Chemical Fingerprint Research Articles

An innovative method for predicting oxidation reaction rate constants by extracting vital information of organic contaminants (OCs) based on diverse molecular representations

The reaction rate constant (k) of oxidants with organic contaminants (OCs) is an important parameter to assess the efficiency of oxidants in removing contaminants. In this study, the degradation of OCs in three oxidation systems was evaluated. The modeling process applied three molecule representations (molecular descriptors (MD), quantum chemical descriptors (QCD) and MACCS fingerprints) and their variable integrations. Models based on integration molecule representations show significant performance improvements. Eventually, the optimal models for ozone, chlorine dioxide and hypochlorite were found to be (MD+QCD)-XGBoost (R2tra = 0.982, Q2tra = 0.715), (MD+QCD+MACCS)-XGBoost (R2tra = 0.982, Q2tra = 0.778), and (MD+QCD+MACCS)-CatBoost (R2tra = 0.856, Q2tra = 0.709) model, respectively. Here, we introduced a new perspective that differed from focusing on machine learning (ML) algorithm optimization. This perspective centered on the input variables (i.e., molecular representations) of models to improve model performance by capturing the key properties of OCs comprehensively. Furthermore, the key effects of pH, ionization potential, orbital energy, polarizability and electronegativity on the oxidation reaction in different oxidation systems were clarified. We hope that the mechanism explanation in this study can provide valuable insights for understanding the mechanism of various oxidation reactions of complex OCs.

Transfer learning-driven artificial intelligence model for glass transition temperature estimation of molecular glass formers mixtures

Predicting binary mixtures' glass transition temperature (Tg) is crucial in various fields, particularly for industrial materials affected by this property during production processes and in service-life. On the other hand, from the fundamental point of view, this predictive capability is relevant for understanding the chemical interactions between the two components and how this affects the Tg of the mixture. In this sense, some models provide different approaches for describing the Tg of the mixture. Among them, the Gordon-Taylor approach has been widely used since it only relies on the relationship between the Tg of the pure components, their weight fraction, and only one fitting parameter. Although simple, this approach still requires measurements of Tg of the pure components and at least some intermediated composition for the fitting procedure. In a previous work, our research has focused on neural networks methods for predicting Tg values directly from the chemical structure of monomers and molecules, but the scarcity of experimental data for binary mixtures limits the application of a similar approach. To address this problem, we propose to use in this work a transfer learning method that relays on the previous acquired knowledge of the chemical structure - Tg relationship, for the prediction of the Tg of the binary mixtures. Therefore, pure component characteristics are derived from chemical fingerprints originated in a pre-trained network, and enables a training process focused on their behavior within the mixtures. This approach successfully estimated K with very low deviations, even allowing for the exploration of the embedded chemical structure's relation to previously unknown mixtures.

Plastic Food Packaging from Five Countries Contains Endocrine- and Metabolism-Disrupting Chemicals.

Plastics are complex chemical mixtures of polymers and various intentionally and nonintentionally added substances. Despite the well-established links between certain plastic chemicals (bisphenols and phthalates) and adverse health effects, the composition and toxicity of real-world mixtures of plastic chemicals are not well understood. To assess both, we analyzed the chemicals from 36 plastic food contact articles from five countries using nontarget high-resolution mass spectrometry and reporter-gene assays for four nuclear receptors that represent key components of the endocrine and metabolic system. We found that chemicals activating the pregnane X receptor (PXR), peroxisome proliferator receptor γ (PPARγ), estrogen receptor α (ERα), and inhibiting the androgen receptor (AR) are prevalent in plastic packaging. We detected up to 9936 chemical features in a single product and found that each product had a rather unique chemical fingerprint. To tackle this chemical complexity, we used stepwise partial least-squares regressions and prioritized and tentatively identified the chemical features associated with receptor activity. Our findings demonstrate that most plastic food packaging contains endocrine- and metabolism-disrupting chemicals. Since samples with fewer chemical features induce less toxicity, chemical simplification is key to producing safer plastic packaging.

