Quantification of binding capacity of natural products to target proteins by sensors integrating SERS labeling and photocrosslinked molecular probes.

Natural products-based screening of active ingredients and their interactions with target proteins is an important ways to discover new drugs. Assessing the binding capacity of target proteins, particularly when multiple components are involved, presents a significant challenge for sensors. As far as we know, there is currently no sensor that can accomplish high-throughput quantitative analysis of natural product-target protein binding capacity based on Raman spectroscopy. In this study, a novel sensor model has been developed for the quantitative analysis of binding capacity based on Surface-Enhanced Raman Spectroscopy (SERS) and Photocrosslinked Molecular Probe (PCMP) technology. This sensor, named SERS-PCMP, leverages the high throughput of molecular probe technology to investigate the active ingredients in natural products, along with the application of SERS labelling technology for target proteins. Thus it significantly improves the efficiency and accuracy of target protein identification. Based on the novel strategy, quantitative analysis of the binding capacity of 20 components from Shenqi Jiangtang Granules (SJG) to α-Glucosidase were completed. Ultimately, the binding capacity of these active ingredients was ranked based on the detected Raman Intensity. The compounds with higher binding capacity were Astragaloside IV (Intensity, 138.17), Ginsenoside Rh2 (Intensity, 87.46), Ginsenoside Rg3 (Intensity, 73.92) and Ginsenoside Rh1 (Intensity, 64.37), which all exceeded the binding capacity of the positive drug Acarbose (Intensity, 28.75). Furthermore, this strategy also performed a high detection sensitivity. The limit of detection for the enzyme using 0.1 mg of molecular probe magnetic nanoparticles (MP MNPs) was determined to be no less than 0.375 µg/mL. SERS-PCMP sensor integrating SERS labeling and photocrosslinked molecular probes which offers a fresh perspective for future drug discovery studies. Such as high-throughput drug screening and the exploration of small molecule-target protein interactions in vitro.

Elucidating the substance basis and pharmacological mechanism of Fufang Qiling granules in modulating xanthine oxidase for intervention in hyperuricemia.

ETHNOPHARMACOLOGICAL RELEVANCE: Fufang Qiling granules (FQG), derived from the traditional Qiling Decoction with a longstanding clinical history, is utilized for the treatment of hyperuricemia (HUA). FQG is formulated with a combination of seven Chinese herbs based on the principles of traditional Chinese medicine (TCM) theories. Clinical evidence indicates that FQG exhibits favorable therapeutic effects in reducing uric acid (UA) levels and attenuating renal damage. AIM OF THIS STUDY: To elucidate the potential active components and pharmacological mechanism of FQG in the treatment of HUA, and to provide an experimental basis for the development of efficient and low-toxicity TCM for HUA treatment. MATERIALS AND METHODS: A HUA rat model induced by potassium oxonate and adenine was established to initially evaluate the hypouricemic effects of FQG. Chemical analyses were conducted using an ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Network pharmacology was used to investigate the active components and mechanism of FQG in the treatment of HUA. Potential Xanthine oxidase (XOD) inhibitors were screened from FQG based on ultrafiltration liquid chromatography and mass spectrometry (UF-LC-MS). Molecular docking, surface plasmon resonance (SPR) and circular dichroism (CD) spectroscopy were applied to validate the interactions between the active components and XOD. RESULTS: In comparison to the model group, treatment with FQG significantly decreased serum UA, serum creatinine (CREA), serum blood urea nitrogen (BUN), and liver XOD activity. Additionally, the FQG administration notably ameliorated HUA-induced renal injury in rats. Through the pharmacodynamics of the HUA rat models and network pharmacology, it was found that XOD was a key pathway enzyme in UA metabolism. 18 XOD inhibitors were screened from FQG by UF-LC-MS, and 11 compounds with strong affinity were verified by SPR, molecular docking and CD spectroscopy. CONCLUSION: In summary, flavonoids, organic acids and saponins may be the active components in FQG that alleviate HUA. The primary mechanism of FQG involves inhibiting XOD enzyme activity in the plasma to reduce UA production, alleviating renal tubular epithelial cell necrosis, tubulointerstitial injury, fibrosis, and urate deposition, ultimately exerting a therapeutic effect on HUA.

