Matrix effects in ultra-high performance supercritical fluid chromatography-mass spectrometry analysis of vitamin E in plasma: The effect of sample preparation and data processing.

The approaches to matrix effects determination and reduction in ultra-high performance supercritical fluid chromatography with mass spectrometry detection have been evaluated in this study using different sample preparation methods and investigation of different calibration models. Five sample preparation methods, including protein precipitation, liquid-liquid extraction, supported liquid extraction, and solid phase extraction based on both "bind and elute" and "interferent removal" modes, were optimized with an emphasis on the matrix effects and recovery of 8 forms of vitamin E, including α-, ß-, γ-, and δ-tocopherols and tocotrienols, from plasma. The matrix effect evaluation included the use and comparison of external and internal calibration using three models, i.e., least square with no transformation and no weighting (1/x0), with 1/x2 weighting, and with logarithmic transformation. The calibration model with logarithmic transformation provided the lowest %-errors and the best fits. Moreover, the type of the calibration model significantly affected not only the fit of the data but also the matrix effects when evaluating them based on the comparison of calibration curve slopes. Indeed, based on the used calibration model, the matrix effects calculated from calibration slopes ranged from +92% to - 72% for α-tocopherol and from -77% to +19% in the case of δ-tocotrienol. Thus, it was crucial to calculate the matrix effect by Matuszewski's post-extraction approach at six concentration levels. Indeed, a strong concentration dependence was observed for all optimized sample preparation methods, even if the stable isotopically labelled internal standards (SIL-IS) were used for compensation. The significant differences between individual concentration levels and compounds were observed, even when the tested calibration range covered only one order of magnitude. In methods with wider calibration ranges, the inappropriate use of calibration slope comparison instead of the post-extraction addition approach could result in false negative results of matrix effects. In the selected example of vitamin E, solid-phase extraction was the least affected by matrix effects when used in interferent removal mode, but supported liquid extraction resulted in the highest recoveries. We showed that the calibration model, the use of a SIL-IS, and the analyte concentration level played a crucial role in the matrix effects. Moreover, the matrix effects can significantly differ for compounds with similar physicochemical properties and close retention times. Thus, in all bioanalytical applications, where different analytes are typically determined in one analytical run, it is necessary to carefully select the data processing in addition to the method for the sample preparation, SIL-IS, and chromatography.

Construction of a Calibration Curve for Lycopene on a Liquid-Handling Platform─Wider Lessons for the Development of Automated Dilution Protocols.

Liquid-handling is a fundamental operation in synthetic biology─all protocols involve one or more liquid-handling operations. It is, therefore, crucial that this step be carefully automated in order to unlock the benefits of automation (e.g., higher throughput, higher replicability). In the paper, we present a study, conducted at the London Biofoundry at SynbiCITE, that approaches liquid-handling and its reliable automation from the standpoint of the construction of the calibration curve for lycopene in dimethyl sulfoxide (DMSO). The study has important practical industrial applications (e.g., lycopene is a carotenoid of industrial interest, DMSO is a popular extractant). The study was also an effective testbed for the automation of liquid-handling. It necessitated the development of flexible liquid-handling methods, which can be generalizable to other automated applications. In addition, because lycopene/DMSO is a difficult mix, it was capable of revealing issues with automated liquid-handling protocols and stress-testing them. An important component of the study is the constraint that, due to the omnipresence of liquid-handling steps, errors should be controlled to a high standard. It is important to avoid such errors propagating to other parts of the protocol. To achieve this, a practical framework based on regression was developed and utilized throughout the study to identify, assess, and monitor transfer errors. The paper concludes with recommendations regarding automation of liquid-handling, which are applicable to a large set of applications (not just to complex liquids such as lycopene in DMSO or calibration curves).

Construction of a nomogram model for predicting the outcome of debulking surgery for ovarian cancer on the basis of clinical indicators.

