Accurate Quantification Research Articles

Functional Tactile Sensor Based on Arrayed Triboelectric Nanogenerators

AbstractIn the era of Internet of Things (IoT) and Artificial Intelligence (AI), sensors have become an integral part of intelligent systems. Although the traditional sensing technology is very mature in long‐term development, there are remaining defects and limitations that make it difficult to meet the growing demands of current applications, such as high‐sensitivity detection and self‐supplied sensing. As a new type of sensor, array triboelectric nanogenerators (TENG)‐based tactile sensors can respond to wide dynamic range of mechanical stimuli in the surrounding environment and converting them into quantifiable electrical signals, thus realizing real‐time self‐supplied tactile sensing. The array structure allows for fine delineation of the sensing area and improved spatial resolution, resulting in accurate localization and quantification of the detected tactile signals, and have been widely used in wearable devices, smart interaction, medical and health detection, and other fields. In this paper, the latest research progress of functional tactile sensors based on arrayed triboelectric nanogenerators is systematically reviewed from the aspects of working mechanism, material selection, material processing, structural design, functional integration, and application. Finally, the challenges faced by arrayed triboelectric tactile sensors are summarized with a view to providing inspiration and guidance for the future development of tactile sensors.

Flat‐Field Lens Grating Microspectrometer for Spectroscopic Multiplex Bioassay

AbstractSpectroscopic multiplex bioassay allows the rapid and simultaneous detection of various biomarkers in biomedical applications. The high cost and bulky configuration of conventional spectrometers hinder the decentralization of biomedical instruments for point‐of‐care diagnostics. Here a flat‐field lens grating‐based microspectrometer (FLG‐µSPEC) is reported for continuous detection of multi‐target nucleic acid amplification. FLG‐µSPEC features the vertical integration of microslit, integrated flat‐field lens and transmission grating (FLG), concave mirror, and CMOS line sensor. FLG effectively corrects the field curvature by flattening the focal plane of dispersed wavelengths and also facilitates compact module packaging. FLG‐µSPEC module is precisely assembled into a dimension of 10 mm × 16 mm × 10 mm. The average spectral resolution exhibits 3.4 ± 0.9 nm over an operating range of 500 to 950 nm. A spectral detection system utilizing FLG‐µSPEC demonstrates the amplification of lambda DNA (𝜆‐DNA) for each target, exhibiting a high correlation with the obtained result from a conventional real‐time PCR (qPCR) machine. Furthermore, multiplex qPCR analysis using FLG‐µSPEC and spectral deconvolution successfully demonstrates simultaneous and accurate quantification of three different targets. The FLG‐µSPEC module offers diverse on‐demand analytical applications, including not only multiplex bioassay but also environmental monitoring.

The added value of diffusion tensor imaging with systematic bias correction for the assessment of liver morphology and physiology.

Diffusion-weighted images of the human liver are prone to artifacts from bulk motions, poor SNR, non-uniformity of magnetic field gradients, and non-optimal choice of diffusion weightings. These factors markedly affect diffusion tensor imaging (DTI) metrics such as mean diffusivity (MD) and fractional anisotropy (FA). This work presents a simple preprocessing pipeline for enhanced magnetic field gradient non-uniformity calibration and analysis of the systematic bias removal attained in each correction step. Liver DTI scans were conducted in two isotropic phantoms and one healthy volunteer. Diffusion tensor was calculated for the original data and after denoising, B1 correction, rigid body registration, and magnetic field gradient non-uniformity correction applying the B-matrix spatial distribution (BSD) method and then, compared with the standard approach (sDTI). MD and FA were determined in three segments of the right lobe from DTI using four different combinations of b-values from the set 0, 400, and 800 s/mm2. Results showed that the proposed preprocessing and BSD methods have a significant impact on MD and FA values in off- and iso-centered isotropic phantoms. The applied corrections applied to the human liver resulted in a 11% change in MD and a - 64% change in FA. By manipulating the b-values used in the diffusion tensor calculation, DTI metrics that reflect only morphology or additional information about liver tissue physiology can be obtained. Accurate quantification of the human liver by diffusion requires appropriate preprocessing and carefully chosen b-value. Noise, B1 inhomogeneity, mis-registration, and non-uniform magnetic field gradients significantly change distributions of DTI metrics in isotropic phantoms and the human liver. Basic preprocessing and the B-matrix spatial distribution (BSD) method perform differently for off-center and isocenter locations. In the human liver, they removed systematic bias of FA and MD by up to -63% and 11%, respectively. Visible variability of FA and MD among b-value sets indicates the possibility of DTI sensitization to different liver compartments.

