Batches Of Samples Research Articles

Network pharmacology integrated with experimental validation revealed the mechanism of Fengshi Gutong Capsule in the treatment of osteoarthritis

Ethnopharmacological relevanceFengshi Gutong Capsule (FSGTC) is a commonly used Chinese medicine for the treatment of joint pain caused by osteoarthritis (OA). However, the mechanism of action of FSGTC for OA remains unclear. Aim of the studyThis study aimed to explore the alleviating effects and potential mechanisms of action of FSGTC for OA through data mining, network pharmacology, and in vitro experiments. Materials and methodsHigh-performance liquid chromatography (HPLC) was performed to establish the fingerprints of FSGTC and detect the components of FSGTC absorbed in the blood. The effects of FSGTC on inflammation, immunity, and liver and kidney functions in patients with OA were evaluated by mining clinical data. The potential targets and pathways of FSGTC were screened using network pharmacology. Subsequently, CCK-8 assay, flow cytometry, western blotting, RT-qPCR, ELISA, and immunofluorescence were performed in IL-1β-stimulated chondrocytes for further validation. ResultsEighty-seven common peaks and 10 components were identified using the HPLC fingerprints of 12 batches of samples, and the similarity was in the range of 0.973–0.998. Retrospective clinical analysis demonstrated a significant reduction in inflammatory response levels among patients with OA who received FSGTC treatment. Network pharmacology analysis revealed that FSGTC potentially targeted processes related to inflammation, oxidative stress, and apoptosis. The phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT), The nuclear factor-κB (NF-κB), and janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) signaling pathways were predicted to be the main pathways involved in the therapeutic effects of FSGTC in OA. In vitro, FSGTC-containing serum aided the proliferation of chondrocytes stimulated by IL-1β, while concurrently mitigating apoptosis, suppressing the expression of inflammatory cytokines and oxidative molecules, and inhibiting the degradation of the chondrocyte extracellular matrix (ECM). ConclusionsFSGTC alleviates the inflammatory response in patients with OA. This therapeutic effect was attributed to its anti-inflammatory and antioxidant properties, and its ability to promote IL-1β-induced chondrocyte proliferation, inhibit apoptosis, and prevent the degradation of extracellular matrix. These favorable results were associated with the inhibition of the PI3K/AKT, NF-κB, and JAK2/STAT3 signaling pathways.

Privacy-Preserving Federated Recurrent Neural Networks

We present RHODE, a novel system that enables privacy-preserving training of and prediction on Recurrent Neural Networks (RNNs) in a cross-silo federated learning setting by relying on multiparty homomorphic encryption. RHODE preserves the confidentiality of the training data, the model, and the prediction data; and it mitigates federated learning attacks that target the gradients under a passive-adversary threat model. We propose a packing scheme, multi-dimensional packing, for a better utilization of Single Instruction, Multiple Data (SIMD) operations under encryption. With multi-dimensional packing, RHODE enables the efficient processing, in parallel, of a batch of samples. To avoid the exploding gradients problem, RHODE provides several clipping approximations for performing gradient clipping under encryption. We experimentally show that the model performance with RHODE remains similar to non-secure solutions both for homogeneous and heterogeneous data distributions among the data holders. Our experimental evaluation shows that RHODE scales linearly with the number of data holders and the number of timesteps, sub-linearly and sub-quadratically with the number of features and the number of hidden units of RNNs, respectively. To the best of our knowledge, RHODE is the first system that provides the building blocks for the training of RNNs and its variants, under encryption in a federated learning setting.