Objective: This study aimed to investigate the risk factors affecting satisfaction with debulking surgery for ovarian cancer and establish a preoperative clinical predictive model. Methods: Clinical data from 131 patients who underwent ovarian cancer debulking surgery at Jiangnan University Affiliated Hospital between 2016 and 2022 were collected. Patients were randomly separated into an experimental group and a control group in a 7:3 ratio. On the basis of intraoperative outcomes, patients were grouped as either surgery-satisfactory or surgery-unsatisfactory. Clinical indicators were compared through single-factor analysis between groups. Significantly different factors (p < 0.1) were further analyzed through multivariate logistic regression. A predictive nomogram model was developed and validated by receiver operating characteristic (ROC), calibration, and clinical decision curves. Results: Single-factor analysis revealed the significance of factors such as albumin levels, alkaline phosphatase (ALP), ECOG scores, CA125, HE4, and lymph node metastasis. Multivariate regression analysis identified albumin levels, ALP, ECOG scores, HE4, and lymph node metastasis as independent risk factors for satisfactory surgical outcomes in patients with ovarian cancer undergoing debulking surgery as (p < 0.05). A clinical predictive model was successfully constructed. ROC curves showed AUC values of 0.818 and 0.796 for the experimental and validation groups, respectively. Internal validation through the bootstrap method confirmed the model's fit in both groups. Meanwhile, the clinical decision curve demonstrated the model's high utility. Conclusion: Independent risk factors associated with satisfactory tumor reduction in patients with ovarian cancer undergoing debulking surgery included decreased albumin levels, ALP > 137 U/L, ECOG = 1 score, HE4 > 140 pmol/L, and lymph node metastasis. Constructing a clinical predictive model through logistic regression analysis enables individualized testing and maximizes clinical benefits.

Development and validation of a lung metastases-predicting nomogram for intermediate- to high-risk differentiated thyroid carcinoma patients.

Aim: This research aimed to construct a clinical model for forecasting the likelihood of lung metastases in differentiated thyroid carcinoma (DTC) with intermediate- to high-risk. Methods: In this study, 375 DTC patients at intermediate to high risk were included. They were randomly divided into a training set (70%) and a validation set (30%). A nomogram was created using the training group and then validated in the validation set using calibration, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve. Results: The calibration curves demonstrated excellent consistency between the predicted and the actual probability. ROC analysis showed that the area under the curve in the training cohort was 0.865 and 0.845 in the validation cohort. Also, the DCA curve indicated that this nomogram had good clinical utility. Conclusion: A user-friendly nomogram was constructed to predict the lung metastases probability with a high net benefit.

[Box: see text].

A novel and simple method for measuring nano/microplastic concentrations in soil using UV-Vis spectroscopy with optimal wavelength selection.

A simple method for measuring the concentration of nano/microplastics (N/MPs) in soil, which is difficult owing to the size of the filter mesh and the resolution of the measuring instrument, was investigated. A spectrophotometer was used for the measurements and polystyrene particles were used as the N/MP samples. When measuring N/MP concentrations in soil suspensions, absorbance was measured at two wavelengths, and the best combination of wavelengths for measurement was extracted because soil particles and leached components interfere with N/MP absorbance. A wavelength combination of 220-260 nm and 280-340 nm was found to be suitable for a variety of soils. As N/MPs are adsorbed on the surface of soil particles and precipitate with soil particles in suspension, a calibration curve was created between the concentration of N/MPs in the soil suspension and the N/MP content in the soil. The calibration curve showed a linear relationship, allowing for the estimation of the concentration of N/MPs in the soil. Although other N/MP materials, such as polyethylene and polyethylene terephthalate, must also still be considered and tested, this simple method has the potential to measure N/MPs in various types of soil.

Quadratic resampling method for the long-term γ-ray background spectra using an HPGe detector.

We developed a novel quadratic resampling method for summing up γ-ray spectra with different calibration parameters. We investigated a long-term environmental background γ-ray spectrum by summing up 114 spectra measured using a 30% HPGe detector between 2017 and 2021. Gain variations in different measurement periods shift γ-ray peak positions by a fractional pulse-height bin size up to around 2 keV. The resampling method was applied to measure low-level background γ-ray peaks in the γ-ray spectrum in a wide energy range from 50 keV to 3 MeV. We additionally document temporal variations in the activities of major γ-ray peaks, such as 40K (1461 keV), 208Tl (2615 keV), and other typical nuclides, along with contributions from cosmic rays. The normal distribution of γ-ray background count rates, as evidenced by quantile-quantile plots, indicates consistent data collection throughout the measurement period. Consequently, we assert that the quadratic resampling method for accumulating γ-ray spectra surpasses the linear method (Bossew, 2005) in various aspects.

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.

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.

Characteristic Evaluation of Gas Chromatography with Different Detectors for Accurate Determination of Sulfur Hexafluoride.