Long-read sequencing transcriptome quantification with lr-kallisto.

RNA abundance quantification has become routine and affordable thanks to high-throughput "short-read" technologies that provide accurate molecule counts at the gene level. Similarly accurate and affordable quantification of definitive full-length, transcript isoforms has remained a stubborn challenge, despite its obvious biological significance across a wide range of problems. "Long-read" sequencing platforms now produce data-types that can, in principle, drive routine definitive isoform quantification. However some particulars of contemporary long-read datatypes, together with isoform complexity and genetic variation, present bioinformatic challenges. We show here, using ONT data, that fast and accurate quantification of long-read data is possible and that it is improved by exome capture. To perform quantifications we developed lr-kallisto, which adapts the kallisto bulk and single-cell RNA-seq quantification methods for long-read technologies.

Predicting Peptide Ionization Efficiencies for Electrospray Ionization Mass Spectrometry Using Machine Learning.

Mass spectrometry (MS) is inherently an information-rich technique. In this era of big data, label-free MS quantification for nontargeted studies has gained increasing popularity, especially for complex systems. One of the cornerstones of successful label-free quantification is the predictive modeling of ionization efficiency (IE) based on solutes' physicochemical properties. While many have studied IE modeling for small molecules, there are limited reports on peptide IEs. In this study, we leverage the stoichiometric relationship in trypsin digests of well-characterized monoclonal antibodies (mAbs) to compile a data set of relative ionization efficiencies (RIEs) for 241 peptides. From each peptide's sequence, we computed a set of physiochemical descriptors, which were then used to train machine learning regression models to predict RIEs. Peptides shorter than 20 amino acids had RIEs that were highly correlated to their molecular weight. A random forest (RF) model was able to best predict the RIEs of a test data set with a mean relative error of 23.9%. For larger peptides, a multilayer perceptron (MLP) model improved RIE prediction compared to current best practices, reducing mean relative error from 60.5% to 32.0%. Finally, we also show the application of the RF model in label-free relative protein quantification and improving the quantification of peptide post-translational modifications (PTMs). This approach to predicting peptide IEs from their sequences enables the development of accurate label-free quantification workflows for peptide and protein analysis.

ThP3.13 - Impact of gastrojejunostomy anastomosis diameter on weight loss following laparoscopic gastric bypass: a systematic review

Abstract Introduction Laparoscopic Roux-en-Y Gastric Bypass (RYGB) is a preeminent procedure for achieving significant weight reduction and alleviating obesity-related comorbidities. However, the influence of gastrojejunostomy (GJ) anastomosis diameter on weight loss is not well-elucidated. This review examines the effect of GJ diameter on post-RYGB weight loss outcomes. Methods A meticulous systematic search of the literature, limited to English-language publications, was executed with the assistance of an expert librarian. The search encompassed all studies available up to 3 August 2023, the date of the last search. Study quality was evaluated utilizing the Newcastle-Ottawa Scale. Due to the heterogeneity of the outcomes, a narrative synthesis approach was adopted. Results From 1,026 records screened, 6 studies met the inclusion criteria, demonstrating a range of GJ diameters and follow-up durations (1-5 years). Generally, smaller GJ diameters were associated with more significant weight loss in the short to medium term. There appears to be a threshold for the reduction in diameter; beyond this, the risk of complications such as stenosis may escalate. The studies indicated a moderate to low risk of bias and underscored the importance of accurate GJ area quantification post-operation. Conclusion This review identifies a negative correlation between smaller GJ diameters and weight loss after RYGB. Future investigations should prioritize the standardization of measurement techniques and investigate the potential of intra-operative and artificial intelligence-based methodologies for optimal GJ diameter determination, with the aim of augmenting patient outcomes in bariatric surgery.

The NSCLC immunotherapy response predicted by tumor-infiltrating T cells via a non-invasive radiomic approach.