The impact of pumpkin seed-derived silver nanoparticles on corrosion and cytotoxicity: a molecular docking study of the simulated AgNPs

ABSTRACT Green-synthesized nanoparticles from pumpkin (Cucurbita pepo L.) seed extracts are economical and eco-friendly. Silver nanoparticles (AgNPs) and their selective cytotoxicity towards HCT116 and African Green Monkey Kidney, Vero cells were investigated. Chemical fingerprinting, heat stability, and 2D-images of nanoparticle size and morphology were determined using Fourier-transform infrared spectroscopy (FTIR), Thermogravimetric Analysis (TGA), Transmission Electron Microscopy (TEM), and Scanning electron microscopy-energy dispersive X-ray (SEM-EDX) on AgNPs. UV-vis examination shows surface plasmons in the wide peak at 417 nm, indicating polydisperse nanoparticles. Small silver nanoparticles (AgNPs) below 2 µm demonstrated a rod-like form and a tendency to agglomerate. SEM-EDX element analysis and fingerprinting confirmed the material as AgNPs. TEM indicated that the nanoparticles were generally spherical or ellipsoidal, equally dispersed, and averaged 26.08 nm in diameter with negligible aggregation. The AgNPs are also stable at a temperature of 220°C, indicating that the green material is quite robust at 150°C to 400°C. According to cytotoxic studies, AgNPs are toxic to cancer cells (HCT 116 cells), however they have no effect on Vero cells. AgNPs and tubulin (TUB) domain active sites have a significant affinity, according to molecular docking analysis. In an electrochemical investigation, biogenic AgNPs effectively prevented mild steel from corroding in a 1.0 M HCl solution.

Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles

BackgroundBreast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades, drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However, with a growing number of potential small-molecule cancer inhibitors, in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However, these models heavily relied on a large number of drug chemical structural fingerprints as their main features, which made model interpretation a challenge.ResultsThis study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models, our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins, we gained insights into the patterns our model recognized as indicative of synergistic effects.ConclusionsThe framework employed in the present study lays the groundwork for future advancements, especially in model interpretation. By combining deep learning techniques and target-specific models, this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment.

Impacts of ship emissions and sea-land breeze on urban air quality using chemical characterization, source contribution and dispersion model simulation of PM2.5 at Asian seaport

Fine particulate matter (PM2.5) emitted from marine transportation, bulk materials handling at the docks, and dust dispersion has garnered increased attention, particularly in the interface between port and urban areas. This study explored the inter-transport of PM2.5 between Kaohsiung Harbor and neighboring Metro Kaohsiung. Chemical analyses of PM2.5 samples from four sites include water-soluble ions, metallic elements, carbons, anhydrosugars, and organic acids to establish PM2.5's chemical fingerprints. The CALPUFF air dispersion model is employed to simulate the spatiotemporal distribution of PM2.5 in Kaohsiung Harbor and adjacent urban areas. A clear seasonal and diurnal variation of PM2.5 concentrations and chemical composition was observed in both harbor and urban areas. The high correlation of nighttime PM2.5 levels between the port and urban areas suggests inter-transport phenomena. Sea salt spray, ship emissions, secondary aerosols, and heavy fuel-oil boilers exhibit higher levels in the port area than in the urban area. In Metro Kaohsiung, mobile sources, fugitive dust, and waste incinerators emerge as major PM2.5 contributors. Furthermore, sea breeze significantly influences PM2.5 dispersion from Kaohsiung Harbor to Metro Kaohsiung, particularly in the afternoon. The average contribution of PM2.5 from ships' main engines in Kaohsiung Harbor ranges from 2.9% to 5.3%, while auxiliary engines contribute 3.8%–8.3% of PM2.5 in Metro Kaohsiung.

Chemical Fingerprinting of Biomass Burning Organic Aerosols from Sugar Cane Combustion: Complementary Findings from Field and Laboratory Studies.