Ultrastructural studies on implants failure with immediate or late loading

Objectives. The purpose of the present study was to evaluate by scanning electron microscope (SEM) and energy dispersive spectrophotometry (EDS), the degree of bone mineralization of the tissues on the surface of failure dental implants with immediate or late loading. Materials and method. In the study, 8 dental explants from 8 clinically healthy, non-smoking patients were taken. All implants were inserted by the same dental surgeon, 4 of them were immediate loaded and 4 were late prosthetically loaded by the same dentist. The ablation of the implants was performed as atraumatically as possible and they were collected in sterile containers and sent to the BIOMAT Research Center, where they were subjected to SEM and EDS analyses. The ratios between the chemical elements calcium and nitrogen (Ca/N), phosphorus and nitrogen (P/N), respectively calcium and phosphorus (Ca/P) were calculated for each of the 8 samples and were statistically analyzed. Results. If we compare the degree of coverage of the implants with bone tissue in different phases of mineralization, we find that the bone structures occupy a larger surface area of the implants in the cases with immediate loading compared to the cases with late loading. Thus, out of 4 immediately loaded implants, 2 were completely covered, one showed very little exposed areas, and the fourth showed alternating covered and uncovered areas. Regarding the late loaded implants, three showed alternation between covered and uncovered areas and only one was completely covered with bone. Conclusions. The degree of coverage of the explants with bone tissue was better represented for the immediate loading cases. The degree of mineralization of the bone tissue covering the explants was higher for cases with immediate loading. To confirm the obtained results, it is necessary to expand the study on larger batches of samples.

Quality testing of mifepristone and misoprostol in 11 countries.

Previous studies have demonstrated quality concerns with misoprostol. Mifepristone, however, has not been extensively assessed for quality. Between 2020 and 2021, Concept Foundation and the International Planned Parenthood Federation conducted a study to determine the quality of these medical abortion drugs in low- and middle-income countries (LMIC). The collection of batch samples of misoprostol and mifepristone was carried out by trained sampling agents in selected LMIC. Single drug packs and combipacks were sampled. A World Health Organization prequalified laboratory conducted testing method verifications and subsequent sample analysis. Tests included identification, assay, related substances, and content uniformity for misoprostol, and identification, assay, related substances, and dissolution for mifepristone. Samples were collected from Burkina Faso, Cambodia, Democratic Republic of Congo, India, Kyrgyzstan, Moldova, Nepal, Nigeria, Pakistan, Uganda and Vietnam. Sixty-four pooled batch samples were tested, consisting of 31 combipacks, 26 misoprostol-only and seven mifepristone-only products. Overall, 54.7% of samples were non-compliant with one or more of the specifications, representing 51.6% of combipack products, 57.1% of misoprostol tablets analyzed and 23.7% of mifepristone tablets. One falsified misoprostol-only product was found. The present study confirms that a significant problem still exists in relation to the quality of medical abortion drugs in LMIC. For misoprostol, our findings suggest that historical concerns around primary packaging may have been largely resolved but that manufacturing processes for both finished product and active pharmaceutical ingredient need to be improved. The present study also provides evidence of mifepristone quality issues.

Isotopic and proteomic evidence for communal stability at Pre-Pottery Neolithic Jericho in the Southern Levant

As one of the key, long-term occupied sites in the Southern Levant, Jericho was one of the most important early Neolithic centres to witness social and economic changes associated with the domestication of plants and animals. This study applies strontium (87Sr/86Sr), oxygen (δ18O) and carbon (δ13C) isotope analyses to the enamel of 52 human teeth from Pre-Pottery Neolithic (PPN) layers of Jericho to directly study human diet and mobility and investigate the degree of consolidation and the flexibility of social organization of Jericho society in the PPN period. The results indicate only two non-local individuals out of the 44 sampled inhabitants identified by strontium isotope analysis and are consistent with the presence of a largely sedentary community at PPN Jericho with no evidence for large-scale migration. We also construct strontium spatial baselines (87Sr/86Sr map) with local 87Sr/86Sr signatures for the sites across the Southern Levant based on systematic compilation and analysis of available data. In addition, we apply proteomic analysis of sex-specific amelogenin peptides in tooth enamel for sex estimation of the sampled individuals (n = 44), the results of which showed a sex-biased ratio (more male than female detected in this sample pool) in Jericho society during the PPN period, which may be due to the limited sample size or selective ritual practices like particular burial zones used for specific groups. We also pretreated a batch of human bone samples recovered from PPNB Jericho for stable carbon and nitrogen isotope analyses for dietary investigations. However, the extracted collagen showed poor preservation and no valid δ13C or δ15N data were obtained.