Identifying patients with non-small cell lung cancer (NSCLC) who are optimal candidates for immunotherapy is a cornerstone in clinical decision-making. The tumor immune microenvironment (TIME) is intricately linked with both the prognosis of the malignancy and the efficacy of immunotherapeutic interventions. CD8+ T cells, and more specifically, tissue-resident memory CD8+ T cells [CD8+ tissue-resident memory T (TRM) cells] are postulated to be pivotal in orchestrating the immune system's assault on tumor cells. Nevertheless, the accurate quantification of immune cell infiltration-and by extension, the prediction of immunotherapeutic efficacy-remains a significant scientific frontier. In this study, we introduce a cutting-edge non-invasive radiomic model, grounded in TIME markers (CD3+ T, CD8+ T, and CD8+ TRM cells), to infer the levels of immune cell infiltration in NSCLC patients receiving immune checkpoint inhibitors and ultimately predict their response to immunotherapy. Data from patients who had surgical resections (cohort 1) were employed to construct a radiomic model capable of predicting the TIME. This model was then applied to forecast the TIME for patients under immunotherapy (cohort 2). Conclusively, the study delved into the association between the predicted TIME from the radiomic model and the immunotherapeutic outcomes of the patients. For the immune cell infiltration radiomic prediction models in cohort 1, the AUC values achieved 0.765, 0.763, and 0.675 in the test set of CD3+ T, CD8+ T, and CD8+ TRM, respectively. While the AUC values for the TIME-immunotherapy predictive value were 0.651, 0.763, and 0.829 in the CD3-immunotherapy response model, CD8-immunotherapy response model, and CD8+ TRM-immunotherapy response model in cohort 2, respectively. The CD8+ TRM-immunotherapy model exhibited the highest predictive value and was significantly better than the CD3-immunotherapy model in predicting the immunotherapy response. The progression-free survival (PFS) analysis based on the predicted levels of CD3+ T, CD8+ T, and CD8+ TRM immune cell infiltration showed that the CD8+ T cell infiltration level was an independent factor (P=0.014, HR=0.218) with an AUC value of 0.938. Our empirical evidence reveals that patients with substantial CD8+ T cell infiltration experience a markedly improved PFS compared with those with minimal infiltration, asserting the status of the CD8+ T cell as an independent prognosticator of PFS in the context of immunotherapy. Although CD8+ TRM cells demonstrated the greatest predictive accuracy for immunotherapy response, their predictive strength for PFS was marginally surpassed by that of CD8+ T cells. These insights advocate for the application of the proposed non-invasive radiomic model, which utilizes TIME analysis, as a reliable predictor for immunotherapy outcomes and PFS in NSCLC patients.

Highly accurate and sensitive absolute quantification of bacterial strains in human fecal samples

BackgroundNext-generation sequencing (NGS) approaches have revolutionized gut microbiome research and can provide strain-level resolution, but these techniques have limitations in that they are only semi-quantitative, suffer from high detection limits, and generate data that is compositional. The present study aimed to systematically compare quantitative PCR (qPCR) and droplet digital PCR (ddPCR) for the absolute quantification of Limosilactobacillus reuteri strains in human fecal samples and to develop an optimized protocol for the absolute quantification of bacterial strains in fecal samples.ResultsUsing strain-specific PCR primers for L. reuteri 17938, ddPCR showed slightly better reproducibility, but qPCR was almost as reproducible and showed comparable sensitivity (limit of detection [LOD] around 104 cells/g feces) and linearity (R2 > 0.98) when kit-based DNA isolation methods were used. qPCR further had a wider dynamic range and is cheaper and faster. Based on these findings, we conclude that qPCR has advantages over ddPCR for the absolute quantification of bacterial strains in fecal samples. We provide an optimized and easy-to-follow step-by-step protocol for the design of strain-specific qPCR assays, starting from primer design from genome sequences to the calibration of the PCR system. Validation of this protocol to design PCR assays for two L. reuteri strains, PB-W1 and DSM 20016 T, resulted in a highly accurate qPCR with a detection limit in spiked fecal samples of around 103 cells/g feces. Applying our strain-specific qPCR assays to fecal samples collected from human subjects who received live L. reuteri PB-W1 or DSM 20016 T during a human trial demonstrated a highly accurate quantification and sensitive detection of these two strains, with a much lower LOD and a broader dynamic range compared to NGS approaches (16S rRNA gene sequencing and whole metagenome sequencing).ConclusionsBased on our analyses, we consider qPCR with kit-based DNA extraction approaches the best approach to accurately quantify gut bacteria at the strain level in fecal samples. The provided step-by-step protocol will allow scientists to design highly sensitive strain-specific PCR systems for the accurate quantification of bacterial strains of not only L. reuteri but also other bacterial taxa in a broad range of applications and sample types.-oGjUmj5e8MXZDxyDjzcm-Video

