Chemical Patterns Research Articles

Application of HPLC Fingerprint Combined with Chemical Pattern Recognition and Multi-Component Determination in Quality Evaluation of Echinacea purpurea (L.) Moench.

Echinacea purpurea (EP) is a common medicinal material for extracting anti-RSV components. However, up to now, there has been no effective and simple method to comprehensively reflect the quality of EP. In our current study, the quality of Echinacea purpurea (L.) Moench samples from six different cultivation locations in China was evaluated by establishing a high-performance liquid chromatography (HPLC) fingerprint, combining chemical pattern recognition and multi-component determination. In this study, the chemical fingerprints of 15 common peaks were obtained using the similarity evaluation system of the chromatographic fingerprints of traditional Chinese medicine (2012A Edition). Among the 15 components, three phenolic acids (caftaric acid, chlorogenic acid and cichoric acid) were identified and determined. The similarity of fingerprints of 16 batches of Echinacea purpurea (L.) Moench samples ranged from 0.905 to 0.998. The similarity between fingerprints of five batches of commercially available Echinacea pupurea (L.) Moench and the standard fingerprint ”R” ranged from 0.980 to 0.997, which proved the successful establishment of the fingerprint. PCA and HCA were performed with the relative peak areas of 15 common peaks (peak 3 as the reference peak) as variables. Anhui and Shaanxi can be successfully distinguished from the other four cultivation areas. In addition, the index components of caftaric acid, chlorogenic acid and cichoric acid were in the range of 1.77–8.60 mg/g, 0.02–0.20 mg/g and 2.27–15.87 mg/g. The results of multi-component index content determination show that the contents of the Shandong cultivation area were higher, followed by Gansu, Henan and Hebei, and the lowest were Anhui and Shaanxi. The results are consistent with PCA and HCA, which proved that the quality of Echinacea purpurea (L.) Moench from different origins was different. HPLC fingerprint combined with chemical pattern recognition and multi-component content determination was a reliable, comprehensive and prospective method for evaluating the quality of Echinacea purpurea (L.) Moench. This method provides a scientific basis for the quality control and evaluation of Echinacea purpurea (L.) Moench.

Chemical and Radiological Characterizations of the Desert Dust Coming from Northern Africa to Batman (Southeastern Turkey)

This work investigates the chemical and radiological characterizations of the dust coming from Sahara in North Africa to Batman city in the southeastern region of Turkey on 20 May 2017. According to meteorological maps, the source region of the dust storm that took place in Batman was found to be Libya, which is supported by the NOAA HYSPLIT model’s back trajectory analysis. XRD analyses show that the common minerals of the dust samples are quartz, feldspars, calcite, dolomite, hematite and rutile. Chemical patterns of some major, minor and trace elements in dust samples are generally consistent with those of Saharan Dust composition. The heavy metals in the dust follow the decreasing concentration order: Mn > Zn > Cr >Ni > Cu > Pb. The enrichment factors (EF) of these elements show anthropogenic contamination effects with regard to Zn, Cr and S elements in dust samples. The corresponding values of the radionuclides, the absorbed dose rates in the air and the annual effective doses in the samples were also evaluated and compared to the internationally recommended values. The findings are supposed to be beneficial for tracking and evaluating any environmental pollution inventory in this area.

The chemical signature of the Galactic spiral arms revealed by Gaia DR3

Taking advantage of the recent Gaia Data Release 3 (DR3), we mapped chemical inhomogeneities in the Milky Way’s disc out to a distance of ∼4 kpc from the Sun, using different samples of bright giant stars. The samples were selected using effective temperatures and surface gravities from the GSP-Spec module, and they are expected to trace stellar populations of a different typical age. The cool (old) giants exhibit a relatively smooth radial metallicity gradient with an azimuthal dependence. Binning in Galactic azimuth ϕ, the slope gradually varies from d[M/H]/dR ∼ −0.054 dex kpc−1 at ϕ ∼ −20° to ∼ − 0.036 dex kpc−1 at ϕ ∼ 20°. On the other hand, the relatively hotter (and younger) stars present remarkable inhomogeneities, which are apparent as three (possibly four) metal-rich elongated features in correspondence with the spiral arms’ locations in the Galactic disc. When projected onto the Galactic radius, those features manifest themselves as statistically significant bumps on top of the observed radial metallicity gradients with amplitudes up to ∼0.05–0.1 dex, making the assumption of a linear radial decrease not applicable for this sample. The strong correlation between the spiral structure of the Galaxy and the observed chemical pattern in the young sample indicates that the spiral arms might be at the origin of the detected chemical inhomogeneities. In this scenario, the spiral arms would leave a strong signature in the younger stars which progressively disappears when cooler (and older) giants are considered.