Centrifugal Extraction-Assisted Fiber-Enhanced Raman Spectroscopy for Online Detection of Trace Furfural in Oil Power Equipment.

Oil-paper insulated equipment is integral in power conversion and supports low-loss electricity transport. As a characteristic byproduct of the oil-paper insulation system, the realization of efficient detection of furfural in oil is crucial to the safe operation of the power grid. We proposed a novel approach using dual-enhanced Raman spectroscopy for sensing trace liquid components. This method employs a centrifugal extractor to separate and enrich the targeted components, achieving selective enhancement. The optimal phase ratio was determined to be 30:1. A liquid-core fiber was used to optimize the laser transmission efficiency and Raman signal collection efficiency, resulting in a nonselective signal enhancement of 44.86. It also investigated the impact of intermolecular interactions on the shift of Raman spectra, identifying the reasons for the differences in Raman signals between pure furfural, furfural in oil, and furfural in water. A batch of samples with furfural dissolved in insulation oil was measured using this system and achieved a limit of detection of 0.091 mg/L. The stability of the dual-enhanced Raman platform was experimentally verified with a spectral intensity fluctuation of 0.68%. This method is fast, stable, adaptable, and suitable for the detection of a wide range of liquid ingredients.

Stereolithography for manufacturing of advanced porous solids

The aim of this paper is focused on benefits of stereolithography (SLA) technology for the fabrication of the lightweight lattice structures with potential for load-bearing function and high absorption of impact energy. SLA is an additive manufacturing technology employing the principle of liquid resins curing moderated by radiation of a wavelength from ultra-violet band where resulting material parameters are tunable by setting of the curing process. The batches of samples manufactured using three different resins were subjected to quasi-static uni-axial tensile and compression tests. Acquired data were processed to derive deformation behaviour expressed as stress-strain diagrams and fundamental material properties. Based on the knowledge obtained from the mechanical tests, the setup of the fabrication parameters, the most suitable resin for manufacturing of the lattice structures and the overall suitability of SLA technology for the fabrication of advanced porous materials, were determined.

ADFSNet: An Adaptive Domain Feature Separation Network for the Classification of Wheat Seed Using Hyperspectral Images.

Wheat seed classification is a critical task for ensuring crop quality and yield. However, the characteristics of wheat seeds can vary due to variations in climate, soil, and other environmental factors across different years. Consequently, the present classification model is no longer adequate for accurately classifying novel samples. To tackle this issue, this paper proposes an adaptive domain feature separation (ADFS) network that utilizes hyperspectral imaging techniques for cross-year classification of wheat seed varieties. The primary objective is to improve the generalization ability of the model at a minimum cost. ADFS leverages deep learning techniques to acquire domain-irrelevant features from hyperspectral data, thus effectively addressing the issue of domain shifts across datasets. The feature spaces are divided into three parts using different modules. One shared module aligns feature distributions between the source and target datasets from different years, thereby enhancing the model's generalization and robustness. Additionally, two private modules extract class-specific features and domain-specific features. The transfer mechanism does not learn domain-specific features to reduce negative transfer and improve classification accuracy. Extensive experiments conducted on a two-year dataset comprising four wheat seed varieties demonstrate the effectiveness of ADFS in wheat seed classification. Compared with three typical transfer learning networks, ADFS can achieve the best accuracy of wheat seed classification with small batch samples updated, thereby addressing new seasonal variability.

