Batches Of Samples Research Articles

NanoString nCounter-Based Assay for Detection of Fusion-Associated Salivary Gland Tumors.

Salivary gland tumors include numerous subtypes that vary from benign to highly aggressive, with many showing overlapping histopathological features that can make diagnosis challenging. Most subtypes express driver fusion genes that are tumor specific, and detection of such fusions is useful for differentiating amongst specific diagnoses, determining appropriate tumor grading, and guiding effective treatment. Currently, fusions can be detected by FISH, RT-PCR or through next-generation sequencing approaches, all of which are highly effective methodologies but can be costly or time consuming. We developed a rapid NanoString nCounter platform-based assay to detect salivary gland tumor fusions using a combination of fusion junction-specific probes and an approach through differential exon expression analysis. The assay includes 68 junction-specific probes and analysis of exon expression across 9 fusion-associated genes in a single multiplex assay. Out of 55 retrospective and 171 prospective cases assayed, we accurately detected the majority of cases of pleomorphic adenoma, adenoid cystic carcinoma, cribriform adenocarcinoma, clear cell carcinoma, secretory carcinoma and NUT-rearranged carcinoma, including cases of these tumor types arising in non-salivary gland sites, with the major drawback being an inability to detect MAML2-containing mucoepidermoid samples. With mucoepidermoid carcinoma excluded, the assay shows an overall sensitivity of 96.1% and specificity of 100%. We show that the majority of salivary gland tumor fusions can be effectively detected with a single rapid NanoString based assay, which can serve as a useful adjunctive tool for routine diagnostic head and neck pathology. The assay is low cost with a rapid turnaround time (30h total assay time per sample batch, with minimal technician input required) compared to alternate detection methods.

Quality Evaluation of Kidney Tea Granules From Different Origins Based on TLC, HPLC Fingerprinting, and Quantitative Analysis Combined With Chemical Pattern Recognition.

Kidney tea is an essential herbal medicine. It is widely used to treat conditions such as urinary stones, gallstones, and rheumatoid arthritis. There is currently no standardized or widely accepted research strategy for evaluating the quality of kidney tea granules (KTGs) after granulation. In this study, we aim to establish a comprehensive strategy for evaluating the quality of KTGs produced from different sources of kidney tea. A TLC combined with HPLC method was established to identify the chemical components in KTGs, and HPLC method was used to determine the contents of rosmarinic acid of KTGs. In order to distinguish samples and identify differential components, similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted. TLC and HPLC detection confirmed three chemical components of KTGs, which are rosmarinic acid, caffeic acid, and lithospermic acid. HPLC fingerprint analysis revealed a total of seven common peaks in 15 batches of KTGs. Similarity analysis showed that the similarity of all 15 batches of KTGs was greater than 0.969. The peak areas of the seven common peaks were identified by chemical pattern recognition, and the results showed that most of the KTGs from different origins were clustered together, with small differences between them. The PCA and OPLS-DA results showed that two principal components can represent 82.638% of the common peaks of KTGs, among which peak 5 represents rosmarinic acid, which is the main differential biomarker of KTGs from different regions. Quantitative analysis of rosmarinic acid in KTG samples was performed using HPLC fingerprint conditions and the content of rosmarinic acid in 15 batches of KTGs samples was measured to be between 8.01-14.61 mg/g. This study combines TLC, HPLC, and chemometrics to establish a stable and reliable method that can quickly and effectively identify the components of KTGs, accurately quantify known components in KTGs, and provide reference for the quality evaluation of KTGs.

The effect of complex extra-furnace treatment of metal melts on the formation of non-metallic inclusions in large-sized ingots