Agricultural fires are a major source of biomass-burning organic aerosols (BBOAs) with impacts on health, the environment, and climate. In this study, globally relevant BBOA emissions from the combustion of sugar cane in both field and laboratory experiments were analyzed using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry. The derived chemical fingerprints of fresh emissions were evaluated using targeted and nontargeted evaluation approaches. The open-field sugar cane burning experiments revealed the high chemical complexity of combustion emissions, including compounds derived from the pyrolysis of (hemi)cellulose, lignin, and further biomass, such as pyridine and oxime derivatives, methoxyphenols, and methoxybenzenes, as well as triterpenoids. In comparison, laboratory experiments could only partially model the complexity of real combustion events. Our results showed high variability between the conducted field and laboratory experiments, which we, among others, discuss in terms of differences in combustion conditions, fuel composition, and atmospheric processing. We conclude that both field and laboratory studies have their merits and should be applied complementarily. While field studies under real-world conditions are essential to assess the general impact on air quality, climate, and environment, laboratory studies are better suited to investigate specific emissions of different biomass types under controlled conditions.

Identifying potential Q-markers for quality evaluation of Zhenyuan capsule by integrating chemical analysis, network pharmacology, molecular docking, and molecular dynamics simulations

Quality markers (Q-markers) are of great significance for quality evaluation of herbal medicines. Zhenyuan Capsule (ZYC) is a kind of Chinese patent medicine used to treat cardiovascular diseases. However, reliable and effective Q-markers for ZYC are still lacking. Herein, a UHPLC-Q/Orbitrap-MS/MS was performed to characterise the preliminary chemical profile of ZYC. A total of 86 components were characterised among which 20 constituents were unambiguously identified by reference compounds. Based on network pharmacology, seven major ginsenosides with great importance in the network were identified as Q-markers among which ginsenoside Re with the highest betweenness was screened to inhibit the development of coronary heart disease (CHD) by binding with vascular endothelial growth factor A (VEGFA). Docking and molecular dynamics simulation studies suggested that ginsenoside Re stably bound to VEGFA. Quantitative determination and chemical fingerprinting analysis were performed using HPLC-DAD. The results showed that ginsenosides screened might function as potential Q-markers for ZYC.

High-Resolution Mass Spectrometry-Based Chemical Fingerprinting of Baijiu, a Traditional Chinese Liquor.

Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry, coupled with electrospray ionization (ESI) or atmospheric-pressure photoionization (APPI), was employed for chemical fingerprinting of baijiu, a traditional Chinese liquor. Baijiu is the most consumed distilled alcoholic beverage globally, with over 10 billion liters sold annually. It is a white (transparent) spirit that exhibits similarities to dark spirits such as whisky or rum in terms of aroma and mouthfeel. In this study, direct-infusion FT-ICR mass spectrometry was used to analyze 10 commercially available baijiu liquors, enabling the examination of both volatile and nonvolatile constituents without the need for tedious sample extractions or compound derivatizations. The chemical fingerprints obtained by FT-ICR MS revealed substantial compositional diversity among different baijiu liquors, reflecting variations in the raw materials and production methods. The main compounds identified included a variety of acids, esters, aldehydes, lactones, terpenes, and phenolic compounds. The use of ESI and APPI provided complementary compositional information; while ESI demonstrated greater selectivity toward polar, aliphatic sample constituents, APPI also ionized semipolar and nonpolar (aromatic) ones.

Study on the Fingerprints of Acanthopanax trifoliatus Fractions with Varying Polarities and the Relationship Between Anti-inflammatory and Antioxidant Activity Using Multivariate Analysis