Sulfur hexafluoride (SF6), which survives in the atmosphere for an extremely long period of time, is the most potent greenhouse gas regulated under the Kyoto Protocol. So, the accurate monitoring of atmospheric SF6 plays an important role in the study of the control policies for reducing greenhouse gas emissions. The instruments for SF6 measurement are typically calibrated using certified reference materials. The concentrations of the commercially available SF6 reference materials usually have a broad range, from 1 µmol/mol to 6000 µmol/mol. Some characteristics including sensitivity, linear range, relative standard deviation, and accuracy are crucial for the determination of SF6 in such a broad concentration range. Therefore, the selection of a proper detector for the accurate determination of SF6 with such a broad range is extremely important to establish a gas chromatography (GC) method for developing SF6 reference materials. In this paper, several typical GC methods with different detectors, including a thermal conductivity detector (TCD), a pulsed discharge helium ionization detector (PDHID), and a flame photometric detector (FPD), were carefully established for the accurate determination of SF6 with different concentrations. The results show that an FPD detector has a relatively narrow linearity range, thus a quadratic equation should be established for building a calibration curve. The PDHID and TCD have good linearity with coefficients of 1.0000 in the concentration range of 10-100 µmol/mol (using a PDHID), and 100-1000 µmol/mol (using a TCD), respectively. Further considering the measurement errors of indication results, the PDHID is suitable for SF6 measurement when the concentrations are below 100 µmol/mol, whereas the TCD is suitable for SF6 measurement when the concentrations are over 100 µmol/mol. These results provide useful guidance in choosing an appropriate GC detector for the accurate determination of SF6, which are especially very helpful for developing SF6 reference materials.

Interlaboratory evaluation of LC-MS-based biomarker assays.

Validation of biomarker assays is crucial for effective drug development and clinical applications. Interlaboratory reproducibility is vital for reliable comparison and combination of data from different centers. This review summarizes interlaboratory studies of quantitative LC-MS-based biomarker assays using reference standards for calibration curves. The following points are discussed: trends in reports, reference and internal standards, evaluation of analytical validation parameters, study sample analysis and normalization of biomarker assay data. Full evaluation of these parameters in interlaboratory studies is limited, necessitating further research. Some reports suggest methods to address variations in biomarker assay data among laboratories, facilitating organized studies and data combination. Method validation across laboratories is crucial for reducing interlaboratory differences and reflecting target biomarker responses.

Introducing the f0% method: a reliable and accurate approach for qPCR analysis.

BACKGROUND: qPCR is a widely used technique in scientific research as a basic tool in gene expression analysis. Classically, the quantitative endpoint of qPCR is the threshold cycle (CT) that ignores differences in amplification efficiency among many other drawbacks. While other methods have been developed to analyze qPCR results, none has statistically proven to perform better than the CT method. Therefore, we aimed to develop a new qPCR analysis method that overcomes the limitations of the CT method. Our f0% [eff naught percent] method depends on a modified flexible sigmoid function to fit the amplification curve with a linear part to subtract the background noise. Then, the initial fluorescence is estimated and reported as a percentage of the predicted maximum fluorescence (f0%). RESULTS: The performance of the new f0% method was compared against the CT method along with another two outstanding methods-LinRegPCR and Cy0. The comparison regarded absolute and relative quantifications and used 20 dilution curves obtained from 7 different datasets that utilize different DNA-binding dyes. In the case of absolute quantification, f0% reduced CV%, variance, and absolute relative error by 1.66, 2.78, and 1.8 folds relative to CT; and by 1.65, 2.61, and 1.71 folds relative to LinRegPCR, respectively. While, regarding relative quantification, f0% reduced CV% by 1.76, 1.55, and 1.25 folds and variance by 3.13, 2.31, and 1.57 folds regarding CT, LinRegPCR, and Cy0, respectively. Finally, f0% reduced the absolute relative error caused by LinRegPCR by 1.83 folds. CONCLUSIONS: We recommend using the f0% method to analyze and report qPCR results based on its reported advantages. Finally, to simplify the usage of the f0% method, it was implemented in a macro-enabled Excel file with a user manual located on https://github.com/Mahmoud0Gamal/F0-perc/releases .