Impact of meteorological conditions on tropospheric ozone and associated with parameterization methods for quantitative assessment and monitoring

In recent years, the heavy ozone pollution events around the world have shown a sudden frequently increase, which has aroused widespread concern in the government and the public. It is well known that O3 is driven by photochemical reactions triggered by solar radiation (direct and indirect solar radiation), the O3 concentration calculated by chemical mechanism is mostly significantly lower than the actual O3 observation. Based on the study of the effect of meteorological conditions on the “additional increment” of O3 in three representative regions of Beijing, Hangzhou and Guangzhou from 2015 to 2020, an innovation diagnostic theory algorithm that the cross-cutting effects of atmospheric clouds on the chemical pattern of O3 solar radiation is established in this study. On this basis, a parametric evaluation method of O3 is established. The novelty of this study is 1) Comprehensive influence of the meteorological conditions and photochemical reactions mechanisms on the cross-cutting effects of O3 concentration are given. Especially low-level clouds in the troposphere, which have significantly large variable effects on the reflection and refraction of O3 through solar radiation. Theory quantitative algorithm of cloud scattering, cloud height, cloud volume and cloud structure changes, as well as feedback effects caused by water vapor condensation, which closely related to the transformation of O3 precursors are given. 2) Based on this, a parameterization method for quantitative O3 assessment and monitoring, which is a Parameterization for Linking Ozone pollution with Meteorological conditions. 3) Applying the theoretical algorithm and parameterization method of this study, comparing the changes of O3 in 2018 with 2019, an objective quantitative distinction between emission reduction and meteorological impact was made, showing that emission reduction still played a leading role, with a contribution rate of about 27%. This shows that the created quantitative algorithm of atmospheric cloud theory and the innovative parameterization method can provide an objective quantitative basis for O3 pollution decision-making and public emission reduction.

Quality evaluation of Panax notoginseng (Burk.) F.H. Chen using supercritical fluid chromatography–mass spectrometry and chemical pattern recognition

An efficient supercritical fluid chromatography-mass spectrometry (SFC-MS) method was developed for the quality evaluation of Panax Notoginseng (Burk) F.H. Chen (P. notoginseng) by combination with chemical pattern recognition (CPR). Design of experiments (DoE) was applied to obtain optimal SFC-MS conditions. Several CPR methods including hierarchical cluster analysis (HCA), principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to establish a classification model based on the peak areas and contents of 12 components in P. notoginseng in order to evaluate the quality difference according to the collecting time (Chunqi and Dongqi) and medicinal parts (fibrous root, rhizome, branch root, and main root). PLS-DA has proved to be a satisfactory method with accurate discrimination of the selected samples. The characteristic variables based on the variable importance in projection (VIP) values were selected using PLS-DA. Three characteristic components (ginsenoside Rg2, ginsenoside Rg1, ginsenoside Rb1) with higher VIP values (>1) were chosen to further build the CPR model. Subsequently, the model was verified by testing another set of samples and the results indicated that the established model was satisfactory. PLS-DA models based on the peak areas of the 12 selected analytes in 30 batches of P. notoginseng could give accurate classification. The obtained results demonstrate that the developed method using SFC-MS and PLS-DA has a great potential for the quality assessment of P. notoginseng.