Objectives To evaluate the Spectrum-Effect relationships between the High-Performance Liquid Chromatography (HPLC) Fingerprints of fractions with different polarities and the anti-inflammatory and antioxidant effects of A. trifoliatus and to identify its pharmacodynamic material basis. Materials and Methods Chemical Fingerprints of A. trifoliatus fractions with different polarities (all fractions and ethyl acetate, n-butanol and water fractions) were obtained by HPLC, and Principal Component Analysis (PCA) was performed. The foot swelling and prostaglandin E2 (PGE2) content in mice induced by carrageenan were used as anti-inflammatory markers, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) methods were used as antioxidant indicators. Moreover, the Spectrum-Effect relationship between common peaks and efficacy value was conducted by Grey Relational Analysis (GRA) and Partial Least Squares analysis (PLS). Results PCA identified three principal components, and all fractions of A. trifoliatus had good anti-inflammatory and antioxidant effects. Based on GRA and PLS analysis, peaks 1 and 2 (neochlorogenic acid), 4 and 5 (chlorogenic acid), 8, 9, 10, 11 (isochlorogenic acid B), and 12 were involved in anti-inflammatory efficacy, and peaks 1 and 2 (neochlorogenic acid), 3, 4, and 5 (chlorogenic acid), and 6, 7, 9, 10, and 13 (isochlorogenic acid A) were involved in antioxidant efficacy. Conclusion This study on the Spectrum-Effect relationship of different polar fractions of A. trifoliatus provides data to support the discovery of the pharmacodynamic material basis of the anti-inflammatory and antioxidant properties of A. trifoliatus.

A novel strategy of coupling artificial intelligence with chemical fingerprinting to predict drug phase behaviors in complex systems

AbstractWith the large‐scale development of drugs, understanding the drug phase behaviors in complex systems becomes more and more important. Among them, the solubility of drugs in biorelevant media needs to be urgently understood. To address this challenge, new strategies based on machine learning models are proposed. First, the strategy trains five machine learning models (extra trees [ET], gradient boosting [GB], k‐nearest neighbors [k‐NN], random forest [RF], and extreme gradient boosting [XGBoost]) based on 15 molecular descriptors of the drug molecular properties. The XGboost model was identified as the best predictive model for predicting drug solubility performance in various solvents. Next, the input feature vectors were expanded for machine learning using the MACCS chemical fingerprint coupled with the XGboost model. The MACCS chemical fingerprint coupled with XGboost model has significantly improved the prediction accuracy of drug solubility. This finding demonstrates that the proposed strategy has solubility prediction capability, which is expected to provide valid information for drug development and drug solvent screening.

Exploring Anticancer Potential of Camptothecin Isolated from Nothapodytes Nimmonianain the Treatment of Prostate and Lung Carcinoma

Background: Cancer is a life-threatening disease which is a major threat to global health which continues to affect predominantly in developing nations. Present research work has been oriented towards the determination of the anticancer potential of camptothecin (CPT) as herbal medicine for the treatment of prostate and lung carcinoma. Methods: Structural determination of camptothecin has been performed by different analytical techniques where anticancer potential was tested by MTT assay, Flow cytometry and DAPI on A549 and LNCaP cells lines. Results: FTIR spectra of camptothecin showed peaks related to specific structure which is nearly equal to standard structure of CPT. NMR spectra of camptothecin showed specific peaks in the region of delta 8.686 - 5.279, the signals of H-7 related to structural features similar to camptothecin. LCMS spectra of camptothecin showed mean retention time at 3.620 and covered 100 % area along with mass spectra gives precursor m/z peak at 349.2 [M+H]+ matches to standard molecular weight of camptothecin. CPT has been used as competent alternative to systemic chemotherapy to cure lung and prostate carcinoma having IC50 value 3.421 μg /ml and 5.253μg /ml respectively. CPT successfully induces apoptosis in A549 and LNCaP cell lines 72.12 ± 3.45 % and 66.41 ± 4.50 % as compared to control 4.28 ± 1.78 and 1.52 ± 0.58 respectively which was proved by DAPI and flow cytometry. Conclusions: Chemical fingerprinting and structural elucidation confirmed that isolated moiety was camptothecin and it has great potential in treatment of lung and prostate carcinoma as a competent alternative to chemotherapy in the form of herbal medicine.