Abstract The article presents the results of studying non-metallic inclusions (NI) after extra-furnace treatment of the 13Cr9Mo2Co1NiVNbNB steel melt to produce large-sized ingots. The objects of the study were laboratory samples melted in a semi-industrial induction furnace with the capacity of 20 tons (the first batch), and samples obtained after melting in an electric arc furnace with the capacity of 120 tons, followed by modification and double vacuuming (the second batch).
Previously, using the Fact Sage software, modeling of the NI formation processes was carried out. The data were obtained on the possible NI composition and amount in this steel.
Studying NIs (the composition, the contamination index, inclusion parameters) was carried out on an ASPEX Explorer electron microscope using the method of automatic X-ray spectral analysis of particles (Automatic Features Analysis, AFA). It was established that in the samples of the first batch, the main part of the NIs consisted of chromium- and manganese-containing oxides: chromium-manganese spinels (Cr-Mn-O and Mn-Cr-Si-O groups), which occupy more than 94% of the total area of the NIs. In the samples of the second batch, the main part of the Nis was represented by Cr-Mn-O; Al-Cr-O; Cr-Al-Mn-O systems.
It was shown that the average size of non-ferrous substances in the samples of the laboratory batch and in industrial samples after extra-furnace treatment differs slightly (5-12%), however, the contamination index in industrial samples after vacuum treatment and especially after modification and repeated vacuum treatment was reduced by more than 10 times. The results obtained allow recommending vacuum treatment and complex modification with calcium and boron as measures to prevent the formation of non-ferrous metals and to improve metallurgical quality when smelting large ingots using complex alloy steels.

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

Rapid assessment of main agronomic indicators in the grain of winter and spring forms of triticale using infrared spectroscopy

Background. Calibration models were developed for rapid assessment of crucial agronomic indicators in the grain of winter and spring triticale from the VIR collection using near-infrared spectroscopy (NIRS). These calibration models will enable users to find the most optimal utilization trends for individual triticale accessions. Materials and methods. Indicators of agronomic value (coloration, moisture, and the content of ash, protein, starch, amylose and fiber) were studied on a set of 32 grain samples collected from winter and spring triticale forms (Triticosecale Wittm. & A. Camus) reproduced in 2021–2022 at the Dagestan Experiment Station, a branch of the N.I. Vavilov All-Russian Institute of Plant Genetic Resources (VIR). Calibration models for the assessment of the said agronomic characters in triticale grain were developed on the platform of the MATRIX-I IR analyzer (Bruker Optics, Germany) and based on the values obtained by classical methods of biochemical analysis adopted at VIR. Results. Reliability of the calibration models was tested on a randomized batch of triticale grain samples: the results of the new calibrations were compared with those of the conventional biochemical analysis. This verification showed that the data obtained through the use of calibration curves were reliable for protein and ash content, while other models required further improvement. Conclusion. Calibration models for measuring protein and ash content developed on the basis of the MATRIX-I IR analyzer can be used to perform rapid assessment of the winter and spring triticale grain. This method makes it possible to accelerate the process of obtaining crop characteristics for a number of basic agronomic characters and preserve valuable breeding material.

Evaluation of Soumbara (Parkia biglobosa) Quality During Storage Using Mid-Infrared Spectroscopy and Physicochemical Methods

The fermented seed of néré (Parkia biglobosa), known as Soumbara, is a condiment widely consumed by the population in West Africa and Guinea in particular. The present study aims to use physicochemical, colorimetric methods and mid-infrared (MIR) spectroscopy to evaluate the evolution of the quality of Soumbara during storage (30 days). Thus, 5 kg of Soumbara were taken, subdivided into 7 batches of samples, and analysed every 5 days for 30 days. Physicochemical analyses (water activity, moisture, proteins, and total ash) would show that the storage duration induced a variation during storage; for example, the total ash contents on days 1, 5, 10, 15, 20, 25 and 30 are respectively 3.96 ± 0.25, 3.89 ± 0.43, 3.97 ± 0, 23, 3.99 ± 0.12, 4.54 ± 0.14, 4.57 ± 0.2 and 4.8 ± 0.21%. During the first 15 days of storage, no significant differences (p < 0.05) were observed between samples. The colorimetric measurements showed that the storage duration did not significantly affect the values of a* and b*, except the L* value which decreased significantly (p<0.05) (42.16 ± 0.015 on day 1 to 40.02 ± 0.015 on day 30). Applying statistical methods to mid-infrared spectroscopy allowed a clear distinction between the Soumbara samples during storage for 30 days. The partial least squares regression (PLSR) applied to MIR spectra made it possible to predict water, moisture and protein activity levels, since R2 greater than 0.99 were obtained. The results demonstrated that mid-infrared spectroscopy coupled with chemometric tools could be used as a rapid screening tool to assess and monitor the quality of Soumbara during storage.