The neutron-capture and α-elements abundance ratios scatter in old stellar populations: cosmological simulations of the stellar halo

ABSTRACT We investigate the origin of the abundance ratios and scatter of the neutron-capture elements Sr, Ba, and Eu in the stellar halo of a Milky Way-mass galaxy formed in a hydrodynamical cosmological simulation, and compare them with those of α elements. For this, we implement a novel treatment for chemical enrichment of Type II supernovae that considers the effects of the rotation of massive stars on the chemical yields and differential enrichment according to the life-times of progenitor stars. We find that differential enrichment has a significant impact on the early enrichment of the interstellar medium which is translated into broader element ratio distributions, particularly in the case of the oldest, most metal-poor stars. We find that the [element/Fe] ratios of the α-elements O, Mg, and Si have systematically lower scatter compared to the neutron-capture elements ratios Sr, Ba, and Eu at [Fe/H] < −2, which is ∼0.1–0.4 dex for the former and between ∼0.5 and 1 dex for the latter. The different scatter levels found for the neutron-capture and α-elements is consistent with observations of old stars in the Milky Way. Our model also predicts a high scatter for the [Sr/Ba] ratio, which results from the treatment of the fast-rotating stars and the dependence of the chemical yields on the metallicity, mass, and rotational velocities. Such chemical patterns appear naturally if the different ejection times associated with stars of different mass are properly described, without the need to invoke for additional mixing mechanisms or a distinct treatment of the α- and neutron-capture elements.

Provenance of life: Chemical autonomous agents surviving through associative learning.

We present a benchmark study of autonomous, chemical agents exhibiting associative learning of an environmental feature. Associative learning systems have been widely studied in cognitive science and artificial intelligence but are most commonly implemented in highly complex or carefully engineered systems, such as animal brains, artificial neural networks, DNA computing systems, and gene regulatory networks, among others. The ability to encode environmental information and use it to make simple predictions is a benchmark of biological resilience and underpins a plethora of adaptive responses in the living hierarchy, spanning prey animal species anticipating the arrival of predators to epigenetic systems in microorganisms learning environmental correlations. Given the ubiquitous and essential presence of learning behaviors in the biosphere, we aimed to explore whether simple, nonliving dissipative structures could also exhibit associative learning. Inspired by previous modeling of associative learning in chemical networks, we simulated simple systems composed of long- and short-term memory chemical species that could encode the presence or absence of temporal correlations between two external species. The ability to learn this association was implemented in Gray-Scott reaction-diffusion spots, emergent chemical patterns that exhibit self-replication and homeostasis. With the novel ability of associative learning, we demonstrate that simple chemical patterns can exhibit a broad repertoire of lifelike behavior, paving the way for in vitro studies of autonomous chemical learning systems, with potential relevance to artificial life, origins of life, and systems chemistry. The experimental realization of these learning behaviors in protocell or coacervate systems could advance a new research direction in astrobiology, since our system significantly reduces the lower bound on the required complexity for autonomous chemical learning.

Combination of ligand and structure based virtual screening approaches for the discovery of potential PARP1 inhibitors.

Poly (ADP-ribose) polymerase 1 (PARP1) has high therapeutic value as biomolecular target for research and development of small molecules with antineoplastic activity, since it is upregulated in many cancers, especially in ovarian and BRCA 1/2 mutated breast cancers. Decades of investigation of PARP inhibitors (PARPi) have led to the approval of several drug compounds, however clinical application of PARPi in cancer therapy is limited due to a number of factors, including low selectivity, weak affinity and undesired side effects. Thus, identification of novel drug-like chemical compounds with alternatives to the known PARPi chemical scaffolds, binding modes and interaction patterns with amino acid residues in the active site is of high therapeutic importance. In this study we applied a combination of ligand- and structure-based virtual screening approaches with the goal of identification of novel potential PARPi.

Integrating Anti-Influenza Virus Activity and Chemical Pattern Recognition to Explore the Quality Evaluation Method of Lonicerae Japonicae Flos.