Pixel-Level Recognition of Trace Mycotoxins in Red Ginseng Based on Hyperspectral Imaging Combined with 1DCNN-Residual-BiLSTM-Attention Model.

Red ginseng is widely used in food and pharmaceuticals due to its significant nutritional value. However, during the processing and storage of red ginseng, it is susceptible to grow mold and produce mycotoxins, generating security issues. This study proposes a novel approach using hyperspectral imaging technology and a 1D-convolutional neural network-residual-bidirectional-long short-term memory attention mechanism (1DCNN-ResBiLSTM-Attention) for pixel-level mycotoxin recognition in red ginseng. The "Red Ginseng-Mycotoxin" (R-M) dataset is established, and optimal parameters for 1D-CNN, residual bidirectional long short-term memory (ResBiLSTM), and 1DCNN-ResBiLSTM-Attention models are determined. The models achieved testing accuracies of 98.75%, 99.03%, and 99.17%, respectively. To simulate real detection scenarios with potential interfering impurities during the sampling process, a "Red Ginseng-Mycotoxin-Interfering Impurities" (R-M-I) dataset was created. The testing accuracy of the 1DCNN-ResBiLSTM-Attention model reached 96.39%, and it successfully predicted pixel-wise classification for other unknown samples. This study introduces a novel method for real-time mycotoxin monitoring in traditional Chinese medicine, with important implications for the on-site quality control of herbal materials.

Prediction of key quality attributes in Salvia miltiorrhiza standard decoction using a Gaussian process regression model.

INTRODUCTION: Nonstationary, nonlinear mass transfer in traditional Chinese medicine (TCM) extraction poses challenges to correlating process characteristics with quality parameters, particularly in defining clear parameter ranges for the process. OBJECTIVES: The aim of the study was to provide a solution for quality consistency analysis in TCM preparation processes. MATERIALS AND METHODS: Salvia miltiorrhiza was taken as an example for 15 batches of standard decoction. Using aqueous extract, alcoholic extract, and the content of salvianolic acid B as herb material key quality attributes, multiple nonlinear regression, Gaussian process regression, and artificial neural network models were employed to predict the key quality attributes including the paste yield, the content of salvianolic acid B, and the transfer rate. The evaluation criteria were root mean square error, mean absolute percentage error, and R2. RESULTS: The Gaussian process regression model had the best prediction effect on the paste yield, the content of salvianolic acid B, and the transfer rate, with R2 being 0.918, 0.934, and 0.919, respectively. Utilizing Gaussian process regression model confidence intervals, along with Shewhart control and intervals optimized through process capability index analysis, the quality control range of the standard decoction was determined as follows: paste yield, 25.14%-33.19%; salvianolic acid B content, 2.62%-4.78%; and transfer rate, 56.88%-64.80%. CONCLUSION: This study combined the preparation process of standard decoction with the Gaussian process regression model, accurately predicted the key quality attributes, and determined the quality parameter range by using process analysis tools, providing a new idea for the quality consistency standard of TCM processes.

Discovery of drug targets based on traditional Chinese medicine microspheres (TCM-MPs) fishing strategy combined with bio-layer interferometry (BLI) technology.

Target discovery of natural products is a key step in the development of new drugs, and it is also a difficult speed-limiting step. In this study, a traditional Chinese medicine microspheres (TCM-MPs) target fishing strategy was developed to discover the key drug targets from complex system. The microspheres are composed of Fe3O4 magnetic nanolayer, oleic acid modified layer, the photoaffinity group (4- [3-(Trifluoromethyl)-3H-diazirin-3-yl] benzoic acid, TAD) layer and active small molecule layer from inside to outside. TAD produces highly reactive carbene under ultraviolet light, which can realize the self-assembly and fixation of drug active small molecules with non-selective properties. Here, taking Shenqi Jiangtang Granules (SJG) as an example, the constructed TCM-MPs was used to fish the related proteins of human glomerular mesangial cells (HMCs) lysate. 28 differential proteins were screened. According to the target analysis based on bioinformatics, GNAS was selected as the key target, which participated in insulin secretion and cAMP signaling pathway. To further verify the interaction effect of GNAS and small molecules, a reverse fishing technique was established based on bio-layer interferometry (BLI) coupled with UHPLC-Q/TOF-MS/MS. The results displayed that 26 small molecules may potentially interact with GNAS, and 7 of them were found to have strong binding activity. In vitro experiments for HMCs have shown that 7 active compounds can significantly activate the cAMP pathway by binding to GNAS. The developed TCM-MPs target fishing strategy combined with BLI reverse fishing technology to screen out key proteins that directly interact with active ingredients from complex target protein systems is significant for the discovery of drug targets for complex systems of TCM.