A Study on a Method of Detecting Heavy Metal Content Using Thioglycolic Acid and Quartz Crucible

In the Chinese Pharmacopoeia, the method of detecting heavy metals commonly uses thioglycolic acid. It is often used as a special method for the limit check of heavy metals in raw pharmaceutical or cosmetic raw materials. Traditional use of porcelain crucibles in sample pretreatment may result in false positive reactions. This study used quartz crucibles to replace the traditional porcelain crucibles to study the possibility of contamination. It was found that the false negative reactions caused by crucible material can be eliminated, and the precision of the method was verified by detecting three batches of samples. The method has good repeatability and accuracy. It can replace the use of traditional porcelain crucibles in the method of detecting heavy metals with thioglycolic acid.

Comprehensive Analysis of Bile Medicines Based on UHPLC-QTOF-MSE and Machine Learning.

Based on UHPLC-QTOF-MSE analysis and quantized processing, combined with machine learning algorithms, data modeling was carried out to realize digital identification of bear bile powder (BBP), chicken bile powder (CIBP), duck bile powder (DBP), cow bile powder (CBP), sheep bile powder (SBP), pig bile powder (PBP), snake bile powder (SNBP), rabbit bile powder (RBP), and goose bile powder (GBP). First, 173 batches of bile samples were analyzed by UHPLC-QTOF-MSE to obtain the retention time-exact mass (RTEM) data pair to identify bile acid-like chemical components. Then, the data were modeled by combining support vector machine (SVM), random forest (RF), artificial neural network (ANN), gradient boosting (GB), AdaBoost (AB), and Naive Bayes (NB), and the models were evaluated by the parameters of accuracy (Acc), precision (P), and area under the curve (AUC). Finally, the bile medicines were digitally identified based on the optimal model. The results showed that the RF model constructed based on the identified 12 bile acid-like chemical constituents and random forest algorithm is optimal with ACC, P, and AUC > 0.950. In addition, the accuracy of external identification verification of 42 batches of bile medicines detected at different times is 100.0%. So based on UHPLC-QTOF-MSE analysis and combined with the RF algorithm, it can efficiently and accurately realize the digital identification of bile medicines, which can provide reference and assistance for the quality control of bile medicines. In addition, hyodeoxycholic acid, glycohyodeoxycholic acid, and taurochenodeoxycholic acid, and so forth are the most important bile acid constituents for the identification of nine bile medicines.

12374 A Prospective Study Of Plasma Levels Of Asprosin And Incident Type 2 Diabetes Among Postmenopausal Women In The Women’s Health Initiative

Abstract Disclosure: B. Yang: None. J. Li: None. E.S. Silva: None. L.S. Phillips: None. J.E. Manson: None. A.P. Reiner: None. A.R. Chopra: None. S. Liu: None. Background: Asprosin, a 30-kDa hormone identified from white adipose tissue, has been shown to play key roles in regulating hepatic glucose release and appetite stimulation. Despite an increasing body of experimental studies demonstrating asprosin as a promising therapeutic target, little is known about its distribution in the general population particularly concerning its association with incident type 2 diabetes (T2D). Objectives: To evaluate whether higher plasma levels of asprosin are associated with incident T2D among postmenopausal women independently of known risk factors for T2D. Methods: We conducted a case-control study nested in the Women’s Health Initiative (WHI), including 5,198 participants free of diabetes, cardiovascular diseases, and cancer at baseline (2,602 incident T2D cases and 2,596 controls). Plasma levels of asprosin were measured using a sandwich enzyme-linked immunosorbent (ELISA) assay. We used conditional logistic regression models to estimate the association between asprosin and T2D risk with adjustment for matching variables (age, race, sampling batch), education, body mass index (BMI), physical activity, tobacco smoking, alcohol drinking, family history of diabetes, age at menopause, total energy intake, the Alternate Healthy Eating Index (AHEI), and multivitamin use. Results: Approximately 43% of participants were white and the mean age at baseline was 63 years. The median plasma levels of asprosin were 38 (IQR: 30, 50) pmoL/L in the controls and 39 (IQR: 31, 50) pmoL/L among those who developed T2D during follow-up. Compared with the participants in the lowest quintile of plasma levels of asprosin at baseline, those in the highest quintile had a 26% increased risk of T2D (adjusted odds ratio: OR: 1.26 [95% CIs, 1.01, 1.57], p for trend=0.03) in the fully adjusted model. The OR estimates exhibited a large variation across race and BMI, although there was a linear dose-response relation between levels of asprosin and T2D risk (p for nonlinear=0.44). Genetic analysis revealed that women with the heterozygous allele (CG) at single nucleotide polymorphisms (SNP) rs56407701 had higher levels of asprosin compared to women with the homozygous reference allele (CC). Further, Mendelian Randomization analysis revealed that genetically predicted asprosin levels were associated with higher T2D risk. Conclusion: Among postmenopausal women, higher plasma levels of asprosin were prospectively associated with elevated T2D risk independent of known T2D risk factors. Further work is needed to confirm the potential causal role of this peptide hormone in the clinical risk stratification and potential prevention of T2D. Presentation: 6/2/2024