Lonicerae japonicae flos (LJF, Lonicera japonica Thunb.) is adopted as a core herb for preventing and treating influenza. However, the anti-influenza virus components of LJF and the impact of quality-affecting factors on the anti-influenza activity of LJF have not been systematically investigated. In this study, a strategy integrating anti-influenza virus activity, ultrahigh-performance liquid chromatography fingerprint and chemical pattern recognition was proposed for the efficacy and quality evaluation of LJF. As a result, six bioactive compounds were screened out and identified as neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, 4,5-Di-O-caffeoylquinic acid, sweroside and secoxyloganin. Based on the bioactive compounds, chemical pattern recognition models of LJF were established by a linear discriminant analysis (LDA). The results of the LDA models and anti-influenza virus activity demonstrated that cultivation pattern significantly affected the anti-influenza effect of LJF and that the neuraminidase inhibition rate of wild LJF was significantly higher than that of cultivated LJF. Moreover, the quality of LJF samples with different processing methods and geographical origins showed no obvious difference. Overall, the proposed strategy in the current study revealed the anti-influenza virus components of LJF and provided a feasible method for thequality evaluation of LJF, which has great importance for assuring the clinical effect against influenza of LJF.

Electrokinetic Janus micromotors moving on topographically flat chemical patterns

Ionic and molecular selectivity are considered unique for the nanoscale and not realizable in microfluidics. This is due to the scale-matching problem—a difficulty to match the dimensions of ions and electrostatic potential screening lengths with micron-sized confinements. Here, we demonstrate a microscale realization of ionic transport processes closely resembling those specific to ionic channels or in nanofluidic junctions, including selectivity, guidance and flow focusing. As a model system, we explore electrokinetic spherical Janus micromotors moving over charged surfaces with complex charge distribution and without any topographical wall. We discuss peculiarities of the long-range electrostatic interaction on the behavior of the system including interface crossing and reflection of positively charged particles from negatively charged interfaces. These results are crucial for understanding the electrokinetic transport of biochemical species under confinement, have the potential to increase the precision of lab-on-chip-based assays, as well as broadening use cases and control strategies of nano-/micromachinery.

Migration and heating in the galactic disc from encounters between Sagittarius and the Milky Way

ABSTRACT Stars born on near-circular orbits in spiral galaxies can subsequently migrate to different orbits due to interactions with non-axisymmetric disturbances within the disc such as bars or spiral arms. This paper extends the study of migration to examine the role of external influences using the example of the interaction of the Sagittarius dwarf galaxy (Sgr) with the Milky Way (MW). We first make impulse approximation estimates to characterize the influence of Sgr disc passages. The tidal forcing from Sgr can produce changes in both guiding radius ΔRg and orbital eccentricity, as quantified by the maximum radial excursion ΔRmax. These changes follow a quadrupole-like pattern across the face of the disc, with amplitude increasing with Galactocentric radius. We next examine a collisionless N-body simulation of a Sgr-like satellite interacting with an MW-like galaxy and find that Sgr’s influence in the outer disc dominates the secular evolution of orbits between disc passages. Finally, we use the same simulation to explore possible observable signatures of Sgr-induced migration by painting the simulation with different age stellar populations. We find that following Sgr disc passages, the migration it induces manifests within an annulus as an approximate quadrupole in azimuthal metallicity variations (δ[Fe/H]), along with systematic variations in orbital eccentricity, ΔRmax. These systematic variations can persist for several rotational periods. We conclude that this combination of signatures may be used to distinguish between the different migration mechanisms shaping the chemical abundance patterns of the MW’s thin disc.

Analysis of components in male annihilation blocks used to manage Queensland fruit fly Bactrocera tryoni (Froggatt)