Detection of trace components in Xiangdan injection of Dalbergia odorifera based on microextraction and back-extraction along with bar-form-diagram strategy.

Xiangdan Injection are commonly used traditional Chinese medicine formulations for the clinical treatment of cardiovascular diseases. However, the trace components of Dalbergia odorifera in Xiangdan Injection pose a challenge for evaluating its quality due to the difficulty of detection. This study proposes a technology combining dispersive liquid-liquid microextraction and back-extraction (DLLME-BE) along with Bar-Form-Diagram (BFD) to address this issue. The proposed combination method involves vortex-mixing tetradecane, which has a lower density than water, with the sample solution to facilitate the transfer of the target components. Subsequently, a new vortex-assisted liquid-liquid extraction step is performed to enrich the components of Dalbergia odorifera in acetonitrile. The sample analysis was performed on HPLC-DAD, and a clear overview of the chemical composition was obtained by integrating spectral and chromatographic information using BFD. The combination of BFD and CRITIC-TOPSIS strategies was used to optimize the process parameters of DLLME-BE. The determined optimal sample pre-treatment process parameters were as follows: 200 µL extraction solvent, 60 s extraction time, 50 µL back-extraction solvent, and 90 s back-extraction time. Based on the above strategy, a total of 29 trace components, including trans-nerolidol, were detected in the Xiangdan Injection. This combination technology provides valuable guidance for the enrichment analysis of trace components in traditional Chinese medicines.

Improving the geographical origin classification of Radix glycyrrhizae (licorice) through hyperspectral imaging assisted by U-Net fine structure recognition.

Radix glycyrrhizae (licorice) is extensively employed in traditional Chinese medicine, and serves as a crucial raw material in industries such as food and cosmetics. The quality of licorice from different origins varies greatly, so classification of its geographical origin is particularly important. This study proposes a technique for fine structure recognition and segmentation of hyperspectral images of licorice using deep learning U-Net neural networks to segment the tissue structure patterns (phloem, xylem, and pith). Firstly, the three partitions were separately labeled using the Labelme tool, which was utilized to train the U-Net model. Secondly, the obtained optimal U-Net model was applied to predict three partitions of all samples. Lastly, various machine learning models (LDA, SVM, and PLS-DA) were trained based on segmented hyperspectral data. In addition, a threshold method and a circumcircle method were applied to segment licorice hyperspectral images for comparison. The results revealed that compared with the threshold segmentation method (which yielded SVM classifier accuracies of 99.17%, 91.15%, and 92.50% on the training set, validation set, and test set, respectively), the U-Net segmentation method significantly enhanced the accuracy of origin classification (99.06%, 94.72% and 96.07%). Conversely, the circumcircle segmentation method did not effectively improve the accuracy of origin classification (99.65%, 91.16% and 92.13%). By integrating Raman imaging of licorice, it can be inferred that the U-Net model, designed for region segmentation based on the inherent tissue structure of licorice, can effectively improve the accuracy origin classification, which has positive significance in the development of intelligence and information technology of Chinese medicine quality control.

High resolution micro-confocal Raman spectrometer-based photo-affinity microarray technology for the investigation of active ingredients - Target protein recognition strategy.