Construction and validation of prediction model for diabetic retinopathy / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To analyze and screen influencing factors of diabetic patients complicated with retinopathy, and establish and validate prediction model of nomogram.METHODS: A total of 1 252 patients from the Diabetes Complications Early Warning Dataset of the National Population Health Data Archive(PHDA)between January 2013 to January 2021 were selected and randomly divided into a modeling group(n=941)and a validation group(n=311). Univariate analysis, LASSO regression and Logistic regression analysis were used to screen out the influencing factors of diabetic retinopathy, and a nomogram prediction model was established. The receiver operating characteristic curve, Hosmer-Lemeshow test and calibration curve were used to evaluate the model. The clinical benefit was evaluated by the decision curve analysis(DCA).RESULTS: Age, hypertension, nephropathy, systolic blood pressure(SBP), glycated hemoglobin(HbA1c), high-density lipoprotein cholesterol(HDL-C), and blood urea(BU)were the influencing factors of diabetic retinopathy. The area under the curve(AUC)of the modeling group was 0.792(95%CI: 0.763-0.821), and the AUC of the validation group was 0.769(95%CI: 0.716-0.822). The Hosmer-Lemeshow goodness of fit test and calibration curve suggested that the theoretical value of the model was in good agreement(modeling group: χ2=14.520, P=0.069; validation group: χ2=14.400, P=0.072). The DCA results showed that the threshold probabilities range was 0.09-0.89 for modeling group and 0.07-0.84 for the validation group, which suggested the clinical net benefit was higher.CONCLUSION: This study constructed a risk prediction model including age, hypertension, nephropathy, SBP, HbA1c, HDL-C, and BU. The model has a high discrimination and consistency, and can be used to predict the risk of diabetic retinopathy in patients with diabetes.

Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model.

BACKGROUD: The current diagnostic criteria for refractory Mycoplasma pneumoniae pneumonia (RMPP) among Mycoplasma pneumoniae Pneumonia (MPP) are insufficient for early identification, and potentially delayed appropriate treatment. This study aimed to develop an effective individualized diagnostic prediction nomogram for pediatric RMPP. METHODS: A total of 517 hospitalized children with MPP, including 131 with RMPP and 386 without RMPP (non-RMPP), treated at Lianyungang Maternal and Child Health Care Hospital from January 2018 to December 2021 were retrospectively enrolled as a development (modeling) cohort to construct an RMPP prediction nomogram. Additionally, 322 pediatric patients with MPP (64 with RMPP and 258 with non-RMPP, who were treated at the Affiliated Hospital of Xuzhou Medical University from June 2020 to May 2022 were retrospectively enrolled as a validation cohort to assess the prediction accuracy of model. Univariable and multivariable logistic regression analyses were used to identify RMPP risk factors among patients with MPP. Nomogram were generated based on these risk factors using the rms package of R, and the predictive performance was evaluated based on receiver operating characteristic (ROC) curves and using decision curve analysis (DCA). RESULTS: Multivariate analysis revealed five significant independent predictors of RMPP among patients with MPP: age (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.08-1.33, P = 0.038), fever duration (HR 1.34, 95%CI 1.20-1.50, P < 0.001), lymphocyte count (HR 0.45, 95%CI 0.23-0.89, P = 0.021), serum D-dimer (D-d) level (HR 1.70, 95%CI 1.16-2.49, P = 0.006), and pulmonary imaging score (HR 5.16, 95%CI 2.38-11.21, P < 0.001). The area under the ROC curve was 90.7% for the development cohort and 96.36% for the validation cohort. The internal and external verification calibration curves were almost linear with slopes of 1, and the DCA curve revealed a net benefit with the final predictive nomogram. CONCLUSION: This study proposes a predictive nomogram only based on five variables. The nomogram can be used for early identification of RMPP among pediatric patients with MPP, thereby facilitating more timely and effective intervention.

Pitfalls and potential of high-throughput plant phenotyping platforms.

Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.

One sample multi-point calibration curve as a novel approach for quantitative LC-MS analysis: The quantitation of six aflatoxins in milk and oat-based milk as an example.

Preparing of calibration curves are critical steps for accurate quantitative LC-MS bioanalysis. Traditional multi-sample external calibration curve (MSCC) is labor-intensive and prone to error. In this study, a novel strategy of one sample multi-point calibration curve (OSCC) using multiple isotopologue reaction monitoring (MIRM) was proposed and validated using LC-MS for the quantitation of six aflatoxins in milk and oat-based milk samples. The developed MIRM-OSCC methodology is comprehensively validated and the results indicated that the established method exhibits good performance in selectivity, sensitivity, accuracy and precision. Furthermore, the OSCC could realize sample dilution by monitoring the MIRM channel with less intensity for samples beyond the upper limit of quantification, without the need of sample dilution, which improves the assay throughput. Considering the advantages of excluding the MSCC preparation and sample dilution in OSCC, this strategy can be widely applied in various fields such as drugs, food safety and environmental analysis.