The male annihilation technique (MAT) is used throughout the world to assist in the management and eradication of fruit flies. In Australia, often MAT was deployed using particle board blocks (50 × 50 mm) and was used in southern New South Wales to manage Queensland fruit fly Bactrocera tryoni. We sampled 120 MAT blocks over two years which were newly manufactured, new and stored (for later use), exposed for a four or twelve months period and analysed them for the effects of environmental exposure on chemical contents and the weight of blocks. Chemicals present in the blocks were malathion (toxicant), cuelure (male lure) and raspberry ketone (male lure). Total weight of the blocks and the weight of the chemicals from the block's central core and edge components were recorded separately. We found that the storage of newly manufactured blocks did not prevent chemical degradation; cuelure and malathion contents were reduced by 25% and 16%, respectively, in a stored block when compared to a new block. Most changes in the chemical contents of the blocks occurred in the first four months of field deployment, with 56% and 35% reduction analysed in cuelure and malathion contents, respectively. Loss in chemical contents and weight of blocks continued with long-term exposure of blocks from four to twelve months. Edges were found to degrade more as compared to the central core of the used blocks. Managers need to be aware of chemical degradation patterns if particle board is to be used in future MAT programs.

Comparison of Volatile Oil between the Ligusticum sinese Oliv. and Ligusticum jeholense Nakai et Kitag. Based on GC-MS and Chemical Pattern Recognition Analysis.

Ligustici Rhizoma et Radix (LReR) is the dried rhizomes and roots of Ligusticum sinese Oliv. (LS) or Ligusticum jeholense Nakai et Kitag. (LJ). However, in the market, LS and LJ are frequently confused with each other. Since the volatile oils are both the main active components and quality control indicators of LReR, a strategy combining gas chromatography-mass spectrometry (GC-MS) and chemical pattern recognition (CPR) was used to compare the volatile components of LJ and LS. Total ion chromatography (TIC) revealed that phthalides (i.e., neocnidilide) and phenylpropanoids (i.e., myristicin) could be thought of as the most critical components in the volatile oils of LJ and LS, respectively. In addition, the chemical components of the volatile oils in LJ and LS were successfully distinguished by hierarchical cluster analysis (HCA) and principal component analysis (PCA). Moreover, two quality markers, including myristicin and neocnidilide, with a very high discriminative value for the classification of LJ and LS, were found by orthogonal partial least squares discriminant analysis (OPLS-DA). The relative contents of myristicin and neocnidilide were 10.86 ± 6.18% and 26.43 ± 19.63% for LJ, and 47.43 ± 12.66% and 2.87 ± 2.31% for LS. In conclusion, this research has developed an effective approach to discriminating LJ and LS based on volatile oils by combining GC-MS with chemical pattern recognition analysis.

Comprehensive Quality Evaluation of the Root Bark of Morus alba L. Based on High-Performance Liquid Chromatography Fingerprinting and Chemometric Analyses.

The quality of the root bark of Morus alba L. (SBP) herbs currently circulating in the market is variable. In order to ensure clinical effectiveness, a high-performance liquid chromatography (HPLC) fingerprinting method combined with chemical pattern recognition should be established to control the quality of SBP herbs. The differences of 23 batches of SBP were analyzed by exploratory cluster analysis based on shared fingerprint peak data, and the results indicated that the processing method to remove the cork layer from SBP materials is an important influencing factor on SBP quality. Principal component analysis indicated that SBP samples with the cork layer removed can be clearly distinguished from samples without cork layer removal. The potential chemical markers (kuwanon G, morusin and oxyresveratrol) were screened by partial least squares discriminant analysis. Finally, the contents of the main components were determined, indicating that the processing method of SBP materials can affect content of bioactive ingredients and that cork layer removal leads to a more uniform chemometric profile. The HPLC-based chemometrics approach described here will support the development of quality standards in SBP products.

Location-aware convolutional neural networks for graph classification

Graph patterns play a critical role in various graph classification tasks, e.g., chemical patterns often determine the properties of molecular graphs. Researchers devote themselves to adapting Convolutional Neural Networks (CNNs) to graph classification due to their powerful capability in pattern learning. The varying numbers of neighbor nodes and the lack of canonical order of nodes on graphs pose challenges in constructing receptive fields for CNNs. Existing methods generally follow a heuristic ranking-based framework, which constructs receptive fields by selecting a fixed number of nodes and dropping the others according to predetermined rules. However, such methods may lose important structure information through dropping nodes, and they also cannot learn task-oriented graph patterns. In this paper, we propose a Location learning-based Convolutional Neural Networks (LCNN) for graph classification. LCNN constructs receptive fields by learning the location of each node according to its embedding that contains structures and features information, then standard CNNs are applied to capture graph patterns. Such a location learning mechanism not only retains the information of all nodes, but also provides the ability for task-oriented pattern learning. Experimental results show the effectiveness of the proposed LCNN, and visualization results further illustrate the valid pattern learning ability of our method for graph classification.

