Amount Of Sample Research Articles

Plant-Derived and Synthetic Nicotine in E-Cigarettes: Is Differentiation with NMR Spectroscopy Possible?

To circumvent regulatory frameworks, many producers start to substitute plant-derived nicotine (tobacco-derived nicotine, TDN) by synthetic nicotine (tobacco-free nicotine, TFN) in e-cigarette products. Due to the higher costs of enantiomeric synthesis and purification of TFN, there is a need to develop an analytical method that clearly distinguishes between the two sources. To trace nicotine's origin, its enantiomeric purity can be postulated by 1H NMR spectroscopy using (R)-(-)-1,1'-binaphthyl-2,2'-diyl hydrogen phosphate (BNPPA) as a chiral complexing agent. Low-field (LF) NMR conditions were optimized for this purpose even using a small amount of e-liquid sample (limit of quantification 8 mg/mL nicotine). All investigated products were found to contain one isomer (most likely (S)-(-)-nicotine). A direct 13C NMR method at natural abundance has been validated to differentiate (S)-TDN and (S)-TFN in e-cigarettes produced using nicotine of different origin. The method is based on calculation of the relative 13C content of 10 C-positions of the nicotine molecule with intraday and interday precisions below <0.2%. The method was applied to 12 commercial e-cigarette products labeled as containing TDN and TFN. Principal component analysis (PCA) was applied to the relative peak areas to visualize the difference between studied products. The LF 1H NMR method is a good alternative to expensive high-field NMR to differentiate between a racemate mixture and single optical isomers, whereas only high-precision 13C NMR can be used to distinguish (S)-TDN and (S)-TFN in e-cigarettes after appropriate sample extraction.

Co-Extraction of DNA and RNA from Candida albicans Using a Chemical Method in Conjunction with Silicon Carbide with Few Cells

Objective: The study aimed to optimize protocols for the joint extraction of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) from 0.025 × 106 CFU of Candida albicans, targeting to overcome the challenges in the extraction of these genetic materials. Materials and methods: From this, treated silicon carbide (SiC) granules were added to fungal samples from methods 1, 2, and 3 obtained from aliquots of BHI or Sabouraud medium to cause cell lysis and enable the isolation of these macromolecules by phenol and chloroform. The concentration and integrity of the extracted nucleic acids were analyzed, respectively, by spectrophotometry using the A260/A280 ratios and 1% agarose gel electrophoresis. Results: Therefore, method 3 is the one that most comprises samples considered pure of both DNA and RNA, simultaneously. Furthermore, the presence of intact RNAs corresponding to the base pair size such as 5.8 S rRNA and tRNA was verified during electrophoresis, considering the particularities of RNA, which makes it very unstable and easily degraded. Conclusions: Thus, it results in a faster and simpler method in addition to obtain promising results using minimal amounts of biological sample and offering a valuable alternative for small laboratories to work with molecular biology.

A Validated Trigonelline-Based Method for the Standardization and Quality Control of Trigonella foenum-graecum L.

Background Fenugreek, or Trigonella foenum-graecum L, is an edible and medicinal plant of the Fabaceae family. Fenugreek seeds contain a variety of phytochemicals, including proteins, lipids, amino acids, vitamins, flavonoids, steroidal saponins, coumarin, and alkaloids. Trigonelline TG is a bioactive plant alkaloid initially extracted from fenugreek seeds. A substantial portion of fenugreek’s health benefits may rely on the presence of TG. This study addresses the research gap for a fast, green, and economical method for quantifying trigonelline (TG) in fenugreek. Methods Fenugreek seeds from various origins were extracted using three green solvents: acetone (ACt), ethanol (EtOH), and water (H2O). The UPLCMSMS method was developed and validated using a green mobile phase of H2O: EtOH, and an r2-value of 0.999 in the linearity range of 0.1-500 ppb was adopted. The method was validated with an accuracy of 98.6% for trace analysis of TG using a small amount (10 mg) of fenugreek samples from five different origins. Results The average extract yield (±SD) was 5.36±6.3, with the highest extract yield observed in H2O. The ESI (+ve) of the UPLCMSMS resulted in the fragmentation pattern (m/z) 138→94.10→92.05→78.20. The TG quantification revealed an average TG concentration of 181.4, with the highest amount of TG in H2O extract (392.7±132.4 ppb), followed by EtOH (91.9±83.3 ppb) and ACt (59.5±30.9 ppb). The TG amount observed in the validation step substantiated the efficiency and reproducibility of the developed method. Conclusions The method may be used as an effective tool for a green, rapid, economical, and eco-friendly extraction and quantification of TG in diverse matrices of pharmaceutical, cosmeceutical, herbal, and food products.