The clinical characteristics of pancreatic colloid carcinoma and the development and validation of its cancer-specific survival prediction nomogram.

Background: Pancreatic colloid carcinoma (CC) is a subtype of pancreatic ductal adenocarcinoma (DAC) with low incidence but high malignancy. Unfortunately, there is no consensus regarding the clinical features and prognostic factors associated with CC, and the prognosis is unpredictable. We aimed to assess the clinicopathological characteristics of this rare disease and develop a nomogram for predicting cancer-specific survival (CSS) in CC. Methods: We gathered comprehensive clinicopathological data from the Surveillance, Epidemiology, and End Results (SEER) database on 17,617 patients with DAC and 561 individuals with CC. Kaplan-Meier was used to plot each survival curve. Subsequently, we split the 561 patients with CC in a 7:3 split ratio between an internal training cohort (n=393) and an external validation cohort (n=168). The independent prognostic factors for CC patients in the training cohort were discovered using univariate and multivariate Cox regression analyses, and a nomogram was created. We assessed the nomogram's performance by using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results: The median for follow-up of CC patients was 15 months (range: 1-163 months), and the 1-, 3-, and 5-year CSS were 58.4%, 30.2% and 22.6%. For CC patients in the training cohort, age [hazard ratio (HR) =1.29; 95% confidence interval (CI): 1.00-1.65], sex (HR =0.64; 95% CI: 0.51-0.81), T3 stage (HR =2.21; 95% CI: 1.26-3.88), T4 stage (HR =2.76; 95% CI: 1.47-5.18), N1 stage (HR =1.29; 95% CI: 1.02-1.63), M1 stage (HR =1.60; 95% CI: 1.17-2.18), surgery (HR =0.30; 95% CI: 0.22-0.42), and radiotherapy (HR =0.76; 95% CI: 0.58-1.01) were the main predictors of the nomogram. The C-indexes of the training cohort and the validation cohort were 0.734 and 0.732, respectively. The 1-, 3-, and 5-year AUC values of the nomogram were predicted to be 0.827, 0.816, and 0.831 in the training cohort, 0.801, 0.841, and 0.835 in the validation cohort, respectively. Conclusions: Based on several clinical features, we established the first predictive model of CC. This nomogram could be used to guide treatment decisions in patients with CC.

A method for estimating the measurement uncertainty of carbon determination in low carbon and low alloy steels by spark atomic emission spectroscopy.

Due to its high analysis speed and acceptable accuracy, the Spark Atomic Emission Spectroscopy method has been used widely in steel production factories. Since the accuracy of the results dramatically impacts the quality of the final product, the measurement uncertainty must be calculated and presented along with the analysis results. The primary purpose of this paper is to present a method for estimating the measurement uncertainty of carbon determination in carbon and low alloy steel by spark atomic emission spectroscopy. After identifying the factors influencing the test results, we presented a method for quantification and calculation of each source contribution to the combined uncertainty using mathematical and statistical methods. Finally, the amount of expanded uncertainty is determined for a 95% of confidence level. It was found that the measurement uncertainty is mainly affected by the calibration curve, while the analyst error has no significant effect on the measurement uncertainty. The extended relative uncertainty of 0.01 was determined at the 95% confidence level with a coverage factor k = 2.

Estimation and Quantification of Toxic Metals in Hugely Consumed Chicken Livers by Advanced Diagnostic Approaches.