Multi-scaled self-attention for drug–target interaction prediction based on multi-granularity representation

BackgroundDrug–target interaction (DTI) prediction plays a crucial role in drug discovery. Although the advanced deep learning has shown promising results in predicting DTIs, it still needs improvements in two aspects: (1) encoding method, in which the existing encoding method, character encoding, overlooks chemical textual information of atoms with multiple characters and chemical functional groups; as well as (2) the architecture of deep model, which should focus on multiple chemical patterns in drug and target representations.ResultsIn this paper, we propose a multi-granularity multi-scaled self-attention (SAN) model by alleviating the above problems. Specifically, in process of encoding, we investigate a segmentation method for drug and protein sequences and then label the segmented groups as the multi-granularity representations. Moreover, in order to enhance the various local patterns in these multi-granularity representations, a multi-scaled SAN is built and exploited to generate deep representations of drugs and targets. Finally, our proposed model predicts DTIs based on the fusion of these deep representations. Our proposed model is evaluated on two benchmark datasets, KIBA and Davis. The experimental results reveal that our proposed model yields better prediction accuracy than strong baseline models.ConclusionOur proposed multi-granularity encoding method and multi-scaled SAN model improve DTI prediction by encoding the chemical textual information of drugs and targets and extracting their various local patterns, respectively.

Unravelling UBC 274: A morphological, kinematical, and chemical analysis of a disrupting open cluster

Context.Open clusters in the process of disruption help to understand the formation and evolution of the Galactic disk. The wealth and homogeneity ofGaiadata have led to the discovery of several open clusters with signs of disruption. Detailed chemical information for these clusters is essential in order to study the timescales and interplay between the star formation process and cluster disruption.Aims.We perform a morphological, kinematic, and chemical analysis of the disrupting cluster UBC 274 (2.5 Gyr,d = 1778 pc), to study its global properties.Methods.We use HDBSCAN to obtain a new membership list up to 50 pc from its centre and up to magnitudeG = 19 usingGaiaEDR3 data. We use high-resolution and high signal-to-noise spectra to obtain atmospheric parameters of six giants and sub-giants, and individual abundances of 18 chemical species.Results.The cluster has a highly eccentric (0.93) component, tilted ∼10 deg with respect to the plane of the Galaxy, which is morphologically compatible with the results of a test-particle simulation of a disrupting cluster. Our abundance analysis shows that the cluster has a sub-solar metallicity of [Fe/H] = −0.08 ± 0.02. Its chemical pattern is compatible with that of Ruprecht 147, of similar age but located closer to the Sun, with the remarkable exception of neutron-capture elements, which present an overabundance of [n/Fe] ∼ 0.1.Conclusions.The cluster’s elongated morphology is associated with the internal part of its tidal tail, following the expected dynamical process of disruption. We find a significant sign of mass segregation, where the most massive stars appear 1.5 times more concentrated than other stars. The cluster’s overabundance of neutron-capture elements can be related to the metallicity dependence of the neutron-capture yields due to the secondary nature of these elements, predicted by some models. UBC 274 presents a high chemical homogeneity at the level of 0.03 dex in the sampled region of its tidal tails.