Natural products has been used for the prevention and treatment of diseases for a long history. Research on the bioactive components from natural products and their interaction with target proteins are essential for drug discovery. However, studying the binding ability of natural products' active ingredients to target proteins is usually time-consuming and laborious due to their complex and diverse chemical structures. In this study, we have developed a high resolution micro-confocal Raman spectrometer-based photo-affinity microarray (HRMR-PM) technology for the investigation of active ingredients-target protein recognition strategy. The novel photo-affinity microarray was constructed by photo-cross-linking the small molecule with the photo-affinity group (4-[3-(Trifluoromethyl)-3H-diazirin-3-yl]benzoic acid, TAD) on the photo-affinity linker coated (PALC) slides under 365 nm ultraviolet irradiation. The small molecules on the microarrays with specific binding ability might immobilize target protein, which were characterized by high resolution micro-confocal Raman spectrometer. Using this method, more than a dozen components of Shenqi Jiangtang granules (SJG) were made into small molecule probe (SMP) microarrays. As a result, 8 of them had been identified to have α-glucosidase binding ability according to characteristic Raman shift at about 3060 cm-1. These compounds were further verified by different small molecule-protein interaction analysis methods, including contact angle D-value, surface plasmon resonance (SPR) and molecular docking. The results showed that Ginsenosides Mb, Formononetin and Gomisin D exhibited the strongest binding ability. In conclusion, the HRMR-PM strategy for investigating the interaction between target proteins and small molecules has the advantages such as high throughput, low sample consumption and fast qualitative characterization. This strategy is universal which can be applied in the study of in vitro binding activity of various types of small molecules to target proteins.

An intelligent extraction approach of feature information from three-dimensional DAD chromatogram for integrated quality control of traditional Chinese medicines: Gardenia jasminoides root as an example.

Traditional Chinese medicine (TCM) fingerprinting, which has the characteristics of holism and ambiguity, is a conventional strategy for the holistic quality control of TCMs. However, the fingerprinting of TCMs at the current stage generally adopts a single wavelength or few wavelengths, lacking the effective utilization of diode-array detector (DAD) chromatogram data. This study proposes an intelligent extraction approach of feature information from a three-dimensional DAD chromatogram to establish a novel bar-form-diagram (BFD) for integrated quality control of TCMs. The BFD was automatically established by the chromatographic and spectral information of a complex hybrid system in a DAD chromatogram. This covered the peak areas of target compositions at the optimal absorption wavelength. Taking 27 batches of Gardenia jasminoides root as samples, the BFD combined with chemometrics was applied for assessing the quality of samples completely, which improved the accuracy of origin classification using hierarchical cluster analysis, principal component analysis, soft independent modeling of class analogy and orthogonal partial least squares discriminant analysis. Single-wavelength fingerprinting and BFD used 23 and 38 common peaks as variables respectively, and the adjusted rand index results of the single wavelength and BFD were 0.559 and 0.819, respectively. Compared with the ergodic methods of each single wavelength, the peak recognition method in this study improved the operation speed from 180 s to 4 s and the computational complexity. The established BFD approach performed more abundant characteristic information of chemical components of TCMs and more accurate origin classification ability, and it had great advantages in the overall quality control of TCMs.

Applications of Hyperspectral Imaging Technology Combined with Machine Learning in Quality Control of Traditional Chinese Medicine from the Perspective of Artificial Intelligence: A Review.

Traditional Chinese medicine (TCM) is the treasure of China, and the quality control of TCM is of crucial importance. In recent years, with the quick rise of artificial intelligence (AI) and the rapid development of hyperspectral imaging (HSI) technology, the combination of the two has been widely used in the quality evaluation of TCM. Machine learning (ML) is the core wisdom of AI, and its progress in rapid analysis and higher accuracy improves the potential of applying HSI to the field of TCM. This article reviewed five aspects of ML applied to hyperspectral data analysis of TCM: partition of data set, data preprocessing, data dimension reduction, qualitative or quantitative models, and model performance measurement. The different algorithms proposed by researchers for quality assessment of TCM were also compared. Finally, the challenges in the analysis of hyperspectral images for TCM were summarized, and the future works were prospected.

Recognition of the rhizome of red ginseng based on spectral-image dual-scale digital information combined with intelligent algorithms.