Microplastic and human health with focus on pediatric well-being: a comprehensive review and call for future studies.

Although humans are highly dependent on plastics from infancy to adolescence, these materials can degrade into ubiquitous microplastics (MPs) that affect individuals at every stage of life. However, information on the sources, mechanisms, detection techniques, and detrimental effects of MPs on children's health from infancy to adolescence is limited. Hence, here we identified and reviewed original research papers published in 2017-2023 across 11 database categories in PubMed, Google Scholar, Scopus, and Web of Science to improve our understanding of MPs with a focus on pediatric well-being. These studies found that milk and infant formulas are common sources of MP exposure in infants. Infant formula is the dominant source of MPs in babies, while plastic toys are a common source of MPs in toddlers. Adolescents are frequently exposed to MPs through the consumption of food contaminated with MPs and the use of plastics in food packaging. Water and air are sources of MP exposure in children from infancy through adolescence. This study thoroughly summarized how MP exposure in children of all ages causes cell damage and leads to adverse health effects such as cancer. With appropriate authorization from the relevant authorities, small amounts of human biological samples (10 g of feces) were collected from volunteers to assess the amounts of MPs in children with the aim of promoting pediatric well-being. The samples were then treated with Fenton's reagent, stored in glass jars, and filtered through nonplastic filters. Finally, MPs in children were quantified using stereomicroscopy and characterized using micro-Fourier transform infrared spectroscopy.

Semi-supervised CWGAN-GP modeling for AHU AFDD with high-quality synthetic data filtering mechanism

Supervised learning methods demonstrated high classification accuracy for air handling unit (AHU) automated fault detection and diagnosis (FDD) scenarios with well-shaped training datasets. However, for imbalanced training datasets, i.e., much less real-world fault training data samples against an enormous amount of normal data samples, the supervised learning-based methods failed to produce satisfactory FDD results. To address the above-mentioned issue, this study proposes a semi-supervised conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) to generate high-quality synthetic fault training samples. The semi-supervised learning-based AHU AFDD framework is completed by identifying high-quality synthetic fault samples and inserting them into the training pool iteratively. With different numbers of real-world fault samples, comparative experiments are conducted on datasets collected by ASHRAE project RP-1312 in the summer and winter seasons. The experimental results show that the proposed AFDD method has obvious advantages over the traditional method with limited numbers of real-world fault samples. Moreover, the proposed CWGAN-GP-SSL framework achieves superior AFDD performance compared to the existing GAN-based AHU AFDD method.

Predicting the productivity of fractured horizontal wells using few-shot learning

Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources. In recent years, machine learning (ML) models have emerged as a new approach for such studies. However, the scarcity of sufficient real data for model training often leads to imprecise predictions, even though the models trained with real data better characterize geological and engineering features. To tackle this issue, we propose an ML model that can obtain reliable results even with a small amount of data samples. Our model integrates the synthetic minority oversampling technique (SMOTE) to expand the data volume, the support vector machine (SVM) for model training, and the particle swarm optimization (PSO) algorithm for optimizing hyperparameters. To enhance the model performance, we conduct feature fusion and dimensionality reduction. Additionally, we examine the influences of different sample sizes and ML models for training. The proposed model demonstrates higher prediction accuracy and generalization ability, achieving a predicted R2 value of up to 0.9 for the test set, compared to the traditional ML techniques with an R2 of 0.13. This model accurately predicts the production of fractured horizontal wells even with limited samples, supplying an efficient tool for optimizing the production of unconventional resources. Importantly, the model holds the potential applicability to address similar challenges in other fields constrained by scarce data samples.

Synthesis of Functionalized Pyridine Modified Magnetic Nanoparticles for Utilization in Magnetic Solid-Phase Extraction of Tebuconazole and Propiconazole Followed by Quantification via High-Performance Liquid Chromatography-Ultraviolet Detection.