Impact of Heat Treatment Conditions and Cold Plastic Deformation on Secondary Hardening and Performance of Cold Work Tool Steel X160CrMoV12

In this study, the effect of the cold plastic deformation of a Bridgman anvil at room temperature on the hardness and wear resistance of X160CrMoV12 steel was investigated by utilizing the hardness test, X-ray diffraction (XRD), abrasive emery wear (AEMW) test, optical examination, and scanning electron microscopy (SEM). Three batches of samples were prepared for the experiment: I—as-hardened, II—after hardening with subsequent tempering at 600 °C for 1.5 h, and III— after hardening with subsequent plastic deformation. The hardening of the samples was performed at three temperatures: 1100 °C, 1150 °C, and 1200 °C. The highest content of retained austenite, as much as 69.02%, was observed during hardening at 1200 °C, while 17.36% and 38.14% were formed at lower temperatures, respectively. After tempering (Batch II), the content of residual austenite decreased proportionally by a factor of about seven for each hardening temperature. The effect of plastic deformation (Batch III) is observed, analyzing the hardness of the samples from the surface to the depth, reaching an average hardened depth of 0.08 mm. To evaluate the wear resistance, the surfaces of the three test batches were subjected to an abrasive emery wear test under a 5 N load. Hardened and plastically deformed samples showed higher wear resistance than hardened and tempered samples. The results confirmed that the optimal hardening temperature to achieve the maximum wear resistance of this steel is 1100 °C.

Pairwise analysis of gene expression for oral squamous cell carcinoma via a large-scale transcriptome integration.

Among all cancers occurring in the head and neck region, oral squamous cell carcinoma (OSCC) is the most common oral malignant tumours characterized by its aggressiveness and metastasis. The development of transcriptomics technology has greatly facilitated the diagnosis of various cancers. However, identifying genetic biomarkers is limited by data from a single batch of OSCC samples, and integrating analysis across different platforms remains a great challenge. In this study, we integrated five OSCC transcriptome datasets using an innovative strategy capable of mitigating batch effect, and extracting information from different datasets based on changes in the relative expression of gene pairs. By leveraging a machine learning method, we developed a prediction model including 27 differential gene pairs (DGPs) to discriminate OSCC from control samples, achieving an area under the receiver operating characteristic curve (AUC) of 0.8987 for the training set. Moreover, the model demonstrated commendable performance in four external validation sets, with AUCs of 0.9926, 0.9688, 0.8052 and 0.8565, respectively. Subsequently, a prognostic model was constructed based on six key gene pairs through univariate and multivariate Cox regression analysis. The AUCs of the model at 1-year and 3-year overall survival time prediction were 0.717 and 0.779 in an independent dataset. Our result demonstrates the effectiveness of this new method of integrating data and identifying DGPs. Using DGPs can significantly improve the performance of both diagnostic and prognostic models.

Automatic immunomagnetic beads cleanup and isotope dilution-based liquid chromatography tandem mass spectrometry for the determination of aflatoxins in feed and feedstuff