Identification and quantitative analysis of genotoxic impurities in rifampicin: Development and validation of a targeted LC-MS/MS method for 1-amino-4-methylpiperazine

Rifampicin, essential for long-term tuberculosis treatment, requires rigorous control of non-therapeutic impurities due to their potential adverse, including mutagenic effects. Reports on control strategies for genotoxic impurities in rifampicin have been limited. This study introduced an analytical method to identify potential genotoxic impurities from the synthesis of raw materials. The structure of the 25-deacetyl-23-acetyl-rifampicin genotoxic impurity was confirmed using nuclear magnetic resonance, high-resolution mass spectrometry (HRMS), and high-performance liquid chromatography (HPLC). An HPLC-HRMS method was established and validated for detecting another genotoxic impurity, 1-amino-4-methylpiperazine, adhering to the International Council on Harmonization guidelines, which include specificity, linearity, detection and quantification limits, accuracy, precision, and robustness. These developments improve the quality control strategy for genotoxic impurities in rifampicin, ensuring product safety.

On the Sources of Discrepancies Between Grain Size Measurements

Accurate quantification of grain size in polycrystals is extraordinarily important for quality assurance and the development of structure-property relationships, as grain size affects nearly all engineering properties of structural alloys, from strength and fatigue life to corrosion resistance and thermal conductivity. Despite nearly a century of the existence of standards for measuring grain size, issues persist in obtaining consistent grain size measurements across several techniques. Of particular interest is comparing optical microscopy or scanning electron microscopy on an etched surface with mapping of crystal orientations using electron backscatter diffraction (EBSD). In this work, discrepancies between methods in the ASTM E 112 and ASTM E 2627 standards are explored through simulated measurements on synthetic microstructures. It is concluded that a small but systematic discrepancy exists between planimetric and lineal intercept-based approaches, and a new empirical relationship between the ASTM grain size number G and lineal intercepts is proposed.

An Automated pre-Dilution Setup for Von Willebrand Factor Activity Assays.

Accurate quantification of von Willebrand factor ristocetin cofactor activity (VWF:RCo) is critical for the diagnosis and classification of von Willebrand disease, the most common hereditary and acquired bleeding disorder in humans. Moreover, it is important to accurately assess the function of von Willebrand factor (VWF) concentrates within the pharmaceutical industry to provide consistent and high-quality biopharmaceuticals. Although the performance of VWF:RCo assay has been improved by using coagulation analyzers, which are specialized devices for blood and blood plasma samples, scientists still report a high degree of intra- and inter-assay variation in clinical laboratories. Moreover, high, manual sample dilutions are required for VWF:RCo determination of VWF concentrates within the pharmaceutical industry, which are a major source for assay imprecision. For the first time, we present a precise and accurate method to determine VWF:RCo, where all critical pipetting and mixing steps are automated. A pre-dilution setup was established on CyBio FeliX (Analytik-Jena) liquid handling system, and an adapted VWF:RCo method on BCS-XP analyzer (Siemens) is used. The automated pre-dilution method was executed on three different, most frequently used coagulation analyzers and compared to manual pre-dilutions performed by an experienced operator. Comparative sample testing revealed a similar assay precision (coefficient of variation = 5.9% automated, 3.1% manual pre-dilution) and no significant differences between the automated approach and manual dilutions of an expert in this method. While no outliers were generated with the automated procedure, the manual pre-dilution resulted in an error rate of 8.3%. Overall, this operator-independent protocol enables standardization and offers an efficient way of fully automating VWF activity assays, while maintaining the precision and accuracy of an expert analyst. Key features • Automated pre-dilution setup for von Willebrand factor concentrates of various natures. • Combination of a liquid handling system (CyBio FeliX) with a coagulation analyzer (BCS-XP). • Simplifies method transfer to other laboratories. • Basic training for CyBio FeliX and BCS-XP is required. Graphical overview VWF:RCo assay principle and measurement setup. Platelets (yellow ellipsoids) with negative surface charge (- - -) are treated with formaldehyde, which partly denatures the cell surface and thus stabilizes platelets for use as assay reagents. Stabilized platelets (dark-yellow-framed yellow ellipsoids) are then brought in contact with ristocetin A (chemical structure shown; black dots), which binds to the platelet surface and facilitates binding of VWF (green circles). The graphs show an example of quantitative determination of platelet agglutination by measurement of light transmission, where increasing amounts of VWF increase light transmission over time. The photo in the left-bottom corner shows the CyBio FeliX setup for VWF sample dilution and the photo in the right-bottom corner displays the BCS-XP system, which is used for VWF:RCo measurements.

