more revascularization procedures, major adverse cardiovascular events, and major adverse limb events. Algorithm performance was evaluated using the area under the receiver operating characteristic curve. Calibration curves were generated to assess the consistency between predicted and actual outcomes. Decision curve analysis was employed to evaluate the clinical utility. Validation was performed using a similar dataset. The bootstrapped sample of 10 000 patients was based on 255 patients. The model was validated using a similar sample of 254 patients. The area under the receiver operating characteristic curves for risk of progression to chronic limb-threatening ischaemia at 2 years (0.892), risk of progression to chronic limb-threatening ischaemia at 5 years (0.866), likelihood of major adverse cardiovascular events within 5 years (0.836), likelihood of major adverse limb events within 5 years (0.891), and likelihood of two or more revascularization procedures within 5 years (0.896) demonstrated excellent discrimination. Calibration curves demonstrated good consistency between predicted and actual outcomes and decision curve analysis confirmed clinical utility. Logistic regression yielded slightly lower area under the receiver operating characteristic curves for these outcomes compared with the least absolute shrinkage and selection operator algorithm (0.728, 0.717, 0.746, 0.756, and 0.733 respectively). External calibration curve and decision curve analysis confirmed the reliability and clinical utility of the model, surpassing traditional logistic regression. The machine-learning algorithm successfully predicts outcomes for patients with intermittent claudication across various initial treatment strategies, offering potential for improved risk stratification and patient outcomes.

Multi-transcriptomics analysis of microvascular invasion-related malignant cells and development of a machine learning-based prognostic model in hepatocellular carcinoma.

Microvascular invasion (MVI) stands as a pivotal pathological hallmark of hepatocellular carcinoma (HCC), closely linked to unfavorable prognosis, early recurrence, and metastatic progression. However, the precise mechanistic underpinnings governing its onset and advancement remain elusive. In this research, we downloaded bulk RNA-seq data from the TCGA and HCCDB repositories, single-cell RNA-seq data from the GEO database, and spatial transcriptomics data from the CNCB database. Leveraging the Scissor algorithm, we delineated prognosis-related cell subpopulations and discerned a distinct MVI-related malignant cell subtype. A comprehensive exploration of these malignant cell subpopulations was undertaken through pseudotime analysis and cell-cell communication scrutiny. Furthermore, we engineered a prognostic model grounded in MVI-related genes, employing 101 algorithm combinations integrated by 10 machine-learning algorithms on the TCGA training set. Rigorous evaluation ensued on internal testing sets and external validation sets, employing C-index, calibration curves, and decision curve analysis (DCA). Pseudotime analysis indicated that malignant cells, showing a positive correlation with MVI, were primarily concentrated in the early to middle stages of differentiation, correlating with an unfavorable prognosis. Importantly, these cells showed significant enrichment in the MYC pathway and were involved in extensive interactions with diverse cell types via the MIF signaling pathway. The association of malignant cells with the MVI phenotype was corroborated through validation in spatial transcriptomics data. The prognostic model we devised demonstrated exceptional sensitivity and specificity, surpassing the performance of most previously published models. Calibration curves and DCA underscored the clinical utility of this model. Through integrated multi-transcriptomics analysis, we delineated MVI-related malignant cells and elucidated their biological functions. This study provided novel insights for managing HCC, with the constructed prognostic model offering valuable support for clinical decision-making.

Combining novel theoretical and experimental insights into the physical electrochemistry of gold and carbon-based electrodes and baicalein flavonoid sensing

Here, both the theoretical and experimental behavior of the flavonoid baicalein (BAI) was evaluated, considering the physical electrochemistry of carbon materials. For this purpose, experimental data based on cyclic voltammetry (CV) measurements were collected using the glassy carbon electrode (GCE) in comparison with a metallic gold electrode (GE). From a theoretical point of view, the focus was on modeling the BAI interaction with substrates through the use of graphene (its functionalizations) and a gold closed-packed slab model to understand the molecular adsorption process and verify important issues related to the analyte-substrate interaction at the atomistic level. The high-level quantum-mechanical calculations were based on density functional theory (DFT). From an analytical point of view, GCE and GE were tested for method development using differential pulse voltammetry (DPV). Regarding analytical purposes, limit of detection (LOD) values of 31.5 nmol L−1 and 68.1 nmol L−1 were estimated for GCE and GE, respectively. The standard addition method was explored with GE, while an external calibration curve approach was applied to overcome surface saturation on GCE. We found two elucidating points that may compromise electroanalysis: (i) in physisorption, the analyte adsorption occurs in parallel orientation to both substrate types, carbon- and gold-based ones, which compromises the useful electrode area; and (ii) in chemisorption, the presence of defects on the carbon-based electrode surface is responsible for a strong interaction that leads to BAI dissociation and electrode poisoning. Thus, the theoretical results support the mechanistic investigation, and novel insights into the electrochemical sensing of flavonoids are presented here.