Data-driven safe control via finite-time Koopman identifier

In this paper, a novel data-driven invariant-based safe control scheme for control of a nonlinear vehicle is developed. First, a novel incremental finite-time Koopman representation-based identifier is introduced to learn approximated lifted-states linear system model of system from a limited budget of incrementally sampled data points which not only minimises the instantaneous Koopman representation's identification errors but also the identification errors for a batch of past samples collected in a history stack. A great advantage of the presented identifier is that the uncertainty of the identified system is also quantified and updated over time on a slower time scale. Using set-theoretic tools, then, the quantified uncertainty will be leveraged in conjunction with the learned model in order to impose robust contractivity on the safe set, which guarantees its safety and stability. The proposed approach significantly reduces conservativeness through quantifying and updating the uncertainty level of the learned model over time.

Qualitative identification and quantitative comparison of Physochlainae Radix from different regions based on chemometric methods.

Physochlainae Radix (PR) is an essential herbal medicine that has been generally applied for treating cough and asthma. In this study, a comprehensive strategy for quality evaluation of PR from different origins was established by integrating qualitative identification, quantitative analysis, and chemometric methods. A total of 58 chemical components were identified by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF-MS/MS), and a sensitive and rapid UHPLC-QqQ-MS/MS method was established for the simultaneous determination of 12 compounds. In addition, multivariate statistical analysis was applied for discriminant analysis to compare the differences among 30 batches of PR samples. The results showed that the 30 batches of PR collected from four provinces could be clustered into three categories, in which scoparone, protocatechuic acid, tropic acid, and scopolin were important components to distinguish the primary and non-primary producing areas, as well as superior and inferior products of PR. Chemometric results were consistent and validated each other, and systematically explained the intrinsic quality characteristics of PR. This study first demonstrated that LC-MS combined with multivariate statistical analysis, provided a comprehensive and effective means for quality evaluation of PR.

MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data

Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolites in the organism quantitatively and to find the relative relationship between metabolites and physiological and pathological changes. A feature construction of mass spectrometry data (MSFS) method is proposed to construct the features of the original mass spectrometry data, so as to reduce the noise in the mass spectrometry data, reduce the redundancy of the original data and improve the information content of the data. Chi-square test is used to select the optimal non-redundant feature subset from high-dimensional features. And the optimal feature subset is visually analyzed and corresponds to the original mass spectrum interval. Training in 10 kinds of supervised learning models, and evaluating the classification effect of the models through various evaluation indexes. Taking two public mass spectrometry datasets as examples, the feasibility of the method proposed in this paper is verified. In the coronary heart disease dataset, during the identification process of mixed batch samples, the classification accuracy on the test set reached 1.000; During the recognition process, the classification accuracy on the test set advanced to 0.979. On the colorectal liver metastases data set, the classification accuracy on the test set reached 1.000. This paper attempts to use a new raw mass spectrometry data preprocessing method to realize the alignment operation of the raw mass spectrometry data, which significantly improves the classification accuracy and provides another new idea for mass spectrometry data analysis. Compared with MetaboAnalyst software and existing experimental results, the method proposed in this paper has obtained better classification results.

Quality markers of Dajianzhong decoction based on multicomponent qualitative and quantitative analysis combined with network pharmacology and chemometric analysis.

Dajianzhong decoction (DJZD), a classic famous prescription, has a long history of medicinal application. Modern studies have demonstrated its clinical utility in the treatment of postoperative ileus (POI). But none of the current quality evaluation methods for this compound is associated with efficacy. This study aimed to identify the quality markers (Q-Markers) connected to the treatment of POI in DJZD. Ultra-performance liquid chromatography quadrupole Exactive Orbitrap mass spectrometry (UPLC-Q-Exactive Orbitrap-MS) was used to identify the main constituents in DJZD. Based on the qualitative results obtained by fingerprinting, chemical pattern recognition (CPR) was used to analyse the key components affecting the quality and finally to establish the network of the active ingredients in DJZD with POI. A total of 64 chemical components were detected. After fingerprint analysis, 13 common peaks were identified. The fingerprint similarity of 15 batches of samples ranged from 0.860 to 1.000. CPR analysis was able to categorically classify 15 batches of DJZD into two groups. And gingerenone A, methyl-6-gingerdiol, 6-gingerol, and hydroxy-β-sanshool contributed to their grouping. Twelve common components interact with the therapeutic targets for treating POI. In addition, the mechanism of this prescription for treating POI may be related to the jurisdiction of the neurological system, the immunological system, and the inflammatory response. This integrated approach can accurately assess and forecast the quality of DJZD, presume the Q-Markers of DJZD for POI, and lay the foundation for studying the theoretical underpinnings and exploring the mechanism of DJZD in the treatment of POI.