In the current study, a new magnetic sorbent (Fe3O4@2,6-diamino-4-(3,4-dihydroxyphenyl) pyridine-3,5-dicarbonitrile) was prepared as a new and efficient sorbent to be used in the magnetic solid-phase extraction method to extract tebuconazole and propiconazole as model compounds. In this regard, effective parameters such as pH of the sample solution, desorption volume, ionic strength, extraction time, desorption time, and amount of the magnetic sorbent were optimized using experimental design. In the optimal condition, the linear dynamic ranges for tebuconazole and propiconazole were between 0.5 and 200.0µg/L. Also, the limits of detection, limits of quantification, and relative standard deviations were 0.21-0.14µg/L, 0.49-0.45µg/L, and 1.3%-4.6%, respectively. The estimated enrichment factors for tebuconazole and propiconazole were 22 and 28, respectively. The absolute recovery (AR%) values for tebuconazole and propiconazole were also 67% and 84%, respectively. Finally, the proposed method was used for determination of tebuconazole and propiconazole in cucumber, tomato, orange, and lemon fruits. The obtained result confirmed that the presented method is capable for the analysis of the selected analytes in complicated matrices. In addition, the ARs% were in the range of 67%-84%. The result proved that the presented method can be an alternative to the extraction process of various pesticides.

A dual-aptamer sandwich assay by detection of c-reactive protein in synovial fluid on an integrated microfluidic system for diagnosis of periprosthetic joint infection

Diagnosing periprosthetic joint infection (PJI) in equivocal cases is always challenging. Synovial fluid (SF) C-reactive protein (CRP) levels have been reported as a surrogate to improve the diagnosis accuracy. This study reported a new integrated microfluidic system (IMS) designed to detect SF-CRP at a dynamic range of 1-50mg/L. The novel, magnetic bead-based, dual-aptamer assay with two CRP-specific aptamers, St1 and St2, enabled rapid and accurate CRP detection. This represents the first attempt to automatically perform the CRP dual-aptamer assay on a microfluidic chip quantifying CRP in SF with optical signals. CRP concentrations in fourteen clinical samples were accurately quantified by using the dual-aptamer assay on the developed IMS in an automatic format, suggesting the assay’s potential as a reliable diagnostic tool for SF-CRP detection and may serve as an armamentarium for PJI diagnosis. Notably, the assay was deemed superior to traditional enzyme-linked immunosorbent assay (ELISA) because it consumes a smaller amount of sample (only 40μL), requires fewer reagents (only 50μL of secondary detection aptamer and horseradish peroxidase), and provides quicker results (~2hours).

Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability

BackgroundCutaneous melanoma is the most aggressive form of skin cancer, responsible for most skin cancer-related deaths. Recent advances in artificial intelligence, jointly with the availability of public dermoscopy image datasets, have allowed to assist dermatologists in melanoma identification. While image feature extraction holds potential for melanoma detection, it often leads to high-dimensional data. Furthermore, most image datasets present the class imbalance problem, where a few classes have numerous samples, whereas others are under-represented.MethodsIn this paper, we propose to combine ensemble feature selection (FS) methods and data augmentation with the conditional tabular generative adversarial networks (CTGAN) to enhance melanoma identification in imbalanced datasets. We employed dermoscopy images from two public datasets, PH2 and Derm7pt, which contain melanoma and not-melanoma lesions. To capture intrinsic information from skin lesions, we conduct two feature extraction (FE) approaches, including handcrafted and embedding features. For the former, color, geometric and first-, second-, and higher-order texture features were extracted, whereas for the latter, embeddings were obtained using ResNet-based models. To alleviate the high-dimensionality in the FE, ensemble FS with filter methods were used and evaluated. For data augmentation, we conducted a progressive analysis of the imbalance ratio (IR), related to the amount of synthetic samples created, and evaluated the impact on the predictive results. To gain interpretability on predictive models, we used SHAP, bootstrap resampling statistical tests and UMAP visualizations.ResultsThe combination of ensemble FS, CTGAN, and linear models achieved the best predictive results, achieving AUCROC values of 87% (with support vector machine and IR=0.9) and 76% (with LASSO and IR=1.0) for the PH2 and Derm7pt, respectively. We also identified that melanoma lesions were mainly characterized by features related to color, while not-melanoma lesions were characterized by texture features.ConclusionsOur results demonstrate the effectiveness of ensemble FS and synthetic data in the development of models that accurately identify melanoma. This research advances skin lesion analysis, contributing to both melanoma detection and the interpretation of main features for its identification.

Leveraging the potential of 1.0-mm i.d. columns in UHPLC-HRMS-based untargeted metabolomics.