In this study, a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method based on immunomagnetic bead cleanup and isotope dilution was established for the simultaneous detection of four aflatoxins (aflatoxins B1, B2, G1, and G2) in feed and feedstuff. The samples were extracted with an acetonitrile–water mixture (84 + 16, v/v), purified using automatic immunomagnetic beads, and detected by LC-MS/MS. Under the optimal LC-MS/MS conditions, the four mycotoxins had a linear relationship in the concentration range of 0.5–50 µg/kg, and the correlation coefficients were all greater than 0.99. The limits of detection of the four aflatoxins ranged from 0.075–0.17 µg/kg, and the limits of quantification ranged from 0.25–0.5 µg/kg. The average spike recoveries of the four common feed and feedstuff matrices (corn, soybean meal, pig compound feed, and chicken compound feed) at three spiked levels (2, 10, and 50 µg/kg) ranged from 80.8–118.3 %, and the relative standard deviations (RSDs) were all less than 7.80 %. Thirty-two actual feed samples were analyzed for the four aflatoxins. Aflatoxin B1 was detected in nineteen samples (0.11–6.39 µg/kg); aflatoxin B2 was detected in four samples (0.12–0.43 µg/kg); aflatoxin G1 was detected in four samples (0.19–0.57 µg/kg), and aflatoxin G2 was not detected. The immunomagnetic beads cleanup system could automatically remove each of the four aflatoxins in feed and feedstuff and realize batch sample processing in 20 min. The established LC-MS/MS method has high accuracy and repeatability, and it can be used for the rapid and simultaneous determination of four aflatoxins in feed samples.

Application of ultrasound for leaching Vietnamese monazite concentrate with NaOH solutions to obtain chlorides of rare earth elements

Relevance. Despite the use of alkali stripping technology for processing monazite concentrate in industry, this method has a number of limitations, such as low efficiency of concentrate stripping, the need to use a large excess of alkali and a long process time. Aim. Improvement of existing technology to overcome these limitations. Methods. Fusion with NaOH under pressure and fusion with NaOH combined with ball milling, as well as fusion with NaOH using high intensity ultrasound. High-intensity ultrasound has long been studied and used in industry to intensify leaching/alloying in various applications to increase efficiency and speed in leaching for copper, vanadium, nickel, bauxite and uranium ores. This article is devoted to studying the influence of high-intensity ultrasound on the efficiency of opening monazite concentrate with NaOH solutions. Experimental data were obtained using mass spectrometric, X-ray diffraction and ICP-OES analysis methods. Results and conclusions. The optimal conditions for leaching batches of samples weighing from 100 g to 1 kg were determined. The authors have carried out the research on the separation of radioactive elements and impurities in a solution of rare earth chlorides, obtaining a solution of rare earth chlorides purified from radioactive components and impurities. It was shown that ultrasonic exposure eliminates the disadvantages of traditional fusion technology with NaOH, which can be used for deep processing of Vietnamese monazite concentrate. The conditions for obtaining REE chlorides from a previously opened monazite concentrate are given.

Rapid identification and on-site analysis by miniature mass spectrometry of chemical markers for fragrant rosewood authentication

Dalbergia odorifera T. Chen (Dalbergiae Odoriferae Lignum, DOL), renowned for its therapeutic applications in traditional Chinese medicine and its value in crafting luxury items, faces challenges due to the slow heartwood formation process and subsequent market adulteration. The proliferation of counterfeit products necessitates reliable methods for rapid, on-site authentication. This study aims to establish a rapid, green, and on-site method to identify the authenticity of DOL. A comprehensive analysis was conducted on 10 batches of DOL samples sourced from diverse locations, utilizing a miniature mass spectrometer (mini MS) equipped with a paper capillary spray (PCS) technique. Multivariate statistical approaches were employed to classify the samples and pinpoint chemical markers indicative of authenticity. Subsequent MS-guided separation and thin-layer chromatography (TLC) verified the markers' validity and assessed the greenness profile by Analytical Eco-scale (AES), Green Analytical Procedure Index (GAPI), and Analytical GREEnness (AGREE). A total of 10 batches of DOL samples detected by PCS-mini MS were classified into authentic and counterfeit, by unsupervised cluster analysis. Sativanone (m/z 301.1, VIP=6.0, p=0.000001) and 3′-O-methylviolanone (m/z 331.1, VIP=3.2, p=0.000382) were regarded as the chemical markers for the rapid identification of DOL. The results of the TLC method were consistent with this method, and the new method is greener. The application of mini MS for on-site authentication of DOL via specific chemical markers offers significant advantages, including operational simplicity, high efficiency, greenness, and accuracy. The deployment of this strategy promises to facilitate the effective regulation of DOL, ensuring authenticity and quality.

Electronic senses and UPLC-Q-TOF/MS combined with chemometrics analyses of Cynanchum species (Baishouwu).