Multivariate statistical visualisation and modelling of GC–MS data for sensory quality prediction of flavoured cider as influenced by storage time and temperature

It is important to cider producers to ensure the optimal quality of their products directly after production, and after extended shelf life. Quality assessments usually involve regular and continuous sensory evaluation conducted by trained panels. The competitive cider production environment necessitates extensive sensory analysis for new product launches, as part of quality control of existing products and during the production process. This could potentially lead to panel fatigue, less accuracy in sensory evaluation and delays in product launches. This paper describes the development of a partial least squares (PLS) model for the prediction of cider quality based on multivariate correlation of sensory and gas chromatography-mass spectroscopy (GC–MS) volatile compound profile data of a flavoured cider. Clear differences between cider samples of various batches and storage conditions could be visualised using principal component analysis (PCA). However, the developed PLS model was able to predict the commercial cider’s quality irrespective of differences in batch, storage temperature or storage time. The main compounds that are strongly associated with sensory quality and its decline during shelf-life storage were identified. This model is the first quality prediction model to be developed for South African flavoured cider which could be used to facilitate fast, consistent and accurate quality assessment in the high-pressure cider production and trade environment. Furthermore, since the model captures the sensory data obtained from a trained technical panel, it can be used to facilitate the calibration of sensory panels across production sites, reduce product development time and better optimise processes that impact sensory quality.

An Ultra-Fast and Green LC-MS Method for Quantitative Analysis of Aesculin and Aesculetin in Cortex Fraxini

This study aims to develop a fast and eco-friendly liquid chromatography–mass spectrometry (LC-MS) method for the determination of aesculin and aesculetin in Cortex Fraxini. Ultrapure water was used as the solvent during the microwave-assisted extraction process to prepare the Cortex Fraxini sample. This extraction method reduces the cost of the harmful solvent (only ultrapure water was used) and microwave extraction time (1 min). The LC separation was conducted using an Agilent InfinityLab Poroshell 120 EC-C18 column (2.1 mm × 30 mm, 2.7 µm) with a mobile phase consisting of 0.1% formic acid and acetonitrile (90:10, v/v) at a flow rate of 0.6 mL/min. Isocratic elution was employed, and the analytes were detected by MS. Through careful optimization and selection of LC-MS conditions, the analysis time was reduced to 1 min, demonstrating the method’s efficiency. The developed method was validated and exhibited excellent specificity, linearity, limit, precision, accuracy, and stability in quantifying aesculin and aesculetin in the Cortex Fraxini samples. The analysis result revealed the presence of aesculin (ranging from 3.55 to 18.8 mg/g) and aesculetin (ranging from 1.01 to 16.2 mg/g) in all ten batches of Cortex Fraxini samples. Compared to the reported LC methods, this approach substantially reduces the total analysis time and requires a minuscule volume of organic solvents. An “Analytical Eco-Scale” assessment was used to evaluate the different assay methods of Cortex Fraxini. The current LC-MS method scored an impressive 90; it was better than the other four reports’ LC methods. Thus, the developed LC-MS method is rapid and green, which is helpful for the quality evaluation of Cortex Fraxini.

Electron paramagnetic resonance spectroscopy for the detection of radiation exposure in dreissenid mussels.