Untargeted metabolomics UHPLC-HRMS workflows typically employ narrowbore 2.1-mm inner diameter (i.d.) columns. However, the wide concentration range of the metabolome and the need to often analyze small sample amounts poses challenges to these approaches. Reducing the column diameter could be a potential solution. Herein, we evaluated the performance of a microbore 1.0-mm i.d. setup compared to the 2.1-mm i.d. benchmark for untargeted metabolomics. The 1.0-mm i.d. setup was implemented on a micro-UHPLC system, while the 2.1-mm i.d. on a standard UHPLC, both coupled to quadrupole-orbitrap HRMS. On polar standard metabolites, a sensitivity gain with an average 3.8-fold increase over the 2.1-mm i.d., along with lower LOD (LODavg 1.48ng/mL vs. 6.18ng/mL) and LOQ (LOQavg 4.94ng/mL vs. 20.60ng/mL), was observed. The microbore method detected and quantified all metabolites at LLOQ with respect to 2.1, also demonstrating good repeatability with lower CV% for retention times (0.29% vs. 0.63%) and peak areas (4.65% vs. 7.27%). The analysis of various samples, in both RP and HILIC modes, including different plasma volumes, dried blood spots (DBS), and colorectal cancer (CRC) patient-derived organoids (PDOs), in full scan-data dependent mode (FS-DDA) reported a significant increase in MS1 and MS2 features, as well as MS/MS spectral matches by 38.95%, 39.26%, and 18.23%, respectively. These findings demonstrate that 1.0-mm i.d. columns in UHPLC-HRMS could be a potential strategy to enhance coverage for low-amount samples while maintaining the same analytical throughput and robustness of 2.1-mm i.d. formats, with reduced solvent consumption.

Analysis of wood anatomy via high-resolution micro-MRI and micro-CT: first approaches and results from Pozzo di Bellafonte (Montaione, Florence, Italy)

Several artefacts were found during the excavation performed at “Pozzo di Bellafonte”, located in an archaeological area in the province of Florence (Italy). The material found in the archaeological site included a large amount of wood samples; therefore, a method for understanding the positioning, the shape and the orientation of the wood sample vessels was necessary for identification of botanical taxa. In recent years, the study of ancient remains has benefited greatly from the use of imaging techniques commonly used for medical diagnosis and biomedical research, such as the micro-Computed Tomography (micro-CT) and high-field micro-Magnetic Resonance Imaging (micro-MRI). In this paper such imaging methodologies were employed for assessing the internal structure of representative wood samples extracted from the above mentioned. The complementarity of micro-CT and micro-MRI for the investigation of wood specimen anatomy is also discussed, by underlining what micro-MRI provides as compared with micro-CT and by proposing a tool constituted by the combination of micro-CT/micro-MRI for the study of such wood samples in a multimodal approach.

Statistical monitoring applied to data science in classification: continuous validation in predictive models

PurposeThis study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.Design/methodology/approachThe research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.FindingsValidation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.Originality/valueThe main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

High-throughput capture and in situ protein analysis of extracellular vesicles by chemical probe-based array.

Extracellular vesicles (EVs) are small particles with phospholipid bilayers that carry a diverse range of cargoes including nucleic acids, proteins and metabolites. EVs have important roles in various cellular processes and are increasingly recognized for their ubiquitous role in cell-cell communications and potential applications in therapeutics and diagnostics. Although many methods have been developed for the characterization and measurement of EVs, analyzing them from biofluids remains a challenge with regard to throughput and sensitivity. Recently, we introduced an approach to facilitate high-throughput analysis of EVs from trace amounts of sample. In this method, an amphiphile-dendrimer supramolecular probe (ADSP) is coated onto a nitrocellulose membrane for array-based capture and to enable an in situ immunoblotting assay. Here, we describe the protocol for our array-based method of EV profiling. We describe an enhanced version of the method that incorporates an automated printing workstation, ensuring high throughput and reproducibility. We further demonstrate the use of our array to profile specific glycosylations on the EV surface using click chemistry of an azide group introduced by metabolic labeling. In this protocol, the synthesis of ADSP and the fabrication of ADSP nitrocellulose membrane array can be completed on the same day. EVs are efficiently captured from biological or clinical samples through a 30-min incubation, followed by an immunoblotting assay within a 3-h window, thus providing a high-throughput platform for EV isolation and in situ targeted analysis of EV proteins and their modifications.