Development of analytical methods and method validation for the qualitative and quantitative determination of Carboxymethyl Cellulose (CMC) in yogurt and ayran

This study presents the development and validation of analytical methods for the qualitative and quantitative determination of Carboxymethyl Cellulose (CMC) in yogurt and ayran. The methods were evaluated for their detection limit (LOD), quantification limit (LOQ), and method detection limit (MDL) across multiple samples, demonstrating robust sensitivity with MDL and LOD values of 0.03% for both yogurt and ayran. Recovery studies showed high accuracy, with recovery rates between 98% and 100%, and consistent recovery within the acceptable range of 97–103%, reinforcing the methods' reliability. The quantitative analysis exhibited strong repeatability and reproducibility, with low relative standard deviations (RSDr and RSDR), confirming minimal intra-day and inter-day variability. The calibration curves for CMC analysis demonstrated excellent linearity with high coefficients of determination (R2 = 0.9985 for yogurt and 0.9951 for ayran), ensuring accurate quantification over a wide concentration range. Measurement uncertainty analysis further validated the methods' precision and accuracy, showing low relative uncertainties for trueness, repeatability, and reproducibility. Robustness evaluations indicated that the methods maintained consistent recovery rates despite variations in pH, heating time, and sample quantity, highlighting their reliability under different conditions. For qualitative analysis, the visual images method showed exceptional performance with perfect sensitivity and specificity, accurately identifying all positive and negative samples without any false positives or negatives. This high level of accuracy makes the visual images method a valuable tool for ensuring product safety and compliance with quality standards in the dairy industry. Overall, the validated analytical methods for CMC detection in yogurt and ayran demonstrated high precision, accuracy, and reliability, making them essential for quality control and regulatory compliance in dairy production.

Formaldehyde quantification using gas chromatography–mass spectrometry reveals high background environmental formaldehyde levels

Formaldehyde (HCHO) is a human toxin that is both a pollutant and endogenous metabolite. HCHO concentrations in human biological samples are reported in the micromolar range; however, accurate quantification is compromised by a paucity of sensitive analysis methods. To address this issue, we previously reported a novel SPME–GC–MS-based HCHO detection method using cysteamine as an HCHO scavenger. This method showed cysteamine to be a more efficient scavenger than the widely used O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine, and enabled detection of aqueous HCHO in the nanomolar range and quantification in the micromolar range. However, quantification in this range required immersive extraction of the HCHO-derived thiazolidine, while a high background signal was also observed. Following on from these studies, we now report an optimised head-space extraction SPME–GC–MS method using cysteamine, which provides similarly sensitive HCHO quantification to the immersive method but avoids extensive wash steps and is therefore more amenable to screening applications. However, high background HCHO levels were still observed A Complementary GC–MS analyses using a 2-aza-Cope-based HCHO scavenger also revealed high background HCHO levels; therefore, the combined results suggest that HCHO exists in high (i.e. micromolar) concentration in aqueous samples that precludes accurate quantification below the micromolar range. This observation has important implications for ongoing HCHO quantification studies in water, including in biological samples.

Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients.

Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of -528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions.