Electron paramagnetic resonance (EPR) spectroscopy, established for radiation measurements in calcified tissues, was identified as a methodology that merits investigation for the purpose of environmental radiation measurements using dreissenid mussels from the Great Lakes. With the refinement of sample preparation and measurement protocols, a linear relationship of dose with the peak-to-peak height of the radiation-induced signal at g = 2.0034 was established. A dedicated analysis algorithm was developed to process batches of samples, eliminating the need for manual peak-to-peak height measurement. Varying background EPR signals were identified in different sampling groups, with samples gathered in winter having a markedly lower background signal. Through optimisation of spectrum acquisition normalisation methods, it was possible to resolve doses as low as 0.2 Gy. This work provides further validation that EPR dosimetry of shelled species has the potential to contribute to better characterisation of absorbed doses in aquatic environments.

Rapid analysis of 226Ra in ultrapure gadolinium sulfate octahydrate

Abstract Numerous particle physics experiments utilize gadolinium (Gd), a rare earth element with the most significant neutron capture cross-section among all elements, to detect anti-neutrinos via inverse beta decays or to remove neutron-induced background events. For example, to load Gd into water Cherenkov detectors, Gd2(SO4)3 · 8H2O is dissolved and rare event search experiments are required to screen for radioactive impurities in Gd2(SO4)3 · 8H2O before dissolution. This study developed a new method to rapidly measure the radium-226 (226Ra) concentration in Gd2(SO4)3 · 8H2O. This method requires only 3 days to measure a batch of samples, as opposed to the usual method using high-purity germanium detectors, which takes approximately 20 days after arrival. The detection limit for the measurement of 226Ra concentration in Gd2(SO4)3 · 8H2O is 0.43 mBq/kg. This method has been already used for Gd2(SO4)3 · 8H2O screening at the Super-Kamiokande Gd (SK-Gd) project, and it can be applied to future experiments.

Integration of non-targeted multicomponent profiling, targeted characteristic chromatograms and quantitative to accomplish systematic quality evaluation strategy of Huo-Xiang-Zheng-Qi oral liquid

Huo-Xiang-Zheng-Qi oral liquid (HXZQOL) is a well-known traditional Chinese medicine formula for the treatment of gastrointestinal diseases, with the pharmacologic effects of antiinflammatory, immune protection and gastrointestinal motility regulation. More significantly, HXZQOL is recommended for the treatment of COVID-19 patients with gastrointestinal symptoms, and it has been clinically proven to reduce the inflammatory response in patients with COVID-19. However, the effective and overall quality control of HXZQOL is currently limited due to its complex composition, especially the large amount of volatile and non-volatile active components involved. In this study, aimed to fully develop a comprehensive strategy based on non-targeted multicomponent identification, targeted authentication and quantitative analysis for quality evaluation of HXZQOL from different batches. Firstly, the non-targeted high-definition MSE (HDMSE) approach is established based on UHPLC/IM-QTOF-MS, utilized for multicomponent comprehensive characterization of HXZQOL. Combined with in house library-driven automated peak annotation and comparison of 47 reference compounds, 195 components were initially identified. In addition, HS-SPME-GC-MS was employed to analyze the volatile organic compounds (VOCs) in HXZQOL, and a total of 61 components were identified by comparison to the NIST database, reference compounds as well as retention indices. Secondly, based on the selective ion monitoring (SIM) of 24 “identity markers” (involving each herbal medicine), characteristic chromatograms (CCs) were established on LC-MS and GC-MS respectively, to authenticate 15 batches of HXZQOL samples. The targeted-SIM CCs showed that all marker compounds in 15 batches of samples could be accurately monitored, which could indicate preparations authenticity. Finally, a parallel reaction monitoring (PRM) method was established and validated to quantify the nine compounds in 15 batches of HXZQOL. Conclusively, this study first reports chemical-material basis, SIM CCs and quality evaluation of HXZQOL, which is of great implication to quality control and ensuring the authenticity of the preparation.