MA08.08 Association of FDG-PET/CT Findings with Sufficient Amount of Tissue Samples for Gene Panel Testing in TBB/TBNA

MA08.08 Association of FDG-PET/CT Findings with Sufficient Amount of Tissue Samples for Gene Panel Testing in TBB/TBNA

Zinc determination in common beans by pXRF: An easy and versatile calibration strategy applied to biofortification studies

Zinc deficiency is observed in millions of individuals, especially in underdeveloped countries. Therefore, agronomical strategies have been implemented to mitigate this public health issue, mainly by the biofortification of staple food crops, e.g., common beans, one of the most consumed leguminous grains worldwide. Accurate Zn determination by an analytical method is one of the most relevant steps in these initiatives. This study evaluated three calibration methods for Zn determination by portable X-ray Fluorescence Spectrometry (pXRF) in pelletized common bean samples. A Ti/Al primary filter and a 10-second irradiation time were selected as optimized operating conditions. The sample with the highest Zn content was submitted to acid extraction with diluted HCl and HNO3 to obtain synthetic blanks. Increasing amounts of the original sample were mixed with the obtained blanks to prepare the calibration curves standards. An additional calibration model with plant-based CRMs was also evaluated. Zinc mass fractions calculated using the calibration model of HCl-extracted and mixed samples showed statistical agreement with the reference data as demonstrated by the Student t-test at a 95 % confidence level. The linear correlation factor (r) was greater than 0.99, with a detection limit as low as 2.23 mg kg−1. The obtained calibration model also exhibited high prediction capability, as revealed by the low RMSEP (Root Mean Square Error of Prediction), i.e., 2.16 mg kg−1. We concluded that pXRF is an attractive and cost-effective method for direct, quick, and environmentally friendly Zn determination in common beans.

Intramuscular myxoma: unusual observation of spontaneous tumor size shrinkage.

Soft tissue tumors, whether benign or malignant, may grow over time or remain stable, but they usually do not spontaneously decrease in size. However, there are exceptions, such as inflammatory conditions, desmoid tumors, or benign cysts. Intramuscular myxomas are benign soft tissue tumors that typically present as a solitary, slow-growing, painless mass. They are generally treated by surgical resection, after which recurrence is rare. Here, we present a brief series of three unusual cases of intramuscular myxomas that spontaneously decreased in size. They were located in the cervical region, the right lower extremity, and the paravertebral lumbar region. Imaging findings and percutaneous biopsies confirmed the diagnosis in all cases. Follow-up imaging showed a spontaneous reduction in lesion volume over time, far exceeding the amount of tissue sample removed during biopsy. This unusual observation of spontaneous shrinkage may call into question the subsequent therapeutic approach to these lesions.

Human health risk assessment of potentially toxic metals in fish (Cynoglossus sp.) commonly consumed in Nigeria

In order to determine the concentrations of certain metals that may be hazardous in widely consumed fish species (Cynoglossus sp.) in Nigeria, atomic absorption spectrophotometry was used. The main objectives were to determine the most likely sources of these metals and assess the potential health concerns connected with consuming fish. Fish samples were collected from Sabo market, Ile-Ife, Osun state. They were digested using HNO3 and H2O2 and subjected to elemental analysis (Fe, Zn, Mn, Pb, Cu, Cd, Co, and Cr) using atomic absorption spectrophotometer. The results showed that Lead (Pb) had the lowest mean concentration of 0.56 ± 0.01 mg/kg, while Zinc (Zn) had the highest average concentration of 12.88 ± 0.18 mg/kg. Strong and substantial correlations between the studied metals were shown by the analysis of the correlation matrix and cluster patterns. The study also evaluated a number of health risk parameters, including target hazard quotient, estimated daily intake, and cancer risk, which together suggested that consuming these fish samples posed health concerns for both adults and children. According to relative risk, seafood consumption raises the most alarm about Cd levels. The study came to the conclusion that due to the propensity for these potentially hazardous metals to bioaccumulate, only a moderate amount of these fish samples should be consumed. Additionally, it is strongly advised to discourage the disposal of domestic and industrial wastes into water bodies.

Development and Validation of RoboCap, a Robotic Capillary Platform to Automate Capillary Electrophoresis Mass Spectrometry En Route to High-Throughput Single-Cell Proteomics.