Contamination of toxins in chicken's liver is a serious concern for human health owing to related threats of cytotoxicity and general pathologies after their digestion. The quantitative investigations were accomplished by calibration curves plotted for all the detected toxins via typical samples arranged in the known concentrations in the chicken liver's matrix. The chicken liver samples were collected from the Khyber Pakhtunkhwa province of Pakistan and found to contain heavy metals like Cu, Mn, Ni, Pb, and Zn. The analytical estimations were performed under the suppositions of local thermodynamic equilibrium (LTE) in terms of optical thin plasma. The maximum concentrations (parts per million) of Cu, Mn, Ni, Pb, and Zn were as 2.87 ± 0.02 ppm, 7.80 ± 0.13 ppm, 2.84 ± 0.02 ppm, 4.00 ± 0.08, and 83.5 ± 2.10 ppm respectively. Abundance of Cu, and Pb was found considerably beyond the maximum accepted boundary of WHO. Likewise, the level of Ni exceeded the permitted bounds of WHO in samples 01 and 02. To validate our laser-induced breakdown spectroscopy investigation, we approximated the abundance of identical (duplicate) chicken livers through digesting the specimens in suitable solvents by a typical technique such as inductively coupled plasma/optical emission spectrometry (ICP/OES) and the results acquired were in outstanding harmony. Furthermore, the existence of detected toxins was also checked using energy dispersive X-ray spectroscopy (EDX). It is worth stating that larger amounts of Cu, Ni, and Pb in poultry may cause a severe hazard to customers which required security actions and precautions. Our findings are extremely important to make an awareness among the people due to associated health hazards after the digestion of toxins through chicken liver and to protect numerous human lives.

Novel one-point calibration strategy for high-throughput quantitation of microcystins in freshwater using LC-MS/MS.

Precise quantification of microcystins (MCs) in freshwater is crucial for environmental monitoring and human health. However, the preparation of traditional multi-sample external calibration curve (MSCC) is time consuming and prone to error. Here, a novel one-point calibration strategy including one sample multi-point calibration curve (OSCC) and in sample calibration curve (ISCC) is proposed for the quantitation of eight MCs in freshwater lakes using liquid chromatography tandem mass spectrometry (LC-MS/MS). The multiple isotopologue reaction monitoring (MIRM) of MCs and its 15N-labelled internal standards were used for OSCC and ISCC, respectively. The isotopic abundance of each MIRM channel could be calculated and measured accurately. Additionally, this strategy was comprehensively validated and showed good performance in selectivity, sensitivity, accuracy and precision as the traditional MSCC. Interestingly, OSCC could realize sample dilution by monitoring the less abundant MIRM transitions, while ISCC remove blank matrixes and generate calibration curve in each study samples. Furthermore, the proposed methodology was successfully applied to analyze several freshwater lake samples contaminated by MCs. Considering the advantages of excluding the MSCC preparation, simplified workflows and improved throughput, OSCC and ISCC will be favored for MCs monitoring and as an emerging approach in environmental pollutant control and prevention.

Novel automated identification and quantification database using liquid chromatography quadrupole time-of-flight mass spectrometry for quick, comprehensive, cheap and extendable organic micro-pollutant analysis in environmental systems.

In order to protect human health and the environment, highly efficient, low-cost, labor-saving, and green analysis of toxic chemicals are urgently required. To achieve this objective, we have developed a novel database-based automated identification and quantification system (AIQS) using LC-QTOF-MS. Since the AIQS uses retention times (RTs), exact MS and MS-MS spectra, and calibration curves of 484 chemicals registered in the database instead of the use of standards, the targets can be determined with low-cost in a short time. The AIQS uses Sequential Window Acquisition of All Theoretical Fragment-ion Spectra as an acquisition method by which we can obtain accurate MS and MS-MS spectra of all detectable substances in a sample with minimal interference from co-eluted peaks. Identification is certainly done using RTs, mass error, ion ratios (a precursor to two product ions), and accurate MS and MS-MS spectra. Consequently, the chance of misidentification is very low even in dirty samples. To examine the accuracy of the AIQS, two collaborative tests were conducted. The first test used 208 pesticide standards at two concentrations (10 and 100 ng mL-1) using 7 instruments, and showed that average trueness was 106 and 95.2%, respectively, with relative standard deviations of 90% of the test compounds below 30%. The second collaborative study involved 5 laboratories carrying out recovery tests on 200 pesticides using 10 river waters. The average recovery was 71.6%; this was 15% lower than that using purified water probably due to the matrix effects. The average relative standard deviation was 30% worse than that of measurement of the standards. Both the recovery and reproducibility, however, satisfied the criteria of Analytical Method Validity Guidelines, Ministry of Health, Labour and Welfare, Japan. Instrument detection limits of 96% of the registered compounds are below 10 pg. The AIQS allows for easy addition of new substances and retrospective analysis after their addition. The results applied to actual samples showed that the AIQS has sufficient identification and quantification performance as a target screening method for a large number of substances in environmental samples.