Large Carbonaceous Chondrite Parent Bodies Favored by Abundance–Volatility Modeling: A Possible Chemical Signature of Pebble Accretion

Primitive meteorite groups such as the Vigarano, Mighei, and Karoonda carbonaceous chondrites have enigmatic patterns of elemental abundances, with moderately volatile elements—those that transition from vapor to condensate between ∼400 and ∼900 K—defining plateaus of subequal abundances despite a wide range in volatility. In detail, each group defines a plateau with distinctive nonmonotonic “chemical fingerprints” that have been attributed to combinations of mixing, vaporization/condensation, and fluid-mediated metasomatism—but the extent to which these processes can reproduce the observed variability has not been quantified. Starting with primitive Ivuna chondrite, a two-stage, two-component equilibrium condensation–vaporization model—with gravity implemented as Jeans escape—can explain large-scale plateaus in these chondrite groups, as well as more complex, nonmonotonic small-scale variations. For all three chondritic meteorite groups, models favor earlier high-temperature fractionation under low-gravity conditions followed by a low-temperature fractionation event that took place on a protoplanet at least as large as Ceres. The second fractionation event may represent the fractionation of incoming materials to the planetesimal during protracted pebble accretion. Models with only thermally driven volatile loss, gravity, and mixing can explain more than 80% of the observed compositional variability in these meteorite groups. In our five-parameter model, using only five randomly selected elements yields uselessly large ranges of planet sizes and temperatures, ranges that converge with increasing numbers of elements. These results suggest that even simple models are prone to generating inaccurate conclusions when constrained by too few observations, a fault likely held by more complex models as well.

Plant metabolic response to stress in an arid ecosystem is mediated by the presence of neighbors.

Plant neighbors in arid environments can ameliorate abiotic stress by reducing insolation, but they also attract herbivores and pathogens, especially when neighbors are close relatives that share similar antagonists. Plants' metabolic profiles provide a chemical fingerprint of the physiological processes behind plant responses to different environmental stresses. For example, abscisic acid and proline, mainly involved in stomatal closure and osmotic adjustment, can induce plant responses to abiotic stress, while jasmonic acid and salicylic acid primarily regulate plant defense to herbivory or pathogens. Neighbor plants can generate contrasting ecological contexts, modulating plant responses to abiotic and biotic stresses. We hypothesize that plant metabolic profile is modulated by its neighbors in a vegetation patch, expecting a higher investment in metabolites related to biotic-stress tolerance (i.e., herbivory or pathogens) when growing associated with other plants, especially to phylogenetically close relatives, compared to plants growing alone. We show that plants from five species growing with neighbors invest more in biotic-stress tolerance while their conspecifics, growing alone, invest more in abiotic-stress tolerance. This tendency in plants' metabolic profiles was not affected by the phylogenetic diversity of their neighborhood. Linking physiological snapshots with community processes can contribute to elucidating metabolic profiles derived from plant-plant interactions.

Rapid and non-destructive quality estimation of cinchona, andrographis paniculata, and black pepper using a portable NIR spectroscopy measuring device

Quality assessment of natural products are mostly done by laboratory methods which are invasive, cost-intensive, with low-throughput and prone to human error. Near infrared (NIR) spectroscopy have emerged as a very useful technique as a rapid non-destructive detection and quality estimation tool in identifying chemical fingerprint sensitive to the vibrational modes in chemical bonds like O-H, C-H and N-H.In this work, a reasonably low-cost and portable NIR spectrometer was developed with a few imported internal components, together with compatible software for its use, like the graphical user interface (GUI) and calibration program. Quinine in Cinchona (Cinchona L) bark, andrographolides in Andrographis paniculata and piperine in black pepper were estimated and the coefficient of determination (R2) of 0.93, 0.94, and 0.90 was obtained respectively for the regression analysis. The model for cinchona, andrographolides, and piperine gave residual prediction deviation (RPDs) of 4.25, 4.21 and 3.0, range error ratio (RERs) of 14.59, 14.42, 10.32 and the ratio between the standard error of calibration (SEC) and the standard error of prediction (SEP) i.e. SEP/SEC of 1.18, 0.21 and 0.92 respectively. The derived partial least squares (PLS) regression models fulfil the requirements of AACC Method 39–00 (AACC in AACC Method, 39–00:15, 1999) used for screening (RPD ≥ 2.5), as per knowledge of the authors. The device can be used for other molecular markers in plants and their byproducts.