Development of an enhanced analytical method utilizing pepper matrix as an analyte protectant for sensitive GC‒MS/MS detection of dimethipin in animal-based food products.

Herein, an analytical method using gas chromatography-tandem mass spectrometry (GC‒MS/MS) was devised to detect the presence of the troublesome pesticide dimethipin in various animal-based food products, including chicken, pork, beef, eggs, and milk. The injection port was primed with a matrix derived from pepper leaves that acts as an analyte protectant (AP) to safeguard the target compound from thermal degradation during gas chromatography. The presence of AP resulted in a remarkable limit of quantification of 0.005 mg/kg for dimethipin in five matrices. Three different versions (original, EN, and AOAC) of the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) method were compared for dimethipin extraction, with a double-layer solid-phase extraction (SPE) cartridge utilized for matrix purification. A seven-point external calibration curve was established for dimethipin in the five matrices, demonstrating excellent linearity with determination coefficients (R2) ≥ 0.998. The developed quantitative method was validated by fortifying each matrix with three different concentrations of standard dimethipin, and the average recovery fell within the acceptable range outlined in the CODEX guidelines (ranging from 88.8% to 110.0%), with a relative standard deviation (RSD) of ≤ 11.97%. This method effectively addresses the challenge of analyzing dimethipin and can therefore be used as a routine monitoring tool for dimethipin across various matrices.

Highly sensitive and selective dopamine determination using a photoelectrochemical sensor based on the BiVO4 and surface molecularly imprinted poly(acrylic acid-co-TRIM) on vinyl functionalized carbon nanotubes

This paper describes the development of a new photoelectrochemical (PEC) sensor for dopamine (DA) determination based on the nanocomposite constituted of a molecularly imprinted poly(acrylic acid-co-TRIM) functionalized onto MWCNT with vinyl groups (MWCNT-MIP) physically mixed with monoclinic bismuth vanadate. Synthesized polymeric materials were characterized by FT-IR, TGA, SEM and TEM. The sensor based on glassy carbon electrode modified with BiVO4/MWCNT-MIP provided a limit of detection (LOD) of 31.1 nmol/L and a linear range from 103.6 to 2500 nmol/L. The proposed sensor exhibited selectivity to DA in competitive analysis with epinephrine, norepinephrine, ascorbic acid, citric acid, uric acid, creatine and urea, and it was applied for analysis of DA in simulated urine samples using external calibration curve with accuracy attested by recovery values between 91 and 106 %.

Comparison of quantitative PCR and digital PCR assays for quantitative detection of infectious bronchitis virus (IBV) genome

The quantitative polymerase chain reaction (qPCR) technique is an extensively used molecular tool for the detection and quantification of viral genome load. However, since the qPCR assay is a relative quantification method that relies on an external calibration curve it has a lower assay precision and sensitivity. The digital PCR (dPCR) technique is a good alternative to the qPCR assay as it offers highly precise and direct quantification of viral genome load in samples. In this study, performance characteristics such as the quantification range, sensitivity, precision, and specificity of the dPCR technique was compared to qPCR technique for the detection and quantification of IBV genome loads in serial dilutions of IBV positive plasmid DNA, and IBV infected chicken tissue and swab samples. The quantification range of the qPCR assay was wider than that of the dPCR assay, however dPCR had a higher sensitivity compared to qPCR. The precision of quantification of DNA in plasmid samples in terms of repeatability and reproducibility of results was higher when using the dPCR assay compared to qPCR assay. The quantification results of IBV genome load in infected samples by the qPCR and dPCR assays displayed a high correlation. Hence, our findings suggest that dPCR could be used in avian virology research for improved precision and sensitivity in detection and quantification of viral genome loads.

Constructing a nomogram based on the distribution of thyroid nodules and suspicious lateral cervical lymph nodes in fine-needle aspiration biopsies to predict metastasis in papillary thyroid carcinoma.