Enhancing trustworthiness in ML-based network intrusion detection with uncertainty quantification

A crucial role in the security of modern networks is played by Intrusion Detection Systems (IDSs), security devices designed to identify and mitigate attacks to networks structure. Data-driven approaches based on Machine Learning (ML) have gained more and more popularity for executing the classification tasks required by signature-based IDSs. However, typical ML models adopted for this purpose do not properly take into account the uncertainty associated with their prediction. This poses significant challenges, as they tend to produce misleadingly high classification scores for both misclassified inputs and inputs belonging to unknown classes (e.g. novel attacks), limiting the trustworthiness of existing ML-based solutions. In this paper, we argue that ML-based IDSs should always provide accurate uncertainty quantification to avoid overconfident predictions. In fact, an uncertainty-aware classification would be beneficial to enhance closed-set classification performance, would make it possible to carry out Active Learning, and would help recognize inputs of unknown classes as truly unknowns, unlocking open-set classification capabilities and Out-of-Distribution (OoD) detection. To verify it, we compare various ML-based methods for uncertainty quantification and open-set classification, either specifically designed for or tailored to the domain of network intrusion detection. Moreover, we develop a custom model based on Bayesian Neural Networks that stands out for its OoD detection capabilities and robustness, with a lower variance in the results over different scenarios, compared to other baselines, thus showing how proper uncertainty quantification can be exploited to significantly enhance the trustworthiness of ML-based IDSs.

Conjugation of primary amine groups in targeted proteomics.

Primary amines, in the form of unmodified N-terminus of peptide/protein and unmodified lysine residue, are perhaps the most important functional groups that can serve as the starting points in proteomic analysis, especially via mass spectrometry-based approaches. A variety of multifunctional probes that conjugate primary amine groups through covalent bonds have been developed and employed to facilitate protein/protein complex characterization, including identification, quantification, structure and localization elucidation, protein-protein interaction investigation, and so forth. As an integral part of more accurate peptide quantification in targeted proteomics, isobaric stable isotope-coded primary amine labeling approaches eventually facilitated protein/peptide characterization at the single-cell level, paving the way for single-cell proteomics. The development and advances in the field can be reviewed in terms of key components of a multifunctional probe: functional groups and chemistry for primary amine conjugation; hetero-bifunctional moiety for separation/enrichment of conjugated protein/protein complex; and functionalized linker/spacer. Perspectives are primarily focused on optimizing primary amine conjugation under physiological conditions to improve characterization of native proteins, especially those associated with the surface of living cells/microorganisms.

Assessing prognosis by quantifying FcγRIIa on fixed platelets.

Introduction: FcγRIIa amplifies platelet activation and higher platelet FcγRIIa identifies patients at greater risk of subsequent cardiovascular events. We report the accuracy and precision of a modified test to quantify FcγRIIa on previously fixed platelets (pFCG test).Methods & results: An antibody clone (5G1) was developed after exposure of mice to formaldehyde treated FcγRIIa. Accuracy and precision of the modified test was evaluated with biologic specimens (platelets) and engineered synthetic cells conjugated with FcγRIIa (Slingshot Biosciences). The modified pFCG test on fixed platelets (using 5G1) consistently identified modestly more (∼300molecules) of FcγRIIa on platelets compared with the pFCG test on nonfixed platelets (using clone FL18.26). With biologic specimens, the intra-assay coefficient of variation (CV) was 2.1±0.1% (standard error of the mean, n=750). The interassay CV was assessed intraday (4.5±1%) and interday (up to 5days after fixation, 6.5±0.4%, n=50). The pFCG test performed on Slingshot Synthetic cells conjugated with FcγRIIa demonstrated accuracy, linearity (R2=0.984) and similar interassay CV both intraday (2%±0.6%) and interday (20nonconsecutive days, 9.9%±2.1%).Conclusion: In summary, modification of the pFCG test to be performed on fixed platelets allows accurate quantification of pFCG with high precision.