Baishouwu, derived from Cynanchum auriculatum (CA) Royle ex Wight, Cynanchum bungei (CB) Decne., and Cynanchum wilfordii (CW) (Maxim.) Hemsl., is a valuable traditional Chinese medicine. CA is also recognized as a new food resource by China's National Health Commission. Given the considerable variations in flavor and chemical composition among these species and lack of their qualitative assessments, accurately differentiating between the species constituting Baishouwu is essential. To develop a method combining electronic tongue (E-tongue), electronic nose (E-nose), and ultra-performance liquid chromatography-quadrupole-time of flight/mass spectrometry (UPLC-Q-TOF/MS) to differentiate between Baishouwu samples. Fifteen batches of Baishouwu samples were analyzed using E-tongue, E-nose, and UPLC-Q-TOF/MS. Flavor differences and key differential metabolites were determined through principal component analysis and orthogonal partial least squares discriminant analysis. E-tongue results revealed umami, sweetness, and richness as the predominant flavors of Baishouwu, with CA having the highest umami response, CW exhibiting the highest bitterness, and CB the highest sweetness. E-nose sensors showed consistent responses across species, with variations in signal strength; W1W and W2W sensors showed the highest response values. A total of 158 and 41 characteristic variables in the positive and negative ion modes, respectively, were selected as candidate differential metabolites, of which 29 and 14 were confirmed through database comparison. Eight critical differential metabolites, including C21 steroids and acetophenone compounds, were identified. This study presents a strategy for differentiating among the species constituting Baishouwu, providing a basis for broader application and establishing quality standards for these medicinal compounds.

Digital identification and adulteration analysis of bupleurum chinense and bupleurum marginatum based on "digital identity" and UHPLC-QTOF-MSE

Background and objectivesone of the legal sources of Bupleuri Radix (BR) is the Bupleurum chinense (BC), rather than the Bupleurum marginatum (BM). However, fake incidents of BM mixed with BC often occur in the market, which makes it more difficult to supervise the quality of BR and even endanger the life and health of patients. To strengthen the quality control and supervision of BP, we carried out digital identification and adulteration analysis of BC and BM based on the "digital identity" and UHPLC-QTOF-MSE. Methodfirstly, the BC and BM were analyzed by UHPLC-QTOF-MSE to obtain the quantized data characterization of chemical components. Secondly, the shared ions were extracted from different batches of BC and BM's control medicinal materials as their "data representation of ions", respectively. Then, the data matrices of unique ions of BC relative to BM and BM relative to BC were screened out, and the Top-N ions were outputted as the "digital identities" of BC and BM, sorted by ionic strength. Finally, the above "digital identities" of BC and BM were used as benchmarks for matching positive samples and commercial samples to feedback on matching credibility (MC). Resultsthe results showed that based on the "digital identities" of BC and BM, the digital identification of BC, BM, and positive samples can be realized efficiently and accurately at the individual level of Chinese medicine, even if 3 % of BM in the mixed samples can still be identified efficiently and accurately. 10 batches of market samples were identified as adulterated samples. Furthermore, chemometric analysis has proven the reliability of BC and BM-based "digital identity" identification. ConclusionIt proved that the identification and adulteration analysis of two herbs can be realized efficiently and quickly through the "digital identities" of BC and BM. It has important reference significance for developing non-targeted digital identification of herbal medicines at the individual level of Chinese medicine based on "digital identity", which was beneficial to the construction of digital Chinese medicine and digital quality control.

Investigations into the chemical composition and nutritional value of different batches of rapeseed meals for turkey poults

ABSTRACT A study was conducted to investigate the chemical composition and feeding value of rapeseed meal (RSM) batches produced at the same plant when fed to turkey poults. In total, seven RSM samples were obtained from a single manufacturer within a period of 90 days. Although the manufacturer followed the same procedures during oil extraction and RSM production, different batches of rapeseed were used. A balancer feed (BF) was formulated to contain 11.85 MJ/kg ME and 265 g/kg crude protein. Seven nutritionally complete test mash diets were prepared by mixing 200 g/kg of each RSM batch sample with 800 g/kg of the BF, totalling 8 diets. Diets were fed to female B.U.T. Premium turkeys from 12 to 21 d of age. Each diet was fed to six raised floor pens, housing two birds, following randomisation. During the experiment, a nitrogen corrected apparent metabolisable energy (AMEn) assay was performed using a total collection technique. The AMEn in RSM samples was calculated based on the differences between the AMEn values of basal and test diets. Associations were examined between AMEn and the chemical composition of the RSM samples. The overall determined AMEn value of the RSM ranged from 5.50 MJ/kg DM to 8.53 MJ/kg DM, giving an average AMEn of 7.29 MJ/kg DM. There was no difference (p > 0.05) in AMEn content between batches. There was a negative correlation (r = – 0.864; p < 0.05) between AMEn values and the neutral detergent fibre (NDF) content of the RSM samples. The results suggest that the NDF could be a good predictor of the AMEn of industry produced RSM. It may be inferred that processing rather than cultivar could be the main factor determining the feeding value of RSM for turkeys.