Unraveling the antimicrobial efficacy and chemical fingerprinting of medicinal plants against the WHO’s prioritized pathogens

BackgroundThe global spread of drug-resistant organisms has necessitated the search for alternative treatments against bacterial and candidal resistant pathogens. Plants have long been used as traditional medicines to ameliorate various diseases, and their antimicrobial properties are still being explored. The aim of the present study is to assess the antimicrobial activity of extracts from Alstonia scholaris, Orthosiphon aristatus, Sphaeranthus amaranthoides, Crateva magna and Garcinia travancorica against bacteria and Candida pathogens.ResultsOut of 60 different sequential extracts tested, several showed moderate to good antimicrobial activity. Among them, ethyl acetate extract of G. travancorica exhibited significant activity against Lactobacillus acidophilus (17 mm) followed by Staphylococcus aureus (16 mm), Escherichia coli (13 mm), Proteus mirabilis (12 mm), Staphylococcus epidermis, Candida krusei (11 mm), Candida glabrata (10 mm) and the chloroform extract from O. aristatus showed good activity against S. epidermis, L. acidophilus (13 mm), S. aureus, Escherichia fergusonii, C. krusei (12 mm), C. glabrata, E. coli (11 mm) and Klebsiella pneumoniae (10 mm), respectively. In addition, GC–MS analysis revealed the presence of nine major compounds in G. travancorica and ten compounds in O. aristatus which were responsible for the significant antimicrobial activity.ConclusionsThese findings highlight the potential of G. travancorica and O. aristatus as sources for developing new antimicrobial agents against the World Health Organization’s (WHO) prioritized pathogens. Further research on these plants could lead to the discovery and synthesis of novel therapeutic agents with enhanced antimicrobial properties.Graphical abstract

11B NMR of the Morphological Evolution of Traditional Chinese Medicine Borax.

This article applies nuclear magnetic resonance technology to the study of boron-containing traditional Chinese medicine, in order to explore the morphological evolution of boron elements in traditional Chinese medicine. Borax is a traditional Chinese medicine with anti-corrosion, anti-inflammatory, antibacterial, and anticonvulsant effects. It is made by boiling, removing stones, and drying borax minerals like borate salts. This article introduces an 11B nuclear magnetic resonance method for identifying and characterizing boron-containing compounds in TCM. We applied this technology to borax aqueous solutions in different chemical environments and found that with boron mixed in the form of SP2 hybridization in equilateral triangles and SP3 hybridization in equilateral tetrahedra, the pH changes in alkaline environments significantly affected the ratio of the two. At the same time, it was found that in addition to the raw material peak, boron signals of other boron-containing compounds were also detected in 20 commercially available boron-containing TCM preparations. These new boron-containing compounds may be true pharmaceutical active ingredients, and adding them directly to the formula can improve quality and safety. This article describes the detection of 11B NMR in boron-containing traditional Chinese medicine preparations. It is simple, non-destructive, and can provide chemical fingerprint studies for boron-containing traditional Chinese medicine.

Source profile of PM2.5 emissions from different primary sources in the coal capital city Dhanbad, India