Current developments in single-cell mass spectrometry (MS) aim to deepen proteome coverage while enhancing analytical speed to study entire cell populations, one cell at a time. Custom-built microanalytical capillary electrophoresis (μCE) played a critical role in the foundation of discovery single-cell MS proteomics. However, requirements for manual operation, substantial expertise, and low measurement throughput have so far hindered μCE-based single-cell studies on large numbers of cells. Here, we design and construct a robotic capillary (RoboCap) platform that grants single-cell CE-MS with automation for proteomes limited to less than ∼100 nL. RoboCap remotely controls precision actuators to translate the sample to the fused silica separation capillary, using vials in this work. The platform is hermetically enclosed and actively pressurized to inject ∼1-250 nL of the sample into a CE separation capillary, with errors below ∼5% relative standard deviation (RSD). The platform and supporting equipment were operated and monitored remotely on a custom-written Virtual Instrument (LabView). Detection performance was validated empirically on ∼5-250 nL portions of the HeLa proteome digest using a trapped ion mobility mass spectrometer (timsTOF PRO). RoboCap improved CE-ESI sample utilization to ∼20% from ∼3% on the manual μCE, the closest reference technology. Proof-of-principle experiments found proteome identification and quantification to robustly return ∼1,800 proteins (∼13% RSD) from ∼20 ng of the HeLa proteome digest on this earlier-generation detector. RoboCap automates CE-MS for limited sample amounts, paving the way to electrophoresis-based high-throughput single-cell proteomics.

Genomic Multicopy Loci Targeted by Current Forensic Quantitative PCR Assays

Modern forensic DNA quantitation assays provide information on the suitability of a DNA extract for a particular type of analysis, on the amount of sample to put into the analysis in order to yield an optimal (or best possible) result, and on the requirement for optional steps to improve the analysis. To achieve a high sensitivity and specificity, these assays are based on quantitative PCR (qPCR) and analyze target DNA loci that are present in multiple copies distributed across the genome. These target loci allow the determination of the amount of DNA, the degree of DNA degradation, and the proportion of DNA from male contributors. In addition, internal control DNA of a known amount is analyzed in order to inform about the presence of PCR inhibitors. These assays are nowadays provided as commercial kits that have been technically validated and are compatible with common qPCR instruments. In this review, the principles of forensic qPCR assays will be explained, followed by information on the nature of DNA loci targeted by modern forensic qPCR assays. Finally, we critically draw attention to the current trend of manufacturers not to disclose the exact nature of the target loci of their commercial kits.

B-148 Bacer: decentralized isotope dilution method with lab blinding for blood alcohol content

Abstract Background In legal medicine, the determination Blood Alcohol Content (BAC) usually faced the problems of insufficient sample size, ethanol degradation and impossible double check. In addition, the integrity of laboratory may also be questioned. To this end, we developed a decentralized solution based on isotope dilution method. Methods Patented deuterated ethanol vacuum tube was used in field as regular type, weight of blood was record to BACer terminal. Arbitrary amount of sample(ca.0.1-1.0g) was pipetted to GC sample vial and sealed tightly, then heated more than 2min at 70°C, 10µL headspace sample was injected into GC-MS. Blinded lab uploaded peak area ratio of ethanol/deuterated ethanol(mz31/33) to BACer to calculate the final BAC. Results Regression between peak area ratios of ethanol/deuterated ethanol showed good linearity in the range of 0.10-5.0 (R2&amp;gt;0.999). Regression equation of different instruments were proved good consistency with slopes ranged from 1.03 to 1.07, and the LOD and LOQ were around 0.01mg/g and 0.05mg/g, 3-level recoveries between 97%-101% with RSD less than 1%. During the 6-month stability test at 4°C and normal temperature, the blood sample test results maintained good stability. The proposed method was applied to 2021 CNAS proficiency testing (2021SF-CNAS002) organized by China Academy of Forensic Sciences with satisfactory results. The comparison with Chinese standard method SF/Z JD0107001-2010 yielded good agreement with Passing&amp;Bablok test (P&amp;gt;0.10). The expanded uncertainty estimated by Monte-carlo method was less than 3%. Conclusions This proposed method showed the possibility of decentralized determination of BAC with lab blinding, the separation of on-site sampling data and laboratory testing also provided the possibility of integrity of results. It also provided a possible solution for the scenarios where the storage condition of blood collection is poor, or the cross check is required. But good chromatographic separation and operation are compulsive due to the possibility of interference in IDMS.