Metabolomics and quantitative analysis to determine differences in the geographical origins and species of Chinese dragon's blood.

The aim of this study was to comprehensively analyze the differences in Chinese dragon's blood (CDB), specifically Dracaena cochinchinensis and Dracaena cambodiana, from different geographical origins. Metabolomic analysis of CDB was performed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A reliable ultrahigh-performance liquid chromatography method with a photodiode array detector (UHPLC-PDA) was developed and applied for the quantitative analysis of 12 phenolic compounds in 51 batches of samples. A total of 1394 metabolites were detected, of which 467 were identified as differentially accumulated metabolites. Multivariate analysis revealed that both origin and species had an effect on the composition of CDB, with greater variation between species. 19 phenolic compounds were selected as quality markers to distinguish D. cochinchinensis (Hdsp) from D. cambodiana (Hdca), and oppositin and spinoflavanone a were identified as quality markers to discriminate D. cochinchinensis samples from Hainan (Hdsp) and Guangxi Provinces (Gdc). Quantitative analysis indicated that four phenolic compounds, including loureirin D, 4H-1-benzopyran-4-one,2,3-dihydro-3,5,7-trihydroxy-3-[(4-methoxyphenyl)methyl]-,(R)-, loureirin B, and pterostilbene, showed significant differences between Gdc and Hdsp. Additionally, five phenolic compounds, namely resveratrol, loureirin D, pinostilbene, 4H-1-benzopyran-4-one,2,3-dihydro-3,5,7-trihydroxy-3-[(4-methoxyphenyl)methyl]-, (R)-, and loureirin B, exhibited significant differences between Hdsp and Hdca. There are significant differences in the quality of CDB from different geographical origins and species, which lays the foundation for the in-depth development and utilization of different sources of CDB.

Label-Free Quantitative Proteomics Analysis of COVID-19 Vaccines by Nano LC-HRMS.

A nanoliter liquid chromatography-high resolution mass spectrometry-based method was developed for quantitative proteomics analysis of COVID-19 vaccines. It can be used for simultaneous qualitative and quantitative analysis of target proteins and host cell proteins (HCPs) in vaccine samples. This approach can directly provide protein information at the molecular level. Based on this, the proteomes of 15 batches of COVID-19 inactivated vaccine samples from two companies and 12 batches of COVID-19 recombinant protein vaccine samples from one company were successfully analyzed, which provided a significant amount of valuable information. Samples produced in different batches or by different companies can be systematically contrasted in this way, offering powerful supplements for existing quality standards. This strategy paves the way for profiling proteomics in complex samples and provides a novel perspective on the quality evaluation of bio-macromolecular drugs.

SEMbap: Bow-free covariance search and data de-correlation.

Large-scale studies of gene expression are commonly influenced by biological and technical sources of expression variation, including batch effects, sample characteristics, and environmental impacts. Learning the causal relationships between observable variables may be challenging in the presence of unobserved confounders. Furthermore, many high-dimensional regression techniques may perform worse. In fact, controlling for unobserved confounding variables is essential, and many deconfounding methods have been suggested for application in a variety of situations. The main contribution of this article is the development of a two-stage deconfounding procedure based on Bow-free Acyclic Paths (BAP) search developed into the framework of Structural Equation Models (SEM), called SEMbap(). In the first stage, an exhaustive search of missing edges with significant covariance is performed via Shipley d-separation tests; then, in the second stage, a Constrained Gaussian Graphical Model (CGGM) is fitted or a low dimensional representation of bow-free edges structure is obtained via Graph Laplacian Principal Component Analysis (gLPCA). We compare four popular deconfounding methods to BAP search approach with applications on simulated and observed expression data. In the former, different structures of the hidden covariance matrix have been replicated. Compared to existing methods, BAP search algorithm is able to correctly identify hidden confounding whilst controlling false positive rate and achieving good fitting and perturbation metrics.