The rise of fine particulate matter (PM2.5) levels in urban areas, driven by traffic, construction, and combustion emissions, has prompted urgent air quality concerns. Understanding source-specific PM2.5 chemical characteristics and developing associated source markers is essential for knowing their accurate contribution to atmospheric PM2.5. This study focuses on developing PM2.5 chemical source profiles from nine different emissions, primarily categorized under traffic and combustion sources. The chemical characterization included the carbonaceous thermal fractions, inorganic ions, and elemental composition. Heterogeneity in chemical composition across emission sources was examined using the coefficient of divergence and diagnostic ratio, and finally, source-specific chemical fingerprints were developed using the ratio normalization approach. The finding revealed significant inter and intra-variation in the chemical composition among traffic and combustion emission sources. Organic matter is observed significantly higher in combustion sources (84%–92%) than in traffic sources (22%–45%). Both OC/EC and char-EC/soot-EC values showed much higher values for combustion sources than traffic emissions, with cow dung cake burning emissions displaying the largest values. Also, char-EC/soot-EC values showed a similar trend with OC/EC values and thus can be used as an additional marker for deciphering emission sources. The ion-balance ratio revealed particle emissions from coal, cow dung, and garbage burning to be highly acidic, while traffic and construction sources were alkaline. Source marker results provide new insights into differences in the chemical fingerprint of specific emission sources. A new set of source markers was seen for garbage burning while coal-burning emissions showed varying chemical fingerprints and were found to be dependent on coal processing. Among elements, bromine and chlorine are found to be the unique markers for cow dung cake-burning emissions. Receptor models can use the database developed from the current work to demarcate the emission sources accurately and benefit the regulatory bodies in developing efficient control measures.

Quality Evaluation of Banlangen Granule based on Bioassays of Anti-influenzal and Anti-inflammatory Effects

Objective:: In order to control the quality better, this study aimed to develop two bioassay methods of Banlangen Granule (BLGG) based on its anti-influenza activity and antiinflammatory activity and to verify the necessity of established methods by relating the results tested by chemical methods. Methods: First, the bioassay methods for determining the biopotency of the anti-influenza effect and anti-inflammatory activity were established and applied, taking neuraminidase and cyclooxygenase- 2 as disease targets, respectively. Secondly, the ultra-high-performance liquid chromatography coupled photo-diode array detector (UPLC-PDA) technique was used to perform fingerprints and quantify chemical compounds. Finally, the correlation analysis was performed on the results of bioassay methods and chemical methods to assist in choosing the effective quality markers for the BLGG. Results:: Two accurate, stable, and repeatable bioassay methods were developed and applied to the determination of 57 batches of samples. The chemical fingerprints and contents of seven quality compounds were obtained based on UPLC-PDA methods. From the results of correlation analysis, the highest intensity correlation between these quality markers was medium with a r=0.495 (P<0.01), which indicates the need for establishing a bioassay method for BLGG. Conclusion:: This present work illuminated that bioassay methods can be a great means to evaluate the quality of BLGG effectively and also provided a paradigm case for the quality control of other traditional Chinese medicine preparations.

Investigation of bioactive components responsible for the antibacterial and anti-biofilm activities of Caroxylon volkensii by LC-QTOF-MS/MS analysis and molecular docking.

Caroxylon volkensii is a wild desert plant of the family Amaranthaceae. This study represents the first report of the metabolomic profiling of C. volkensii by liquid chromatography quadrupole-time-of-flight tandem mass spectrometry (LC-QTOF-MS/MS). The dereplication study of its secondary metabolites led to the characterization of 66 known compounds. These compounds include catecholamines, tyramine derivatives, phenolic acids, triterpenoids, flavonoids, and others. A new tyramine derivative, alongside other known compounds, was reported for the first time in the Amaranthaceae family. The new derivative and the first-reported compounds were putatively identified through MS/MS fragmentation data. Given the notorious taxonomical challenges within the genus Salsola, to which C. volkensii previously belonged, our study could offer a valuable insight into its chemical fingerprint and phylogenetic relationship to different Salsola species. The antibacterial potential of C. volkensii methanolic extract (CVM) against Pseudomonas aeruginosa was screened. The minimum inhibitory concentration (MIC) of CVM ranged from 32 to 256 μg mL-1. The anti-quorum sensing potential of CVM resulted in a decrease in the percentage of strong and moderate biofilm-forming isolates from 47.83% to 17.39%. It revealed a concentration-dependent inhibitory activity on violacein formation by Chromobacterium violaceum. Moreover, CVM exhibited an in vivo protective potential against the killing capacity of P. aeruginosa isolates. A molecular docking study revealed that the quorum-sensing inhibitory effect of CVM can be attributed to the binding of tyramine conjugates, ethyl-p-digallate, and isorhamnetin to the transcriptional global activator LasR.