Chemical pattern recognition of Atractylodes chinensis from different producing areas and establishment of quantitative analysis of multi-components by single marker (QAMS) method for four components

This study established the fingerprint and combined it with chemical pattern recognition to evaluate the quality of Atractylodes chinensis samples from different producing areas and then employed the quantitative analysis of multi-components by single marker(QAMS) method to verify the feasibility and applicability of the established method in the quality evaluation of A. chinensis. The fingerprints of A. chinensis samples were constructed via high performance liquid chromatography(HPLC) to evaluate the inter-batch consistency. With the quality control component atractylodin as the internal reference, the relative correction factors(RCFs) were established for atractylenolide Ⅰ, atractylenolide Ⅲ, and β-eudesmol and the content of the four components was calculated. The external standard method was used to verify the accuracy of QAMS method. The quality of A. chinensis was further evaluated by similarity analysis, clustering analysis, and principal component analysis. The fingerprints of 13 batches of samples were calibrated with 21 common peaks, and 4 common peaks were identified with the similarities all above 0.9. The RCFs established with atractylodin as the internal reference represented good reproducibility under different experimental conditions. Specifically, the RCFs of atractylenolide Ⅰ, atractylenolide Ⅲ, and β-eudesmol in A. chinensis were 2.091, 4.253, and 6.010, respectively. QAMS and ESM showed no significant difference in the results, indicating that the QAMS method established in this study was stable and reliable. Thus, HPLC fingerprint combined with QAMS can be used for the quality evaluation of A. chinensis, providing a basis for comprehensive and rapid quality evaluation of A. chinensis.

Anti-inflammatory/anti-oxidant properties and the UPLC-QTOF/MS-based metabolomics discrimination of three yellow camellia species

The species of Camellia nitidissima Chi (CC) and C. euphlebia Merr. ex Sealy (CE) are two most important plant sources for commercialized herbal tea (Jinhuacha) worldwide. However, some other species of camellia genus are also sold as alternatives in market due to the great commercial value. In this study, the similarity and difference of CC and CE as well as C.insularis (CI) are comprehensively compared both in chemistry and pharmacology. Based on the ultraperformance liquid chromatography coupled with a hybrid quadrupole orthogonal time-of-flight mass spectrometer(UPLC-QTOF-MS) analysis, a sequential-optimization based new statistical model has been developed by combining the untargeted metabolomics and fingerprint analyses, and successfully applied for chemical pattern recognition and discrimination of three yellow camellias species. The results indicated that CC, CE and CI could be well discriminated with the optimized chemical combination including quercetin-3-O-rhamnoside (C2), okicamelliaside (C4), Kaempferol 7-O-rhamnoside (C6), Corymboside (C9), asiatic acid-glc-rha-xyl (C11) and 3′-methy-4′-glucoside-ellagic acid (C14). Moreover, the 30 % ethanolic extracts of yellow camellias species presented the optimal activities on anti-inflammation/anti-oxidation in LPS-stimulated Raw264.7 macrophages dose-dependently. The averaged 50 % inhibitory concentrations (IC50) on NO production were 754.68 ± 50.96, 1182.39 ± 22.10, 1527.83 ± 106.24 μg(herb)/mL, and ROS production were 311.70 ± 26.57, 332.64 ± 25.46, 917.60 ± 41.36 μg(herb)/mL for CC, CE and CI, respectively. The results indicated a certain similarity of CC and CE, as well as their significant difference from CI.

The role of herbal plants in the inhibition of SARS-CoV-2 main protease: A computational approach

COVID-19 has quickly spread across the globe, becoming a pandemic. This disease has a variable impact in different countries depending on their cultural norms, mitigation efforts and health infrastructure. This study aims to assess the herbal plants in the pursuit of potential SARS-CoV-2 Mpro inhibitors using in silico approaches. We have considered 16 extracted compounds of 10 different species of these plants. In order to explain their inhibition properties and chemical reactivity pattern, we have performed the density functional theory based calculations of frontier molecular orbitals, molecular electrostatic potential surface and chemical reactivity descriptors. Our calculated lipophilicity, aqueous solubility and binding affinity of the extracted compounds suggest that the inhibition potentials in the order; harsingar > aloe vera > giloy > turmeric > neem > ginger > red onion > tulsi > cannabis > black pepper. On comparing the binding affinity with hydroxychloroquine, we note that the inhibition potentials of the extracts of harsingar, aloe vera and giloy are very promising. In order to validate this, we have also performed MD simulation and MM-PBSA binding free energy analysis. Therefore, we believe that these findings will open further possibilities and accelerate the works towards finding an antidote for this malady.