High-Performance Thin-Layer Chromatography Coupled with Single Quadrupole: Application the Identification and Differentiation of Rehmanniae Radix and Its Different Processing Products from Raw Materials to Commercial Products.

The authentication of ingredients in formulas is crucial yet challenging, particularly for constituents with comparable compositions but vastly divergent efficacy. Rehmanniae Radix and its derivatives are extensively utilized in food supplements, which contain analogous compositions but very distinct effects. Rehmanniae Radix, also a difficult-to-detect herbal ingredient, was chosen as a case to explore a novel HPTLC-QDa MS technique for the identification of herbal ingredients in commercial products. Through systematic condition optimization, including thin layer and mass spectrometry, a stable and reproducible HPTLC-QDa MS method was established, which can simultaneously detect oligosaccharides and iridoids. Rehmannia Radix and its processed products were then analyzed to screen five markers that could distinguish between raw and prepared Rehmannia Radix. An HPTLC-QDa-SIM method was further established for formula detection by using the five markers and validated using homemade prescriptions and negative controls. Finally, this method was applied to detect raw and prepared Rehmannia Radix in 12 commercial functional products and supplements.

Comprehensive HRMS Screening and Risk Assessments of Aristolochic Acid Analogues in Asari Radix et Rhizoma and Related Commercial Health Products.

Aristolochic acid analogues (AAAs) are well-known toxins. We performed the first comprehensive screening on AAAs in Asari Radix et Rhizoma (underground part of Asarum heterotropoides Schmidt), the only Aristolochiaceae plant widely used in clinical practice. LC-HRMS revealed 70 trace AAAs using polygonal mass defect filtering and precursor ion list strategies, 38 of which were newly discovered in A. heterotropoides. UHPLC-QTrap-MS/MS was then utilized for quantitative/semiquantitative analysis of 26 abundant compounds. Seventeen AAAs were detected from 91 batches of A. heterotropoides and 20 AAAs from 166 consumable products. For 141 Asari-containing proprietary products, aristolactam I and aristolactam II-glucoside exhibited the widest distribution, present in 98% products. AA IVa was the most abundant, detected in 91%. Notably, 60% of the products contained AA I (0.03-0.79 ppm). The safety was assessed using linear extrapolation, permitted daily exposure, cumulative amount, and the margin of exposure. It is recommended that AA I content be limited to 3 ppm.

Systematical targeted multicomponent characterization and comparison of Arnebiae Radix and its three confusing species by offline two-dimensional liquid chromatography/LTQ-Orbitrap mass spectrometry.

Arnebiae Radix, commonly known as "Zicao," can be easily confused with other compounding species, posing challenges for its clinical use. Here, we developed a comprehensive strategy to systematically characterize the diverse components across Arnebiae Radix and its three confusing species. First, an offline two-dimensional liquid chromatography (2D-LC) system integrating hydrophilic interaction chromatography (HILIC) and reverse phase (RP) separations was established, enabling effective separation and detection of more trace constituents. Second, a polygonal mass defect filtering (MDF) workflow was implemented to screen target ions and generate a precursor ion list (PIL) to guide multistage mass (MSn) data acquisition. Third, a three-step characterization strategy utilizing diagnostic ions and neutral losses was developed for rapid determination of molecular formulas, structure classes, and compound identification. This approach enabled systematic characterization of Arnebiae Radix and its three confusing species, with 437 components characterized including 112 shikonins, 22 shikonfurans, 144 phenolic acids, 131 glycosides, 18 flavonoids, and 10 other compounds. Additionally, 361, 230, 340, and 328 components were identified from RZC, YZC, DZC, and ZZC, respectively, with 142 common components and 30 characteristic components that may serve as potential markers for distinguishing the four species. In summary, this is the first comprehensive characterization and comparison of the phytochemical profiles of Arnebiae Radix and its three confusing species, advancing our understanding of this herbal medicine for quality control.

MATLAB language assisted data acquisition and processing in liquid chromatography Orbitrap mass spectrometry: Application to the identification and differentiation of Radix Bupleuri from its adulterants.

Comprehensive and rapid analysis of secondary metabolites like saponins remains challenging. This study aimed to establish a semi-automated workflow for filtration, identification, and characterization of saikosaponins in six Bupleurum species. Radix Bupleuri, a high-sales herbal medicine, is often adulterated, restricting its quality control and applications. Two authentic Radix Bupleuri species and four major adulterants were analyzed through UHPLC-LTQ-Orbitrap-MS for targeted saikosaponin analysis. To reveal trace saikosaponins and obtain quality fragment data, a MATLAB-based process automatically enumerating "sugar chain + aglycone + side chain" combinations and deduplicating generated a predicted saikosaponin database covering all possible saikosaponins as a precursor ion list for comprehensive targeted acquisition. To focus on informative ions and reduce MS analysis workload, we utilized MATLAB to automatically filtrate the false positive ions by MS1 and MS2 spectrometry. The newly established MATLAB-assisted data acquisition approach exhibited 50 % improvement in characterization of targeted saikosaponins. Furthermore, positive and negative ionization workflows were designed for accurate saikosaponins characterization based on fragmentation rules. In total, 707 saikosaponins were characterized, including over 500 potential new compounds and previously unreported C29 aglycones. We identified 25 saikosaponins present in both authentic species but absent in adulterants as potential markers. This unprecedented comprehensive multi-origin species differentiation demonstrates the promise of MATLAB-assisted acquisition and processing to advance saponin identification and standardize the Radix Bupleuri market.

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC.

Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.

Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations.

Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method's sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm's stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall.

A Novel Histogram Region Merging Based Multithreshold Segmentation Algorithm for MR Brain Images.

Multithreshold segmentation algorithm is time-consuming, and the time complexity will increase exponentially with the increase of thresholds. In order to reduce the time complexity, a novel multithreshold segmentation algorithm is proposed in this paper. First, all gray levels are used as thresholds, so the histogram of the original image is divided into 256 small regions, and each region corresponds to one gray level. Then, two adjacent regions are merged in each iteration by a new designed scheme, and a threshold is removed each time. To improve the accuracy of the merger operation, variance and probability are used as energy. No matter how many the thresholds are, the time complexity of the algorithm is stable at O(L). Finally, the experiment is conducted on many MR brain images to verify the performance of the proposed algorithm. Experiment results show that our method can reduce the running time effectively and obtain segmentation results with high accuracy.

Dynamic biometric identification from multiple views using the GLBP-TOP method.

To realize effective and rapid dynamic biometric identification with low computational complexity, a video-based facial texture program that extracts local binary patterns from three orthogonal planes in the frequency domain of the Gabor transform (GLBP-TOP) was proposed. Firstly, each normalized face was transformed by Gabor wavelet to get the enhanced Gabor magnitude map, and then the LBP-TOP operator was applied to the maps to extract video texture. Finally, weighted Chi square statistics based on the Fisher Criterion were used to realize the identification. The proposed algorithm was proved effective through the biometric experiments using the Honda/UCSD database, and was robust against changes of illumination and expressions.