A STABILITY, ACCURACY, AND ROBUSTNESS REPRESENTING LIQUID CHROMATOGRAPHIC METHOD FOR THE QUANTIFICATION OF ZANUBRUTINIB AND ITS SPECIFIED IMPURITIES

Objective: An innovative RP-HPLC isocratic method was established and then validated using Zanubrutinib and its specified impurities (Impurity-1, Impurity-2, Impurity-3, Impurity-4, and Impurity-5). Methods: In this method, effective chromatographic separation was given an X-Bridge Phenyl column measuring 250 mm x 4.6 mm, packed column with 5μ as a particle size. Acetonitrile, 1% Ortho Phosphoric acid (pH: 2.7), and methanol in the volume ratios 40, 40, and 20 were utilized as a mobile phase at room temperature with an optimized 1.0 ml/min flow rate. Wavelength was detected at 225 nm by using a PDA detector. Results: Retention times of zanubrutinib and its specified impurities were recorded at 13.284, 4.730, 6.816, 9.583, 10.726, and 12.287. Moreover, other parameters USP tailing is good, USP plate count above 4000, and USP resolution is greater than are equal to 2. The Obtained peaks are homogeneous, hence the purity angle is less than the purity threshold and No Purity Flag. According to ICH guidelines, this method was validated. Zanubrutinib (5-75 µg/ml), their quantified impurity-1, impurity-2, impurity-4, impurity-5 (0.1-1.5 µg/ml), and impurity-3 (0.1-1.5 µg/ml) are proved through linearity method in between LOQ to 75 quantified levels. The % recovery was present between 100.18-95.85, 103.15-93.80, which is a good and acceptance range (amongst 85% and 115%) for drug and specified impurities. The limit of quantitation (LOQ) and limit of detection (LOD) values were assessed for zanubrutinib and its specified impurities were tabulated. These values were calculated using slope (σ) and standard deviation (SD) methods. Method precision (M. P.) and Intermediate (I. P.) Intermediate (I. P.) precision was estimated by evaluating several (six) samples of a similar batch as per the planned technique on the day and the next day, using different columns and systems. Robustness information significantly affects the resolution between Zanubrutinib and specified impurities. The remaining parameters do not impact the parameter’s system suitability. Conclusion: Hence this method was chosen for common analysis. Finally, the system-suitable parameters and validation parameters values are acceptable limits.

Identifying early life stages of Great Lakes fishes using a metabarcoding approach

Detection of early life stages of fishes is important for understanding life history patterns and critical spawning habitats. When feasible, identifying early life stages of fishes using morphology requires taxonomic expertise and can be challenging, time consuming, and imprecise. In this study, we used DNA metabarcoding to identify egg and larval batch samples from two sites in the species-rich East Sydenham River, Ontario, Canada. We used a two-step PCR metabarcoding approach to amplify a highly variable region of the mitochondrial COI gene from 1075 mixed species batch samples. Amplicon libraries were sequenced with Illumina Mi-seq and the sequencing reads were filtered and assembled using the software package mothur. Barcodes were then classified using a reference library comprised of Great Lakes fishes and potential invaders. In total, 34 species, including three at-risk species and three invasive species, were detected at the two sampling sites. This study shows the potential utility of metabarcoding for detection and identification of early life stage Great Lake fishes.

Inter-laboratory validation of liquid chromatography-tandem mass spectrometry multi-mycotoxin determination in animal feed - method transfer from the reference laboratory to regional laboratories.

The results are presented of the inter-laboratory validation of a liquid chromatography-tandem mass spectrometry method for the determination of eight mycotoxins (aflatoxin B1, deoxynivalenol, fumonisin B1, fumonisin B2, ochratoxin A, toxin T-2, toxin HT-2 and zearalenone) in animal feeds. This study was an essential part of the method's transfer from the National Reference Laboratory to six regional laboratories in Poland working in the official survey of mycotoxins in feed. The laboratories received a batch of standard solutions, blank samples and quality control materials on which to perform analysis with one procedure and different liquid chromatography-tandem mass spectrometry conditions. The validation results show good precision (reproducibility coefficient of variation 3.7-20.5%) and accuracy of the method (recovery 89-120% and trueness 94-103%) and sufficient skills of the laboratory personnel. The study is an example of the successful transfer of the method among laboratories.