Red ginseng is a widely used and extensively researched food and medicinal product with high nutritional value, derived from steamed fresh ginseng. The components in various parts of red ginseng differ significantly, resulting in distinct pharmacological activities and efficacies. This study proposed to establish a hyperspectral imaging technology combined with intelligent algorithms for the recognition of different parts of red ginseng based on the dual-scale of spectrum and image information. Firstly, the spectral information was processed by the best combination of first derivative as pre-processing method and partial least squares discriminant analysis (PLS-DA) as classification model. The recognition accuracy of the rhizome and the main root of red ginseng is 96.79% and 95.94% respectively. Then, the image information was processed by the You Only Look Once version 5 small (YOLO v5s) model. The best parameter combination is epoch = 30, learning rate = 0.01, and activation function is leaky ReLU. In the red ginseng dataset, the highest accuracy, recall and mean Average Precision at IoU (Intersection over Union) threshold 0.5 (mAP@0.5) were 99.01%, 98.51% and 99.07% respectively. The application of spectrum-image dual-scale digital information combined with intelligent algorithms in the recognition of red ginseng is successful, which provides a positive significance for the online and on-site quality control and authenticity identification of crude drugs or fruits.

Multi-wavelength fusion column fingerprint technology combined with chemometric analysis to evaluate the overall quality of the Gardenia jasminoides root.

Chromatographic fingerprinting provides effective technical means for quality evaluation of traditional Chinese medicine. In this work, a novel multi-wavelength fusion column fingerprint was obtained by intelligent selection of chromatographic peaks from different wavelengths, which displayed the maximum peak area information under the optimal wavelength at the same retention time. Here, the Gardenia jasminoides root was selected as a sample. The multi-wavelength fusion column fingerprint graph of the Gardenia jasminoides root was constructed from five wavelengths (203 nm, 210 nm, 238 nm, 250 nm and 330 nm). The peak capacity, peak resolution, the number of common peaks and similarity were used to evaluate the performance. The 19 batches of Gardenia jasminoides root were classified into three categories with clear distinction between origin categories based on the multi-wavelength fusion column fingerprint combined with chemometrics, including hierarchical cluster analysis and principal component analysis. Nine markers of variation that led to differences between batches were screened by orthogonal partial least squares discriminant analysis. This study demonstrated that the classification model based on the multi-wavelength fusion column fingerprint was better than that on a single-wavelength, and the fusion fingerprint was suitable for the identification and quality control of traditional Chinese medicine with more comprehensive chemical composition information and more accurate prediction ability.

[Discrimination of quality markers of Shujin Huoxue Capsules based on five principles coupled with cobweb model].

The quality markers(Q-markers) of Shujin Huoxue Capsules were comprehensively discriminated based on the five principles of transfer and traceability, specificity, compatibility, effectiveness and measurability. The compounds that could be transferred from the original medicinal materials to the preparation were selected with the principle of transfer and traceability. The specific components in the prescription were screened by reviewing literature with the principle of specificity. According to the principle of compatibility, the attributes of compounds were evaluated by the sovereign, minister, assistant and guide combination rules of the original medicinal materials in the prescription. According to the principle of measurability, the measurable components were summarized by reference to the pharmacopoeia and literature combined with the content. The mechanism of Shujin Huoxue Capsules in the treatment of osteoporosis was studied through network pharmacology based on the principle of effectiveness, which was the evaluation index of effectiveness. The chemical components screened out above were regarded as candidate Q-markers, and the cobweb model was plotted to obtain the comprehensive score of Q-markers. Hydroxysafflor yellow A, trachelosid, eleutheroside B, α-cyperone, protocatechuic acid, protocatechualdehyde and 4-methoxy salicylaldehyde were discriminated as the Q-markers of Shujin Huoxue Capsules based on the five principles combined with cobweb model.

[Integrated smart hyperspectral imaging and CARS-based characteristic band selection for rapid determination of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix].