Determination of Some Element’s Migrants in Aqueous Simulant from Plastic Food Contact Products by Inductively Coupled Plasma Mass Spectrometer

Various chemicals present at different stages in the food supply chain can lead to the leaching of heavy metals. These metals can accumulate in the human body through the consumption of contaminated food. Consequently, it is necessary to validate an analytical technique for the quantification chemical that could contaminate food. This study presents a rapid, straightforward, and efficient analytical method for the direct quantification of some potentially toxic elements in aqueous simulants from plastic food contact products using an inductively coupled mass spectrometer (ICP-MS). The method’s validation encompassed the study of the estimated detection limits, practical quantification limits, linearity, accuracy, and measurement uncertainty of aluminium (Al), antimony (Sb), arsenic (As), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), nickel (Ni), and zinc (Zn) under optimized ICP-MS conditions. The estimated detection limits ranged from 7.5 × 10−4 to 0.074 mg/kg, while practical quantification limits spanned from 0.02 to 0.8 mg/kg. The average recoveries ± standard deviations at different spiking levels were varied between 85.7 ± 1.51 and 115.6 ± 0.88% with coefficients of variation between 0.42 and 5.85%. The method trueness was verified by using references materials (test material in aqueous acetic acid) purchased from Food Chemistry Proficiency Testing and Analysis (FAPAS) yielding satisfactory results within acceptable recovery and Z-score values. The method precision, in terms of relative standard deviation (RSD), was being below 4.22%. The method uncertainty expressed as expanded uncertainty of all validated elements was found to be ≤ 21.9%. Validated method was employed to determine specific elements in aqueous simulants of thirty commercial plastic food packaging samples, representing three distinct types of plastic polymers. The results showed that the mean concentrations, in mg/kg, were as follows: 2.04 (Al), 0.02 (As), 0.02 (Cd), 0.02 (Co), 0.06 (Cr), 0.41 (Cu), 1.55 (Fe), 0.09 (Mn), 0.15 (Ni), 0.07 (Pb), 0.05 (Sb), and 0.81 (Zn). Furthermore, 30% of analyzed samples exceeding the maximum permissible limits of Al for plastic materials and articles intended to come into contact with food.

Multi-Biomarker Profiling for Precision Diagnosis of Lung Cancer.

Multi-biomarker analysis can enhance the accuracy of the single-biomarker analysis by reducing the errors caused by genetic and environmental differences. For this reason, multi-biomarker analysis shows higher accuracy in early and precision diagnosis. However, conventional analysis methods have limitations for multi-biomarker analysis because of their long pre-processing times, inconsistent results, and large sample requirements. To solve these, a fast and accurate precision diagnostic method is introduced for lung cancer by multi-biomarker profiling using a single drop of blood. For this, surface-enhanced Raman spectroscopic immunoassay (SERSIA) is employed for the accurate, quick, and reliable quantification of biomarkers. Then, it is checked the statistical relation of the multi-biomarkers to differentiate between healthy controls and lung cancer patients. This approach has proven effective; with 20µL of blood serum, lung cancer is diagnosed with 92% accuracy. It also accurately identifies the type and stage of cancer with 87% and 85%, respectively. These results show the importance of multi-biomarker analysis in overcoming the challenges posed by single-biomarker diagnostics. Furthermore, it markedly improves multi-biomarker-based analysis methods, illustrating its important impact on clinical diagnostics.

Detection of PCBs and OCPs in the Irtysh River Water (GC-MS/MS) and ecological risk assessment

This study optimized a gas chromatography-tandem triple quadrupole mass spectrometry (GC-MS/MS) method for the determination of 21 persistent organic pollutants (POPs) in Irtysh River water, including 14 organochlorines (OCPs) and 7 polychlorinated biphenyls (PCBs). Factors such as column temperature ramping, selection of qualitative and quantitative ion pairs and collision energy were considered to achieve perfect separation and accurate quantification of all 21 target compounds. The limits of detection (LOD) for PCBs and OCPs ranged from 0.21 to 1.18 ng/L. Applying this method to detect POPs in the Irtysh River revealed concentrations of OCPs ranging from ND to 20.2 ng/L and PCBs from ND to 0.411 ng/L. Source analysis indicated that POPs in the Irtysh River mainly originate from historical industrial and agricultural activities, particularly the deliberate use of pesticides. To ensure ecological safety and human health, expanding the range of target analytes and monitoring periods is necessary. This study provides:•Qualitative and quantitative analysis methods for 7 PCBs and 14 OCPs.•Recoveries achieved ranged between 74.6 to 109 % with RSD less than 15 %.•Analysis of sources, transport pathways, accumulation status, and ecological risks of PCBs and OCPs in the Irtysh River.