Elevated concentrations of thyroglobulin eluent is a risk factor for lateral cervical lymph node metastasis (LLNM) in patients with papillary thyroid cancer (PTC). We aimed to develop a practical nomogram based on the distribution of thyroid nodules and the presence of suspicious lateral cervical lymph nodes in fine-needle aspiration biopsies (LN-FNABs), including the cytopathology and the suspicious lateral cervical lymph node (LLN) thyroglobulin eluent (Tg), to predict the possibility of LLNM preoperatively in patients with PTC. The clinical data of PTC patients who were admitted to the Third Affiliated Hospital of Soochow University from January 2022 to May 2023 to undergo fine-needle aspiration biopsy (FNAB) were included in this study. A total of 208 patients in 2022 served as the training set (70%), and 89 patients in 2023 served as the validation set (30%). The clinical characteristics and LN-FNAB results were collected to determine the risk factors of LLNM. A preoperative nomogram was developed for predicting LLNM based on the results of the univariate and multivariate analyses. Internal calibration, external calibration, and decision curve analysis (DCA) were performed for these models. The multivariate logistic regression analysis showed that the maximum thyroid nodule diameter (Odds Ratio (OR) 2.323, 95% CI 1.383 to 3.904; p = 0.001), Tg level (OR 1.007, 95% CI 1.005 to 1.009; p = 0.000), Tg divided by serum thyroglobulin, (Tg/sTg) [odds ratio (OR) 1.005, 95% CI 1.001 to 1.008; p = 0.009], and cytopathology (OR 9.738, 95% CI 3.678 to 25.783; p = 0.000) (all p < 0.05) had a significant impact on the LLNM of patients with suspicious LLNs. The nomogram showed a better predictive value in both the training cohort [area under the curve, (AUC) 0.937, 95% CI 0.895 to 0.966] and the validation cohort (AUC 0.957, 95% CI 0.892 to 0.989). The nomogram also showed excellent internal and external calibration in predicting LLNM. According to the DCA, the diagnostic performance of this model was dependent on the following variables: maximum thyroid nodule diameter, Tg level, Tg/sTg, and cytopathology. Based on the aforementioned risk factors, we believe that it is necessary to establish a personalized LLNM model for patients with PTC. Using this practical nomogram, which combines clinical and Tg risk factors, surgeons could accurately predict the possibility of LLNM preoperatively. The nomogram will also help surgeons to establish personalized treatment plans before surgery.

A new direct extraction by gas-chromatography with flame ionization detector coupled to head space method for the determination of alcohol content of high matrix wine products

Detection, identification, and quantitation of alcohol in any matrix rich medium is a common practice although sample preparation is inevitable and time consuming. A sensitive, precise and ultimately wide range method for detection, identification and quantification of main content/residual/impurity alcohols without any matrix interference that can be used for production phase quality control, pharmaceutical and/or bio-technological refinement or toxicological evaluation and for forensics is always needed. Even for quality control also for toxicological considerations, ethanol (EtOH) and very similar compound methanol has to be detected and identified definitely becomes vital. However, with the fermented products, the matrix becomes a challenging process, makes the methods inefficient or more extraction methods have to be implanted. Here we propose a new simple and reliable direct extraction method has been developed for the determination of alcohol content of high matrix wine products using the gas-chromatography with flame ionization detector coupled to head space. The method was developed with a rich and complex component mixture of fermented alcoholic beverages (wine) with very high matrix effects. Isopropanol (IPA) was preferred as an internal standard, and Triton X-100 (TX-100) was used as diluting solution in this method. The amount of TX-100, extraction temperature, and the total volume of solution in head space vial (20 mL) were optimized. 2.5% TX-100, 80 °C extraction temperature, and 2.0 ml of total volume were used as optimum condition. Stationary phase was the fused silica, Agilent J&amp;W DB-624 column (30 m x 320 m x 1.8 m) and Helium was used as a mobile phase. GC oven temperature programme was 40C (5 min), 5C/min ramp to 60C (0 min) and 30C/min to 150C (1 min). Performance of the method was assessed by evaluating the recovery, accuracy, precision, linearity, limits of detection (LOD) and limit of quantification (LOQ). Calibration curve was drawn between the concentration of 2.5% to 15.0% EtOH (y = 1.572x – 0.702, R² = 0.9960, y; the ratio of peak area of EtOH to IPA, x: EtOH%). The slopes of standard addition and external calibration curve were statistically same. Recovery of the method was 97.5 ± 3.5 for tree different concentrations and the precision was %5.8 (n= 11). LOD and LOQ were calculated as 0.80% and 2.5%, respectively. The proposed method has a potential for application into the industry and academia with determination of the alcohol content/residual/impurity and also check the quality and content of the fermented medium without the effect of matrix.