In order to realize the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, this paper first prepared the sulphur-fumigated Achyranthis Bidentatae Radix samples with the usage amount of sulphur being 0, 2.5%, and 5% of the mass of Achyranthis Bidentatae Radix pieces. The SO_2 content in different batches of sulphur-fumigated Achyranthis Bidentatae Radix was determined using the method in Chinese Pharmacopoeia, followed by the acquisition of their hyperspectral data within both visible-near infrared(435-1 042 nm) and short-wave infrared(898-1 751 nm) regions by hyperspectral imaging. Meanwhile, the first derivative, AUTO, multiplicative scatter correction, Savitzky-Golay(SG) smoothing, and standard normal variable transformation algorithms were used to pre-process the original hyperspectral data, which were then subjected to characteristic band extraction based on competitive adaptive reweighted sampling(CARS) and the partial least square regression analysis for building a quantitative model of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix. It was found that the accuracy of the quantitative model built depending on the visible-near infrared spectra was high, with the determination coefficient of prediction set(R■) reaching 0.900 1. The established quantitative model has enabled the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, which can serve as an effective supplement to the method described in Chinese Pharmacopeia.

Process optimization for the synthesis of functionalized Au@AgNPs for specific detection of Hg2+ based on quality by design (QbD).

The current study highlights the advantages of using the quality by design (QbD) approach to synthesise and optimize SERS substrates for the detection of Hg2+. Considering that the performance of Au@AgNPs is affected by many factors, Plackett-Burman (PB) experimental design was used to determine the critical process parameters (CPPs) for evaluating the performance of Au@AgNPs. The quantitative relationships between the CPPs and the critical quality attributes (CQAs) were assessed by Box-Behnken Design (BBD). The optimal design space for Au@AgNPs was calculated via a Monte Carlo algorithm. Finally, detection of Hg2+ in the range of 1 ∼ 100 ng mL-1 (R 2 = 0.9891) was achieved by SERS in combination with 4,4-bipyridine (Dpy) as signal molecules. The recoveries for licorice ranged from 83.53% to 92.96%. Specificity and practicality studies indicated that the method based on the QbD concept and design space not only met the optimal performance of Au@AgNPs but also improved the rapid detection of Hg2+ in Chinese medicine samples.

A simple and sensitive preconcentration strategy by coupling salting-out assisted liquid-liquid extraction with online three-step stacking for the determination of potent anti-tumour compound vinblastine and its precursor in biological samples by capillary electrophoresis.

A sensitive capillary electrophoresis strategy was developed by combining salting-out assisted liquid-liquid extraction and online three-step stacking to detect trace quantities of antitumour indole alkaloids in complex biological samples. The proposed strategy fully exploits these two technologies such that extraction, online stacking and separation are combined in a fast and efficient manner. First, salting-out assisted liquid-liquid extraction was used to extract three indole alkaloids (vinblastine sulfate, catharanthine sulfate and vindoline) from complex biomasses. An appropriate volume of acetonitrile (ACN) was mixed with a faecal aqueous solution to precipitate proteins. The mixed solution was vortexed, followed by the addition of ammonium sulfate ((NH4)2SO4) to induce two-phase separation. Alkaloids were effectively extracted into the organic phase, which was then subjected to capillary electrophoresis (CE) analysis. The sensitivity of capillary electrophoresis was effectively improved by online three-step stacking. Good linearity of the calibration curve for each indole alkaloid was obtained in the concentration range of 0.1-1 µg/mL. Under optimal conditions, the sensitivity of ordinary injection was increased by up to 2366-fold, confirming the applicability of the proposed strategy for the sensitive determination of trace indole alkaloids in complex biological samples.

Two new nor-lignans from Selaginella pulvinata (Hook. & Grev.) Maxim and their antihyperglycemic activities.

Two new nor-lignans, pulvin A (1) and moellenoside C (2), along with two known compounds (3-4) were isolated from the whole plant of Selaginella pulvinate (Hook. & Grev.) Maxim. The structures of the new compounds were established on the basis of spectroscopic data and acid hydrolysis. All the isolates were investigated for their antihyperglycemic activities in 3T3-L1 adipocytes. The results showed that compounds 1 and 2 promoted the glucose consumption prominently in 3T3-L1 adipocytes in a dose-response manner. Compound 1 and 2 induced 1.14-1.73 folds and 1.03-1.55 folds changes relative to the basal level, respectively, in the concentration range of 12.5 µM to 50 µM.

A novel high-resolution monophenolase/diphenolase/radical scavenging profiling for the rapid screening of natural whitening candidates from Peaonia lactiflora root and their mechanism study with molecular docking.

ETHNOPHARMACOLOGICAL RELEVANCE: The root of Paeonia lactiflora is a traditionally-used whitening medicine in China for thousands of years. Although some tyrosinase inhibitors and/or antioxidants such as 1,2,3,4,6-pentagalloylglucose, gallic acid, have been isolated and identified, their tyrosinase inhibition pathway (monophenolase or diphenolase inhibition, or both two) have not been systematically studied and the underlying tyrosinase inhibition mechanism has not been revealed yet. Moreover, the exploring of new natural tyrosinase inhibitors and antioxidants is urgently needed. AIM OF THE STUDY: This review aimed to develop a new microplate-based high-resolution tyrosinase inhibition profiling assay and establish a furthermore triple high-resolution monophenolase/diphenolase/radical scavenging profiling for accelerating identification bioactive compounds from complicated plant extract. MATERIALS AND METHODS: The targeted isolation and structure elucidation were performed with high-performance liquid chromatography-high-resolution mass spectrometry and preparative high-performance liquid chromatography. It allows to be a proof of concept with the root of Paeonia lactiflora crude extract as a natural whitening herbal drug. RESULTS: The result showed that galloylpaeoniflorin specifically inhibited monophenolase activity. While 1,2,3,4,6-pentagalloylglucose, gallic acid and catechin demonstrated the inhibition towards both monophenolase and diphenolase. Among them, 1,2,3,4,6-pentagalloylglucose can inhibit monophenolase activity was reported for the first time. In addition, antioxidant properties were attributed to catechin, 1,2,3,4,6-pentagalloylglucose and gallic acid. Due to its low content and complicated configuration in the root of Paeonia lactiflora, a new potential tyrosinase inhibitor and radical scavenger which tentatively identified as hexagalloylglucose by high-resolution MS was still need further verification. What's more, the molecular docking unveiled that bioactive enzymatic inhibitors mainly interacted with amino acid catalytic residues of tyrosinase via H-bonds and van der wals, which may be helpful to understand their inhibition mechanisms with tyrosinase in the skin whitening. CONCLUSIONS: The platform provided a promising and efficient strategy for the rapid screening of whitening active components from natural sources.

Tracing anti-osteoporosis components from raw and salt-processed semen of Cuscuta chinensis by employing a biochemometrics strategy that integrates ultrasonic-assisted extraction, quantitation, efficacy assessment in zebrafish, and grey relationship analysis.

Semen of Cuscuta chinensis has been reported to have an anti-osteoporosis effect, however, the components which account for the anti-osteoporosis effect have not been clarified. In this work we propose a biochemometrics strategy that integrates quantitation, anti-osteoporosis evaluation in zebrafish, and grey relationship analysis for the identification of anti-osteoporosis components from the semen of Cuscuta chinensis. In the beginning, a precise and accurate liquid chromatography-tandem mass spectrometry method was established for simultaneous quantitation of seven major components in crude and salt-processed Cuscuta chinensis. The mode of multiple reaction monitoring was used. Chloramphenicol was selected as the internal standard. The method showed good linearity and repeatability. The recovery rates of each component ranged from 95.4 to 103.9%. The precisions of intra-day and inter-day were all within 5.0%. The method was then applied for quantitation of the seven major components in 11 batches of crude and salt-processed Cuscuta chinensis. Subsequently, the anti-osteoporosis effects of crude and salt-processed Cuscuta chinensis were evaluated in zebrafish. Principle component analysis, grey relationship analysis, and partial least squares regression were applied for deciphering the relationship between the contents of seven major components and the anti-osteoporosis effects. Hyperin, p-hydroxycinnamic acid, and astragalin were found to be the major anti-osteoporosis components.

Metal-Free Tandem Oxidative Cyclization for the Synthesis of 1,2-Dihydropyridazines and Pyrazoles.

Mediated by 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ), a novel oxidative coupling of hydrazones and 1,3-diarylpropenes has been disclosed to generate appealing ß,γ-unsaturated hydrazones, which further undergo 5-exo-trig or 6-endo-trig cascade cyclization to give the respective 1,2-dihydropyridazines or pyrazoles selectively under metal-free conditions. The mechanisms of the coupling and subsequent cyclization are proposed.